[UCR Center for Geospatial Sciences](https://spatial.ucr.edu) Annually BSD aws-pds, urban, geospatial, demographics
Global 30m Height Above Nearest Drainage (HAND) Notifications for new data arn:aws:sns:us-west-2:879002409890:glo-30-hand-object_created us-west-2 SNS Topic https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://regis Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus Wo aws-pds, elevation, hydrology, agriculture, disaster response, satellite imagery, geospatial, cog, stac
Global 30m Height Above Nearest Drainage (HAND) GLO-30 HAND S3 bucket arn:aws:s3:::glo-30-hand us-west-2 S3 Bucket https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://regis Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus Wo aws-pds, elevation, hydrology, agriculture, disaster response, satellite imagery, geospatial, cog, stac ['[STAC V1.0.0 endpoint](https://stac.asf.alaska.edu/collections/glo-30-hand)', '[Via STAC Browser](https://radiantearth.github.io/stac-browser/#/external/stac.asf.alaska.edu/collections/glo-30-hand)']
-Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:s3:::gbif-open-data-sa-east-1 sa-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-sa-east-1.s3.sa-east-1.amazonaws.com/index.html)']
-Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-eu-central-1-object_created eu-central-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
-Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (us-east-1 region) arn:aws:s3:::gbif-open-data-us-east-1 us-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-us-east-1.s3.us-east-1.amazonaws.com/index.html)']
-Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-sa-east-1-object_created sa-east-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
-Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-ap-southeast-2-object_created ap-southeast-2 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:s3:::gbif-open-data-af-south-1 af-south-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-af-south-1.s3.af-south-1.amazonaws.com/index.html)']
Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (us-east-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-us-east-1-object_created us-east-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
-Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-af-south-1-object_created af-south-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
-Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:s3:::gbif-open-data-eu-central-1 eu-central-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-eu-central-1.s3.eu-central-1.amazonaws.com/index.html)']
Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:s3:::gbif-open-data-ap-southeast-2 ap-southeast-2 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-ap-southeast-2.s3.ap-southeast-2.amazonaws.com/index.html)']
+Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:s3:::gbif-open-data-eu-central-1 eu-central-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-eu-central-1.s3.eu-central-1.amazonaws.com/index.html)']
+Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:s3:::gbif-open-data-sa-east-1 sa-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-sa-east-1.s3.sa-east-1.amazonaws.com/index.html)']
+Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (us-east-1 region) arn:aws:s3:::gbif-open-data-us-east-1 us-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-us-east-1.s3.us-east-1.amazonaws.com/index.html)']
+Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-af-south-1-object_created af-south-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
+Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-ap-southeast-2-object_created ap-southeast-2 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
+Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-eu-central-1-object_created eu-central-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
+Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-sa-east-1-object_created sa-east-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences
Global Database of Events, Language and Tone (GDELT) Project data files arn:aws:s3:::gdelt-open-data us-east-1 S3 Bucket http://www.gdeltproject.org/ http://www.gdeltproject.org/about.html#contact Unmanaged Not currently being updated http://www.gdeltproject.org/about.html#termsofuse aws-pds, events, disaster response
Global Database of Events, Language and Tone (GDELT) Notifications for new data arn:aws:sns:us-east-1:928094251383:gdelt-csv us-east-1 SNS Topic http://www.gdeltproject.org/ http://www.gdeltproject.org/about.html#contact Unmanaged Not currently being updated http://www.gdeltproject.org/about.html#termsofuse aws-pds, events, disaster response
-Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter Data Set 1x1 degree tiled data and metadata in a S3 bucket, Global VRT mosaics (vrt arn:aws:s3:::sentinel-1-global-coherence-earthbigdata/data/tiles us-west-2 S3 Bucket http://sentinel-1-global-coherence-earthbigdata.s3-website-us-west-2.amazonaws.c For questions regarding data methodology or delivery, contact info@earthbigdata. [Earth Big Data LLC](https://earthbigdata.com/) The data set covers the time period from 1-Dec-2019 to 30-Nov-2020. No updates a The use of these data fall under the terms and conditions of the [Creative Commo global, satellite imagery, ecosystems, agriculture, urban, infrastructure, earth observation, earthquakes, environmental, geology, geophysics, geospatial, mapping, natural resource, cog, synthetic aperture radar, aws-pds
Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter Data Set Global mosaics at 001 degree pixel spacing as cloud optimized GeoTIFFs in a S3 arn:aws:s3:::sentinel-1-global-coherence-earthbigdata/data/mosaics us-west-2 S3 Bucket http://sentinel-1-global-coherence-earthbigdata.s3-website-us-west-2.amazonaws.c For questions regarding data methodology or delivery, contact info@earthbigdata. [Earth Big Data LLC](https://earthbigdata.com/) The data set covers the time period from 1-Dec-2019 to 30-Nov-2020. No updates a The use of these data fall under the terms and conditions of the [Creative Commo global, satellite imagery, ecosystems, agriculture, urban, infrastructure, earth observation, earthquakes, environmental, geology, geophysics, geospatial, mapping, natural resource, cog, synthetic aperture radar, aws-pds
+Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter Data Set 1x1 degree tiled data and metadata in a S3 bucket, Global VRT mosaics (vrt arn:aws:s3:::sentinel-1-global-coherence-earthbigdata/data/tiles us-west-2 S3 Bucket http://sentinel-1-global-coherence-earthbigdata.s3-website-us-west-2.amazonaws.c For questions regarding data methodology or delivery, contact info@earthbigdata. [Earth Big Data LLC](https://earthbigdata.com/) The data set covers the time period from 1-Dec-2019 to 30-Nov-2020. No updates a The use of these data fall under the terms and conditions of the [Creative Commo global, satellite imagery, ecosystems, agriculture, urban, infrastructure, earth observation, earthquakes, environmental, geology, geophysics, geospatial, mapping, natural resource, cog, synthetic aperture radar, aws-pds
Golden Retriever Lifetime Study: Whole genome genotyping of Golden Retrievers on Axiom HD Arrays Two zip files containing genotyping data from Axiom Array sets A and B, as well arn:aws:s3:::mafgrlsgenome us-west-2 S3 Bucket https://datacommons.morrisanimalfoundation.org/artisanal_dataset/151 datacommons@morrisanimalfoundation.org [Morris Animal Foundation](https://www.morrisanimalfoundation.org/) Static CC-BY-SA aws-pds, genome, genotyping, golden retriever lifetime study, morris animal foundation False
Google Books Ngrams A data set containing Google Books n-gram corpora in a Hadoop friendly file form arn:aws:s3:::datasets.elasticmapreduce/ngrams/books/ us-east-1 S3 Bucket http://books.google.com/ngrams/ https://books.google.com/ngrams Not managed Not updated Creative Commons Attribution 3.0 Unported License amazon.science, natural language processing
Google Brain Genomics Sequencing Dataset for Benchmarking and Development FASTQ files for nine human samples comprising three parent-child trios at 40X 30 arn:aws:s3:::genomics-benchmark-datasets/google-brain us-east-1 S3 Bucket https://genomics-benchmark-datasets.s3.amazonaws.com/README.txt genomics-benchmark-datasets@amazon.com Amazon Web Services Occasionally as new derived files (alignment files or variant call files) are ge [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) amazon.science, bioinformatics, life sciences, genetic, genomic, fastq, short read sequencing, long read sequencing, whole exome sequencing, whole genome sequencing
@@ -404,11 +403,11 @@ Harvard Electroencephalography Database Harvard Electroencephalography Database
Harvard-Emory ECG Database Harvard-Emory ECG Database arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-ecg-accesspoint/ECG/ us-east-1 S3 Bucket More documentation can be found [here](https://doi.org/10.60508/g072-7n95) contact@bdsp.io [Brain Data Science Platform](https://bdsp.io/) New data is added as soon as it is available. "BDSP Restricted Health Data License 1.0.0 ""[BDSP Licence](https://bdsp.io/conten" aws-pds, neurophysiology, medicine, machine learning, neuroscience, deep learning, life sciences, bioinformatics https://doi.org/10.60508/g072-7n95
Hecatomb Databases Hecatomb Database Resources arn:aws:s3:::hecatombdatabases us-west-2 S3 Bucket https://hecatomb.readthedocs.io/en/latest/ shandley@wustl.edu [Washington University in St. Louis](https://wustl.edu/) Every 6 to 12 months [MIT](https://opensource.org/licenses/MIT) aws-pds, life sciences, genetic, genomic, metagenomics, bioinformatics, whole genome sequencing, virus ['[Explore Bucket](https://hecatombdatabases.s3.us-west-2.amazonaws.com/index.html)']
Helpful Sentences from Reviews Helpful Sentences from Reviews arn:aws:s3:::helpful-sentences-from-reviews us-east-1 S3 Bucket https://helpful-sentences-from-reviews.s3.amazonaws.com/README.md gkutiel@amazon.com [Amazon](https://www.amazon.com/) Not updated This data is available for anyone to use under the terms of the CDLA-Sharing lic amazon.science, information retrieval, natural language processing, text analysis, json ['[train.json](https://helpful-sentences-from-reviews.s3.amazonaws.com/train.json)', '[test.json](https://helpful-sentences-from-reviews.s3.amazonaws.com/test.json)']
-High Resolution Canopy Height Maps by WRI and Meta Global Canopy Height maps GeoTIFF files and Geojson files with observation date arn:aws:s3:::dataforgood-fb-data/forests/v1/alsgedi_global_v6_float/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial
High Resolution Canopy Height Maps by WRI and Meta California Canopy Height maps GeoTIFF files and Geojson files with observation arn:aws:s3:::dataforgood-fb-data/forests/v1/California/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial
+High Resolution Canopy Height Maps by WRI and Meta Global Canopy Height maps GeoTIFF files and Geojson files with observation date arn:aws:s3:::dataforgood-fb-data/forests/v1/alsgedi_global_v6_float/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial
+High Resolution Canopy Height Maps by WRI and Meta Sao Paulo Canopy Height maps GeoTIFF files and Geojson files with observation d arn:aws:s3:::dataforgood-fb-data/forests/v1/sao_paulo/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial
High Resolution Canopy Height Maps by WRI and Meta Sub Saharan Africa Height maps GeoTIFF files and Geojson files with observation arn:aws:s3:::dataforgood-fb-data/forests/v1/subsaharan_africa/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial
High Resolution Canopy Height Maps by WRI and Meta Model weights arn:aws:s3:::dataforgood-fb-data/forests/v1/models/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial
-High Resolution Canopy Height Maps by WRI and Meta Sao Paulo Canopy Height maps GeoTIFF files and Geojson files with observation d arn:aws:s3:::dataforgood-fb-data/forests/v1/sao_paulo/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial
High Resolution Downscaled Climate Data for Southeast Alaska High-res dynamically downscaled climate data for Southeast Alaska arn:aws:s3:::wrf-se-ak-ar5 us-west-2 S3 Bucket https://www.sciencebase.gov/catalog/item/5f93658882ce720ee2d598a7 http://directory.iarc.uaf.edu/richard-lader Scenarios Network for Alaska + Arctic Planning at the International Arctic Resea as needed https://creativecommons.org/licenses/by/4.0/ aws-pds, agriculture, climate, coastal, earth observation, environmental, weather, aws-pds, sustainability ['[Browse Bucket](http://wrf-se-ak-ar5.s3-website-us-west-2.amazonaws.com/)']
High Resolution Population Density Maps + Demographic Estimates by CIESIN and Meta Cloud-optimized GeoTIFF files arn:aws:s3:::dataforgood-fb-data/hrsl-cogs/ us-east-1 S3 Bucket [Project overview](https://dataforgood.facebook.com/dfg/docs/methodology-high-re disastermaps@fb.com [Meta](https://dataforgood.fb.com/) Quarterly https://creativecommons.org/licenses/by/4.0/ population, demographics, machine learning, aerial imagery, satellite imagery, image processing, geospatial, disaster response, aws-pds
High Resolution Population Density Maps + Demographic Estimates by CIESIN and Meta CSV files arn:aws:s3:::dataforgood-fb-data/csv/ us-east-1 S3 Bucket [Project overview](https://dataforgood.facebook.com/dfg/docs/methodology-high-re disastermaps@fb.com [Meta](https://dataforgood.fb.com/) Quarterly https://creativecommons.org/licenses/by/4.0/ population, demographics, machine learning, aerial imagery, satellite imagery, image processing, geospatial, disaster response, aws-pds
@@ -426,8 +425,8 @@ I-CARE:International Cardiac Arrest REsearch consortium Electroencephalography D
IBL Behavioral Data on AWS Project data in public bucket arn:aws:s3:::ibl-brain-wide-map-public us-east-1 S3 Bucket https://int-brain-lab.github.io/iblenv/dj_docs/dj_public.html info@internationalbrainlab.org [International Brain Laboratory](https://www.internationalbrainlab.com) TBD CC-BY 4.0 aws-pds, life sciences, neuroscience, neurophysiology, open source software, Mus musculus
IBL Neuropixels Brainwide Map on AWS Project data in public bucket arn:aws:s3:::ibl-brain-wide-map-public us-east-1 S3 Bucket https://int-brain-lab.github.io/iblenv/notebooks_external/data_release_brainwide info@internationalbrainlab.org [International Brain Laboratory](https://www.internationalbrainlab.com) TBD CC-BY 4.0 aws-pds, life sciences, neuroscience, neurophysiology, open source software, Mus musculus
IBL Neuropixels Reproducible Ephys Data on AWS Project data in public bucket arn:aws:s3:::ibl-brain-wide-map-public us-east-1 S3 Bucket https://int-brain-lab.github.io/iblenv/notebooks_external/data_release_repro_eph info@internationalbrainlab.org [International Brain Laboratory](https://www.internationalbrainlab.com) TBD CC-BY 4.0 aws-pds, life sciences, neuroscience, neurophysiology, open source software, Mus musculus
-ICGC on AWS This public Amazon S3 bucket contains analysis metadata in XML format for genome arn:aws:s3:::oicr.icgc.meta/metadata us-east-1 S3 Bucket https://dcc.icgc.org/icgc-in-the-cloud/aws dcc-support@icgc.org [International Cancer Genome Collaboratory](https://dcc.icgc.org/) New data is added as soon as it is available. Data use is subject to the access and publication polices of the source. Distrib aws-pds, cancer, genetic, genomic, life sciences, bam, vcf
ICGC on AWS Raw sequencing and other primary data from non-TCGA ICGC projects arn:aws:s3:::oicr.icgc us-east-1 S3 Bucket https://dcc.icgc.org/icgc-in-the-cloud/aws dcc-support@icgc.org [International Cancer Genome Collaboratory](https://dcc.icgc.org/) New data is added as soon as it is available. Data use is subject to the access and publication polices of the source. Distrib aws-pds, cancer, genetic, genomic, life sciences, bam, vcf https://docs.icgc.org/download/guide/
+ICGC on AWS This public Amazon S3 bucket contains analysis metadata in XML format for genome arn:aws:s3:::oicr.icgc.meta/metadata us-east-1 S3 Bucket https://dcc.icgc.org/icgc-in-the-cloud/aws dcc-support@icgc.org [International Cancer Genome Collaboratory](https://dcc.icgc.org/) New data is added as soon as it is available. Data use is subject to the access and publication polices of the source. Distrib aws-pds, cancer, genetic, genomic, life sciences, bam, vcf
ICGC on AWS BAM and VCF files from the The PanCancer Analysis of Whole Genomes (PCAWG) study arn:aws:s3:::icgc us-east-1 S3 Bucket https://dcc.icgc.org/icgc-in-the-cloud/aws dcc-support@icgc.org [International Cancer Genome Collaboratory](https://dcc.icgc.org/) New data is added as soon as it is available. Data use is subject to the access and publication polices of the source. Distrib aws-pds, cancer, genetic, genomic, life sciences, bam, vcf https://icgc.org/daco
IDEAM - Colombian Radar Network Level II data arn:aws:s3:::s3-radaresideam us-east-1 S3 Bucket http://www.pronosticosyalertas.gov.co/archivos-radar atencionalciudadano@ideam.gov.co, radares_ideam@ideam.gov.co [IDEAM](http://www.ideam.gov.co/) Updated level II data is added as soon as it is available. Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, natural resource, weather, meteorological
ISERV ISERV Optical Imagery arn:aws:s3:::nasa-iserv us-west-2 S3 Bucket https://stacindex.org/collections/nasa-iserv support@radiant.earth [Radiant Earth Foundation](https://www.radiant.earth/) Not updated The data is released under a ODC Public Domain Dedication & License 1.0 ([PDDL-1 aws-pds, geospatial, earth observation, satellite imagery, environmental
@@ -437,34 +436,34 @@ InRad COVID-19 X-Ray and CT Scans Radiographs in png format CT-Scans in nii form
Indexes for Kaiju This AWS S3 bucket contains pre-built indexes for Kaiju arn:aws:s3:::kaiju-idx eu-central-1 S3 Bucket https://bioinformatics-centre.github.io/kaiju/downloads.html https://github.com/bioinformatics-centre/kaiju/issues [Peter Menzel](https://github.com/pmenzel) roughly yearly Public Domain aws-pds, bioinformatics, biology, genomic, life sciences, whole genome sequencing, reference index, metagenomics, microbiome
Indiana Statewide Digital Aerial Imagery Catalog State of Indiana digital orthophotography archive arn:aws:s3:::gisimageryingov us-east-2 S3 Bucket https://imagery-ingov.hub.arcgis.com/ sscholer@iot.in.gov Indiana Geographic Information Office The State of Indiana has had a 4-year cycle collecting imagery. The collections Access to Indiana Geographic Information Office Orthoimagery is governed by Crea aerial imagery, aws-pds, earth observation, geospatial, imaging, mapping, cog, natural resource, sustainability, agriculture
Integrative Analysis of Lung Adenocarcinoma in Environment and Genetics Lung cancer Etiology (Phase 2) Whole Genome Sequencing, Whole Exome Sequencing arn:aws:s3:::gdc-cddp-eagle-1-phs001239-2-open us-east-1 S3 Bucket https://ftp.ncbi.nlm.nih.gov/dbgap/studies/phs001239/phs001239.v1.p1 support@nci-gdc.datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month (NIH Genomic Data Sharing Policy)[https://gdc.cancer.gov/access-data/data-access cancer, whole exome sequencing, whole genome sequencing, aws-pds, life sciences, STRIDES, genomic, epigenomics
-Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) Notifications for new data arn:aws:sns:us-west-2:367587189974:its-live-data-object_created us-west-2 SNS Topic https://its-live-data.s3.us-west-2.amazonaws.com/README.html If you have questions about the data itself or the processing methods used, plea [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Up to daily, as new satellite imagery is made available. [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/ aws-pds, ice, earth observation, satellite imagery, geophysics, geospatial, global, cog, netcdf, zarr, stac
Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) ITS_LIVE Data S3 Bucket arn:aws:s3:::its-live-data us-west-2 S3 Bucket https://its-live-data.s3.us-west-2.amazonaws.com/README.html If you have questions about the data itself or the processing methods used, plea [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Up to daily, as new satellite imagery is made available. [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/ aws-pds, ice, earth observation, satellite imagery, geophysics, geospatial, global, cog, netcdf, zarr, stac ['[Browse Bucket](https://its-live-data.s3.amazonaws.com/index.html)']
+Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) Notifications for new data arn:aws:sns:us-west-2:367587189974:its-live-data-object_created us-west-2 SNS Topic https://its-live-data.s3.us-west-2.amazonaws.com/README.html If you have questions about the data itself or the processing methods used, plea [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Up to daily, as new satellite imagery is made available. [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/ aws-pds, ice, earth observation, satellite imagery, geophysics, geospatial, global, cog, netcdf, zarr, stac
International Neuroimaging Data-Sharing Initiative (INDI) Neuroimaging data for the International Neuroimaging Data-Sharing Initiative (IN arn:aws:s3:::fcp-indi us-east-1 S3 Bucket http://fcon_1000.projects.nitrc.org/ [FCP-INDI](alexandre.franco@childmind.org) [Child Mind Institute](https://childmind.org/our-research/) Each dataset within INDI has its own release schedule. See release date and freq ODC-By v1.0 for imaging data and BSD 3-Clause for CPAC, unless otherwise specifi aws-pds, life sciences, imaging, neuroimaging, neuroscience, magnetic resonance imaging, Homo sapiens
JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Digital Terrain Models Digital terrain models, orthoimages, shaded reliefs, and quality assurance docum arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/usgs_dtms/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. At least one update to this dataset is [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, elevation, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_usgs_dtms)']
-JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations Scenes and metadata for monoscopic observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/monoscopic/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_monoscopic_uncontrolled_observations)']
JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations Scenes and metadata for stereoscopic observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/stereoscopic/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_stereoscopic_uncontrolled_observations)']
+JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations Scenes and metadata for monoscopic observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/monoscopic/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_monoscopic_uncontrolled_observations)']
JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations Scenes and metadata for spectral profiler (spsupport) observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/spsupport/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_spsupport_uncontrolled_observations)']
-JMA Himawari-8/9 Himawari-9 Imagery arn:aws:s3:::noaa-himawari9 us-east-1 S3 Bucket https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-himawari9.s3.amazonaws.com/index.html)']
JMA Himawari-8/9 New data notifications for Himawari-8, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewHimawari8Object us-east-1 SNS Topic https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery
-JMA Himawari-8/9 New data notifications for Himawari-9, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewHimawariNineObject us-east-1 SNS Topic https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery
JMA Himawari-8/9 Himawari-8 Imagery arn:aws:s3:::noaa-himawari8 us-east-1 S3 Bucket https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-himawari8.s3.amazonaws.com/index.html)']
+JMA Himawari-8/9 New data notifications for Himawari-9, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewHimawariNineObject us-east-1 SNS Topic https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery
+JMA Himawari-8/9 Himawari-9 Imagery arn:aws:s3:::noaa-himawari9 us-east-1 S3 Bucket https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-himawari9.s3.amazonaws.com/index.html)']
Japanese Tokenizer Dictionaries Dictionary Files arn:aws:s3:::cotonoha-dic ap-northeast-1 S3 Bucket This dataset includes dictionaries for tokenization and morphologicalanalysis of polm@cotonoha.io Cotonoha Infrequently (typically less than once a year) Versions of Unidic offered here are available under the GPL/LGPL/BSD license.IPA aws-pds, natural language processing, csv, japanese
K2 Mission Data K2 Mission data files arn:aws:s3:::stpubdata/k2 us-east-1 S3 Bucket https://archive.stsci.edu/missions-and-data/k2 archive@stsci.edu [Space Telescope Science Institute](http://www.stsci.edu/) Never STScI hereby grants the non-exclusive, royalty free, non-transferable, worldwide astronomy, aws-pds False
KITTI Vision Benchmark Suite http://wwwcvlibsnet/datasets/kitti/raw_dataphp arn:aws:s3:::avg-kitti eu-central-1 S3 Bucket http://www.cvlibs.net/datasets/kitti/ http://www.cvlibs.net/people.php [Max Planck Campus Tübingen](http://tue.mpg.de/) Not updated Creative Commons Attribution-NonCommercial-ShareAlike 3.0 http://creativecommons aws-pds, autonomous vehicles, computer vision, robotics, machine learning, deep learning
Kepler Mission Data Kepler Mission data files arn:aws:s3:::stpubdata/kepler us-east-1 S3 Bucket https://archive.stsci.edu/missions-and-data/kepler archive@stsci.edu [Space Telescope Science Institute](http://www.stsci.edu/) Never STScI hereby grants the non-exclusive, royalty-free, non-transferable, worldwide astronomy, aws-pds False
-Korea Meteorological Administration (KMA) GK-2A Satellite Data GK2A Imagery arn:aws:s3:::noaa-gk2a-pds us-east-1 S3 Bucket https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fa For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2 minutes for 4 visible channels and 12 infrared chan Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-gk2a-pds.s3.amazonaws.com/index.html)']
Korea Meteorological Administration (KMA) GK-2A Satellite Data New data notifications for GK2A, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewGK2AObject us-east-1 SNS Topic https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fa For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2 minutes for 4 visible channels and 12 infrared chan Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery
+Korea Meteorological Administration (KMA) GK-2A Satellite Data GK2A Imagery arn:aws:s3:::noaa-gk2a-pds us-east-1 S3 Bucket https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fa For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2 minutes for 4 visible channels and 12 infrared chan Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-gk2a-pds.s3.amazonaws.com/index.html)']
Kraken2 NCBI RefSeq Complete V205 database on AWS Kraken2 NCBI RefSeq Complete V205 database arn:aws:s3:::kraken2-ncbi-refseq-complete-v205 us-west-2 S3 Bucket https://github.com/R-Wright-1/kraken_metaphlan_comparison/wiki robyn.wright@dal.ca Robyn Wright This database is currently what was published in our 2023 paper comparing the pe There are no restrictions on the use of this data. aws-pds, metagenomics, microbiome, benchmark, bioinformatics ['[Browse Bucket](https://kraken2-ncbi-refseq-complete-v205.s3.amazonaws.com/)']
-KyFromAbove on AWS KyFromAbove Topographic Contours, digital elevation models, point cloud, spot el arn:aws:s3:::kyfromabove/elevation/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/)'] False
-KyFromAbove on AWS LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a arn:aws:s3:::kyfromabove/elevation/PointCloud/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/PointCloud/)'] False
+KyFromAbove on AWS LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky or arn:aws:s3:::kyfromabove/elevation/DEM/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/DEM/)'] False
KyFromAbove on AWS KyFromAbove ortho imagery for the Commonwealth of Kentucky organized in a 5000x5 arn:aws:s3:::kyfromabove/imagery/orthos/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/orthos/)'] False
-KyFromAbove on AWS KyFromAbove oblique imagery can be found in this folder The four oblique views arn:aws:s3:::kyfromabove/imagery/obliques/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/obliques/)'] False
-KyFromAbove on AWS There are three data resources in this folder - 1) KyTopo Map Series quadrangles arn:aws:s3:::kyfromabove/elevation/KyTopoMapSeries/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/KyTopoMapSeries/)'] False
-KyFromAbove on AWS Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital arn:aws:s3:::kyfromabove/elevation/Contours/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/Contours/)'] False
-KyFromAbove on AWS KyFromAbove aerial imagery, both nadir and oblique views, can be found in this b arn:aws:s3:::kyfromabove/imagery/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/)'] False
KyFromAbove on AWS Elevation and imagery data resources for the Commonwealth of Kentucky are organi arn:aws:s3:::kyfromabove us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html)'] False
-KyFromAbove on AWS LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky or arn:aws:s3:::kyfromabove/elevation/DEM/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/DEM/)'] False
+KyFromAbove on AWS KyFromAbove Topographic Contours, digital elevation models, point cloud, spot el arn:aws:s3:::kyfromabove/elevation/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/)'] False
+KyFromAbove on AWS KyFromAbove aerial imagery, both nadir and oblique views, can be found in this b arn:aws:s3:::kyfromabove/imagery/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/)'] False
KyFromAbove on AWS The data in this bucket includes spot elevations for the entire Commonwealth of arn:aws:s3:::kyfromabove/elevation/SpotElevations/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/SpotElevations/)'] False
+KyFromAbove on AWS There are three data resources in this folder - 1) KyTopo Map Series quadrangles arn:aws:s3:::kyfromabove/elevation/KyTopoMapSeries/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/KyTopoMapSeries/)'] False
+KyFromAbove on AWS Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital arn:aws:s3:::kyfromabove/elevation/Contours/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/Contours/)'] False
+KyFromAbove on AWS LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a arn:aws:s3:::kyfromabove/elevation/PointCloud/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/PointCloud/)'] False
+KyFromAbove on AWS KyFromAbove oblique imagery can be found in this folder The four oblique views arn:aws:s3:::kyfromabove/imagery/obliques/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/obliques/)'] False
LOFAR ELAIS-N1 cycle 2 observations on AWS LOFAR ELAIS-N1 cycle 2 observations arn:aws:s3:::lofar-elais-n1 us-east-1 S3 Bucket https://www.lofarcloud.uk/data.html https://www.lofarcloud.uk/ Institute for Astronomy, University of Edinburgh Not updated "The data are considered ""LOFAR data in the public domain"" and their use must adh" astronomy, aws-pds, survey, imaging
Learning to Rank and Filter - community question answering S3 bucket with dataset arn:aws:s3:::ltrf-cqa-dataset us-west-2 S3 Bucket https://ltrf-cqa-dataset.s3-us-west-2.amazonaws.com/readme.txt ekravi@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated. [CDLA-Permissive](https://cdla.dev/permissive-1-0/) amazon.science, natural language processing, machine learning
Legal Entity Identifier (LEI) and Legal Entity Reference Data (LE-RD) Legal Entity Identifier (LEI) and Legal Entity Reference Data (LE-RD) arn:aws:s3:::gleif eu-central-1 S3 Bucket https://www.gleif.org/en/lei-data/gleif-golden-copy/download-the-golden-copy#/ info@gleif.org [GLEIF](http://www.gleif.org/) Three times daily (about every 8 hours) Creative Commons (CC0) license analytics, aws-pds, blockchain, climate, commerce, copyright monitoring, csv, financial markets, governance, government spending, json, market data, statistics, transparency, xml, socioeconomic
@@ -473,26 +472,26 @@ Longitudinal Nutrient Deficiency Folder contains the terms of use, paper, and zi
Low Altitude Disaster Imagery (LADI) Dataset LADI dataset: images and labels arn:aws:s3:::ladi us-west-2 S3 Bucket https://github.com/LADI-Dataset/ladi-overview ladi-dataset-admin@mit.edu [MIT Lincoln Laboratory Humanitarian Assistance and Disaster Relief group](https Periodically Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, aerial imagery, coastal, computer vision, disaster response, earth observation, earthquakes, geospatial, imaging, image processing, infrastructure, land, machine learning, mapping, natural resource, seismology, transportation, urban, water
Low Context Name Entity Recognition (NER) Datasets with Gazetteer Data files arn:aws:s3:::lowcontext-ner-gaz us-east-1 S3 Bucket https://lowcontext-ner-gaz.s3.amazonaws.com/readme.html njfn@amazon.com [Amazon](https://www.amazon.com/) N/A [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) amazon.science, natural language processing
MIMIC-III (‘Medical Information Mart for Intensive Care’) Project data files arn:aws:s3:::mimic-iii-physionet us-east-1 S3 Bucket https://mimic.physionet.org/ https://mimic.physionet.org/help/ [MIT Laboratory for Computational Physiology](https://lcp.mit.edu/) Not updated https://physionet.org/content/mimiciii/view-license/1.4/ aws-pds, bioinformatics, health, life sciences, natural language processing, us https://mimic.physionet.org/gettingstarted/access/#request-access-to-mimic-iii
-MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 Imagery and metadata arn:aws:s3:::astraea-opendata us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
-MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 S3 Inventory files for MOD11A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD11A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
-MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 S3 Inventory files for MOD13A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD13A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 S3 Inventory files for MYD11A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MYD11A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
+MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 S3 Inventory files for MOD13A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD13A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
+MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 S3 Inventory files for MOD11A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD11A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 S3 Inventory files for MCD43A4 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MCD43A4 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
-MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 New data notifications arn:aws:sns:us-west-2:791757209086:astraea-opendata-events us-west-2 SNS Topic Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response
+MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 Imagery and metadata arn:aws:s3:::astraea-opendata us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 S3 Inventory files for MYD13A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MYD13A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True
+MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 New data notifications arn:aws:sns:us-west-2:791757209086:astraea-opendata-events us-west-2 SNS Topic Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response
MONKEY PAS- and IHC-stained whole slide images with corresponding dot annotaions for in arn:aws:s3:::monkey-training us-west-2 S3 Bucket https://monkey.grand-challenge.org/ linda.studer@radboudumc.nl Radboud University Medical Center As required CC BY-NC-SA 4.0 aws-pds, life sciences, cancer, computational pathology, deep learning, grand-challenge.org, histopathology, computer vision, digital pathology, medical image computing, medical imaging, classification, imaging, machine learning
MWIS VR Instances Conflict graphs for maximum weight independent set arn:aws:s3:::mwis-vr-instances us-east-1 S3 Bucket https://mwis-vr-instances.s3.amazonaws.com/vrInstances.pdf resendem@amazon.com [Amazon](https://www.amazon.com/) Infrequent MIT-0 amazon.science, traffic, transportation, graph
Mars Spectrometry 2: Gas Chromatography for the Sample Analysis at Mars Data (SAM) Instrument GCMS SAM Instrument Exploration Dataset arn:aws:s3:::gcms-samdata-mlchallenge us-west-2 S3 Bucket https://pds-geosciences.wustl.edu/missions/msl/sam.htm victoria.dapoian@nasa.gov, eric.i.lyness@nasa.gov NASA """Static dataset for the commercial data. When more data is collected on the SAM " There are no restrictions on the use, access, and/or download of data from the N aws-pds, analytics, archives, deep learning, planetary, machine learning, NASA SMD AI False
Mars Spectrometry: Detect Evidence for Past Habitability EGAMS SAM Instrument Exploration Dataset arn:aws:s3:::egams-samdata-mlchallenge us-west-2 S3 Bucket https://pds-geosciences.wustl.edu/missions/msl/sam.htm victoria.dapoian@nasa.gov , eric.i.lyness@nasa.gov NASA Static dataset for the commercial dataWhen more data is collected on the SAM ins There are no restrictions on the use, access, and/or download of data from the N aws-pds, analytics, archives, deep learning, planetary, machine learning, NASA SMD AI False
-Materials Project Data Build Data arn:aws:s3:::materialsproject-build us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography ['[Browse Bucket](https://materialsproject-build.s3.amazonaws.com/index.html)']
-Materials Project Data Parsed Data arn:aws:s3:::materialsproject-parsed us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography ['[Browse Bucket](https://materialsproject-parsed.s3.amazonaws.com/index.html)']
Materials Project Data Raw Data arn:aws:s3:::materialsproject-raw us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography
+Materials Project Data Parsed Data arn:aws:s3:::materialsproject-parsed us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography ['[Browse Bucket](https://materialsproject-parsed.s3.amazonaws.com/index.html)']
+Materials Project Data Build Data arn:aws:s3:::materialsproject-build us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography ['[Browse Bucket](https://materialsproject-build.s3.amazonaws.com/index.html)']
Maxar Open Data Program Imagery and metadata arn:aws:s3:::maxar-opendata us-west-2 S3 Bucket https://www.maxar.com/open-data https://www.maxar.com/open-data [Maxar](https://www.maxar.com/) New data is released in response to activations. Older data may be migrated to t Creative Commons Attribution Non Commercial 4.0 aws-pds, earth observation, disaster response, geospatial, satellite imagery, cog, stac ['[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/maxar-opendata.s3.dualstack.us-west-2.amazonaws.com/events/catalog.json)', '[STAC Catalog](https://stacindex.org/catalogs/maxar-open-data-catalog-ard-format#/)']
-Medical Segmentation Decathlon Ten tasks from the Medical Segmentation Decathlon Challenge Tasks are organized arn:aws:s3:::msd-for-monai us-west-2 S3 Bucket http://medicaldecathlon.com [Medical Decathlon Organisers](medicaldecathlon@gmail.com) [MONAI Development Team](https://github.com/Project-MONAI/MONAI) This is a static dataset; however, tutorials and resources will be updated as th [CC-BY-SA 4.0 International](https://creativecommons.org/licenses/by/4.0/) aws-pds, health, life sciences, medicine, imaging, magnetic resonance imaging, nifti, computed tomography, segmentation
Medical Segmentation Decathlon This is a mirror of s3://msd-for-monai in eu-west-2 arn:aws:s3:::msd-for-monai-eu eu-west-2 S3 Bucket http://medicaldecathlon.com [Medical Decathlon Organisers](medicaldecathlon@gmail.com) [MONAI Development Team](https://github.com/Project-MONAI/MONAI) This is a static dataset; however, tutorials and resources will be updated as th [CC-BY-SA 4.0 International](https://creativecommons.org/licenses/by/4.0/) aws-pds, health, life sciences, medicine, imaging, magnetic resonance imaging, nifti, computed tomography, segmentation
+Medical Segmentation Decathlon Ten tasks from the Medical Segmentation Decathlon Challenge Tasks are organized arn:aws:s3:::msd-for-monai us-west-2 S3 Bucket http://medicaldecathlon.com [Medical Decathlon Organisers](medicaldecathlon@gmail.com) [MONAI Development Team](https://github.com/Project-MONAI/MONAI) This is a static dataset; however, tutorials and resources will be updated as th [CC-BY-SA 4.0 International](https://creativecommons.org/licenses/by/4.0/) aws-pds, health, life sciences, medicine, imaging, magnetic resonance imaging, nifti, computed tomography, segmentation
MegaScenes The MegaScenes Dataset is an extensive collection of around 430k scenes, featuri arn:aws:s3:::megascenes us-east-1 S3 Bucket https://megascenes.github.io jt664@cornell.edu; gene@cs.cornell.edu Cornell University (https://www.cs.cornell.edu/~snavely/) The dataset will be updated periodically. Creative Commons Attribution 4.0 International License. The photos have their ow internet, benchmark, computer vision, deep learning
-Met Office Global Deterministic 10km on a 2-year rolling archive Met Office Global Deterministic 10km on a 2-year rolling archive arn:aws:s3:::met-office-atmospheric-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office] (https://www.metoffice.gov.uk/) The Global Deterministic available time steps are provided every hour from 0 to Creative Commons Attribution-ShareAlike (CC BY-SA) aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)']
Met Office Global Deterministic 10km on a 2-year rolling archive Notifications for new atmospheric model data arn:aws:sns:eu-west-2:633885181284:met-office-atmospheric-model-data-object_created eu-west-2 SNS Topic https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office] (https://www.metoffice.gov.uk/) The Global Deterministic available time steps are provided every hour from 0 to Creative Commons Attribution-ShareAlike (CC BY-SA) aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather
+Met Office Global Deterministic 10km on a 2-year rolling archive Met Office Global Deterministic 10km on a 2-year rolling archive arn:aws:s3:::met-office-atmospheric-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office] (https://www.metoffice.gov.uk/) The Global Deterministic available time steps are provided every hour from 0 to Creative Commons Attribution-ShareAlike (CC BY-SA) aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)']
Met Office UK Deterministic (UKV)2km on a 2-year rolling archive Notifications for new atmospheric model data arn:aws:sns:eu-west-2:633885181284:met-office-atmospheric-model-data-object_created eu-west-2 SNS Topic https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office] (https://www.metoffice.gov.uk/) The UKV provides hourly forecasts covering the period T+0 to T+48 hours, produce Creative Commons Attribution-ShareAlike (CC BY-SA) aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather
Met Office UK Deterministic (UKV)2km on a 2-year rolling archive Met Office UK Deterministic (UKV)2km on a 2-year rolling archive arn:aws:s3:::met-office-atmospheric-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office] (https://www.metoffice.gov.uk/) The UKV provides hourly forecasts covering the period T+0 to T+48 hours, produce Creative Commons Attribution-ShareAlike (CC BY-SA) aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)']
Molecular Profiling to Predict Response to Treatment (phs001965) miRNA Sequencing, Total RNA Sequencing, Whole Genome Sequencing, Targeted Sequen arn:aws:s3:::gdc-mp2prt-wt-phs001965-2-open us-east-1 S3 Bucket https://datacatalog.ccdi.cancer.gov/dataset/GDC-MP2PRT support@nci-gdc.datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month [NIH Genomic Data Sharing Policy](https://gdc.cancer.gov/access-data/data-access aws-pds, life sciences, cancer, genomic, whole genome sequencing, STRIDES
@@ -501,16 +500,16 @@ Mouse Brain Anatomy: MouseLight Imagery Imagery and Metadata arn:aws:s3:::janeli
Multi Token Completion multi-token-completion Datasets arn:aws:s3:::multi-token-completion us-east-1 S3 Bucket https://multi-token-completion.s3.amazonaws.com/README.txt guyk@amazon.com, orenk@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated Datasets are published under [CC-NC-SA-3.0](https://creativecommons.org/licenses amazon.science, natural language processing, machine learning
Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST) MUR Level 4 SST dataset in Zarr format The zarr-v1/ directory contains a zarr s arn:aws:s3:::mur-sst/zarr-v1 us-west-2 S3 Bucket https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1 podaac@podaac.jpl.nasa.gov [Farallon Institute](https://faralloninstitute.org) The temporal extent of the Zarr store is 2002-06-01 to 2020-01-20. There are no restrictions on the use of these data. aws-pds, earth observation, environmental, natural resource, oceans, satellite imagery, climate, water, weather
Multi-robot, Multi-Sensor, Multi-Environment Event Dataset (M3ED) Multi-robot, Multi-Sensor, Multi-Environment Event Dataset (M3ED) arn:aws:s3:::m3ed-dist us-west-2 S3 Bucket You can access the overview of the data [here](https://m3ed.io/overview/). https://m3ed.io/contact-us/ [Daniilidis Group](https://www.grasp.upenn.edu/people/kostas-daniilidis/), [Kuma The dataset will be uploaded sporadically, when bugs are found and new features Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) aws-pds, autonomous vehicles, computer vision, deep learning, event camera, global shutter camera, GNSS, GPS, h5, hdf5, IMU, lidar, machine learning, perception, robotics, RTK
-MultiCoNER Datasets MultiCoNER 2 Data files arn:aws:s3:::multiconer/multiconer2023/ us-west-2 S3 Bucket https://multiconer.s3.us-west-2.amazonaws.com/readme.html besnikf@amazon.com [Amazon](https://www.amazon.com/) N/A [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) natural language processing
MultiCoNER Datasets MultiCoNER 1 Data files arn:aws:s3:::multiconer/multiconer2022/ us-west-2 S3 Bucket https://multiconer.s3.us-west-2.amazonaws.com/readme.html besnikf@amazon.com [Amazon](https://www.amazon.com/) N/A [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) natural language processing
+MultiCoNER Datasets MultiCoNER 2 Data files arn:aws:s3:::multiconer/multiconer2023/ us-west-2 S3 Bucket https://multiconer.s3.us-west-2.amazonaws.com/readme.html besnikf@amazon.com [Amazon](https://www.amazon.com/) N/A [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) natural language processing
Multilingual Name Entity Recognition (NER) Datasets with Gazetteer Data file arn:aws:s3:::code-mixed-ner us-east-1 S3 Bucket https://code-mixed-ner.s3.amazonaws.com/readme.html njfn@amazon.com [Amazon](https://www.amazon.com/) N/A [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) amazon.science, natural language processing
Multimedia Commons This directory has 99,171,688 image files and 787,479 video files The videos ad arn:aws:s3:::multimedia-commons us-west-2 S3 Bucket https://multimediacommons.wordpress.com/yfcc100m-core-dataset/ multimedia-commons@icsi.berkeley.edu [Multimedia Commons](http://mmcommons.org/) Not updated. The International Computer Science Institute and Lawrence Livermore National Lab aws-pds, computer vision, machine learning, multimedia, video ['[Browse Bucket](https://multimedia-commons.s3.amazonaws.com/index.html)']
Multiview Extended Video with Activities (MEVA) AVI video clips and collection site map arn:aws:s3:::mevadata-public-01 us-east-1 S3 Bucket http://mevadata.org mevadata@kitware.com [Kitware](http://www.kitware.com/) We anticipate two or three updates as morevideo data is released to the public. http://mevadata.org/resources/MEVA-data-license.txt aws-pds, computer vision, urban, us, video
My School Today Using open-source georeferenced data and satellite data products, we construct t arn:aws:s3:::my-school-today us-west-2 S3 Bucket https://sdgstoday.org/dataset/my-school-today sdgstoday@unsdsn.org SDSN SDGs Today Monthly (weekly updates in progress) This work is licensed under a Creative Commons by Attribution (CC BY 4.0) licens aws-pds, education, geospatial, infrastructure, schools
NA-CORDEX - North American component of the Coordinated Regional Downscaling Experiment Project data files arn:aws:s3:::ncar-na-cordex us-west-2 S3 Bucket https://doi.org/10.26024/9xkm-fp81 rdahelp@ucar.edu [National Center for Atmospheric Research](https://ncar.ucar.edu/) Rare. NA-CORDEX is complete, but we may occasionally copy additional fields from https://na-cordex.org/terms-use.html climate, model, climate model, atmosphere, land, geospatial, aws-pds, sustainability, zarr
-NAIP on AWS The data in this bucket is 4-band (RGB + NIR) in MRF format and Cloud Optimized arn:aws:s3:::naip-analytic us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True
-NAIP on AWS The data in this bucket is Original Imagery in Geotiff format 4-band (RGB + NIR) arn:aws:s3:::naip-source us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True
NAIP on AWS The data in this bucket is 3 band RGB in Geotiff format It is converted and man arn:aws:s3:::naip-visualization us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True
+NAIP on AWS The data in this bucket is Original Imagery in Geotiff format 4-band (RGB + NIR) arn:aws:s3:::naip-source us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True
+NAIP on AWS The data in this bucket is 4-band (RGB + NIR) in MRF format and Cloud Optimized arn:aws:s3:::naip-analytic us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True
NASA / USGS Controlled Europa DTMs Scenes and metadata arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_dtms/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/europa_controlled_us https://answers.usgs.gov/ [NASA](https://www.nasa.gov) HiRISE data will be updated as new releases are made to the Planetary Data Syste [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_dtms?.language=en)']
NASA / USGS Controlled THEMIS Mosaics Scenes and metadata arn:aws:s3:::astrogeo-ard/mars/mo/themis/controlled_mosaics/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/themis_controlled_mosaics/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) None planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mo_themis_controlled_mosaics)']
NASA / USGS Europa Controlled Observation Mosaics Scenes and metadata arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_mosaics/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_sequence_mos https://answers.usgs.gov/ [NASA](https://www.nasa.gov) No future updates planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_mosaics)']
@@ -524,12 +523,9 @@ NASA Earth Exchange (NEX) Data Collection Global Daily Downscaled Projections (N
NASA Earth Exchange (NEX) Data Collection Localized Constructed Analogs (LOCA) arn:aws:s3:::nasanex/LOCA us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/nasa-nex https://www.nasa.gov/nex/team NASA Infrequently US Government work aws-pds, CMIP5, natural resource, climate, sustainability
NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) The NEX-GDDP-CMIP6-COG archive Files are in Cloud-Optimized GeoTiff (COG arn:aws:s3:::nex-gddp-cmip6-cog us-west-2 S3 Bucket https://doi.org/10.7917/OFSG3345 support@nccs.nasa.gov [NASA](https://www.nasa.gov) No future updates planned. As noted in the metadata of each file, the NEX-GDDP-CMIP6 archive wasinitially m aws-pds, CMIP6, climate, climate model, model, global, environmental, earth observation, climate projections, netcdf, near-surface relative humidity, near-surface specific humidity, precipitation, air temperature, NASA Center for Climate Simulation (NCCS), cog ['[Browse Bucket](https://nex-gddp-cmip6-cog.s3.us-west-2.amazonaws.com/index.html)'] False
NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) The NEX-GDDP-CMIP6 archive Files are in NetCDF format with the CF-17 metadata arn:aws:s3:::nex-gddp-cmip6 us-west-2 S3 Bucket https://doi.org/10.7917/OFSG3345 support@nccs.nasa.gov [NASA](https://www.nasa.gov) No future updates planned. As noted in the metadata of each file, the NEX-GDDP-CMIP6 archive wasinitially m aws-pds, CMIP6, climate, climate model, model, global, environmental, earth observation, climate projections, netcdf, near-surface relative humidity, near-surface specific humidity, precipitation, air temperature, NASA Center for Climate Simulation (NCCS), cog ['[Browse Bucket](https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html)'] False
-NASA High Energy Astrophysics Mission Data The NuSTAR Mission Data Archive For more information, see the NuSTAR pages at arn:aws:s3:::heasarc-public/nustar/data/obs/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The HEAO-1 Mission Data Archive Total size 84 GB arn:aws:s3:::nasa-heasarc/heao1/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The HaloSat Mission Data Archive More information available at the mission su arn:aws:s3:::nasa-heasarc/halosat/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The Ginga Mission Data Archive Total size 19 GB arn:aws:s3:::nasa-heasarc/ginga/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data Fermi LAT weekly Data Products as well as GBM bursts and triggers (The full mi arn:aws:s3:::heasarc-public/fermi/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The NICER Mission Data Archive For more information, see the NICER Science Ce arn:aws:s3:::heasarc-public/nicer/data/obs/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The HEAO-1 Mission Data Archive Total size 84 GB arn:aws:s3:::nasa-heasarc/heao1/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The The Einstein Observatory (aka HEAO-2) Data Archive Total size 15 GB arn:aws:s3:::nasa-heasarc/einstein/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The DXS Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/dxs/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The COS-B Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/cosb/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The COPERNICUS Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/copernicus/data/uclrawfits us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
@@ -538,28 +534,31 @@ NASA High Energy Astrophysics Mission Data Chandra Mission Data Archive supporte
NASA High Energy Astrophysics Mission Data The BBXRT Mission Data Archive Total size of 1 GB arn:aws:s3:::nasa-heasarc/bbxrt us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The ASCA Mission Data Archive More information available at the mission suppor arn:aws:s3:::nasa-heasarc/asca/data/rev2 us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The Ariel5 Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/ariel5/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The Ginga Mission Data Archive Total size 19 GB arn:aws:s3:::nasa-heasarc/ginga/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The HaloSat Mission Data Archive More information available at the mission su arn:aws:s3:::nasa-heasarc/halosat/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The The EXOSAT Observatory Data Archive Total size 80 GB arn:aws:s3:::nasa-heasarc/exosat/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The HETE-2 Mission Data Archive Total size 287 GB arn:aws:s3:::nasa-heasarc/hete2/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The The Einstein Observatory (aka HEAO-2) Data Archive Total size 15 GB arn:aws:s3:::nasa-heasarc/einstein/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data Swift Mission Data Archive For more information, see the website of the Swift arn:aws:s3:::nasa-heasarc/swift/data/obs/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The VELA 5B Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/vela5b/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The BeppoSAX Mission Data Archive More information available at the mission s arn:aws:s3:::nasa-heasarc/sax/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The ROSAT Mission Data Archive For more information, see the website of the R arn:aws:s3:::nasa-heasarc/rosat/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The OSO-8 Mission Data Archive Total size 5 GB arn:aws:s3:::nasa-heasarc/oso8/data/gcxse/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The NuSTAR Mission Data Archive For more information, see the NuSTAR pages at arn:aws:s3:::heasarc-public/nustar/data/obs/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The NICER Mission Data Archive For more information, see the NICER Science Ce arn:aws:s3:::heasarc-public/nicer/data/obs/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The SAS-2 Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/sas2/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The Suzaku Mission Data Archive For more information, see the website of the arn:aws:s3:::nasa-heasarc/suzaku/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data Swift Mission Data Archive For more information, see the website of the Swift arn:aws:s3:::nasa-heasarc/swift/data/obs/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The Hitomi Mission Data Archive Total size 115 GB arn:aws:s3:::nasa-heasarc/hitomi/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The VELA 5B Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/vela5b/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The WASS Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/wass/data/maps/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
+NASA High Energy Astrophysics Mission Data The RXTE Mission Data Archive For more information, see the website of the RXT arn:aws:s3:::nasa-heasarc/xte/data/archive/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data XMM-Newton Mission Data Archive For more information, see the website of the arn:aws:s3:::nasa-heasarc/xmm/data/rev0/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA High Energy Astrophysics Mission Data The XQC Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/xqc/data/spectra/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The SAS-2 Mission Data Archive Total size < 1 GB arn:aws:s3:::nasa-heasarc/sas2/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The RXTE Mission Data Archive For more information, see the website of the RXT arn:aws:s3:::nasa-heasarc/xte/data/archive/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The Suzaku Mission Data Archive For more information, see the website of the arn:aws:s3:::nasa-heasarc/suzaku/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The OSO-8 Mission Data Archive Total size 5 GB arn:aws:s3:::nasa-heasarc/oso8/data/gcxse/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA High Energy Astrophysics Mission Data The ROSAT Mission Data Archive For more information, see the website of the R arn:aws:s3:::nasa-heasarc/rosat/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
-NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) The COBE/Differential Microwave Radiometer (DMR) Total size of 25 GB arn:aws:s3:::nasa-lambda/cobe/dmr us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery
-NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) The COBE/Far-InfraRed Absolute Spectrophotometer (FIRAS) Total size of 94 GB arn:aws:s3:::nasa-lambda/cobe/firas us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery
+NASA High Energy Astrophysics Mission Data The HETE-2 Mission Data Archive Total size 287 GB arn:aws:s3:::nasa-heasarc/hete2/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray
NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) the Wilkinson Microwave Anisotropy Probe (WMAP)otal size of 2 TB arn:aws:s3:::nasa-lambda/cobe/map/dr5 us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery
+NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) The COBE/Far-InfraRed Absolute Spectrophotometer (FIRAS) Total size of 94 GB arn:aws:s3:::nasa-lambda/cobe/firas us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery
NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) The COBE/Diffuse Infrared Background Experiment (DIRBE) Total size of 95 GB arn:aws:s3:::nasa-lambda/cobe/dirbe us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery
+NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) The COBE/Differential Microwave Radiometer (DMR) Total size of 25 GB arn:aws:s3:::nasa-lambda/cobe/dmr us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery
NASA Physical Sciences Informatics (PSI) Directional Solidification Insert (DSI) and Observation and Analysis of Smectic arn:aws:s3:::nasa-psi us-west-2 S3 Bucket https://www.nasa.gov/PSI https://www.nasa.gov/content/psi-contacts-tutorials-and-related-links NASA New research data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, chemistry, space biology, fluid dynamics, physics, materials science ['[Browse Bucket](https://nasa-psi.s3.us-west-2.amazonaws.com/index.html)']
-NASA Prediction of Worldwide Energy Resources (POWER) POWER's Zarr Analysis Ready Data (ARD) Datasets arn:aws:s3:::power-analysis-ready-datastore us-west-2 S3 Bucket https://power.larc.nasa.gov/docs/ larc-power-project@mail.nasa.gov NASA Near Real Time (NRT); as soon as source data becomes available from our source d There are no restrictions on the use, access, and/or download of data from the N agriculture, air quality, analytics, archives, atmosphere, climate, climate model, data assimilation, deep learning, earth observation, energy, environmental, forecast, geoscience, geospatial, global, netcdf, history, imaging, industry, machine learning, machine translation, metadata, meteorological, model, opendap, radiation, satellite imagery, solar, statistics, sustainability, time series forecasting, water, weather, zarr, aws-pds ['[Browse Bucket](https://power-analysis-ready-datastore.s3.us-west-2.amazonaws.com/index.html)'] False
NASA Prediction of Worldwide Energy Resources (POWER) POWER's NetCDF Datastore arn:aws:s3:::power-datastore us-west-2 S3 Bucket https://power.larc.nasa.gov/docs/ larc-power-project@mail.nasa.gov NASA Near Real Time (NRT); as soon as source data becomes available from our source d There are no restrictions on the use, access, and/or download of data from the N agriculture, air quality, analytics, archives, atmosphere, climate, climate model, data assimilation, deep learning, earth observation, energy, environmental, forecast, geoscience, geospatial, global, netcdf, history, imaging, industry, machine learning, machine translation, metadata, meteorological, model, opendap, radiation, satellite imagery, solar, statistics, sustainability, time series forecasting, water, weather, zarr, aws-pds ['[Browse Bucket](https://power-datastore.s3.us-west-2.amazonaws.com/index.html)'] False
+NASA Prediction of Worldwide Energy Resources (POWER) POWER's Zarr Analysis Ready Data (ARD) Datasets arn:aws:s3:::power-analysis-ready-datastore us-west-2 S3 Bucket https://power.larc.nasa.gov/docs/ larc-power-project@mail.nasa.gov NASA Near Real Time (NRT); as soon as source data becomes available from our source d There are no restrictions on the use, access, and/or download of data from the N agriculture, air quality, analytics, archives, atmosphere, climate, climate model, data assimilation, deep learning, earth observation, energy, environmental, forecast, geoscience, geospatial, global, netcdf, history, imaging, industry, machine learning, machine translation, metadata, meteorological, model, opendap, radiation, satellite imagery, solar, statistics, sustainability, time series forecasting, water, weather, zarr, aws-pds ['[Browse Bucket](https://power-analysis-ready-datastore.s3.us-west-2.amazonaws.com/index.html)'] False
NASA SOHO/LASCO2 comet challenge on AWS AI/ML ready data set created using NASA/SOHO/LASCO2 archive data arn:aws:s3:::nasa-comets-training-data us-east-1 S3 Bucket https://ssa.esac.esa.int/ssa/#/pages/home ekaterina.m.verner@nasa.gov [NASA](http://www.nasa.gov/) No updates There are no restrictions on the use of this data. aws-pds, astronomy, machine learning, NASA SMD AI
NASA SOTERIA Simulation Testbed Data SOTERIA Testbed Data arn:aws:s3:::nasa-soteria-data us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/ chad.l.stephens@nasa.gov; tyler.fettrow@nasa.gov [NASA](http://www.nasa.gov/) As required [Creative Commons Attribution 4.0 International](https://creativecommons.org/lic workload analysis, neuroimaging, transportation, life sciences
NASA Space Biology Open Science Data Repository (OSDR) Biological research data from spaceflight and space relevant experiments arn:aws:s3:::nasa-osdr us-west-2 S3 Bucket https://osdr.nasa.gov/bio/repo https://osdr.nasa.gov/bio/help/contact.html NASA New research data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, biology, bioinformatics, GeneLab, genomic, imaging, life sciences, space biology ['[Browse Bucket](http://nasa-osdr.s3-website-us-west-2.amazonaws.com)']
@@ -567,18 +566,18 @@ NEOWISE Post-Cryo Data | Wide-field Infrared Survey Explorer (WISE) NEOWISE Post
NEOWISE Reactivation Data | Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) The Single-exposure Image Sets consist of more than 20 million calibrated 1016x1 arn:aws:s3:::nasa-irsa-wise/wise/neowiser us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The NEOWISE-R dataset is updated annually. The data may also be presented in new https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, survey False False
NEXRAD on AWS NEXRAD Level II archive data arn:aws:s3:::noaa-nexrad-level2 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://noaa-nexrad-level2.s3.amazonaws.com/index.html)']
NEXRAD on AWS NEXRAD Level II real-time data arn:aws:s3:::unidata-nexrad-level2-chunks us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological
-NEXRAD on AWS Notifications for the Level II archival bucket arn:aws:sns:us-east-1:811054952067:NewNEXRADLevel2Archive us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological
NEXRAD on AWS NEXRAD Level III real-time select data arn:aws:s3:::unidata-nexrad-level3 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://unidata-nexrad-level3.s3.amazonaws.com/index.html)']
NEXRAD on AWS Rich notifications for real-time data with filterable fields arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2ObjectFilterable us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological
+NEXRAD on AWS Notifications for the Level II archival bucket arn:aws:sns:us-east-1:811054952067:NewNEXRADLevel2Archive us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological
NEXRAD on AWS Notifications for the Level III bucket arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel3Object us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological
NIFS Large Helical Device (LHD) Experiment LHD Diagnostic data arn:aws:s3:::nifs-lhd ap-northeast-1 S3 Bucket https://www-lhd.nifs.ac.jp/pub/Repository_en.html For any questions regarding data delivery or any general questions regarding the [NIFS](https://www.nifs.ac.jp/) Archived data files are updated nightly when new or revised data are generated i "This data is available for anyone to use under the ""[Rights and Terms](https://w" analytics, anomaly detection, archives, aws-pds, computed tomography, datacenter, digital assets, electricity, energy, fluid dynamics, image processing, physics, post-processing, radiation, signal processing, source code, turbulence, video, x-ray, x-ray tomography ['[Browse Bucket](https://nifs-lhd.s3.amazonaws.com/index.html)']
NIH NCBI PubMed Central (PMC) Article Datasets - Full-Text Biomedical and Life Sciences Journal Articles on AWS xml and txt files with the full-text of articles; txt and csv file lists for arn:aws:s3:::pmc-oa-opendata us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/pmc/tools/pmcaws pubmedcentral@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [PMC Copyright](https://www.ncbi.nlm.nih.gov/pmc/about/copyright/) aws-pds, life sciences, STRIDES, txt, xml, csv
-NIH NCBI Sequence Read Archive (SRA) on AWS Update notifications for s3://sra-pub-run-odp Users can subscribe to this SNS t arn:aws:sns:us-east-1:867126678632:sra-pub-run-odp-objects us-east-1 SNS Topic https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/ sra@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomi aws-pds, life sciences, genetic, genomic, whole genome sequencing, whole exome sequencing, transcriptomics, fastq, bam, cram, STRIDES
-NIH NCBI Sequence Read Archive (SRA) on AWS bam, cram, and fastq files in a public S3 bucket This is the first of two S3 arn:aws:s3:::sra-pub-src-1 us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/ sra@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomi aws-pds, life sciences, genetic, genomic, whole genome sequencing, whole exome sequencing, transcriptomics, fastq, bam, cram, STRIDES
NIH NCBI Sequence Read Archive (SRA) on AWS Metadata files for the Sequence Read Archive, ready to load into AWS Glue and qu arn:aws:s3:::sra-pub-metadata-us-east-1/sra/metadata us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/ sra@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomi aws-pds, life sciences, genetic, genomic, whole genome sequencing, whole exome sequencing, transcriptomics, fastq, bam, cram, STRIDES
+NIH NCBI Sequence Read Archive (SRA) on AWS Update notifications for s3://sra-pub-run-odp Users can subscribe to this SNS t arn:aws:sns:us-east-1:867126678632:sra-pub-run-odp-objects us-east-1 SNS Topic https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/ sra@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomi aws-pds, life sciences, genetic, genomic, whole genome sequencing, whole exome sequencing, transcriptomics, fastq, bam, cram, STRIDES
NIH NCBI Sequence Read Archive (SRA) on AWS sra files in a controlled-access S3 bucket This bucket contains controlled-acc arn:aws:s3:::sra-ca-run-odp us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/ sra@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomi aws-pds, life sciences, genetic, genomic, whole genome sequencing, whole exome sequencing, transcriptomics, fastq, bam, cram, STRIDES https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?page=login
NIH NCBI Sequence Read Archive (SRA) on AWS sra files in a public S3 bucket This bucket contains all open access SRA submi arn:aws:s3:::sra-pub-run-odp us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/ sra@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomi aws-pds, life sciences, genetic, genomic, whole genome sequencing, whole exome sequencing, transcriptomics, fastq, bam, cram, STRIDES
NIH NCBI Sequence Read Archive (SRA) on AWS bam, cram, and fastq files in a public S3 bucket This is the second of two S arn:aws:s3:::sra-pub-src-2 us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/ sra@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomi aws-pds, life sciences, genetic, genomic, whole genome sequencing, whole exome sequencing, transcriptomics, fastq, bam, cram, STRIDES
+NIH NCBI Sequence Read Archive (SRA) on AWS bam, cram, and fastq files in a public S3 bucket This is the first of two S3 arn:aws:s3:::sra-pub-src-1 us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud/ sra@ncbi.nlm.nih.gov [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomi aws-pds, life sciences, genetic, genomic, whole genome sequencing, whole exome sequencing, transcriptomics, fastq, bam, cram, STRIDES
NLP - fast.ai datasets Datasets arn:aws:s3:::fast-ai-nlp us-east-1 S3 Bucket http://course.fast.ai/datasets info@fast.ai [fast.ai](http://www.fast.ai/) As required Varies by dataset - see documentation link aws-pds, deep learning, natural language processing, machine learning
NOAA - hourly position, current, and sea surface temperature from drifters Hourly position, current, and sea surface temperature data from drifters arn:aws:s3:::noaa-oar-hourly-gdp-pds us-east-1 S3 Bucket https://www.aoml.noaa.gov/phod/gdp/hourly_data.php Please direct scientific inquiries to Dr. Rick Lumpkin (Rick.Lumpkin@noaa.gov) a [NOAA](http://www.noaa.gov/) New data is added as soon as it's available. Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, sustainability, weather, oceans, environmental ['[Browse Bucket](https://noaa-oar-hourly-gdp-pds.s3.amazonaws.com/index.html)']
NOAA - hourly position, current, and sea surface temperature from drifters New data notifications for Hourly position, current, and sea surface temperature arn:aws:sns:us-east-1:709902155096:NewHourlyGDPObject us-east-1 SNS Topic https://www.aoml.noaa.gov/phod/gdp/hourly_data.php Please direct scientific inquiries to Dr. Rick Lumpkin (Rick.Lumpkin@noaa.gov) a [NOAA](http://www.noaa.gov/) New data is added as soon as it's available. Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, sustainability, weather, oceans, environmental
diff --git a/gee_catalog.json b/gee_catalog.json
index bb00845..5a34a3e 100644
--- a/gee_catalog.json
+++ b/gee_catalog.json
@@ -114,7 +114,7 @@
"snippet": "ee.ImageCollection('ASTER/AST_L1T_003')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-03-04",
- "end_date": "2024-09-23",
+ "end_date": "2024-09-26",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aster, eos, imagery, nasa, nir, radiance, swir, terra, thermal, tir, toa, usgs, vnir",
@@ -726,7 +726,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S1_GRD')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2014-10-03",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel",
@@ -744,7 +744,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2015-06-27",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -56, 180, 83",
"deprecated": true,
"keywords": "copernicus, esa, eu, msi, radiance, sentinel",
@@ -762,7 +762,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')",
"provider": "European Union/ESA/Copernicus/SentinelHub",
"state_date": "2015-06-27",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -56, 180, 83",
"deprecated": false,
"keywords": "cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub",
@@ -780,7 +780,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2_HARMONIZED')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2015-06-27",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -56, 180, 83",
"deprecated": false,
"keywords": "copernicus, esa, eu, msi, radiance, sentinel",
@@ -798,7 +798,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2_SR')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2017-03-28",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -56, 180, 83",
"deprecated": true,
"keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr",
@@ -816,7 +816,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2017-03-28",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -56, 180, 83",
"deprecated": false,
"keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr",
@@ -834,7 +834,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S3/OLCI')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2016-10-18",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "copernicus, esa, eu, olci, radiance, sentinel, toa",
@@ -852,7 +852,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai",
@@ -870,7 +870,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai",
@@ -888,7 +888,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-05",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi",
@@ -906,7 +906,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-11-22",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi",
@@ -924,7 +924,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-10-02",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi",
@@ -942,7 +942,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi",
@@ -960,7 +960,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi",
@@ -978,7 +978,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi",
@@ -996,7 +996,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-04",
- "end_date": "2024-09-25",
+ "end_date": "2024-09-26",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai",
@@ -1104,7 +1104,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-06-28",
- "end_date": "2024-09-18",
+ "end_date": "2024-09-19",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi",
@@ -1140,7 +1140,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-04-30",
- "end_date": "2024-09-12",
+ "end_date": "2024-09-13",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi",
@@ -1572,7 +1572,7 @@
"snippet": "ee.ImageCollection('ECMWF/CAMS/NRT')",
"provider": "European Centre for Medium-Range Weather Forecasts (ECMWF)",
"state_date": "2016-06-22",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter",
@@ -1626,7 +1626,7 @@
"snippet": "ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR')",
"provider": "Daily Aggregates: Google and Copernicus Climate Data Store",
"state_date": "1950-01-02",
- "end_date": "2024-09-19",
+ "end_date": "2024-09-20",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind",
@@ -1644,7 +1644,7 @@
"snippet": "ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY')",
"provider": "Copernicus Climate Data Store",
"state_date": "1950-01-01",
- "end_date": "2024-09-20",
+ "end_date": "2024-09-22",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind",
@@ -2400,7 +2400,7 @@
"snippet": "ee.ImageCollection('FIRMS')",
"provider": "NASA / LANCE / EOSDIS",
"state_date": "2000-11-01",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal",
@@ -2688,7 +2688,7 @@
"snippet": "ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')",
"provider": "Google Earth Engine",
"state_date": "2015-06-27",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "google, cloud, sentinel2_derived",
@@ -2706,7 +2706,7 @@
"snippet": "ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1')",
"provider": "World Resources Institute",
"state_date": "2015-06-27",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "global, google, landcover, landuse, nrt, sentinel2_derived",
@@ -2994,7 +2994,7 @@
"snippet": "ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')",
"provider": "University of California Merced",
"state_date": "1979-01-01",
- "end_date": "2024-09-25",
+ "end_date": "2024-09-26",
"bbox": "-124.9, 24.9, -66.8, 49.6",
"deprecated": false,
"keywords": "climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind",
@@ -3732,7 +3732,7 @@
"snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3')",
"provider": "Global Change Observation Mission (GCOM)",
"state_date": "2021-11-29",
- "end_date": "2024-09-25",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index",
@@ -3786,7 +3786,7 @@
"snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3')",
"provider": "Global Change Observation Mission (GCOM)",
"state_date": "2021-11-29",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst",
@@ -3840,7 +3840,7 @@
"snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3')",
"provider": "Global Change Observation Mission (GCOM)",
"state_date": "2021-11-29",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-26",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color",
@@ -3894,7 +3894,7 @@
"snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3')",
"provider": "Global Change Observation Mission (GCOM)",
"state_date": "2021-11-29",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-26",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst",
@@ -3912,7 +3912,7 @@
"snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational')",
"provider": "JAXA Earth Observation Research Center",
"state_date": "2014-03-01",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -60, 180, 60",
"deprecated": false,
"keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather",
@@ -3948,7 +3948,7 @@
"snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational')",
"provider": "JAXA Earth Observation Research Center",
"state_date": "2014-03-01",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -60, 180, 60",
"deprecated": false,
"keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather",
@@ -3966,7 +3966,7 @@
"snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational')",
"provider": "JAXA Earth Observation Research Center",
"state_date": "1998-01-01",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -60, 180, 60",
"deprecated": false,
"keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather",
@@ -5424,7 +5424,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')",
"provider": "USGS",
"state_date": "2013-03-18",
- "end_date": "2024-09-18",
+ "end_date": "2024-09-24",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs",
@@ -5442,7 +5442,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT')",
"provider": "USGS",
"state_date": "2013-03-18",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs",
@@ -5460,7 +5460,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA')",
"provider": "USGS/Google",
"state_date": "2013-03-18",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l8, landsat, lc8, toa, usgs",
@@ -5514,7 +5514,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T2_L2')",
"provider": "USGS",
"state_date": "2013-03-18",
- "end_date": "2024-09-18",
+ "end_date": "2024-09-24",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs",
@@ -5550,7 +5550,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1')",
"provider": "USGS",
"state_date": "2021-10-31",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs",
@@ -5586,7 +5586,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA')",
"provider": "USGS/Google",
"state_date": "2021-10-31",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, landsat, toa, usgs",
@@ -5604,7 +5604,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2')",
"provider": "USGS",
"state_date": "2021-11-02",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs",
@@ -5640,7 +5640,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA')",
"provider": "USGS/Google",
"state_date": "2021-11-02",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l9, landsat, lc9, toa, usgs",
@@ -7548,7 +7548,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD19A1_GRANULES')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-12-21",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs",
@@ -7566,7 +7566,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD19A2_GRANULES')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs",
@@ -7818,7 +7818,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD11A1')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2024-09-25",
+ "end_date": "2024-09-26",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs",
@@ -8232,7 +8232,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MYD09GA')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2002-07-04",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs",
@@ -9690,7 +9690,7 @@
"snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf')",
"provider": "NASA / GMAO",
"state_date": "2022-10-01",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "composition, forecast, geos, gmao, nasa",
@@ -9708,7 +9708,7 @@
"snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr')",
"provider": "NASA / GMAO",
"state_date": "2022-10-01",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "composition, forecast, geos, gmao, nasa",
@@ -9726,7 +9726,7 @@
"snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf')",
"provider": "NASA / GMAO",
"state_date": "2018-01-01",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "composition, forecast, geos, gmao, nasa",
@@ -9744,7 +9744,7 @@
"snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr')",
"provider": "NASA / GMAO",
"state_date": "2018-01-01",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "composition, forecast, geos, gmao, nasa",
@@ -10176,7 +10176,7 @@
"snippet": "ee.ImageCollection('NASA/HLS/HLSL30/v002')",
"provider": "NASA LP DAAC",
"state_date": "2013-04-11",
- "end_date": "2024-09-20",
+ "end_date": "2024-09-23",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "landsat, nasa, sentinel, usgs",
@@ -10212,7 +10212,7 @@
"snippet": "ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2')",
"provider": "NASA / LANCE / NOAA20_VIIRS",
"state_date": "2023-10-08",
- "end_date": "2024-09-25",
+ "end_date": "2024-09-27",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs",
@@ -10338,7 +10338,7 @@
"snippet": "ee.ImageCollection('NASA/NLDAS/FORA0125_H002')",
"provider": "NASA GES DISC at NASA Goddard Space Flight Center",
"state_date": "1979-01-01",
- "end_date": "2024-09-23",
+ "end_date": "2024-09-24",
"bbox": "-125.15, 24.85, -66.85, 53.28",
"deprecated": false,
"keywords": "climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind",
@@ -10482,7 +10482,7 @@
"snippet": "ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006')",
"provider": "Google and NSIDC",
"state_date": "2023-12-04",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -84, 180, 84",
"deprecated": false,
"keywords": "drought, nasa, smap, soil_moisture, surface, weather",
@@ -10500,7 +10500,7 @@
"snippet": "ee.ImageCollection('NASA/SMAP/SPL4SMGP/007')",
"provider": "Google and NSIDC",
"state_date": "2015-03-31",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -84, 180, 84",
"deprecated": false,
"keywords": "drought, nasa, smap, soil_moisture, surface, weather",
@@ -10698,7 +10698,7 @@
"snippet": "ee.ImageCollection('NCEP_RE/sea_level_pressure')",
"provider": "NCEP",
"state_date": "1948-01-01",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis",
@@ -10716,7 +10716,7 @@
"snippet": "ee.ImageCollection('NCEP_RE/surface_temp')",
"provider": "NCEP",
"state_date": "1948-01-01",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature",
@@ -10734,7 +10734,7 @@
"snippet": "ee.ImageCollection('NCEP_RE/surface_wv')",
"provider": "NCEP",
"state_date": "1948-01-01",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor",
@@ -10968,7 +10968,7 @@
"snippet": "ee.ImageCollection('NOAA/CDR/OISST/V2_1')",
"provider": "NOAA",
"state_date": "1981-09-01",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature",
@@ -11040,7 +11040,7 @@
"snippet": "ee.ImageCollection('NOAA/CFSR')",
"provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)",
"state_date": "2018-12-13",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather",
@@ -11058,7 +11058,7 @@
"snippet": "ee.ImageCollection('NOAA/CFSV2/FOR6H')",
"provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)",
"state_date": "1979-01-01",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather",
@@ -11112,7 +11112,7 @@
"snippet": "ee.ImageCollection('NOAA/GFS0P25')",
"provider": "NOAA/NCEP/EMC",
"state_date": "2015-07-01",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind",
@@ -11490,7 +11490,7 @@
"snippet": "ee.ImageCollection('NOAA/NWS/RTMA')",
"provider": "NOAA/NWS",
"state_date": "2011-01-01",
- "end_date": "2024-09-27",
+ "end_date": "2024-09-28",
"bbox": "-130.17, 20.15, -60.81, 52.91",
"deprecated": false,
"keywords": "climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind",
@@ -11868,7 +11868,7 @@
"snippet": "ee.ImageCollection('OREGONSTATE/PRISM/AN81d')",
"provider": "PRISM / OREGONSTATE",
"state_date": "1981-01-01",
- "end_date": "2024-09-24",
+ "end_date": "2024-09-25",
"bbox": "-125, 24, -66, 50",
"deprecated": false,
"keywords": "climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather",
@@ -13002,7 +13002,7 @@
"snippet": "ee.ImageCollection('TOMS/MERGED')",
"provider": "NASA / GES DISC",
"state_date": "1978-11-01",
- "end_date": "2024-09-25",
+ "end_date": "2024-09-26",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms",
@@ -14640,7 +14640,7 @@
"snippet": "ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1')",
"provider": "Institute of Industrial Science, The University of Tokyo, Japan",
"state_date": "2007-01-01",
- "end_date": "2024-09-26",
+ "end_date": "2024-09-27",
"bbox": "60, -60, 180, 60",
"deprecated": false,
"keywords": "drought, kbdi, lst_derived, rainfall, utokyo, wtlab",
diff --git a/gee_catalog.tsv b/gee_catalog.tsv
index 2983651..648d96b 100644
--- a/gee_catalog.tsv
+++ b/gee_catalog.tsv
@@ -5,7 +5,7 @@ ACA/reef_habitat/v2_0 Allen Coral Atlas (ACA) - Geomorphic Zonation and Benthic
AHN/AHN2_05M_INT AHN Netherlands 0.5m DEM, Interpolated image ee.Image('AHN/AHN2_05M_INT') AHN 2012-01-01 2012-01-01 3.35, 50.74, 7.24, 53.55 False ahn, dem, elevation, geophysical, lidar, netherlands https://storage.googleapis.com/earthengine-stac/catalog/AHN/AHN_AHN2_05M_INT.json https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_INT CC0-1.0
AHN/AHN2_05M_NON AHN Netherlands 0.5m DEM, Non-Interpolated image ee.Image('AHN/AHN2_05M_NON') AHN 2012-01-01 2012-01-01 3.35, 50.74, 7.24, 53.55 False ahn, dem, elevation, geophysical, lidar, netherlands https://storage.googleapis.com/earthengine-stac/catalog/AHN/AHN_AHN2_05M_NON.json https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_NON CC0-1.0
AHN/AHN2_05M_RUW AHN Netherlands 0.5m DEM, Raw Samples image ee.Image('AHN/AHN2_05M_RUW') AHN 2012-01-01 2012-01-01 3.35, 50.74, 7.24, 53.55 False ahn, dem, elevation, geophysical, lidar, netherlands https://storage.googleapis.com/earthengine-stac/catalog/AHN/AHN_AHN2_05M_RUW.json https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_RUW CC0-1.0
-ASTER/AST_L1T_003 ASTER L1T Radiance image_collection ee.ImageCollection('ASTER/AST_L1T_003') NASA LP DAAC at the USGS EROS Center 2000-03-04 2024-09-23 -180, -90, 180, 90 False aster, eos, imagery, nasa, nir, radiance, swir, terra, thermal, tir, toa, usgs, vnir https://storage.googleapis.com/earthengine-stac/catalog/ASTER/ASTER_AST_L1T_003.json https://developers.google.com/earth-engine/datasets/catalog/ASTER_AST_L1T_003 proprietary
+ASTER/AST_L1T_003 ASTER L1T Radiance image_collection ee.ImageCollection('ASTER/AST_L1T_003') NASA LP DAAC at the USGS EROS Center 2000-03-04 2024-09-26 -180, -90, 180, 90 False aster, eos, imagery, nasa, nir, radiance, swir, terra, thermal, tir, toa, usgs, vnir https://storage.googleapis.com/earthengine-stac/catalog/ASTER/ASTER_AST_L1T_003.json https://developers.google.com/earth-engine/datasets/catalog/ASTER_AST_L1T_003 proprietary
AU/GA/AUSTRALIA_5M_DEM Australian 5M DEM image_collection ee.ImageCollection('AU/GA/AUSTRALIA_5M_DEM') Geoscience Australia 2015-12-01 2015-12-01 114.09, -43.45, 153.64, -9.88 False australia, dem, elevation, ga, geophysical, geoscience_australia, lidar https://storage.googleapis.com/earthengine-stac/catalog/AU/AU_GA_AUSTRALIA_5M_DEM.json https://developers.google.com/earth-engine/datasets/catalog/AU_GA_AUSTRALIA_5M_DEM CC-BY-4.0
AU/GA/DEM_1SEC/v10/DEM-H DEM-H: Australian SRTM Hydrologically Enforced Digital Elevation Model image ee.Image('AU/GA/DEM_1SEC/v10/DEM-H') Geoscience Australia 2010-02-01 2010-02-01 112.99, -44.06, 154, -9.99 False australia, dem, elevation, ga, geophysical, geoscience_australia, smoothed, srtm https://storage.googleapis.com/earthengine-stac/catalog/AU/AU_GA_DEM_1SEC_v10_DEM-H.json https://developers.google.com/earth-engine/datasets/catalog/AU_GA_DEM_1SEC_v10_DEM-H CC-BY-4.0
AU/GA/DEM_1SEC/v10/DEM-S DEM-S: Australian Smoothed Digital Elevation Model image ee.Image('AU/GA/DEM_1SEC/v10/DEM-S') Geoscience Australia 2010-02-01 2010-02-01 112.99, -44.06, 154, -9.99 False australia, dem, elevation, ga, geophysical, geoscience_australia, smoothed, srtm https://storage.googleapis.com/earthengine-stac/catalog/AU/AU_GA_DEM_1SEC_v10_DEM-S.json https://developers.google.com/earth-engine/datasets/catalog/AU_GA_DEM_1SEC_v10_DEM-S CC-BY-4.0
@@ -39,30 +39,30 @@ COPERNICUS/CORINE/V20/100m Copernicus CORINE Land Cover image_collection ee.Imag
COPERNICUS/DEM/GLO30 Copernicus DEM GLO-30: Global 30m Digital Elevation Model image_collection ee.ImageCollection('COPERNICUS/DEM/GLO30') Copernicus 2010-12-01 2015-01-31 -180, -90, 180, 90 False copernicus, dem, elevation, geophysical https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_DEM_GLO30.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_DEM_GLO30 proprietary
COPERNICUS/Landcover/100m/Proba-V-C3/Global Copernicus Global Land Cover Layers: CGLS-LC100 Collection 3 image_collection ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V-C3/Global') Copernicus 2015-01-01 2019-12-31 -180, -90, 180, 90 False copernicus, eea, esa, eu, landcover, proba, probav, vito https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_Landcover_100m_Proba-V-C3_Global.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global proprietary
COPERNICUS/Landcover/100m/Proba-V/Global Copernicus Global Land Cover Layers: CGLS-LC100 Collection 2 [deprecated] image_collection ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V/Global') Copernicus 2015-01-01 2015-01-01 -180, -90, 180, 90 True copernicus, eea, esa, eu, landcover, proba, probav, vito https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_Landcover_100m_Proba-V_Global.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V_Global proprietary
-COPERNICUS/S1_GRD Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling image_collection ee.ImageCollection('COPERNICUS/S1_GRD') European Union/ESA/Copernicus 2014-10-03 2024-09-27 -180, -90, 180, 90 False backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S1_GRD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD proprietary
-COPERNICUS/S2 Sentinel-2 MSI: MultiSpectral Instrument, Level-1C [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2') European Union/ESA/Copernicus 2015-06-27 2024-09-27 -180, -56, 180, 83 True copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 proprietary
-COPERNICUS/S2_CLOUD_PROBABILITY Sentinel-2: Cloud Probability image_collection ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') European Union/ESA/Copernicus/SentinelHub 2015-06-27 2024-09-27 -180, -56, 180, 83 False cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY proprietary
-COPERNICUS/S2_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C image_collection ee.ImageCollection('COPERNICUS/S2_HARMONIZED') European Union/ESA/Copernicus 2015-06-27 2024-09-27 -180, -56, 180, 83 False copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED proprietary
-COPERNICUS/S2_SR Sentinel-2 MSI: MultiSpectral Instrument, Level-2A [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2_SR') European Union/ESA/Copernicus 2017-03-28 2024-09-27 -180, -56, 180, 83 True copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR proprietary
-COPERNICUS/S2_SR_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A image_collection ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') European Union/ESA/Copernicus 2017-03-28 2024-09-27 -180, -56, 180, 83 False copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED proprietary
-COPERNICUS/S3/OLCI Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution image_collection ee.ImageCollection('COPERNICUS/S3/OLCI') European Union/ESA/Copernicus 2016-10-18 2024-09-26 -180, -90, 180, 90 False copernicus, esa, eu, olci, radiance, sentinel, toa https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S3_OLCI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI proprietary
-COPERNICUS/S5P/NRTI/L3_AER_AI Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') European Union/ESA/Copernicus 2018-07-10 2024-09-27 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI proprietary
-COPERNICUS/S5P/NRTI/L3_AER_LH Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH') European Union/ESA/Copernicus 2018-07-10 2024-09-27 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH proprietary
-COPERNICUS/S5P/NRTI/L3_CLOUD Sentinel-5P NRTI CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD') European Union/ESA/Copernicus 2018-07-05 2024-09-27 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD proprietary
-COPERNICUS/S5P/NRTI/L3_CO Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') European Union/ESA/Copernicus 2018-11-22 2024-09-27 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO proprietary
-COPERNICUS/S5P/NRTI/L3_HCHO Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') European Union/ESA/Copernicus 2018-10-02 2024-09-27 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO proprietary
-COPERNICUS/S5P/NRTI/L3_NO2 Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') European Union/ESA/Copernicus 2018-07-10 2024-09-27 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2 proprietary
-COPERNICUS/S5P/NRTI/L3_O3 Sentinel-5P NRTI O3: Near Real-Time Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3') European Union/ESA/Copernicus 2018-07-10 2024-09-27 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3 proprietary
-COPERNICUS/S5P/NRTI/L3_SO2 Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2') European Union/ESA/Copernicus 2018-07-10 2024-09-27 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2 proprietary
-COPERNICUS/S5P/OFFL/L3_AER_AI Sentinel-5P OFFL AER AI: Offline UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI') European Union/ESA/Copernicus 2018-07-04 2024-09-25 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI proprietary
+COPERNICUS/S1_GRD Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling image_collection ee.ImageCollection('COPERNICUS/S1_GRD') European Union/ESA/Copernicus 2014-10-03 2024-09-28 -180, -90, 180, 90 False backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S1_GRD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD proprietary
+COPERNICUS/S2 Sentinel-2 MSI: MultiSpectral Instrument, Level-1C [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2') European Union/ESA/Copernicus 2015-06-27 2024-09-28 -180, -56, 180, 83 True copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 proprietary
+COPERNICUS/S2_CLOUD_PROBABILITY Sentinel-2: Cloud Probability image_collection ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') European Union/ESA/Copernicus/SentinelHub 2015-06-27 2024-09-28 -180, -56, 180, 83 False cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY proprietary
+COPERNICUS/S2_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C image_collection ee.ImageCollection('COPERNICUS/S2_HARMONIZED') European Union/ESA/Copernicus 2015-06-27 2024-09-28 -180, -56, 180, 83 False copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED proprietary
+COPERNICUS/S2_SR Sentinel-2 MSI: MultiSpectral Instrument, Level-2A [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2_SR') European Union/ESA/Copernicus 2017-03-28 2024-09-28 -180, -56, 180, 83 True copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR proprietary
+COPERNICUS/S2_SR_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A image_collection ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') European Union/ESA/Copernicus 2017-03-28 2024-09-28 -180, -56, 180, 83 False copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED proprietary
+COPERNICUS/S3/OLCI Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution image_collection ee.ImageCollection('COPERNICUS/S3/OLCI') European Union/ESA/Copernicus 2016-10-18 2024-09-27 -180, -90, 180, 90 False copernicus, esa, eu, olci, radiance, sentinel, toa https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S3_OLCI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI proprietary
+COPERNICUS/S5P/NRTI/L3_AER_AI Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') European Union/ESA/Copernicus 2018-07-10 2024-09-28 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI proprietary
+COPERNICUS/S5P/NRTI/L3_AER_LH Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH') European Union/ESA/Copernicus 2018-07-10 2024-09-28 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH proprietary
+COPERNICUS/S5P/NRTI/L3_CLOUD Sentinel-5P NRTI CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD') European Union/ESA/Copernicus 2018-07-05 2024-09-28 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD proprietary
+COPERNICUS/S5P/NRTI/L3_CO Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') European Union/ESA/Copernicus 2018-11-22 2024-09-28 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO proprietary
+COPERNICUS/S5P/NRTI/L3_HCHO Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') European Union/ESA/Copernicus 2018-10-02 2024-09-28 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO proprietary
+COPERNICUS/S5P/NRTI/L3_NO2 Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') European Union/ESA/Copernicus 2018-07-10 2024-09-28 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2 proprietary
+COPERNICUS/S5P/NRTI/L3_O3 Sentinel-5P NRTI O3: Near Real-Time Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3') European Union/ESA/Copernicus 2018-07-10 2024-09-28 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3 proprietary
+COPERNICUS/S5P/NRTI/L3_SO2 Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2') European Union/ESA/Copernicus 2018-07-10 2024-09-28 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2 proprietary
+COPERNICUS/S5P/OFFL/L3_AER_AI Sentinel-5P OFFL AER AI: Offline UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI') European Union/ESA/Copernicus 2018-07-04 2024-09-26 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI proprietary
COPERNICUS/S5P/OFFL/L3_AER_LH Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH') European Union/ESA/Copernicus 2018-07-04 2024-09-25 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH proprietary
COPERNICUS/S5P/OFFL/L3_CH4 Sentinel-5P OFFL CH4: Offline Methane image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4') European Union/ESA/Copernicus 2019-02-08 2024-09-25 -180, -90, 180, 90 False climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4 proprietary
COPERNICUS/S5P/OFFL/L3_CLOUD Sentinel-5P OFFL CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD') European Union/ESA/Copernicus 2018-07-04 2024-09-25 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD proprietary
COPERNICUS/S5P/OFFL/L3_CO Sentinel-5P OFFL CO: Offline Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO') European Union/ESA/Copernicus 2018-06-28 2024-09-25 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO proprietary
COPERNICUS/S5P/OFFL/L3_HCHO Sentinel-5P OFFL HCHO: Offline Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO') European Union/ESA/Copernicus 2018-12-05 2024-09-25 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO proprietary
-COPERNICUS/S5P/OFFL/L3_NO2 Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') European Union/ESA/Copernicus 2018-06-28 2024-09-18 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 proprietary
+COPERNICUS/S5P/OFFL/L3_NO2 Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') European Union/ESA/Copernicus 2018-06-28 2024-09-19 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 proprietary
COPERNICUS/S5P/OFFL/L3_O3 Sentinel-5P OFFL O3: Offline Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3') European Union/ESA/Copernicus 2018-09-08 2024-09-25 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3 proprietary
-COPERNICUS/S5P/OFFL/L3_O3_TCL Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL') European Union/ESA/Copernicus 2018-04-30 2024-09-12 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL proprietary
+COPERNICUS/S5P/OFFL/L3_O3_TCL Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL') European Union/ESA/Copernicus 2018-04-30 2024-09-13 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL proprietary
COPERNICUS/S5P/OFFL/L3_SO2 Sentinel-5P OFFL SO2: Offline Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2') European Union/ESA/Copernicus 2018-12-05 2024-09-25 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2 proprietary
CPOM/CryoSat2/ANTARCTICA_DEM CryoSat-2 Antarctica 1km DEM image ee.Image('CPOM/CryoSat2/ANTARCTICA_DEM') CPOM 2010-07-01 2016-07-01 -180, -88, 180, -60 False antarctica, cpom, cryosat_2, dem, elevation, polar https://storage.googleapis.com/earthengine-stac/catalog/CPOM/CPOM_CryoSat2_ANTARCTICA_DEM.json https://developers.google.com/earth-engine/datasets/catalog/CPOM_CryoSat2_ANTARCTICA_DEM proprietary
CSIC/SPEI/2_8 SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.8 [deprecated] image_collection ee.ImageCollection('CSIC/SPEI/2_8') Spanish National Research Council (CSIC) 1901-01-01 2021-01-01 -180, -90, 180, 90 True climate, climate_change, drought, evapotranspiration, global, monthly, palmer, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_8.json https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_8 CC-BY-4.0
@@ -86,11 +86,11 @@ CSP/ERGo/1_0/US/topoDiversity US NED Topographic Diversity image ee.Image('CSP/E
CSP/HM/GlobalHumanModification CSP gHM: Global Human Modification image_collection ee.ImageCollection('CSP/HM/GlobalHumanModification') Conservation Science Partners 2016-01-01 2016-12-31 -180, -90, 180, 90 False csp, fragmentation, human_modification, landcover, landscape_gradient, stressors, tnc https://storage.googleapis.com/earthengine-stac/catalog/CSP/CSP_HM_GlobalHumanModification.json https://developers.google.com/earth-engine/datasets/catalog/CSP_HM_GlobalHumanModification CC-BY-NC-SA-4.0
DLR/WSF/WSF2015/v1 World Settlement Footprint 2015 image ee.Image('DLR/WSF/WSF2015/v1') Deutsches Zentrum für Luft- und Raumfahrt (DLR) 2015-01-01 2016-01-01 -180, -90, 180, 90 False landcover, landsat_derived, sentinel1_derived, settlement, urban https://storage.googleapis.com/earthengine-stac/catalog/DLR/DLR_WSF_WSF2015_v1.json https://developers.google.com/earth-engine/datasets/catalog/DLR_WSF_WSF2015_v1 CC0-1.0
DOE/ORNL/LandScan_HD/Ukraine_202201 LandScan High Definition Data for Ukraine, January 2022 image ee.Image('DOE/ORNL/LandScan_HD/Ukraine_202201') Oak Ridge National Laboratory 2022-01-01 2022-02-01 22.125, 44.175, 40.225, 52.4 False landscan, population, ukraine https://storage.googleapis.com/earthengine-stac/catalog/DOE/DOE_ORNL_LandScan_HD_Ukraine_202201.json https://developers.google.com/earth-engine/datasets/catalog/DOE_ORNL_LandScan_HD_Ukraine_202201 CC-BY-4.0
-ECMWF/CAMS/NRT Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time image_collection ee.ImageCollection('ECMWF/CAMS/NRT') European Centre for Medium-Range Weather Forecasts (ECMWF) 2016-06-22 2024-09-27 -180, -90, 180, 90 False aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_CAMS_NRT.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT proprietary
+ECMWF/CAMS/NRT Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time image_collection ee.ImageCollection('ECMWF/CAMS/NRT') European Centre for Medium-Range Weather Forecasts (ECMWF) 2016-06-22 2024-09-28 -180, -90, 180, 90 False aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_CAMS_NRT.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT proprietary
ECMWF/ERA5/DAILY ERA5 Daily Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service image_collection ee.ImageCollection('ECMWF/ERA5/DAILY') ECMWF / Copernicus Climate Change Service 1979-01-02 2020-07-09 -180, -90, 180, 90 False climate, copernicus, dewpoint, ecmwf, era5, precipitation, pressure, reanalysis, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_DAILY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY proprietary
ECMWF/ERA5/MONTHLY ERA5 Monthly Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service image_collection ee.ImageCollection('ECMWF/ERA5/MONTHLY') ECMWF / Copernicus Climate Change Service 1979-01-01 2020-06-01 -180, -90, 180, 90 False climate, copernicus, dewpoint, ecmwf, era5, precipitation, pressure, reanalysis, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY proprietary
-ECMWF/ERA5_LAND/DAILY_AGGR ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR') Daily Aggregates: Google and Copernicus Climate Data Store 1950-01-02 2024-09-19 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR proprietary
-ECMWF/ERA5_LAND/HOURLY ERA5-Land Hourly - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY') Copernicus Climate Data Store 1950-01-01 2024-09-20 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_HOURLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY proprietary
+ECMWF/ERA5_LAND/DAILY_AGGR ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR') Daily Aggregates: Google and Copernicus Climate Data Store 1950-01-02 2024-09-20 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR proprietary
+ECMWF/ERA5_LAND/HOURLY ERA5-Land Hourly - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY') Copernicus Climate Data Store 1950-01-01 2024-09-22 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_HOURLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY proprietary
ECMWF/ERA5_LAND/MONTHLY ERA5-Land Monthly Averaged - ECMWF Climate Reanalysis [deprecated] image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY') Copernicus Climate Data Store 1950-02-01 2023-04-01 -180, -90, 180, 90 True cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY proprietary
ECMWF/ERA5_LAND/MONTHLY_AGGR ERA5-Land Monthly Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_AGGR') Monthly Aggregates: Google and Copernicus Climate Data Store 1950-02-01 2024-08-01 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR proprietary
ECMWF/ERA5_LAND/MONTHLY_BY_HOUR ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_BY_HOUR') Climate Data Store 1950-01-01 2024-08-01 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR proprietary
@@ -132,7 +132,7 @@ FAO/WAPOR/2/L1_NPP_D WAPOR Dekadal Net Primary Production 2.0 image_collection e
FAO/WAPOR/2/L1_RET_D WAPOR Dekadal Reference Evapotranspiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_RET_D') FAO UN 2009-01-01 2023-03-11 -30.15, -39.9953437, 65.13, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_RET_D.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_D proprietary
FAO/WAPOR/2/L1_RET_E WAPOR Daily Reference Evapotranspiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_RET_E') FAO UN 2009-01-01 2023-03-20 -30.15, -39.9953437, 65.13, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_RET_E.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_E proprietary
FAO/WAPOR/2/L1_T_D WAPOR Dekadal Transpiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_T_D') FAO UN 2009-01-01 2023-03-01 -30.0044643, -40.0044644, 65.0044644, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_T_D.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_T_D proprietary
-FIRMS FIRMS: Fire Information for Resource Management System image_collection ee.ImageCollection('FIRMS') NASA / LANCE / EOSDIS 2000-11-01 2024-09-26 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal https://storage.googleapis.com/earthengine-stac/catalog/FIRMS/FIRMS.json https://developers.google.com/earth-engine/datasets/catalog/FIRMS proprietary
+FIRMS FIRMS: Fire Information for Resource Management System image_collection ee.ImageCollection('FIRMS') NASA / LANCE / EOSDIS 2000-11-01 2024-09-27 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal https://storage.googleapis.com/earthengine-stac/catalog/FIRMS/FIRMS.json https://developers.google.com/earth-engine/datasets/catalog/FIRMS proprietary
FORMA/FORMA_500m FORMA Global Forest Watch Deforestation Alerts, 500m [deprecated] image ee.Image('FORMA/FORMA_500m') Global Forest Watch, World Resources Institute 2006-01-01 2015-06-10 -180, -90, 180, 90 True alerts, deforestation, forest, forma, geophysical, gfw, modis, nasa, wri https://storage.googleapis.com/earthengine-stac/catalog/FORMA/FORMA_FORMA_500m.json https://developers.google.com/earth-engine/datasets/catalog/FORMA_FORMA_500m proprietary
Finland/MAVI/VV/50cm Finland NRG NLS orthophotos 50 cm by Mavi image_collection ee.ImageCollection('Finland/MAVI/VV/50cm') NLS orthophotos 2015-01-01 2018-01-01 18, 59, 29.2, 69.4 False falsecolor, finland, mavi, nrg, orthophoto https://storage.googleapis.com/earthengine-stac/catalog/Finland/Finland_MAVI_VV_50cm.json https://developers.google.com/earth-engine/datasets/catalog/Finland_MAVI_VV_50cm CC-BY-4.0
Finland/SMK/V/50cm Finland RGB NLS orthophotos 50 cm by SMK image_collection ee.ImageCollection('Finland/SMK/V/50cm') NLS orthophotos 2015-01-01 2023-01-01 18, 59, 29.2, 69.4 False finland, orthophoto, rgb, smk https://storage.googleapis.com/earthengine-stac/catalog/Finland/Finland_SMK_V_50cm.json https://developers.google.com/earth-engine/datasets/catalog/Finland_SMK_V_50cm proprietary
@@ -148,8 +148,8 @@ GLIMS/20230607 GLIMS 2023: Global Land Ice Measurements From Space table ee.Feat
GLIMS/current GLIMS Current: Global Land Ice Measurements From Space table ee.FeatureCollection('GLIMS/current') National Snow and Ice Data Center (NSDIC) 1750-01-01 2023-06-07 -180, -90, 180, 90 False glacier, glims, ice, landcover, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/GLIMS/GLIMS_current.json https://developers.google.com/earth-engine/datasets/catalog/GLIMS_current proprietary
GLOBAL_FLOOD_DB/MODIS_EVENTS/V1 Global Flood Database v1 (2000-2018) image_collection ee.ImageCollection('GLOBAL_FLOOD_DB/MODIS_EVENTS/V1') Cloud to Street (C2S) / Dartmouth Flood Observatory (DFO) 2000-02-17 2018-12-10 -180, -90, 180, 90 False c2s, cloudtostreet, dartmouth, dfo, flood, gfd, inundation, surface, water https://storage.googleapis.com/earthengine-stac/catalog/GLOBAL_FLOOD_DB/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1.json https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1 CC-BY-NC-4.0
GOOGLE/AirView/California_Unified_2015_2019 Google Street View Air Quality: High Resolution Air Pollution Mapping in California table ee.FeatureCollection('GOOGLE/AirView/California_Unified_2015_2019') Google / Aclima 2015-05-28 2019-06-07 -180, -90, 180, 90 False air_quality, nitrogen_dioxide, pollution https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_AirView_California_Unified_2015_2019.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_AirView_California_Unified_2015_2019 CC-BY-NC-4.0
-GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED Cloud Score+ S2_HARMONIZED V1 image_collection ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED') Google Earth Engine 2015-06-27 2024-09-27 -180, -90, 180, 90 False google, cloud, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED CC-BY-4.0
-GOOGLE/DYNAMICWORLD/V1 Dynamic World V1 image_collection ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') World Resources Institute 2015-06-27 2024-09-27 -180, -90, 180, 90 False global, google, landcover, landuse, nrt, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_DYNAMICWORLD_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 CC-BY-4.0
+GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED Cloud Score+ S2_HARMONIZED V1 image_collection ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED') Google Earth Engine 2015-06-27 2024-09-28 -180, -90, 180, 90 False google, cloud, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED CC-BY-4.0
+GOOGLE/DYNAMICWORLD/V1 Dynamic World V1 image_collection ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') World Resources Institute 2015-06-27 2024-09-28 -180, -90, 180, 90 False global, google, landcover, landuse, nrt, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_DYNAMICWORLD_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 CC-BY-4.0
GOOGLE/GLOBAL_CCDC/V1 Google Global Landsat-based CCDC Segments (1999-2019) image_collection ee.ImageCollection('GOOGLE/GLOBAL_CCDC/V1') Google 1999-01-01 2020-01-01 -180, -60, 180, 72 False change_detection, google, landcover, landsat_derived, landuse https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_GLOBAL_CCDC_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1 CC-BY-4.0
GOOGLE/Research/open-buildings-temporal/v1 Open Buildings Temporal V1 image_collection ee.ImageCollection('GOOGLE/Research/open-buildings-temporal/v1') Google Research - Open Buildings 2016-06-30 2023-06-30 -180, -90, 180, 90 False building_height, height, annual, built_up, open_buildings, africa, asia, south_asia, southeast_asia, high_resolution https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings-temporal_v1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings-temporal_v1 CC-BY-4.0
GOOGLE/Research/open-buildings/v1/polygons Open Buildings V1 Polygons [deprecated] table ee.FeatureCollection('GOOGLE/Research/open-buildings/v1/polygons') Google Research - Open Buildings 2021-04-30 2021-04-30 -180, -90, 180, 90 True africa, building, built_up, open_buildings, structure https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings_v1_polygons.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings_v1_polygons CC-BY-4.0
@@ -165,7 +165,7 @@ HYCOM/GLBu0_08/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Ve
HYCOM/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation image_collection ee.ImageCollection('HYCOM/sea_surface_elevation') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False elevation, hycom, nopp, ocean, ssh, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_surface_elevation.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_surface_elevation proprietary
HYCOM/sea_temp_salinity HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity image_collection ee.ImageCollection('HYCOM/sea_temp_salinity') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, salinity, sst, water, water_temp https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_temp_salinity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_temp_salinity proprietary
HYCOM/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Velocity image_collection ee.ImageCollection('HYCOM/sea_water_velocity') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, velocity, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_water_velocity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_water_velocity proprietary
-IDAHO_EPSCOR/GRIDMET GRIDMET: University of Idaho Gridded Surface Meteorological Dataset image_collection ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') University of California Merced 1979-01-01 2024-09-25 -124.9, 24.9, -66.8, 49.6 False climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET proprietary
+IDAHO_EPSCOR/GRIDMET GRIDMET: University of Idaho Gridded Surface Meteorological Dataset image_collection ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') University of California Merced 1979-01-01 2024-09-26 -124.9, 24.9, -66.8, 49.6 False climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET proprietary
IDAHO_EPSCOR/MACAv2_METDATA MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models image_collection ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA') University of California Merced 1900-01-01 2100-12-31 -124.9, 24.9, -67, 49.6 False climate, conus, geophysical, idaho, maca, monthly https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA CC0-1.0
IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models image_collection ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY') University of California Merced 1900-01-01 2099-12-31 -124.9, 24.9, -67, 49.6 False climate, conus, geophysical, idaho, maca, monthly https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY CC0-1.0
IDAHO_EPSCOR/PDSI PDSI: University of Idaho Palmer Drought Severity Index [deprecated] image_collection ee.ImageCollection('IDAHO_EPSCOR/PDSI') University of California Merced 1979-03-01 2020-06-20 -124.9, 24.9, -66.8, 49.6 True climate, conus, crop, drought, geophysical, merced, palmer, pdsi https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_PDSI.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_PDSI proprietary
@@ -206,20 +206,20 @@ JAXA/ALOS/PALSAR/YEARLY/SAR Global PALSAR-2/PALSAR Yearly Mosaic, version 1 imag
JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH Global PALSAR-2/PALSAR Yearly Mosaic, version 2 image_collection ee.ImageCollection('JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH') JAXA EORC 2015-01-01 2023-01-01 -180, -90, 180, 90 False alos, alos2, eroc, jaxa, palsar, palsar2, sar https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH proprietary
JAXA/GCOM-C/L3/LAND/LAI/V1 GCOM-C/SGLI L3 Leaf Area Index (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V1 proprietary
JAXA/GCOM-C/L3/LAND/LAI/V2 GCOM-C/SGLI L3 Leaf Area Index (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V2 proprietary
-JAXA/GCOM-C/L3/LAND/LAI/V3 GCOM-C/SGLI L3 Leaf Area Index (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-09-25 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V3 proprietary
+JAXA/GCOM-C/L3/LAND/LAI/V3 GCOM-C/SGLI L3 Leaf Area Index (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-09-27 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V3 proprietary
JAXA/GCOM-C/L3/LAND/LST/V1 GCOM-C/SGLI L3 Land Surface Temperature (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V1 proprietary
JAXA/GCOM-C/L3/LAND/LST/V2 GCOM-C/SGLI L3 Land Surface Temperature (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V2 proprietary
-JAXA/GCOM-C/L3/LAND/LST/V3 GCOM-C/SGLI L3 Land Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-09-26 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V3 proprietary
+JAXA/GCOM-C/L3/LAND/LST/V3 GCOM-C/SGLI L3 Land Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-09-27 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V3 proprietary
JAXA/GCOM-C/L3/OCEAN/CHLA/V1 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V1 proprietary
JAXA/GCOM-C/L3/OCEAN/CHLA/V2 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V2 proprietary
-JAXA/GCOM-C/L3/OCEAN/CHLA/V3 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-09-24 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V3 proprietary
+JAXA/GCOM-C/L3/OCEAN/CHLA/V3 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-09-26 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V3 proprietary
JAXA/GCOM-C/L3/OCEAN/SST/V1 GCOM-C/SGLI L3 Sea Surface Temperature (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V1 proprietary
JAXA/GCOM-C/L3/OCEAN/SST/V2 GCOM-C/SGLI L3 Sea Surface Temperature (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V2 proprietary
-JAXA/GCOM-C/L3/OCEAN/SST/V3 GCOM-C/SGLI L3 Sea Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-09-24 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V3 proprietary
-JAXA/GPM_L3/GSMaP/v6/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V6 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational') JAXA Earth Observation Research Center 2014-03-01 2024-09-27 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational proprietary
+JAXA/GCOM-C/L3/OCEAN/SST/V3 GCOM-C/SGLI L3 Sea Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-09-26 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V3 proprietary
+JAXA/GPM_L3/GSMaP/v6/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V6 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational') JAXA Earth Observation Research Center 2014-03-01 2024-09-28 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational proprietary
JAXA/GPM_L3/GSMaP/v6/reanalysis GSMaP Reanalysis: Global Satellite Mapping of Precipitation image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/reanalysis') JAXA Earth Observation Research Center 2000-03-01 2014-03-12 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_reanalysis.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_reanalysis proprietary
-JAXA/GPM_L3/GSMaP/v7/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V7 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational') JAXA Earth Observation Research Center 2014-03-01 2024-09-27 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v7_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational proprietary
-JAXA/GPM_L3/GSMaP/v8/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V8 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational') JAXA Earth Observation Research Center 1998-01-01 2024-09-27 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v8_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational proprietary
+JAXA/GPM_L3/GSMaP/v7/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V7 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational') JAXA Earth Observation Research Center 2014-03-01 2024-09-28 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v7_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational proprietary
+JAXA/GPM_L3/GSMaP/v8/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V8 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational') JAXA Earth Observation Research Center 1998-01-01 2024-09-28 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v8_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational proprietary
JCU/Murray/GIC/global_tidal_wetland_change/2019 Murray Global Tidal Wetland Change v1.0 (1999-2019) image ee.Image('JCU/Murray/GIC/global_tidal_wetland_change/2019') Murray/JCU 1999-01-01 2019-12-31 -180, -90, 180, 90 False coastal, ecosystem, intertidal, landsat_derived, mangrove, murray, saltmarsh, tidal_flat, tidal_marsh https://storage.googleapis.com/earthengine-stac/catalog/JCU/JCU_Murray_GIC_global_tidal_wetland_change_2019.json https://developers.google.com/earth-engine/datasets/catalog/JCU_Murray_GIC_global_tidal_wetland_change_2019 CC-BY-4.0
JRC/CEMS_GLOFAS/FloodHazard/v1 JRC Global River Flood Hazard Maps Version 1 image_collection ee.ImageCollection('JRC/CEMS_GLOFAS/FloodHazard/v1') Joint Research Centre 2024-03-16 2024-03-16 -180, -90, 180, 90 False flood, monitoring, wri https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_CEMS_GLOFAS_FloodHazard_v1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_CEMS_GLOFAS_FloodHazard_v1 CC-BY-4.0
JRC/D5/EUCROPMAP/V1 EUCROPMAP image_collection ee.ImageCollection('JRC/D5/EUCROPMAP/V1') Joint Research Center (JRC) 2018-01-01 2022-01-01 -16.171875, 34.313433, 36.386719, 72.182526 False crop, eu, jrc, lucas, sentinel1_derived https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_D5_EUCROPMAP_V1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_D5_EUCROPMAP_V1 CC-BY-4.0
@@ -300,19 +300,19 @@ LANDSAT/GLS2005 Landsat Global Land Survey 2005, Landsat 5+7 scenes image_collec
LANDSAT/GLS2005_L5 Landsat Global Land Survey 2005, Landsat 5 scenes image_collection ee.ImageCollection('LANDSAT/GLS2005_L5') USGS 2003-08-14 2008-05-29 -180, -90, 180, 90 False etm, gls, l5, landsat, radiance, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_GLS2005_L5.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L5 PDDL-1.0
LANDSAT/GLS2005_L7 Landsat Global Land Survey 2005, Landsat 7 scenes image_collection ee.ImageCollection('LANDSAT/GLS2005_L7') USGS 2003-07-29 2008-07-29 -180, -90, 180, 90 False etm, gls, l7, landsat, radiance, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_GLS2005_L7.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L7 PDDL-1.0
LANDSAT/LC08/C02/T1 USGS Landsat 8 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1') USGS 2013-03-18 2024-09-26 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1 PDDL-1.0
-LANDSAT/LC08/C02/T1_L2 USGS Landsat 8 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') USGS 2013-03-18 2024-09-18 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 proprietary
-LANDSAT/LC08/C02/T1_RT USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT') USGS 2013-03-18 2024-09-27 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT PDDL-1.0
-LANDSAT/LC08/C02/T1_RT_TOA USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA') USGS/Google 2013-03-18 2024-09-27 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA PDDL-1.0
+LANDSAT/LC08/C02/T1_L2 USGS Landsat 8 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') USGS 2013-03-18 2024-09-24 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 proprietary
+LANDSAT/LC08/C02/T1_RT USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT') USGS 2013-03-18 2024-09-28 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT PDDL-1.0
+LANDSAT/LC08/C02/T1_RT_TOA USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA') USGS/Google 2013-03-18 2024-09-28 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA PDDL-1.0
LANDSAT/LC08/C02/T1_TOA USGS Landsat 8 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA') USGS/Google 2013-03-18 2024-09-26 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_TOA PDDL-1.0
LANDSAT/LC08/C02/T2 USGS Landsat 8 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2') USGS 2021-10-28 2024-09-26 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2 PDDL-1.0
-LANDSAT/LC08/C02/T2_L2 USGS Landsat 8 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_L2') USGS 2013-03-18 2024-09-18 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_L2 proprietary
+LANDSAT/LC08/C02/T2_L2 USGS Landsat 8 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_L2') USGS 2013-03-18 2024-09-24 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_L2 proprietary
LANDSAT/LC08/C02/T2_TOA USGS Landsat 8 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_TOA') USGS/Google 2021-10-28 2024-09-26 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_TOA PDDL-1.0
-LANDSAT/LC09/C02/T1 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2024-09-27 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1 PDDL-1.0
+LANDSAT/LC09/C02/T1 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2024-09-28 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1 PDDL-1.0
LANDSAT/LC09/C02/T1_L2 USGS Landsat 9 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') USGS 2021-10-31 2024-09-24 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2 proprietary
-LANDSAT/LC09/C02/T1_TOA USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA') USGS/Google 2021-10-31 2024-09-27 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA PDDL-1.0
-LANDSAT/LC09/C02/T2 USGS Landsat 9 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2') USGS 2021-11-02 2024-09-27 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2 PDDL-1.0
+LANDSAT/LC09/C02/T1_TOA USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA') USGS/Google 2021-10-31 2024-09-28 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA PDDL-1.0
+LANDSAT/LC09/C02/T2 USGS Landsat 9 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2') USGS 2021-11-02 2024-09-28 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2 PDDL-1.0
LANDSAT/LC09/C02/T2_L2 USGS Landsat 9 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_L2') USGS 2021-10-31 2024-09-24 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_L2 proprietary
-LANDSAT/LC09/C02/T2_TOA USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA') USGS/Google 2021-11-02 2024-09-27 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA PDDL-1.0
+LANDSAT/LC09/C02/T2_TOA USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA') USGS/Google 2021-11-02 2024-09-28 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA PDDL-1.0
LANDSAT/LE07/C02/T1 USGS Landsat 7 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False c2, etm, global, l7, landsat, le7, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1 PDDL-1.0
LANDSAT/LE07/C02/T1_L2 USGS Landsat 7 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1_L2') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False cfmask, cloud, etm, fmask, global, landsat, lasrc, le07, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2 proprietary
LANDSAT/LE07/C02/T1_RT USGS Landsat 7 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1_RT') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False c2, etm, global, l7, landsat, le7, nrt, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_RT PDDL-1.0
@@ -418,8 +418,8 @@ MODIS/061/MCD12Q2 MCD12Q2.006 Land Cover Dynamics Yearly Global 500m image_colle
MODIS/061/MCD15A3H MCD15A3H.061 MODIS Leaf Area Index/FPAR 4-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MCD15A3H') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-09-17 -180, -90, 180, 90 False 4_day, fpar, global, lai, mcd15a3h, modis, nasa, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD15A3H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD15A3H proprietary
MODIS/061/MCD18A1 MCD18A1.061 Surface Radiation Daily/3-Hour image_collection ee.ImageCollection('MODIS/061/MCD18A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-06-01 -180, -90, 180, 90 False par, radiation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18A1 proprietary
MODIS/061/MCD18C2 MCD18C2.061 Photosynthetically Active Radiation Daily 3-Hour image_collection ee.ImageCollection('MODIS/061/MCD18C2') NASA LP DAAC at the USGS EROS Center 2002-02-24 2024-06-01 -180, -90, 180, 90 False par, radiation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18C2 proprietary
-MODIS/061/MCD19A1_GRANULES MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km image_collection ee.ImageCollection('MODIS/061/MCD19A1_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-12-21 2024-09-24 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A1_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES proprietary
-MODIS/061/MCD19A2_GRANULES MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km image_collection ee.ImageCollection('MODIS/061/MCD19A2_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-09-24 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES proprietary
+MODIS/061/MCD19A1_GRANULES MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km image_collection ee.ImageCollection('MODIS/061/MCD19A1_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-12-21 2024-09-25 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A1_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES proprietary
+MODIS/061/MCD19A2_GRANULES MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km image_collection ee.ImageCollection('MODIS/061/MCD19A2_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-09-25 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES proprietary
MODIS/061/MCD43A1 MCD43A1.061 MODIS BRDF-Albedo Model Parameters Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-09-16 -180, -90, 180, 90 False albedo, brdf, daily, global, mcd43a1, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A1 proprietary
MODIS/061/MCD43A2 MCD43A2.061 MODIS BRDF-Albedo Quality Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-09-16 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, quality, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A2 proprietary
MODIS/061/MCD43A3 MCD43A3.061 MODIS Albedo Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-09-16 -180, -90, 180, 90 False albedo, black_sky, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 proprietary
@@ -433,7 +433,7 @@ MODIS/061/MOD09GA MOD09GA.061 Terra Surface Reflectance Daily Global 1km and 500
MODIS/061/MOD09GQ MOD09GQ.061 Terra Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09GQ') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-09-25 -180, -90, 180, 90 False daily, global, mod09gq, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GQ proprietary
MODIS/061/MOD09Q1 MOD09Q1.061 Terra Surface Reflectance 8-Day Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09Q1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-09-13 -180, -90, 180, 90 False 8_day, global, mod09q1, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09Q1 proprietary
MODIS/061/MOD10A1 MOD10A1.061 Terra Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MOD10A1') NASA NSIDC DAAC at CIRES 2000-02-24 2024-09-25 -180, -90, 180, 90 False albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A1 proprietary
-MODIS/061/MOD11A1 MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-09-25 -180, -90, 180, 90 False daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1 proprietary
+MODIS/061/MOD11A1 MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-09-26 -180, -90, 180, 90 False daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1 proprietary
MODIS/061/MOD11A2 MOD11A2.061 Terra Land Surface Temperature and Emissivity 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-09-13 -180, -90, 180, 90 False 8_day, emissivity, global, lst, mod11a2, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A2 proprietary
MODIS/061/MOD13A1 MOD13A1.061 Terra Vegetation Indices 16-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD13A1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-08-28 -180, -90, 180, 90 False 16_day, evi, global, mod13a1, modis, nasa, ndvi, terra, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A1 proprietary
MODIS/061/MOD13A2 MOD13A2.061 Terra Vegetation Indices 16-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD13A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-08-28 -180, -90, 180, 90 False 16_day, evi, global, mod13a2, modis, nasa, ndvi, terra, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A2 proprietary
@@ -456,7 +456,7 @@ MODIS/061/MOD21C3 MOD21C3.061 Terra Land Surface Temperature and 3-Band Emissivi
MODIS/061/MYD08_M3 MYD08_M3.061 Aqua Atmosphere Monthly Global Product image_collection ee.ImageCollection('MODIS/061/MYD08_M3') NASA LAADS DAAC at NASA Goddard Space Flight Center 2002-07-01 2024-08-01 -180, -90, 180, 90 False aqua, atmosphere, geophysical, global, modis, monthly, myd08, myd08_m3, nasa, temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD08_M3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD08_M3 proprietary
MODIS/061/MYD09A1 MYD09A1.061 Aqua Surface Reflectance 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD09A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-09-13 -180, -90, 180, 90 False 8_day, aqua, global, modis, myd09a1, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09A1 proprietary
MODIS/061/MYD09CMG MYD09CMG.061 Aqua Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD09CMG') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-09-25 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, aqua https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09CMG proprietary
-MODIS/061/MYD09GA MYD09GA.061 Aqua Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MYD09GA') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-09-24 -180, -90, 180, 90 False aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA proprietary
+MODIS/061/MYD09GA MYD09GA.061 Aqua Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MYD09GA') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-09-25 -180, -90, 180, 90 False aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA proprietary
MODIS/061/MYD09GQ MYD09GQ.061 Aqua Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09GQ') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-09-25 -180, -90, 180, 90 False aqua, daily, global, modis, myd09gq, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GQ proprietary
MODIS/061/MYD09Q1 MYD09Q1.061 Aqua Surface Reflectance 8-Day Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09Q1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-09-13 -180, -90, 180, 90 False 8_day, aqua, global, modis, myd09q1, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09Q1 proprietary
MODIS/061/MYD10A1 MYD10A1.061 Aqua Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MYD10A1') NASA NSIDC DAAC at CIRES 2002-07-04 2024-09-25 -180, -90, 180, 90 False albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD10A1 proprietary
@@ -537,10 +537,10 @@ NASA/EMIT/L2B/CH4ENH Earth Surface Mineral Dust Source Investigation- Methane En
NASA/EMIT/L2B/CH4PLM Earth Surface Mineral Dust Source Investigation- Methane Plume Complexes image_collection ee.ImageCollection('NASA/EMIT/L2B/CH4PLM') NASA Jet Propulsion Laboratory 2022-08-10 2024-09-02 -180, -90, 180, 90 False daily, emit, nasa, methane https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L2B_CH4PLM.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2B_CH4PLM proprietary
NASA/FLDAS/NOAH01/C/GL/M/V001 FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System image_collection ee.ImageCollection('NASA/FLDAS/NOAH01/C/GL/M/V001') NASA GES DISC at NASA Goddard Space Flight Center 1982-01-01 2024-08-01 -180, -60, 180, 90 False climate, evapotranspiration, famine, fldas, humidity, ldas, monthly, nasa, runoff, snow, soil_moisture, soil_temperature, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001.json https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001 proprietary
NASA/GDDP-CMIP6 NEX-GDDP-CMIP6: NASA Earth Exchange Global Daily Downscaled Climate Projections image_collection ee.ImageCollection('NASA/GDDP-CMIP6') NASA / Climate Analytics Group 1950-01-01 2100-12-31 -180, -90, 180, 90 False cag, climate, gddp, geophysical, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GDDP-CMIP6.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GDDP-CMIP6 various
-NASA/GEOS-CF/v1/fcst/htf GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf') NASA / GMAO 2022-10-01 2024-09-26 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf proprietary
-NASA/GEOS-CF/v1/fcst/tavg1hr GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr') NASA / GMAO 2022-10-01 2024-09-26 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr proprietary
-NASA/GEOS-CF/v1/rpl/htf GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf') NASA / GMAO 2018-01-01 2024-09-26 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf proprietary
-NASA/GEOS-CF/v1/rpl/tavg1hr GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr') NASA / GMAO 2018-01-01 2024-09-26 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr proprietary
+NASA/GEOS-CF/v1/fcst/htf GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf') NASA / GMAO 2022-10-01 2024-09-27 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf proprietary
+NASA/GEOS-CF/v1/fcst/tavg1hr GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr') NASA / GMAO 2022-10-01 2024-09-27 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr proprietary
+NASA/GEOS-CF/v1/rpl/htf GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf') NASA / GMAO 2018-01-01 2024-09-27 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf proprietary
+NASA/GEOS-CF/v1/rpl/tavg1hr GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr') NASA / GMAO 2018-01-01 2024-09-27 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr proprietary
NASA/GIMMS/3GV0 GIMMS NDVI From AVHRR Sensors (3rd Generation) image_collection ee.ImageCollection('NASA/GIMMS/3GV0') NASA/NOAA 1981-07-01 2013-12-16 -180, -90, 180, 90 False avhrr, gimms, nasa, ndvi, noaa, vegetation https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GIMMS_3GV0.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GIMMS_3GV0 proprietary
NASA/GLDAS/V021/NOAH/G025/T3H GLDAS-2.1: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H') NASA GES DISC at NASA Goddard Space Flight Center 2000-01-01 2024-09-21 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V021_NOAH_G025_T3H.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H proprietary
NASA/GLDAS/V022/CLSM/G025/DA1D GLDAS-2.2: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V022/CLSM/G025/DA1D') NASA GES DISC at NASA Goddard Earth Sciences Data and Information Services Center 2003-01-01 2024-05-31 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V022_CLSM_G025_DA1D.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V022_CLSM_G025_DA1D proprietary
@@ -564,16 +564,16 @@ NASA/GSFC/MERRA/flx/2 MERRA-2 M2T1NXFLX: Surface Flux Diagnostics V5.12.4 image_
NASA/GSFC/MERRA/lnd/2 MERRA-2 M2T1NXLND: Land Surface Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/lnd/2') NASA/MERRA 1980-01-01 2024-09-01 -180, -90, 180, 90 False evaporation, ice, merra, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_lnd_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_lnd_2 proprietary
NASA/GSFC/MERRA/rad/2 MERRA-2 M2T1NXRAD: Radiation Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/rad/2') NASA/MERRA 1980-01-01 2024-09-01 -180, -90, 180, 90 False albedo, emissivity, merra, shortwave, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_rad_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_rad_2 proprietary
NASA/GSFC/MERRA/slv/2 MERRA-2 M2T1NXSLV: Single-Level Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/slv/2') NASA/MERRA 1980-01-01 2024-09-01 -180, -90, 180, 90 False condensation, humidity, merra, nasa, omega, pressure, slv, temperature, vapor, water, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_slv_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_slv_2 proprietary
-NASA/HLS/HLSL30/v002 HLSL30: HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m image_collection ee.ImageCollection('NASA/HLS/HLSL30/v002') NASA LP DAAC 2013-04-11 2024-09-20 -180, -90, 180, 90 False landsat, nasa, sentinel, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_HLS_HLSL30_v002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002 proprietary
+NASA/HLS/HLSL30/v002 HLSL30: HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m image_collection ee.ImageCollection('NASA/HLS/HLSL30/v002') NASA LP DAAC 2013-04-11 2024-09-23 -180, -90, 180, 90 False landsat, nasa, sentinel, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_HLS_HLSL30_v002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002 proprietary
NASA/JPL/global_forest_canopy_height_2005 Global Forest Canopy Height, 2005 image ee.Image('NASA/JPL/global_forest_canopy_height_2005') NASA/JPL 2005-05-20 2005-06-23 -180, -90, 180, 90 False canopy, forest, geophysical, glas, jpl, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_JPL_global_forest_canopy_height_2005.json https://developers.google.com/earth-engine/datasets/catalog/NASA_JPL_global_forest_canopy_height_2005 proprietary
-NASA/LANCE/NOAA20_VIIRS/C2 VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2') NASA / LANCE / NOAA20_VIIRS 2023-10-08 2024-09-25 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_NOAA20_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2 proprietary
+NASA/LANCE/NOAA20_VIIRS/C2 VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2') NASA / LANCE / NOAA20_VIIRS 2023-10-08 2024-09-27 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_NOAA20_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2 proprietary
NASA/LANCE/SNPP_VIIRS/C2 VNP14IMGTDL_NRT Daily Raster: VIIRS (S-NPP) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2') NASA / LANCE / SNPP_VIIRS 2023-09-03 2024-09-24 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_SNPP_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_SNPP_VIIRS_C2 proprietary
NASA/MEASURES/GFCC/TC/v3 Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m image_collection ee.ImageCollection('NASA/MEASURES/GFCC/TC/v3') NASA LP DAAC at the USGS EROS Center 2000-01-01 2015-01-01 -180, -90, 180, 90 False forest, glcf, landsat_derived, nasa, umd https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_MEASURES_GFCC_TC_v3.json https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3 proprietary
NASA/NASADEM_HGT/001 NASADEM: NASA NASADEM Digital Elevation 30m image ee.Image('NASA/NASADEM_HGT/001') NASA / USGS / JPL-Caltech 2000-02-11 2000-02-22 -180, -56, 180, 60 False dem, elevation, geophysical, nasa, nasadem, srtm, topography, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NASADEM_HGT_001.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NASADEM_HGT_001 proprietary
NASA/NEX-DCP30 NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-DCP30') NASA / Climate Analytics Group 1950-01-01 2099-12-01 -125.03, 24.07, -66.47, 53.74 False cag, climate, cmip5, geophysical, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-DCP30.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30 proprietary
NASA/NEX-DCP30_ENSEMBLE_STATS NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-DCP30_ENSEMBLE_STATS') NASA / Climate Analytics Group 1950-01-01 2099-12-01 -125.03, 24.07, -66.47, 49.93 False cag, climate, cmip5, geophysical, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-DCP30_ENSEMBLE_STATS.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30_ENSEMBLE_STATS proprietary
NASA/NEX-GDDP NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-GDDP') NASA / Climate Analytics Group 1950-01-01 2100-12-31 -180, -90, 180, 90 False cag, climate, cmip5, gddp, geophysical, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-GDDP.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-GDDP proprietary
-NASA/NLDAS/FORA0125_H002 NLDAS-2: North American Land Data Assimilation System Forcing Fields image_collection ee.ImageCollection('NASA/NLDAS/FORA0125_H002') NASA GES DISC at NASA Goddard Space Flight Center 1979-01-01 2024-09-23 -125.15, 24.85, -66.85, 53.28 False climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NLDAS_FORA0125_H002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002 proprietary
+NASA/NLDAS/FORA0125_H002 NLDAS-2: North American Land Data Assimilation System Forcing Fields image_collection ee.ImageCollection('NASA/NLDAS/FORA0125_H002') NASA GES DISC at NASA Goddard Space Flight Center 1979-01-01 2024-09-24 -125.15, 24.85, -66.85, 53.28 False climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NLDAS_FORA0125_H002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002 proprietary
NASA/OCEANDATA/MODIS-Aqua/L3SMI Ocean Color SMI: Standard Mapped Image MODIS Aqua Data image_collection ee.ImageCollection('NASA/OCEANDATA/MODIS-Aqua/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 2002-07-03 2022-02-28 -180, -90, 180, 90 False biology, chlorophyll, climate, modis, nasa, ocean, oceandata, reflectance, sst, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_MODIS-Aqua_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI proprietary
NASA/OCEANDATA/MODIS-Terra/L3SMI Ocean Color SMI: Standard Mapped Image MODIS Terra Data image_collection ee.ImageCollection('NASA/OCEANDATA/MODIS-Terra/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 2000-02-24 2022-02-28 -180, -90, 180, 90 False biology, chlorophyll, climate, modis, nasa, ocean, oceandata, reflectance, sst, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_MODIS-Terra_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Terra_L3SMI proprietary
NASA/OCEANDATA/SeaWiFS/L3SMI Ocean Color SMI: Standard Mapped Image SeaWiFS Data image_collection ee.ImageCollection('NASA/OCEANDATA/SeaWiFS/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 1997-09-04 2010-12-10 -180, -90, 180, 90 False biology, chlorophyll, climate, nasa, ocean, oceandata, reflectance, seawifs, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_SeaWiFS_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_SeaWiFS_L3SMI proprietary
@@ -581,8 +581,8 @@ NASA/ORNL/DAYMET_V3 Daymet V3: Daily Surface Weather and Climatological Summarie
NASA/ORNL/DAYMET_V4 Daymet V4: Daily Surface Weather and Climatological Summaries image_collection ee.ImageCollection('NASA/ORNL/DAYMET_V4') NASA ORNL DAAC at Oak Ridge National Laboratory 1980-01-01 2023-12-31 -150.8, 1.6, -1.1, 84 False climate, daily, daylight, daymet, flux, geophysical, nasa, ornl, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ORNL_DAYMET_V4.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V4 proprietary
NASA/ORNL/biomass_carbon_density/v1 Global Aboveground and Belowground Biomass Carbon Density Maps image_collection ee.ImageCollection('NASA/ORNL/biomass_carbon_density/v1') NASA ORNL DAAC at Oak Ridge National Laboratory 2010-01-01 2010-12-31 -180, -61.1, 180, 84 False aboveground, belowground, biomass, carbon, density, forest, nasa, ornl, vegetation https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ORNL_biomass_carbon_density_v1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_biomass_carbon_density_v1 proprietary
NASA/SMAP/SPL3SMP_E/005 SPL3SMP_E.005 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') Google and NSIDC 2015-03-31 2023-12-03 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_005.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_005 proprietary
-NASA/SMAP/SPL3SMP_E/006 SPL3SMP_E.006 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006') Google and NSIDC 2023-12-04 2024-09-24 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_006.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_006 proprietary
-NASA/SMAP/SPL4SMGP/007 SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL4SMGP/007') Google and NSIDC 2015-03-31 2024-09-24 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007 proprietary
+NASA/SMAP/SPL3SMP_E/006 SPL3SMP_E.006 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006') Google and NSIDC 2023-12-04 2024-09-25 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_006.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_006 proprietary
+NASA/SMAP/SPL4SMGP/007 SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL4SMGP/007') Google and NSIDC 2015-03-31 2024-09-25 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007 proprietary
NASA/VIIRS/002/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09GA') NASA Land SIPS 2012-01-19 2024-09-24 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09GA proprietary
NASA/VIIRS/002/VNP09H1 VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09H1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-09-13 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09H1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09H1 proprietary
NASA/VIIRS/002/VNP13A1 VNP13A1.002: VIIRS Vegetation Indices 16-Day 500m image_collection ee.ImageCollection('NASA/VIIRS/002/VNP13A1') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-09-05 -180, -90, 180, 90 False 16_day, evi, nasa, ndvi, noaa, npp, vegetation, viirs, vnp13a1 https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP13A1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP13A1 proprietary
@@ -593,9 +593,9 @@ NASA/VIIRS/002/VNP21A1N VNP21A1N.002: Night Land Surface Temperature and Emissiv
NASA_USDA/HSL/SMAP10KM_soil_moisture NASA-USDA Enhanced SMAP Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/SMAP10KM_soil_moisture') NASA GSFC 2015-04-02 2022-08-02 -180, -60, 180, 90 True geophysical, hsl, nasa, smap, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP10KM_soil_moisture proprietary
NASA_USDA/HSL/SMAP_soil_moisture NASA-USDA SMAP Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/SMAP_soil_moisture') NASA GSFC 2015-04-02 2020-12-31 -180, -60, 180, 90 True geophysical, hsl, nasa, smap, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_SMAP_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture proprietary
NASA_USDA/HSL/soil_moisture NASA-USDA Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/soil_moisture') NASA GSFC 2010-01-13 2020-12-31 -180, -60, 180, 90 True geophysical, hsl, nasa, smos, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture proprietary
-NCEP_RE/sea_level_pressure NCEP/NCAR Reanalysis Data, Sea-Level Pressure image_collection ee.ImageCollection('NCEP_RE/sea_level_pressure') NCEP 1948-01-01 2024-09-24 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_sea_level_pressure.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure proprietary
-NCEP_RE/surface_temp NCEP/NCAR Reanalysis Data, Surface Temperature image_collection ee.ImageCollection('NCEP_RE/surface_temp') NCEP 1948-01-01 2024-09-24 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_temp.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp proprietary
-NCEP_RE/surface_wv NCEP/NCAR Reanalysis Data, Water Vapor image_collection ee.ImageCollection('NCEP_RE/surface_wv') NCEP 1948-01-01 2024-09-24 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_wv.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv proprietary
+NCEP_RE/sea_level_pressure NCEP/NCAR Reanalysis Data, Sea-Level Pressure image_collection ee.ImageCollection('NCEP_RE/sea_level_pressure') NCEP 1948-01-01 2024-09-25 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_sea_level_pressure.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure proprietary
+NCEP_RE/surface_temp NCEP/NCAR Reanalysis Data, Surface Temperature image_collection ee.ImageCollection('NCEP_RE/surface_temp') NCEP 1948-01-01 2024-09-25 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_temp.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp proprietary
+NCEP_RE/surface_wv NCEP/NCAR Reanalysis Data, Water Vapor image_collection ee.ImageCollection('NCEP_RE/surface_wv') NCEP 1948-01-01 2024-09-25 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_wv.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv proprietary
NOAA/CDR/ATMOS_NEAR_SURFACE/V2 NOAA CDR: Ocean Near-Surface Atmospheric Properties, Version 2 image_collection ee.ImageCollection('NOAA/CDR/ATMOS_NEAR_SURFACE/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False air_temperature, atmospheric, cdr, hourly, humidity, noaa, ocean, osb, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_ATMOS_NEAR_SURFACE_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_ATMOS_NEAR_SURFACE_V2 proprietary
NOAA/CDR/AVHRR/AOT/V3 NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v03 [deprecated] image_collection ee.ImageCollection('NOAA/CDR/AVHRR/AOT/V3') NOAA 1981-01-01 2022-03-31 -180, -90, 180, 90 True aerosol, aot, atmospheric, avhrr, cdr, daily, noaa, optical, pollution https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V3.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V3 proprietary
NOAA/CDR/AVHRR/AOT/V4 NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v04 image_collection ee.ImageCollection('NOAA/CDR/AVHRR/AOT/V4') NOAA 1981-01-01 2024-06-30 -180, -90, 180, 90 False aerosol, aot, atmospheric, avhrr, cdr, daily, noaa, optical, pollution https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V4 proprietary
@@ -608,15 +608,15 @@ NOAA/CDR/AVHRR/SR/V5 NOAA CDR AVHRR: Surface Reflectance, Version 5 image_collec
NOAA/CDR/GRIDSAT-B1/V2 NOAA CDR GRIDSAT-B1: Geostationary IR Channel Brightness Temperature image_collection ee.ImageCollection('NOAA/CDR/GRIDSAT-B1/V2') NOAA 1980-01-01 2024-03-31 -180, -90, 180, 90 False brightness, cdr, fundamental, geostationary, infrared, isccp, noaa, reflectance, sr https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_GRIDSAT-B1_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_GRIDSAT-B1_V2 proprietary
NOAA/CDR/HEAT_FLUXES/V2 NOAA CDR: Ocean Heat Fluxes, Version 2 image_collection ee.ImageCollection('NOAA/CDR/HEAT_FLUXES/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False atmospheric, cdr, flux, heat, hourly, noaa, ocean, osb https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_HEAT_FLUXES_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_HEAT_FLUXES_V2 proprietary
NOAA/CDR/OISST/V2 NOAA CDR OISST v2: Optimum Interpolation Sea Surface Temperature [deprecated] image_collection ee.ImageCollection('NOAA/CDR/OISST/V2') NOAA 1981-09-01 2020-04-26 -180, -90, 180, 90 True avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2 proprietary
-NOAA/CDR/OISST/V2_1 NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/OISST/V2_1') NOAA 1981-09-01 2024-09-24 -180, -90, 180, 90 False avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1 proprietary
+NOAA/CDR/OISST/V2_1 NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/OISST/V2_1') NOAA 1981-09-01 2024-09-25 -180, -90, 180, 90 False avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1 proprietary
NOAA/CDR/PATMOSX/V53 NOAA CDR PATMOSX: Cloud Properties, Reflectance, and Brightness Temperatures, Version 5.3 image_collection ee.ImageCollection('NOAA/CDR/PATMOSX/V53') NOAA 1979-01-01 2022-01-01 -180, -90, 180, 90 False atmospheric, avhrr, brightness, cdr, cloud, metop, noaa, optical, poes, reflectance, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_PATMOSX_V53.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_PATMOSX_V53 proprietary
NOAA/CDR/SST_PATHFINDER/V53 NOAA AVHRR Pathfinder Version 5.3 Collated Global 4km Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/SST_PATHFINDER/V53') NOAA 1981-08-24 2023-12-30 -180, -90, 180, 90 False avhrr, noaa, pathfinder, sea_ice, sst, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_SST_PATHFINDER_V53.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_PATHFINDER_V53 proprietary
NOAA/CDR/SST_WHOI/V2 NOAA CDR WHOI: Sea Surface Temperature, Version 2 image_collection ee.ImageCollection('NOAA/CDR/SST_WHOI/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False atmospheric, cdr, hourly, noaa, ocean, oisst, osb, sst, whoi https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_SST_WHOI_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_WHOI_V2 proprietary
-NOAA/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2024-09-26 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR proprietary
-NOAA/CFSV2/FOR6H CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products image_collection ee.ImageCollection('NOAA/CFSV2/FOR6H') NOAA NWS National Centers for Environmental Prediction (NCEP) 1979-01-01 2024-09-26 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H proprietary
+NOAA/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2024-09-28 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR proprietary
+NOAA/CFSV2/FOR6H CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products image_collection ee.ImageCollection('NOAA/CFSV2/FOR6H') NOAA NWS National Centers for Environmental Prediction (NCEP) 1979-01-01 2024-09-28 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H proprietary
NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4 DMSP OLS: Global Radiance-Calibrated Nighttime Lights Version 4, Defense Meteorological Program Operational Linescan System image_collection ee.ImageCollection('NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 1996-03-16 2011-07-31 -180, -65, 180, 75 False calibrated, dmsp, eog, imagery, lights, nighttime, ols, radiance, visible, yearly https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4 proprietary
NOAA/DMSP-OLS/NIGHTTIME_LIGHTS DMSP OLS: Nighttime Lights Time Series Version 4, Defense Meteorological Program Operational Linescan System image_collection ee.ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 1992-01-01 2014-01-01 -180, -65, 180, 75 False dmsp, eog, imagery, lights, nighttime, ols, visible, yearly https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS proprietary
-NOAA/GFS0P25 GFS: Global Forecast System 384-Hour Predicted Atmosphere Data image_collection ee.ImageCollection('NOAA/GFS0P25') NOAA/NCEP/EMC 2015-07-01 2024-09-27 -180, -90, 180, 90 False climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GFS0P25.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 proprietary
+NOAA/GFS0P25 GFS: Global Forecast System 384-Hour Predicted Atmosphere Data image_collection ee.ImageCollection('NOAA/GFS0P25') NOAA/NCEP/EMC 2015-07-01 2024-09-28 -180, -90, 180, 90 False climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GFS0P25.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 proprietary
NOAA/GOES/16/FDCC GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/16/FDCC') NOAA 2017-05-24 2024-09-27 -152.11, 14, -49.18, 56.77 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC proprietary
NOAA/GOES/16/FDCF GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/FDCF') NOAA 2017-05-24 2024-09-27 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF proprietary
NOAA/GOES/16/MCMIPC GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPC') NOAA 2017-07-10 2024-09-27 -152.11, 14, -49.18, 56.77 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC proprietary
@@ -637,7 +637,7 @@ NOAA/NCEP_DOE_RE2/total_cloud_coverage NCEP-DOE Reanalysis 2 (Gaussian Grid), To
NOAA/NGDC/ETOPO1 ETOPO1: Global 1 Arc-Minute Elevation image ee.Image('NOAA/NGDC/ETOPO1') NOAA 2008-08-01 2008-08-01 -180, -90, 180, 90 False bedrock, dem, elevation, geophysical, ice, noaa, topography https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NGDC_ETOPO1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NGDC_ETOPO1 proprietary
NOAA/NHC/HURDAT2/atlantic NOAA NHC HURDAT2 Atlantic Hurricane Catalog table ee.FeatureCollection('NOAA/NHC/HURDAT2/atlantic') NOAA NHC 1851-06-25 2018-11-04 -109.5, 7.2, 63, 81 False hurricane, nhc, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NHC_HURDAT2_atlantic.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_atlantic proprietary
NOAA/NHC/HURDAT2/pacific NOAA NHC HURDAT2 Pacific Hurricane Catalog table ee.FeatureCollection('NOAA/NHC/HURDAT2/pacific') NOAA NHC 1949-06-11 2018-11-09 -180, 0.4, 180, 63.1 False hurricane, nhc, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NHC_HURDAT2_pacific.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_pacific proprietary
-NOAA/NWS/RTMA RTMA: Real-Time Mesoscale Analysis image_collection ee.ImageCollection('NOAA/NWS/RTMA') NOAA/NWS 2011-01-01 2024-09-27 -130.17, 20.15, -60.81, 52.91 False climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NWS_RTMA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA proprietary
+NOAA/NWS/RTMA RTMA: Real-Time Mesoscale Analysis image_collection ee.ImageCollection('NOAA/NWS/RTMA') NOAA/NWS 2011-01-01 2024-09-28 -130.17, 20.15, -60.81, 52.91 False climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NWS_RTMA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA proprietary
NOAA/PERSIANN-CDR PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record image_collection ee.ImageCollection('NOAA/PERSIANN-CDR') NOAA NCDC 1983-01-01 2024-03-31 -180, -60, 180, 60 False cdr, climate, geophysical, ncdc, noaa, persiann, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_PERSIANN-CDR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_PERSIANN-CDR proprietary
NOAA/VIIRS/001/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km [deprecated] image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP09GA') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-06-16 -180, -90, 180, 90 True daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09GA proprietary
NOAA/VIIRS/001/VNP09H1 VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m [deprecated] image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP09H1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-06-09 -180, -90, 180, 90 True daily, nasa, noaa, npp, reflectance, sr, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09H1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09H1 proprietary
@@ -658,7 +658,7 @@ NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG VIIRS Nighttime Day/Night Band Composites Versi
NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 image_collection ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 2014-01-01 2024-05-01 -180, -65, 180, 75 False dnb, eog, lights, monthly, nighttime, noaa, stray_light, viirs, visible https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG proprietary
NRCan/CDEM Canadian Digital Elevation Model image_collection ee.ImageCollection('NRCan/CDEM') NRCan 1945-01-01 2011-01-01 -142, 41, -52, 84 False canada, cdem, dem, elevation, geophysical, nrcan, topography https://storage.googleapis.com/earthengine-stac/catalog/NRCan/NRCan_CDEM.json https://developers.google.com/earth-engine/datasets/catalog/NRCan_CDEM OGL-Canada-2.0
Netherlands/Beeldmateriaal/LUCHTFOTO_RGB Netherlands orthophotos image_collection ee.ImageCollection('Netherlands/Beeldmateriaal/LUCHTFOTO_RGB') Beeldmateriaal Nederland 2021-01-01 2022-12-31 3.2, 50.75, 7.22, 53.7 False orthophoto, rgb, netherlands https://storage.googleapis.com/earthengine-stac/catalog/Netherlands/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB.json https://developers.google.com/earth-engine/datasets/catalog/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB CC-BY-4.0
-OREGONSTATE/PRISM/AN81d PRISM Daily Spatial Climate Dataset AN81d image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81d') PRISM / OREGONSTATE 1981-01-01 2024-09-24 -125, 24, -66, 50 False climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d proprietary
+OREGONSTATE/PRISM/AN81d PRISM Daily Spatial Climate Dataset AN81d image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81d') PRISM / OREGONSTATE 1981-01-01 2024-09-25 -125, 24, -66, 50 False climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d proprietary
OREGONSTATE/PRISM/AN81m PRISM Monthly Spatial Climate Dataset AN81m image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81m') PRISM / OREGONSTATE 1895-01-01 2024-08-01 -125, 24, -66, 50 False climate, geophysical, monthly, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m proprietary
OREGONSTATE/PRISM/Norm81m PRISM Long-Term Average Climate Dataset Norm81m [deprecated] image_collection ee.ImageCollection('OREGONSTATE/PRISM/Norm81m') PRISM / OREGONSTATE 1981-01-01 2010-12-31 -125, 24, -66, 50 True 30_year, climate, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm81m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm81m proprietary
OREGONSTATE/PRISM/Norm91m PRISM Long-Term Average Climate Dataset Norm91m image_collection ee.ImageCollection('OREGONSTATE/PRISM/Norm91m') PRISM / OREGONSTATE 1991-01-01 2020-12-31 -125, 24, -66, 50 False 30_year, climate, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm91m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm91m proprietary
@@ -721,7 +721,7 @@ TIGER/2018/States TIGER: US Census States 2018 table ee.FeatureCollection('TIGER
TIGER/2020/BG TIGER: US Census Block Groups (BG) 2020 table ee.FeatureCollection('TIGER/2020/BG') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_BG.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_BG proprietary
TIGER/2020/TABBLOCK20 TIGER: 2020 Tabulation (Census) Block table ee.FeatureCollection('TIGER/2020/TABBLOCK20') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_TABBLOCK20.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TABBLOCK20 proprietary
TIGER/2020/TRACT TIGER: US Census Tracts table ee.FeatureCollection('TIGER/2020/TRACT') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_TRACT.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TRACT proprietary
-TOMS/MERGED TOMS and OMI Merged Ozone Data image_collection ee.ImageCollection('TOMS/MERGED') NASA / GES DISC 1978-11-01 2024-09-25 -180, -90, 180, 90 False atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED proprietary
+TOMS/MERGED TOMS and OMI Merged Ozone Data image_collection ee.ImageCollection('TOMS/MERGED') NASA / GES DISC 1978-11-01 2024-09-26 -180, -90, 180, 90 False atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED proprietary
TRMM/3B42 TRMM 3B42: 3-Hourly Precipitation Estimates image_collection ee.ImageCollection('TRMM/3B42') NASA GES DISC at NASA Goddard Space Flight Center 1998-01-01 2019-12-31 -180, -50, 180, 50 False 3_hourly, climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather https://storage.googleapis.com/earthengine-stac/catalog/TRMM/TRMM_3B42.json https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B42 proprietary
TRMM/3B43V7 TRMM 3B43: Monthly Precipitation Estimates image_collection ee.ImageCollection('TRMM/3B43V7') NASA GES DISC at NASA Goddard Space Flight Center 1998-01-01 2019-12-01 -180, -50, 180, 50 False climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather https://storage.googleapis.com/earthengine-stac/catalog/TRMM/TRMM_3B43V7.json https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B43V7 proprietary
TUBerlin/BigEarthNet/v1 TUBerlin/BigEarthNet/v1 image_collection ee.ImageCollection('TUBerlin/BigEarthNet/v1') BigEarthNet 2017-06-01 2018-05-31 -9, 36.9, 31.6, 68.1 False chip, copernicus, corine_derived, label, ml, sentinel, tile https://storage.googleapis.com/earthengine-stac/catalog/TUBerlin/TUBerlin_BigEarthNet_v1.json https://developers.google.com/earth-engine/datasets/catalog/TUBerlin_BigEarthNet_v1 proprietary
@@ -812,7 +812,7 @@ USGS/WBD/2017/HUC06 HUC06: USGS Watershed Boundary Dataset of Basins table ee.Fe
USGS/WBD/2017/HUC08 HUC08: USGS Watershed Boundary Dataset of Subbasins table ee.FeatureCollection('USGS/WBD/2017/HUC08') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC08.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC08 proprietary
USGS/WBD/2017/HUC10 HUC10: USGS Watershed Boundary Dataset of Watersheds table ee.FeatureCollection('USGS/WBD/2017/HUC10') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC10.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC10 proprietary
USGS/WBD/2017/HUC12 HUC12: USGS Watershed Boundary Dataset of Subwatersheds table ee.FeatureCollection('USGS/WBD/2017/HUC12') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC12.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC12 proprietary
-UTOKYO/WTLAB/KBDI/v1 KBDI: Keetch-Byram Drought Index image_collection ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1') Institute of Industrial Science, The University of Tokyo, Japan 2007-01-01 2024-09-26 60, -60, 180, 60 False drought, kbdi, lst_derived, rainfall, utokyo, wtlab https://storage.googleapis.com/earthengine-stac/catalog/UTOKYO/UTOKYO_WTLAB_KBDI_v1.json https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1 CC-BY-4.0
+UTOKYO/WTLAB/KBDI/v1 KBDI: Keetch-Byram Drought Index image_collection ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1') Institute of Industrial Science, The University of Tokyo, Japan 2007-01-01 2024-09-27 60, -60, 180, 60 False drought, kbdi, lst_derived, rainfall, utokyo, wtlab https://storage.googleapis.com/earthengine-stac/catalog/UTOKYO/UTOKYO_WTLAB_KBDI_v1.json https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1 CC-BY-4.0
VITO/PROBAV/C1/S1_TOC_100M PROBA-V C1 Top Of Canopy Daily Synthesis 100m image_collection ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_100M') Vito / ESA 2013-10-17 2021-10-31 -180, -90, 180, 90 False esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_100M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_100M proprietary
VITO/PROBAV/C1/S1_TOC_333M PROBA-V C1 Top Of Canopy Daily Synthesis 333m image_collection ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_333M') Vito / ESA 2013-10-17 2021-10-31 -180, -90, 180, 90 False esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_333M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_333M proprietary
VITO/PROBAV/S1_TOC_100M PROBA-V C0 Top Of Canopy Daily Synthesis 100m [deprecated] image_collection ee.ImageCollection('VITO/PROBAV/S1_TOC_100M') Vito / ESA 2013-10-17 2016-12-14 -180, -90, 180, 90 True esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_S1_TOC_100M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_S1_TOC_100M proprietary
diff --git a/nasa_cmr_catalog.json b/nasa_cmr_catalog.json
index be33927..9a729d1 100644
--- a/nasa_cmr_catalog.json
+++ b/nasa_cmr_catalog.json
@@ -14282,7 +14282,7 @@
"bbox": "-135, -59, -15, 59",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877465-POCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877465-POCLOUD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wiZ2hyc3N0IGxldmVsIDQgZ2Ftc3NhXzI4a20gZ2xvYmFsIGZvdW5kYXRpb24gc2VhIHN1cmZhY2UgdGVtcGVyYXR1cmUgYW5hbHlzaXMgdjEuMCBkYXRhc2V0IChnZHMyKVwiLFwiUE9DTE9VRFwiLFwiR0FNU1NBXzI4a20tQUJPTS1MNC1HTE9CLXYwMVwiLFwiMS4wXCIsMjAzNjg4MTczNSwxMF0iLCJ1bW0iOiJbXCJnaHJzc3QgbGV2ZWwgNCBnYW1zc2FfMjhrbSBnbG9iYWwgZm91bmRhdGlvbiBzZWEgc3VyZmFjZSB0ZW1wZXJhdHVyZSBhbmFseXNpcyB2MS4wIGRhdGFzZXQgKGdkczIpXCIsXCJQT0NMT1VEXCIsXCJHQU1TU0FfMjhrbS1BQk9NLUw0LUdMT0ItdjAxXCIsXCIxLjBcIiwyMDM2ODgxNzM1LDEwXSJ9/ABI_G16-STAR-L2P-v2.70_2.70",
+ "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wiZ2hyc3N0IGxldmVsIDQgY21jMC4yZGVnIGdsb2JhbCBmb3VuZGF0aW9uIHNlYSBzdXJmYWNlIHRlbXBlcmF0dXJlIGFuYWx5c2lzIChnZHMgdmVyc2lvbiAyKVwiLFwiUE9DTE9VRFwiLFwiQ01DMC4yZGVnLUNNQy1MNC1HTE9CLXYyLjBcIixcIjIuMFwiLDI0OTk5NDA1MjEsN10iLCJ1bW0iOiJbXCJnaHJzc3QgbGV2ZWwgNCBjbWMwLjJkZWcgZ2xvYmFsIGZvdW5kYXRpb24gc2VhIHN1cmZhY2UgdGVtcGVyYXR1cmUgYW5hbHlzaXMgKGdkcyB2ZXJzaW9uIDIpXCIsXCJQT0NMT1VEXCIsXCJDTUMwLjJkZWctQ01DLUw0LUdMT0ItdjIuMFwiLFwiMi4wXCIsMjQ5OTk0MDUyMSw3XSJ9/ABI_G16-STAR-L2P-v2.70_2.70",
"description": "GOES-16 (G16) is the first satellite in the US NOAA third generation of Geostationary Operational Environmental Satellites (GOES), a.k.a. GOES-R series (which will also include -S, -T, and -U). G16 was launched on 19 Nov 2016 and initially placed in an interim position at 89.5-deg W, between GOES-East and -West. Upon completion of Cal/Val in Dec 2018, it was moved to its permanent position at 75.2-deg W, and declared NOAA operational GOES-East on 18 Dec 2018. NOAA is responsible for all GOES-R products, including Sea Surface Temperature (SST) from the Advanced Baseline Imager (ABI). The ABI offers vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P Imager, including five narrow bands (centered at 3.9, 8.4, 10.3, 11.2, and 12.3 um) out of 16 that can be used for SST, as well as accurate sensor calibration, image navigation and co-registration, spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping). From altitude 35,800 km, G16/ABI can accurately map SST in a Full Disk (FD) area from 15-135-deg W and 60S-60N, with spatial resolution 2km at nadir (degrading to 15km at view zenith angle, 67-deg) and temporal sampling of 10min (15min prior to 2 Apr 2019). The Level 2 Preprocessed (L2P) SST product is derived at the native sensor resolution using NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO first processes every 10min FD data SSTs are derived from BTs using the ACSPO clear-sky mask (ACSM; Petrenko et al., 2010) and Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Currently, only 4 longwave bands centered at 8.4, 10.3, 11.2, and 12.3 um are used (the 3.9 microns was initially excluded, to minimize possible discontinuities in the diurnal cycle). The regression is tuned against quality controlled in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min FD data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise, compared to each individual 10min image. In the collated L2P, SSTs and BTs are only reported in clear-sky water pixels (defined as ocean, sea, lake or river, and up to 5 km inland) and fill values elsewhere. The L2P is reported in netCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume of 0.6GB/day. In addition to SST, ACSPO files also include sun-sensor geometry, four BTs in ABI bands 11 (8.4um), 13 (10.3um), 14 (11.2um), and 15 (12.3um) and two reflectances in bands 2 and 3 (0.64um and 0.86um; used for cloud identification). The l2p_flags layer includes day/night, land, ice, twilight, and glint flags. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). Pixel-level earth locations are not reported in the granules, as they remain unchanged from granule to granule. To obtain those, user has a choice of using a flat lat-lon file, or a Python script, both available at ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel. The ACSPO VIIRS L2P product is monitored and validated against in situ data (Xu and Ignatov, 2014) using the Satellite Quality Monitor SQUAM (Dash et al, 2010), and BTs are validated against RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3C product is also available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L3C-v2.70, where gridded L2P SSTs are reported, and BT layers omitted.",
"license": "proprietary"
},
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"bbox": "-135, -59, -15, 59",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877612-POCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877612-POCLOUD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wiZ2hyc3N0IGxldmVsIDQgZ2Ftc3NhXzI4a20gZ2xvYmFsIGZvdW5kYXRpb24gc2VhIHN1cmZhY2UgdGVtcGVyYXR1cmUgYW5hbHlzaXMgdjEuMCBkYXRhc2V0IChnZHMyKVwiLFwiUE9DTE9VRFwiLFwiR0FNU1NBXzI4a20tQUJPTS1MNC1HTE9CLXYwMVwiLFwiMS4wXCIsMjAzNjg4MTczNSwxMF0iLCJ1bW0iOiJbXCJnaHJzc3QgbGV2ZWwgNCBnYW1zc2FfMjhrbSBnbG9iYWwgZm91bmRhdGlvbiBzZWEgc3VyZmFjZSB0ZW1wZXJhdHVyZSBhbmFseXNpcyB2MS4wIGRhdGFzZXQgKGdkczIpXCIsXCJQT0NMT1VEXCIsXCJHQU1TU0FfMjhrbS1BQk9NLUw0LUdMT0ItdjAxXCIsXCIxLjBcIiwyMDM2ODgxNzM1LDEwXSJ9/ABI_G16-STAR-L3C-v2.70_2.70",
+ "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wiZ2hyc3N0IGxldmVsIDQgY21jMC4yZGVnIGdsb2JhbCBmb3VuZGF0aW9uIHNlYSBzdXJmYWNlIHRlbXBlcmF0dXJlIGFuYWx5c2lzIChnZHMgdmVyc2lvbiAyKVwiLFwiUE9DTE9VRFwiLFwiQ01DMC4yZGVnLUNNQy1MNC1HTE9CLXYyLjBcIixcIjIuMFwiLDI0OTk5NDA1MjEsN10iLCJ1bW0iOiJbXCJnaHJzc3QgbGV2ZWwgNCBjbWMwLjJkZWcgZ2xvYmFsIGZvdW5kYXRpb24gc2VhIHN1cmZhY2UgdGVtcGVyYXR1cmUgYW5hbHlzaXMgKGdkcyB2ZXJzaW9uIDIpXCIsXCJQT0NMT1VEXCIsXCJDTUMwLjJkZWctQ01DLUw0LUdMT0ItdjIuMFwiLFwiMi4wXCIsMjQ5OTk0MDUyMSw3XSJ9/ABI_G16-STAR-L3C-v2.70_2.70",
"description": "The ACSPO G16/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G16/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L2P-v2.70. The L3C output files are 1hr granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules per 24hr interval, with a total data volume of 0.2GB/day. Fill values are reported at all invalid pixels, including pixels with 5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). All valid SSTs in L3C are recommended for users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).",
"license": "proprietary"
},
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"bbox": "163, -60, -77, 60",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877626-POCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877626-POCLOUD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wiZ2hyc3N0IGxldmVsIDQgZ2Ftc3NhXzI4a20gZ2xvYmFsIGZvdW5kYXRpb24gc2VhIHN1cmZhY2UgdGVtcGVyYXR1cmUgYW5hbHlzaXMgdjEuMCBkYXRhc2V0IChnZHMyKVwiLFwiUE9DTE9VRFwiLFwiR0FNU1NBXzI4a20tQUJPTS1MNC1HTE9CLXYwMVwiLFwiMS4wXCIsMjAzNjg4MTczNSwxMF0iLCJ1bW0iOiJbXCJnaHJzc3QgbGV2ZWwgNCBnYW1zc2FfMjhrbSBnbG9iYWwgZm91bmRhdGlvbiBzZWEgc3VyZmFjZSB0ZW1wZXJhdHVyZSBhbmFseXNpcyB2MS4wIGRhdGFzZXQgKGdkczIpXCIsXCJQT0NMT1VEXCIsXCJHQU1TU0FfMjhrbS1BQk9NLUw0LUdMT0ItdjAxXCIsXCIxLjBcIiwyMDM2ODgxNzM1LDEwXSJ9/ABI_G17-STAR-L2P-v2.71_2.71",
+ "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wiZ2hyc3N0IG5vYWEvc3RhciBnb2VzLTE2IGFiaSBsMnAgYW1lcmljYSByZWdpb24gc3N0IHYyLjcwIGRhdGFzZXQgaW4gZ2RzMlwiLFwiUE9DTE9VRFwiLFwiQUJJX0cxNi1TVEFSLUwyUC12Mi43MFwiLFwiMi43MFwiLDIwMzY4Nzc0NjUsOV0iLCJ1bW0iOiJbXCJnaHJzc3Qgbm9hYS9zdGFyIGdvZXMtMTYgYWJpIGwycCBhbWVyaWNhIHJlZ2lvbiBzc3QgdjIuNzAgZGF0YXNldCBpbiBnZHMyXCIsXCJQT0NMT1VEXCIsXCJBQklfRzE2LVNUQVItTDJQLXYyLjcwXCIsXCIyLjcwXCIsMjAzNjg3NzQ2NSw5XSJ9/ABI_G17-STAR-L2P-v2.71_2.71",
"description": "GOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019. Advanced Baseline Imager (ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, 12.3 um) are suitable for SST. From altitude 35,800km, G17/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with spatial resolution 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The ABI L2P SST is derived at the native sensor resolution using NOAA ACSPO system. ACSPO processes every 10-min FD, identifies good-quality ocean pixels (Petrenko et al., 2010) and derives SST using Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Unfortunately, the G17 ABI loop heat pipe (LHP) that should maintain the ABI at its intended temperature, is not operating at its designed capacity, which required mitigations to the ACSPO algorithms and releasing an updated ACSPO version 2.71 (Pennybacker et al, 2019). In particular, band 11.2um, most subject to calibration problems, is not used leading to a 3-band (8.4, 10.3, and 12.3um) NLSST, and increased calibration problems prevent SST retrievals at night. As a result, the G17 SST is only reported for 13 out of 24hrs/day, from 20UTC to 08UTC. The 10-min FD data are subsequently collated in time, to produce 1-hr product, with improved coverage and reduced cloud leakages and image noise. The collation algorithm also reduces G17 excessive sensor noise and striping to levels similar to G16. The collated SSTs are only reported over clear-sky water pixels. All pixels with valid SSTs are recommended for use. The L2P is reported in NetCDF4 GDS2 format, 13 granules per day, with a total data volume 0.3GB/day. ACSPO files also report sun-sensor geometry, wind speed and l2p_flags (day/night, land, ice, twilight, glint flags). Per GDS2 specifications, two Sensor-Specific Error Statistics (bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). The ACSPO G17 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L3C-v2.71.",
"license": "proprietary"
},
@@ -14321,7 +14321,7 @@
"bbox": "163, -60, -77, 60",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877645-POCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877645-POCLOUD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wiZ2hyc3N0IGxldmVsIDQgZ2Ftc3NhXzI4a20gZ2xvYmFsIGZvdW5kYXRpb24gc2VhIHN1cmZhY2UgdGVtcGVyYXR1cmUgYW5hbHlzaXMgdjEuMCBkYXRhc2V0IChnZHMyKVwiLFwiUE9DTE9VRFwiLFwiR0FNU1NBXzI4a20tQUJPTS1MNC1HTE9CLXYwMVwiLFwiMS4wXCIsMjAzNjg4MTczNSwxMF0iLCJ1bW0iOiJbXCJnaHJzc3QgbGV2ZWwgNCBnYW1zc2FfMjhrbSBnbG9iYWwgZm91bmRhdGlvbiBzZWEgc3VyZmFjZSB0ZW1wZXJhdHVyZSBhbmFseXNpcyB2MS4wIGRhdGFzZXQgKGdkczIpXCIsXCJQT0NMT1VEXCIsXCJHQU1TU0FfMjhrbS1BQk9NLUw0LUdMT0ItdjAxXCIsXCIxLjBcIiwyMDM2ODgxNzM1LDEwXSJ9/ABI_G17-STAR-L3C-v2.71_2.71",
+ "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wiZ2hyc3N0IG5vYWEvc3RhciBnb2VzLTE2IGFiaSBsMnAgYW1lcmljYSByZWdpb24gc3N0IHYyLjcwIGRhdGFzZXQgaW4gZ2RzMlwiLFwiUE9DTE9VRFwiLFwiQUJJX0cxNi1TVEFSLUwyUC12Mi43MFwiLFwiMi43MFwiLDIwMzY4Nzc0NjUsOV0iLCJ1bW0iOiJbXCJnaHJzc3Qgbm9hYS9zdGFyIGdvZXMtMTYgYWJpIGwycCBhbWVyaWNhIHJlZ2lvbiBzc3QgdjIuNzAgZGF0YXNldCBpbiBnZHMyXCIsXCJQT0NMT1VEXCIsXCJBQklfRzE2LVNUQVItTDJQLXYyLjcwXCIsXCIyLjcwXCIsMjAzNjg3NzQ2NSw5XSJ9/ABI_G17-STAR-L3C-v2.71_2.71",
"description": "The ACSPO G17/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G17/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L2P-v2.71. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Due to the loop heat pipe (LHP) issue on G17 ABI, there are only 13 granules available per 24hr interval, from 20UTC to 08UTC, followed by a break from 09UTC to 19UTC, with a total data volume of 0.1GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. The following additional layers are also reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).",
"license": "proprietary"
},
@@ -19150,27 +19150,27 @@
},
{
"id": "AERIALDIGI_Not provided",
- "title": "Aircraft Scanners - AERIALDIGI",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "title": "Aircraft Scanners",
+ "catalog": "USGS_LTA STAC Catalog",
"state_date": "1987-10-06",
"end_date": "",
"bbox": "-180, 24, -60, 72",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json",
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+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/USGS_LTA/collections/AERIALDIGI_Not%20provided",
"description": "The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees.",
"license": "proprietary"
},
{
"id": "AERIALDIGI_Not provided",
- "title": "Aircraft Scanners",
- "catalog": "USGS_LTA STAC Catalog",
+ "title": "Aircraft Scanners - AERIALDIGI",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1987-10-06",
"end_date": "",
"bbox": "-180, 24, -60, 72",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/USGS_LTA/collections/AERIALDIGI_Not%20provided",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.html",
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"description": "The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees.",
"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877480-POCLOUD.html",
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"description": "Himawari-8 (H08) was launched on 7 October 2014 into its nominal position at 140.7-deg E, and declared operational on 7 July 2015. The Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, and 12.3 um) are suitable for SST. Accurate sensor calibration, image navigation and (co)registration, high spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping) offer vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P and MTSAT-2 Imagers. From altitude 35,800km, H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km at view zenith angle 67-deg, with a 10-min temporal sampling. The AHI L2P (swath) SST product is derived at the native sensor resolution using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO processes every 10-min FD data, identifies good quality ocean pixels (Petrenko et al., 2010) and derives SST using the four-band (8.4, 10.3, 11.2 and 12.3um) Non-Linear SST (NLSST) regression algorithm (Petrenko et al., 2014), trained against in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise. The collated L2P reports SSTs and brightness temperatures (BTs) in clear-sky water pixels (defined as ocean, sea, lake or river), and fill values elsewhere. All pixels with valid SSTs are recommended for use. ACSPO files also include sun-sensor geometry, l2p_flags (day/night, land, ice, twilight, and glint flags), and NCEP wind speed. The L2P is reported in NetCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume 0.6GB/day. Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). The H08 AHI SSTs and BTs are continuously validated against in situ data in SQUAM (Dash et al, 2010), and RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), 0.02-deg equal-angle gridded ACSPO L3C product is available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L3C-v2.70.",
"license": "proprietary"
},
@@ -19599,7 +19599,7 @@
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877660-POCLOUD.html",
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"description": "The ACSPO H08/AHI L3C (Level 3 Collated) product is a gridded version of the ACSPO H08/AHI L2P product available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L2P-v2.70. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules available per 24hr interval, with a total data volume of 0.2GB/day. Valid SSTs are found over clear-sky oceans, sea, lakes or rivers, with fill values reported elsewhere. The following layers are reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST (Petrenko et al., 2016). The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).",
"license": "proprietary"
},
@@ -21718,7 +21718,7 @@
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2251465126-POCLOUD.html",
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"description": "These data are near-real-time (NRT) (within 7-9 hours of measurement) sea surface height anomalies (SSHA) from the AltiKa altimeter onboard the Satellite with ARgos and ALtiKa (SARAL). SARAL is a French(CNES)/Indian(SARAL) collaborative mission to measure sea surface height using the Ka-band AltiKa altimeter and was launched February 25, 2013. The major difference between these data and the Operational Geophysical Data Record (OGDR) data produced by the project is that the orbit from SARAL has been adjusted using SSHA differences with those from the OSTM/Jason-2 GPS-OGDR-SSHA product at inter-satellite crossover locations. This produces a more accurate NRT orbit altitude for SARAL with accuracy of 1.5 cm (RMS), taking advantage of the 1 cm (radial RMS) accuracy of the GPS-based orbit used for the OSTM/Jason-2 GPS-OGDR-SSHA product. This dataset also contains all data from the project (reduced) OGDR, and improved altimeter wind speeds and sea state bias correction. More information on the SARAL mission can be found at: http://www.aviso.oceanobs.com/en/missions/current-missions/saral.html",
"license": "proprietary"
},
@@ -21757,7 +21757,7 @@
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"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205120784-POCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205120784-POCLOUD.html",
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"description": "The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through November 4, 2009. SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast. Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region. Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756.",
"license": "proprietary"
},
@@ -21770,7 +21770,7 @@
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
},
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"description": "This product contains a \u201cFinal\u201d (Refined) Level-3U Sea Surface Temperature (SST) (currently identified by \"v8.2\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The \u201cFinal\u201d SSTs are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v8.2\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 2 days. The v8.2 L3U SST supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-3UR8A.",
"license": "proprietary"
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"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL061Mv03 dataset, which can be found at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability are provided as an ASCII table.",
"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD7.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.",
"license": "proprietary"
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"description": "This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD7.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.",
"license": "proprietary"
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"description": "This dataset contains model output interpolated in space and time to the ESDR product from the MetOp-A ASCAT (ASCAT-A) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement the scatterometer observations. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The modeled fields are provided on a non-uniform grid within the sampled locations of the ASCAT-A Level 2 product, and at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.
The dataset represents the first science quality release of these data with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) cleaned up ancillary data points in between the left/right swaths for improved collocation with available satellite data, 2) improved variable metadata, 3) removed the GlobCurrent stokes drift variables, and 4) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this Version 1.1 release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
"license": "proprietary"
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"description": "This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.
The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
"license": "proprietary"
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"description": "This dataset represents the first historically reprocessed Level 2 ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 25 km grid. Products at 25-km sampling are less noisy than 12.5-km products, but also contain less geophysical information on small scales and near the coasts. The wind vector retrievals are currently processed using the CMOD7 geophysical model function using a Hamming filter to spatially average the Level 1 Sigma-0 data over 25 km swath grid cells. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 25-km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-25km. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.",
"license": "proprietary"
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"description": "This dataset represents the first historically reprocessed Level 2 coastal ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 12.5 km grid. This coastal dataset utilizes a spatial box filter to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset and obtains additional winds near the coast. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the vector cell wind using the same CMOD7 geophysical model function as in the operational OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds are computed as close to ~15 km from the coast. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 12.5km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-Coastal. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.",
"license": "proprietary"
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"description": "This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.",
"license": "proprietary"
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"description": "This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 12.5 and 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD5.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.",
"license": "proprietary"
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"description": "This dataset contains model output interpolated in space and time to observations from the MetOp-B ASCAT (ASCAT-B) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement the scatterometer observations. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The modeled fields are provided on a non-uniform grid within the sampled locations of the ASCAT-B Level 2 product, and at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.
The dataset represents the first science quality release of this product with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) cleaned up ancillary data points in between the left/right swaths for improved collocation with available satellite data, 2) improved variable metadata, 3) removed the GlobCurrent stokes drift variables, and 4) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this Version 1.1 release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
"license": "proprietary"
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"description": "This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.
The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
"license": "proprietary"
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},
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"id": "ATL02_006",
"title": "ATLAS/ICESat-2 L1B Converted Telemetry Data V006",
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+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL02_006",
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},
{
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"title": "ATLAS/ICESat-2 L1B Converted Telemetry Data V006",
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- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL02_006",
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"license": "proprietary"
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- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL04_006",
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+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.html",
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"license": "proprietary"
},
{
"id": "ATL04_006",
"title": "ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006",
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"description": "ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
{
"id": "ATL06_006",
"title": "ATLAS/ICESat-2 L3A Land Ice Height V006",
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- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL06_006",
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+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2564427300-NSIDC_ECS.html",
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"license": "proprietary"
},
{
"id": "ATL06_006",
"title": "ATLAS/ICESat-2 L3A Land Ice Height V006",
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+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2670138092-NSIDC_CPRD.html",
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"license": "proprietary"
},
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"license": "proprietary"
},
{
"id": "ATL07_006",
"title": "ATLAS/ICESat-2 L3A Sea Ice Height V006",
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"license": "proprietary"
},
{
"id": "ATL17_005",
"title": "ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005",
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+ "catalog": "NSIDC_ECS STAC Catalog",
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"end_date": "",
"bbox": "-180, -90, 180, 90",
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- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL17_005",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.html",
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"license": "proprietary"
},
{
"id": "ATL17_005",
"title": "ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005",
- "catalog": "NSIDC_ECS STAC Catalog",
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"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
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+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL17_005",
"description": "This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.",
"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.html",
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"license": "proprietary"
},
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"description": "ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.",
"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737912334-NSIDC_ECS.html",
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"description": "ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids.",
"license": "proprietary"
},
@@ -32079,7 +32079,7 @@
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.html",
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"description": "ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.",
"license": "proprietary"
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@@ -32105,7 +32105,7 @@
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2692731693-NSIDC_ECS.html",
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"description": "This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.",
"license": "proprietary"
},
@@ -32131,7 +32131,7 @@
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617197627-POCLOUD.html",
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"description": "Contains wind speeds and directions derived from the Seasat-A Scatterometer (SASS), presented chronologically by swath for the period between 7 July 1978 and 10 October 1978. Robert Atlas et al. (1987) produced this product using an objective ambiguity removal scheme to dealias the wind vector data binned at 100 km cells, which were calculated by Frank Wentz.",
"license": "proprietary"
},
@@ -33301,7 +33301,7 @@
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"license": "proprietary"
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"license": "proprietary"
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"description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC). HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. This particular dataset is derived from LAC data. Further binning and averaging of the 1.1 km LAC pixels results in a final dataset resolution of 2.2 km. The coverage of the LAC data can vary but generally contains scenes over the oceans adjacent to Australia and the North Indian Ocean.",
"license": "proprietary"
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"description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. MetOp-A launched on 19 October 2006 is the first in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1 ), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1 ). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346 ).The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN. A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is available at https://doi.org/10.5067/GHMTA-3US28",
"license": "proprietary"
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"description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite A (Metop-A) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MA-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-A AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MA-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).",
"license": "proprietary"
},
@@ -33366,7 +33366,7 @@
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo- France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
"license": "proprietary"
},
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA-19 platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from 1km AVHRR data that are re-mapped onto a 0.02 degree equal angle grid. In the processing chain, global AVHRR level 1b data are acquired at Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. A cloud mask is applied and SST is retrieved from the AVHRR infrared (IR) channels by using a multispectral technique. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
"license": "proprietary"
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"description": "This dataset of Harmonic Constants for Baroclinic Tide Prediction was produced by Edward Zaron (Oregon State University) and Shane Elipot (University of Miami). It provides sea surface height and ocean surface currents associated with the predictable astronomical tide at the M2, S2, N2, K1, and O1 frequencies. The tidal harmonic constants, in-phase and quadrature with respect to the equilibrium potential, are provided on a latitude/longitude at 1/20-deg resolution. Using the software available at the Github repository, the dataset can be used to predict baroclinic tidal sea surface height and surface ocean currents at arbitrary time and location throughout the world oceans.
The harmonic constants were estimated within the time period from 1993 to 2021 and incorporate roughly 30 years of multi-satellite altimeter data and 20 years of data from drifting buoys. The observations were combined with a kinematic wave model and the internal wave polarization relations to prepare uniformly gridded estimates from the sparse and irregular data sampling. These files may be used by the altimeter community to compute corrections intended to remove baroclinic tidal variability from sea level anomaly observations. Researchers with an interest in ocean surface currents may also use these data to predict baroclinic tidal surface currents. Such information may be used to plan observational campaigns or optimize the design of future surface current mapping satellite missions.
This dataset is funded by NASA SWOT Science Team award #80NSSC21K0346 and NSF Physical Oceanography Program award #1850961. The software to make baroclinic tidal calculations using this dataset is regularly updated at the provided Github link, and an archived snapshot of the software is also provided in the documentation. The harmonic constants and prediction software may be updated every few years as additional data for mapping the tides becomes available.",
"license": "proprietary"
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"description": "This data set contains a 6-hourly, 0.25 degree resolution, near-global gridded analysis of ocean surface vector winds from the Cross-Calibrated Multi-Platform (CCMP) project, produced by Remote Sensing Systems (RSS). CCMP is a combination of inter-calibrated 10 m ocean surface wind retrievals from multiple types of satellite microwave sensors and a background field from reanalysis. The wind retrievals are derived by RSS and include most of the wind-sensing U.S., Japanese, and European satellites flown to date. The background field is from ERA5 10m Neutral Stability winds. The result is a product that remains closely tied to the satellite retrievals where they are available and closely collocated in time and space. Data files are available in netCDF format, with one file per day. This time record is ongoing, with an expected latency of 2-3 months for new files.
Version 3.1 updates include but are not limited to: (1) Improved performance and agreement with satellite winds at high wind speed, (2) Minimized spurious trends caused by the interaction between the amount of satellite measurements available and the satellite/model biases, and (3) improving the quality of the wind after 2012.
Version 3.1 is produced and maintained by RSS with support from a NASA grant (ROSES proposal 17-OVWST-17-0023). Previous versions were funded by the NASA Making Earth Science data records for Use in Research Environments (MEaSUREs) program, with the original V1.0 led by Dr. Robert Atlas at Goddard Space Flight Center.",
"license": "proprietary"
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Version 3.1 updates include but are not limited to: (1) Improved performance and agreement with satellite winds at high wind speed, (2) Minimized spurious trends caused by the interaction between the amount of satellite measurements available and the satellite/model biases, and (3) improving the quality of the wind after 2012.
Version 3.1 is produced and maintained by RSS with support from a NASA grant (ROSES proposal 17-OVWST-17-0023). Previous versions were funded by the NASA Making Earth Science data records for Use in Research Environments (MEaSUREs) program, with the original V1.0 led by Dr. Robert Atlas at Goddard Space Flight Center.",
"license": "proprietary"
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"license": "proprietary"
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"description": "The Potential Impacts of Climate Change on World Food Supply: Datasets from a Major Crop Modeling Study contain projected country and regional changes in grain crop yields due to global climate change. Equilibrium and transient scenarios output from General Circulation Models (GCMs) with three levels of farmer adaptations to climate change were utilized to generate crop yield estimates of wheat, rice, coarse grains (barley and maize), and protein feed (soybean) at 125 agricultural sites representing major world agricultural regions. Projected yields at the agricultural sites were aggregated to major trading regions, and fed into the Basic Linked Systems (BLS) global trade model to produce country and regional estimates of potential price increases, food shortages, and risk of hunger. These datasets are produced by the Goddard Institute for Space Studies (GISS) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).",
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"description": "The Pilot 2006 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using sixteen indicators tracked in six well-established policy categories: Environmental Health, Air Quality, Water Resources, Productive Natural Resources, Biodiversity and Habitat, and Sustainable Energy. The Pilot 2006 EPI utilizes a proximity-to-target methodology focused on a core set of environmental outcomes linked to policy goals for which every government should be held accountable. By identifying specific targets and measuring how close each country comes to them, the EPI provides a factual foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The Pilot 2006 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission.",
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"description": "The HANPP Collection: Human Appropriation of Net Primary Productivity (HANPP) by Country and Product contains tabular data on carbon-equivalents of consumption by country and by type of product. The data were compiled from national-level FAO statistics on consumption of products such as vegetables, meat, paper, and wood. HANPP represents the amount of carbon required to derive food and fibre products consumed by humans including organic matter that is lost during harvesting and processing. Net primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems. These tabular data were used to allocate country level NPP consumption to a spatial surface of NPP consumption (Global Patterns in Human Appropriation of Net Primary Productivity), which is part of this collection.",
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"description": "The HANPP Collection: Human Appropriation of Net Primary Productivity as a Percentage of Net Primary Productivity represents a map identifying regions in which human consumption of NPP is greatly in excess of production by local ecosystems. Humans appropriate net primary productivity through the consumption of food, paper, wood and fiber, which alters the composition of the atmosphere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services. Net primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems.",
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"description": "The Annual Chlorophyll-a Concentrations component of the Indicators of Coastal Water Quality Collection consists of gridded satellite measurements of chlorophyll-a concentrations (in nanogram/cubic meter) in a band extending between 10 and 100 km from the shoreline. Chlorophyll-a concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grids are based on annual composites of SeaWiFS satellite data provided by the SeaWiFS Project, NASA/Goddard Space Flight Center and GeoEye in the form of HDF files at a resolution of 5 arc-minutes (approximately 9 x 9 km at the equator). The source files are true-color images generated from sub-sampled, calibrated, Rayleigh-corrected level-2 data, which are derived from raw radiance counts by applying sensor calibration, atmospheric corrections, and bio-optical algorithms. To arrive at chlorophyll-a concentrations, radiance counts were converted using the conversion formula provided as part of the original data files. The gridding is done by the Columbia University Center for International Earth Science Information Network (CIESIN).",
"license": "proprietary"
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"description": "The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset consists of population, human development, economic, water resources, land cover, land use, agriculture, food, energy and biodiversity data . This dataset was collated by IPCC from a variety of sources such as The World Bank, United Nations Environment Programme (UNEP), and Food and Agriculture Organization of the United Nations (FAO), and is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).",
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"license": "proprietary"
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"license": "proprietary"
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"description": "The SDG Indicator 11.2.1: Urban Access to Public Transport, 2023 Release, part of the SDGI collection, measures the proportion of the population in a city that has convenient access to public transport. UN SDG 11 is \"make cities and human settlements inclusive, safe, resilient and sustainable\". Improving access to public transport services is integral to achieving the objectives of SDG 11. According to the UN Sustainable Transport, Sustainable Development 2021 Interagency Report, \"only about half the world's urban population have convenient access to public transport\". The report highlights that access to sustainable transport can help reduce food insecurity, boost economies, empower women, and connect people to key health, education, and financial services. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.2.1. The indicator was computed as the proportion of WorldPop gridded population within either 0.5 kilometer walking distance to a low-capacity OpenStreetMap (OSM) public transport point or 1 kilometer walking distance to a high-capacity OSM public transport point. Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (UCDB). The SDG indicator 11.2.1 data set provides estimates for the proportion of population with convenient access to public transport for 5,749 urban centers across 178 countries.",
"license": "proprietary"
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"description": "The SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release, part of the SDGI collection, measures the average share of the built-up area of a city that is open space for public use for all. UN SDG 11 is \"make cities and human settlements inclusive, safe, resilient and sustainable\". Aside from environmental benefits, public space can also help improve public health, bolster commUnity, and encourage economic exchange. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.7.1. The indicator was computed by measuring both the proportion of OpenStreetMap (OSM) public space within a given urban center and the proportion of WorldPop gridded population within 400 meters to Open Public Space (OPS). Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (GHS-UCDB). The SDG indicator 11.7.1 data set provides estimates of the average share of the built-up area of cities that is open space for public use for all for 8,873 urban centers across 180 countries.",
"license": "proprietary"
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"description": "The Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021 consists of 37 columns of bibliographic data, methodological and analytical insights, from 155 articles published from 2014 to 2021 that extended the narratives of global SSPs. Local and regional scale Shared Socioeconomic Pathways (SSPs) have grown largely in addressing Climate Change Impact, Adaptation, and Vulnerability (CCIAV) assessments at sub-global levels. Common elements of these studies, besides their focus on CCIAV, are the use of both quantitative and qualitative elements of the SSPs. To explore and learn from current literature on novel methods and insights on extending SSPs, the sub-global extended SSPs literature database is constructed in the research for analyses. The database was developed in four stages: searches; screening; data extraction; and coding. The search stage incorporated three approaches: using a search string in three academic databases (Scopus, Web of Science Core Collection, ScienceDirect); a targeted search of a specific relevant database (ICONICS); and a targeted selection in Google Scholar of all papers that cited the publication of the global SSP narratives. In the screening step, criteria were assessed for full-text papers for eligibility including relevant typologies, methodologies, and other criteria. Finally, data from eligible papers was extracted and entered in a coding framework in an Excel workbook spreadsheet. The coding framework resulted in 37 columns to systematize coding of data from the 155 papers selected along several different dimensions, including categories of papers or analysis, several subcategories for SSP Applications and SSP Extensions, specific SSPs used, specific Representative Concentration Pathways (RCPs) used, typologies of extensions of qualitative and quantitative SSPs, and the types of models and nature of the extended SSPs.",
"license": "proprietary"
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"description": "The Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019 consists of biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019 that used the SSPs. The database was generated from a Google Scholar search, followed by a manual examination of the results for papers that made substantial use of the SSPs. Each paper was then coded along a number of different dimensions, including categories of types of papers or analysis, number of subcategories for SSP Applications and SSP Extensions, particular Shared Socioeconomic Pathways (SSPs) used, particular Representative Concentration Pathways (RCPs) used, and particular SSP-RCP combinations used. Over the past ten years, the climate change research commUnity developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. This framework consists of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), which together describe alternative visions of how society and climate may evolve over the coming decades, while providing a framework for combining these pathways in integrated studies. The tracking of the use of this framework in the literature allows for assessment of how it is being used, whether it is achieving its original goals, and what improvements to the framework would benefit future research.",
"license": "proprietary"
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"description": "The Historical Urban Population, 3700 BC - AD 2000, originally developed by the Yale School of Forestry & Environmental Studies, is the first spatially explicit global data set containing location and size of urban populations over the last 6,000 years. The data set was created by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data. Each data point consists of a city name, latitude, longitude, year, population, and a reliability ranking to assess the geographic uncertainty of each data point. Despite spatial and temporal gaps, no other geocoded data set at this resolution exists. It can therefore be used to investigate long-term historical urbanization trends and patterns, evaluate the current era of urbanization, and build a richer record of urban population through history.",
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the Advanced Very High Resolution Radiometer (AVHRR) from NOAA-18,19, the European Meteorological Operational-A (METOP-A) and Operational-B (METOP-B), and microwave data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
"license": "proprietary"
},
@@ -54855,7 +54855,7 @@
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the (A)TSR series of radiometers from ERS-1, ERS-2 and Envisat, AVHRR from NOAA-16,17,18,19 and METOP-A, and microwave data from TMI, AMSR-E and Windsat in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
"license": "proprietary"
},
@@ -56406,6 +56406,32 @@
"description": "The archive and new tasking X-band SAR products are available from COSMO-Skymed (CSK) and COSMO-SkyMed Second Generation (CSG) missions in ScanSAR and Stripmap modes, right and left looking acquisition (20 to 60\u00b0 incidence angle). COSMO-SkyMed modes: Acquisition Mode\t/\tSingle look Resolution [Az. X. Rg, SCS] (m)\t/\tScene size [Az. X. Rg] (km)\t/\tPolarisation\t/\tScene duration (seconds)\t/\tNumber of looks\t/\tMultilook resolution (m)\t/\tGeolocation accuracy \u00b13 s (m)\t// \t/\t/\t/\t/\t/\t[DGM, GEC, GTC]\t// Stripmap Himage\t\t/\t2.6 x 3\t/\t40 x 40\t/\tSingle: HH, HV, VH, VV\t/\t7\t/\t3\t/\t5\t/\t25\t// Stripmap PingPong\t/\t9.7 x 11\t/\t30 x 30\t/\tAlternate: HH/VV, HH/HV, VV/VH\t/\t6\t/\t3\t/\t20\t/\t25\t// ScanSAR Wide\t/\t23 x 13.5\t/\t100 x 100\t/\tSingle: HH, HV, VH, VV\t/\t15\t/\t4 - 9\t/\t30\t/\t30\t// ScanSAR Huge\t/\t38 x 13.5\t/\t200 x 200\t/\tSingle: HH, HV, VH, VV\t/\t30\t/\t25 - 66\t/\t100\t/\t100\t// COSMO-Skymed Second Generation Modes: Acquisition Mode\t/\tSingle look Resolution [Az. X. Rg, SCS] (m)\t/\tScene size [Az. X. Rg] (km)\t/\tPolarisation\t/\tScene duration (seconds)\t/\tNumber of looks\t/\tMultilook resolution (m)10/05/2021 10:28\t/\tGeolocation accuracy \u00b13 s (m)\t// \t/\t/\t/\t/\t/\t[DGM, GEC, GTC]\t// Stripmap\t/\t3 x 3\t/\t40 x 40\t/\tSingle (HH, VV, HV, VH) or Dual (HH+HV, VV+VH)\t/\t7\t/\t2 x 2; 4 x 4\t/\t5 x 5; 11 x 11\t/\t3.75\t// Stripmap PingPong\t/\t12 x 5\t/\t30 x 30\t/\tAlternate (HH/VV, HH/VH-HV/VV)\t/\t6\t/\t1 x 2; 2 x 5\t/\t12 x 10; 22 x 25/\t12\t// ScanSAR 1\t/\t20 x 4\t/\t100 x 100\t/\tSingle (HH, VV, HV, VH) or Dual (HH+HV, VV+VH)\t/\t15\t/\t1 x 3; 1 x 5\t/\t20 x 13; 23 x 27; 35 x 40\t/\t12\t// ScanSAR 2\t/\t40 x 6\t/\t200 x 200\t/\tSingle (HH, VV, HV, VH) or Dual (HH+HV, VV+VH)\t/\t30\t/\t1 x 4; 1 x 7; 3 x 16\t/\t40 x 27; 47 x 54; 115 x 135\t/\t12\t// Following Processing Levels are available, for both CSK and CSG: - SCS (Level 1A, Single-look Complex Slant): data in complex format, in slant range projection (the sensor's natural acquisition projection) and zero doppler projection, weighted and radiometrically equalised; the coverage corresponds to the full resolution area illuminated by the SAR instrument - DGM (Level 1B, Detected Ground Multi-look): product obtained detecting, multi-looking and projecting the Single-look Complex Slant data onto a grid regular in ground: it contains focused data, amplitude detected, optionally despeckled by multi-looking approach, radiometrically equalised and represented in ground/azimuth projection - GEC (Level 1C, Geocoded Ellipsoid Corrected): focused data, amplitude detected, optionally despeckled by multi-looking approach, geolocated on the reference ellipsoid and represented in a uniform preselected cartographic presentation. Any geometric correction derived by usage of terrain model isn't applied to this product by default - GTC (Level 1D, Geocoded Terrain Corrected): focused data, fully calibrated with the usage of terrain model, amplitude detected, optionally despeckled by multi-looking approach, geolocated on a DEM and represented in a uniform preselected cartographic presentation. The image scene is located and accurately rectified onto a map projection, through the use of Ground Control Points (GCPs) and Digital Elevation Model (DEM); it differs from GEC for the use of the DEM (instead of reference ellipsoid) for the accurate conversion from slant to ground range and to approximate the real earth surface The list of available data can be retrieved using the _$$CLEOS COSMO-SkyMed products catalogue$$ https://www.cleos.earth/ . User registration is requested to navigate the catalogue.",
"license": "proprietary"
},
+ {
+ "id": "COWVR_STPH8_L1_TSDR_V10.0_10.0",
+ "title": "COWVR STP-H8 Antenna and Microwave Brightness Temperatures Version 10.0",
+ "catalog": "POCLOUD STAC Catalog",
+ "state_date": "2022-01-08",
+ "end_date": "2024-12-31",
+ "bbox": "-180, -61, 180, 61",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3237785963-POCLOUD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3237785963-POCLOUD.html",
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+ "description": "!!!Temporary notice posted Sept. 27th, 2024!!! These data are in the process of being ingested and not all files are available yet. The data were made public early to allow assessment by early science users. Accordingly, not all data set resources may be available yet. Please check over the next 2-3 weeks for finalization of this data set and PO.DAAC's release announcement.
This data set includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 18.7 GHz, 23.8 GHz, and 34.5 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. Its swath width is 1012 km and spatial resolution is <35 km. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbers of the project team prior to release
The COWVR sensor is a fully polarimetric, conically imaging microwave radiometer for measuring ocean surface wind vectors. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. It was deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. A successful COWVR mission will demonstrate a lower-cost sensor architecture (e.g. in comparison to WindSat) for providing imaging passive microwave data, including ocean surface vector wind products for the Department of Defense (DoD). COWVR was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.",
+ "license": "proprietary"
+ },
+ {
+ "id": "COWVR_STPH8_L2_EDR_V10.0_10.0",
+ "title": "COWVR STP-H8 Surface Wind Vector and Column-Integrated Atmospheric Water Measurements Version 10.0",
+ "catalog": "POCLOUD STAC Catalog",
+ "state_date": "2022-01-08",
+ "end_date": "",
+ "bbox": "-180, -61, 180, 61",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3237678855-POCLOUD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3237678855-POCLOUD.html",
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+ "description": "!!!Temporary notice posted Sept. 27th, 2024!!! These data are in the process of being ingested and not all files are available yet. The data were made public early to allow assessment by early science users. Accordingly, not all data set resources may be available yet. Please check over the next 2-3 weeks for finalization of this data set and PO.DAAC's release announcement.
This dataset includes satellite-based observations of geolocated surface wind vectors, precipitable water vapor, and integrated cloud liquid water, as well as the microwave brightness temperatures used to derive them. Theses measurements make up the environmental data record (EDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), beginning in January 2022 with forward-streaming to PO.DAAC. Data over the satellite swath are available in HDF5 format with roughly one file per hour (the ISS orbit period is ~90 minutes), and coverage shown in the thumbnail is for a full day. The file metadata formats may be different than what an average user is familiar with \u2013 please see the User Guide to learn more. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release.
The COWVR sensor is a fully polarimetric, conically imaging microwave radiometer for measuring ocean surface wind vectors. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. It was deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. A successful COWVR mission will demonstrate a lower-cost sensor architecture (e.g. in comparison to WindSat) for providing imaging passive microwave data, including ocean surface vector wind products for the Department of Defense (DoD). COWVR was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.",
+ "license": "proprietary"
+ },
{
"id": "CPEXAW-ADM-Aeolus_1",
"title": "CPEX-AW ADM-Aeolus Datasets",
@@ -56857,7 +56883,7 @@
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"description": "The CYGNSS Level 1 Calibrated Raw IF Version 1.0 dataset is produced by the CYGNSS Science Team of the University of Michigan, and it contains the first release, Version 1.0, of the CYGNSS Calibrated Raw Intermediate Frequency (IF) based L1 Product. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.
This product includes several established signal coherence detectors, including the power-ratio Pratio, complex zero-Doppler delay waveform and full entropy Efull, and a novel fast entropy detector Efast. Both entropy detectors are provided with two temporal resolutions: 2 ms and 50 ms. Several scattered signal strength products are included: Signal-to-Noise Ratio SNR, reflected power Pg, reflectivity \u0393, and Normalized Bistatic Radar Cross-Section NBRCS. Each of these products is derived using a coherent integration time of Tc = 1 ms and incoherent integration times of Ninc = 1000, 500, 250, 100, 50, and 2 ms. Signal strength time series at the shorter (2 and 50 ms) times provides excellent detection of land-water transitions in heterogeneous scenes. Delay Doppler Maps (DDMs) are also generated with high delay (\u2206\u03c4 = 1/16 chip) and Doppler (\u2206f= 50 Hz) resolution. This suite of coherence detection methods can be used to detect the presence of small inland water bodies. ",
"license": "proprietary"
},
@@ -56870,7 +56896,7 @@
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"license": "proprietary"
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"description": "This Level 1 (L1) dataset contains the Version 3.0 (v3.0) Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time delay and Doppler frequency shift. DDMs are normally sampled over a restricted range of delay and Doppler values centered on the values at the specular point of reflection. The bit resolution of scattered signal strength is also truncated by a lossy data compression algorithm. Full DDMs are sampled over a wider range of delay and Doppler values and retain their full (lossless) bit resolution. Full DDM data records are typically 10-15 min in duration and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest. This version supersedes the Full DDM Version 1.0 (https://doi.org/10.5067/CYGNS-L1FDD) for data retrieved during or after August 2018. For data retrieved prior to August 2018, users will need to continue using the Full DDM Version 1.0. This version links the Full DDMs to the CYGNSS v3.0 L1 files (https://doi.org/10.5067/CYGNS-L1X30) whereas the version 1.0 Full DDM linked the Full DDMs to the CYGNSS v2.1 L1 files (https://doi.org/10.5067/CYGNS-L1X21). The calibration of the Full DDMs has not been modified for this release.",
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"license": "proprietary"
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"description": "This Level 1 (L1) dataset contains the Version 2.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) data is now available when the CYGNSS satellites are rolled away from nadir during orbital high beta-angle periods, resulting in a significant amount of additional data; 2) correction to coordinate frames result in more accurate estimates of receiver antenna gain at the specular point; 3) improved calibration for analog-to-digital conversion results in better consistency between CYGNSS satellites measurements at nearly the same location and time; 4) improved GPS EIRP and transmit antenna pattern calibration results in significantly reduced PRN-dependence in the observables; 5) improved estimation of the location of the specular point within the DDM; 6) an altitude-dependent scattering area is used to normalize the scattering cross section (v2.0 used a simpler scattering area model that varied with incidence and azimuth angles but not altitude); 7) corrections added for noise floor-dependent biases in scattering cross section and leading edge slope of delay waveform observed in the v2.0 data. Users should also note that the receiver antenna pattern calibration is not applied per-DDM-bin in this v2.1 release.",
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"description": "This Level 1 (L1) dataset contains the Version 3.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L1X21 . Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.0 data: 1) the transmitted GPS signal strength in the direction of the DDM scattering surface is determined in real time from measurements of the direct signal from the GPS satellite to the CYGNSS navigation receiver, allowing for the BRCS calibration to be corrected for variations in GPS transmit power; 2) the NBRCS has been validated using comparisons with a large population of modeled values derived from coincident ocean surface roughness spectra produced by the NOAA WAVEWATCH-3 oceanographic wave model; 3) L1 calibration parameters have been adjusted to produce a best fit to the model population.",
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"description": "This Level 1 (L1) dataset contains the Version 3.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L1X30. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.1 data: The CYGNSS science antenna gain patterns have been adjusted to improve the accuracy of the ocean surface scattering cross section (a.k.a. the NBRCS) calibration. They are adjusted so that the annual average observed NBRCS matches the model-predicted average as derived from Wavewatch-3 estimates of the surface roughness with the appropriate spectral tail extension added to the roughness spectrum. The adjustment is made independently at each position in the science antenna pattern. A correction for coarse quantization effects by the on-board digital processor has also been added. This reduces the effects of radio frequency interference, which appeared as calibration biases in the v3.0 L1 NBRCS and retrieval biases in the v3.0 L2 wind speed that were persistent at certain locations.",
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"description": "This CYGNSS Level 1 (L1) science data record dataset contains the version 3.2 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L1X31. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time.
The correction for coarse quantization effects that was implemented in v3.1 for the signal portion of the DDM has been updated to include a correction to the noise floor portion of the DDM. This update is found to improve the sensitivity to soil moisture over land and to have a minimal effect on the sensitivity to wind speed over ocean. An update is made to the correction for the temperature dependence of the receiver electronics. This update reduces slow variations in calibration bias associated with a ~60 day oscillation in the mean temperature of the satellites. L1 variables over land and ocean are now combined in common netcdf data files, with additional details added regarding the specular point calculation over land. Nadir (science) antenna pattern and NBRCS rescaling has been updated to improve the inter-satellite consistency of the L1 calibration.
The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.",
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"description": "This dataset contains the Version 1.0 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 2 months (or better) from the last recorded measurement time. The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.",
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"description": "This dataset contains the Version 1.1 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.1 CDR represents is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L2X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L2C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.",
"license": "proprietary"
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"description": "This dataset contains the Version 1.2 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.2 CDR represents is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L2X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L2C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.",
"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset contains the Version 1.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Only one netCDF-4 data file is produced each day (each file containing data from a combination of up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. Version 1.0 represents the first release. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. The 35 degree orbital inclination allows CYGNSS to measure ocean surface winds between approximately 38 degrees North and 38 degrees South latitude using an innovative combination of all-weather performance Global Positioning System (GPS) L-band ocean surface reflectometry to penetrate the clouds and heavy precipitation. The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), which uses data assimilation to combine all available in situ and satellite observation data with an initial estimate of the atmospheric state, provided by a global atmospheric model. Since the MERRA-2 data is only updated on monthly intervals, this corresponding heat flux dataset is likewise updated on a monthly interval to reflect the latest data available from MERRA-2, thus accounting for measurement latency, with respect to CYGNSS observables, ranging from 1 to 2 months. The data from this release compares well with in situ buoy data, including: Kuroshio Extension Observatory (KEO), National Data Buoy Center (NDBC), Ocean Sustained Interdisciplinary Time-series Environment observation System (OceanSITES), Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and the Tropical Atmosphere Ocean (TAO) array. As this marks only the first data release, future work is expected to provide comparisons and validation with various field campaigns (e.g., PISTON, CAMP2Ex) as well as more buoy data, especially at higher flux estimates.",
"license": "proprietary"
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"description": "This dataset contains the Version 2.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Version 2.0 represents the second release of this product, which now uses CYGNSS Level 2 (L2) Science Data Record (SDR) Version 3.1 surface wind speeds and ECMWF Reanalysis, Version 5 (ERA5). Version 1.0 used CYGNSS L2 SDR Version 2.1 surface wind speeds and NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2). The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from this dataset obtains these values from ERA5. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. As a result of the CYGNSS constellation coverage, this data is made available from 1 August 2018 to present with an approximate 1 week latency in the netCDF-4 formatted data files, where each file contains data within a 24-hour UTC period from a combination of up to 8 unique CYGNSS spacecraft. More information on CYGNSS can be found on the CYGNSS mission page.",
"license": "proprietary"
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"description": "The CYGNSS level 2 ocean surface heat flux science data record version 3.2 dataset is provided as a service to the oceanographic and meteorological research communities on behalf of the CYGNSS Science Team in direct collaboration with the Cyclone Global Navigation Satellite System (CYGNSS) Mission. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.
This dataset provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Version 3.2 uses CYGNSS Level 2 (L2) Science Data Record (SDR) Version 3.2 surface wind speeds and ECMWF Reanalysis, Version 5 (ERA5). The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from this dataset obtains these values from ERA5. This dataset is made available from 1 August 2018 to present with an approximate 1 week latency in the netCDF-4 formatted data files, where each file contains data within a 24-hour UTC period from a combination of up to 8 unique CYGNSS spacecraft. More information on CYGNSS can be found on the CYGNSS mission page.",
"license": "proprietary"
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"description": "This dataset contains the Version 2.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) revised Geophysical Model Functions (GMFs) for both Fully Developed Seas (FDS) and Young Seas with Limited Fetch conditions, to be consistent with the calibration changes made to the v2.1 Level 1 science data products.; 2) Revised covariance matrix between DDMA and LES versions of the FDS wind speed retrieval, used by the minimum variance estimator, resulting from changes made to the v2.1 Level 1 science data products; 3) Revised debiasing algorithm coefficients used by the FDS L2 retrieval algorithm, resulting from changes made to the v2.1 Level 2 science data products; 4) revised quality control (Q/C) flags related to the required level of consistency between DDMA and LES versions of the FDS wind speed retrieval (the errors in the two retrievals are now less correlated so larger discrepancies are allowed; if either retrieval is not available, the sample receives a fatal Q/C flag); 5) new Q/C flag related to the block type of the GPS satellite which provided the transmitted signal. Samples using block II-F signals receive a fatal Q/C flag due to the higher level of uncertainty in their radiated power; 6) revised wind speed uncertainty values as a function of RCG and wind speed, plus a new dependence of the uncertainty on GPS block type to reflect the higher uncertainty in GPS radiated power for block II-F satellites.",
"license": "proprietary"
},
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"description": "This dataset contains the Version 3.0 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L2X21. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Here is a summary of processing changes reflected in the v3.0 data: 1) the changes to calibration and validation of the Level 1 Normalized Bistatic Radar Cross Section (NBRCS) necessitated updates to the Geophysical Model Functions (GMFs) used to retrieve wind speed; 2) the GMF for fully developed seas (FDS) conditions was generating using matchups between NBRCS measurements and coincident wind speeds produced by NASAs Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis model; 3) the GMF for young seas with limited fetch (YSLF) was generated using matchups between NBRCS and coincident wind speeds produced by NOAAs Hurricane Weather Research and Forecast (HWRF) System; 4) YSLF wind speed is a tapered linear combination of wind speeds derived from the FDS and YSLF GMFs, where the taper gives more weight to FDS at low wind speeds and more to YSLF at high wind speeds and accounts for the transition from FDS to YSLF sea state conditions near cyclonic storms; 5) re-introduces measurements using transmissions from previously discarded GPS satellite block types; in prior versions, Block II-F was completely discarded due to large variations in GPS transmit power. The real time transmit power monitoring and correction implemented in Level 1 v3.0 data now allows Block II-F signals to be used.",
"license": "proprietary"
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"description": "This dataset contains the Version 3.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L2X30. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Here is a summary of processing changes reflected in the v3.1 data: The L2 Geophysical Model Functions (GMFs) that map L1 observables to ocean surface wind speed were rederived to be consistent with the v3.1 L1 calibration. The method used for deriving the GMFs is the same as for v3.0. A new correction has been added to both the Fully Developed Seas (FDS) and Young Seas Limited Fetch (YSLF) wind speed products that is a function of the Significant Wave Height (SWH) of the ocean surface. The correction is based on an observed correlation between the wind speed error and SWH. The SWH value used by the correction algorithm is the ERA5 reanalysis product, coincident in space and time with a CYGNSS measurement. The FDS and YSLF retrieval algorithms are otherwise the same as v3.0.",
"license": "proprietary"
},
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"description": "This dataset contains the version 3.2 CYGNSS level 2 science data record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L2X31. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time.
The L2 Geophysical Model Function (GMF) that maps L1 observables to ocean surface wind speed and the Significant Wave Height (SWH) second order correction to the wind speed retrievals were rederived to be consistent with the v3.2 L1 calibration. The method used for deriving the GMF and SWH correction is the same as for v3.1. An additional swell wave correction has been added to better account for the long wave dependence at low wind speeds. The FDS and YSLF retrieval algorithms are otherwise the same as v3.1. The v3.2 L2 YSLF wind speed is now designated as an intermediate product and should not be used \u2018as is\u2019. Additional quality control filters have been added to the Level 3 gridded product derived from the L2 YSLF wind speed to detect and remove outlier L2 samples, and use of the L3 product is recommended.
The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.",
"license": "proprietary"
},
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"description": "This dataset contains the Version 1.0 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.",
"license": "proprietary"
},
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"description": "This dataset contains the Version 1.1 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 1 to 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L3X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L3C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.",
"license": "proprietary"
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"description": "This dataset contains the Version 1.2 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 5 days latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L3X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L3C11 ), which was derived from SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.",
"license": "proprietary"
},
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"description": "The CYGNSS L3 Ocean Microplastic Concentration V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.
This dataset contains the version 3.2 CYGNSS Level 3 ocean microplastic concentration data record, which provides daily netCDF files, each file containing a gridded map of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021). User caution is advised in regions containing independent, non-correlative factors affecting ocean surface roughness, such as anomalous atmospheric conditions within the Intertropical Convergence Zone, biogenic surfactants (such as algal blooms), oil spills, etc. This product reports microplastic concentration on a daily temporal and 0.25-degree latitude/longitude spatial grid with 30-day, 1 degree latitude/longitude feature resolution, as constrained by the binning and spatial temporal averaging of the Mean Square Slope (MSS) anomaly (i.e., difference between measured and predicted ocean surface roughness for a given wind speed). Version 3.2 uses CYGNSS MSS measurements that are derived from updated v3.2 Level 1 scattering cross section data and has updated the parameterizations in the data processing algorithm to use v3.2 data correctly.",
"license": "proprietary"
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"description": "This dataset contains the version 3.2.1 CYGNSS Level 3 Merged (MRG) Science Data Record Near Real Time (NRT) Storm Wind Speed derived from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. It combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 Young Seas Limited Fetch (YSLF) winds for a region surrounding a given tropical cyclone (TC), with L2 Fully Developed Seas (FDS) winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid.
L3 MRG is a product which combines the L2 FDS and YSLF winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and starts from the September 1, 2024 through the present with an approximate latency between 2 and 24 hours. A tapered weighted averaging scheme is used centered on the 25 m/s wind radius of the storm. The 34 knot wind radius (R34) algorithm has been updated for v3.2.1 release to center around the National Hurricane Center or the Joint Typhoon Warning Center (NHC/JTWC) reported storm center instead of the CYGNSS Vmax location The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis.
The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement. ",
"license": "proprietary"
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L3 MRG is a product which combines the L2 FDS and YSLF winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and starts from the June 11, 2024 through the present with an approximate latency between 2 and 24 hours . A tapered weighted averaging scheme is used centered on the 34-knot wind radius (R34) of the storm. The R34 value in each storm quadrant is also reported. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis.
The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement",
"license": "proprietary"
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L3 MRG combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and extend from 1 August 2018 to the present with an approximate 6 day latency. A tapered weighted averaging scheme is used centered on the 25 m/s wind radius of the storm. The 34 knot wind radius (R34) algorithm has been updated for v3.2.1 release to center around the National Hurricane Center or the Joint Typhoon Warning Center (NHC/JTWC) reported storm center instead of the CYGNSS Vmax location. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netCDF files are output on a storm-by-storm basis.
The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.",
"license": "proprietary"
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"description": "This dataset contains the version 3.2 CYGNSS level 3 science data record merged storm (MRG) wind speed which combines CYGNSS storm-centric gridded (SCG) wind speeds, which are derived from the L2 YSLF winds for a region surrounding a given tropical cyclone (TC), with L3 FDS winds away from the TC center on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation.
L3 MRG is a new product which combines the L2 FDS and Young Seas Limited Fetch (YSLF) winds and eliminates the need to choose between them depending on sea state development and the proximity to storms. The data are provided in netCDF-4 format and extend from 1 August 2018 to the present with an approximate 6 day latency. A tapered weighted averaging scheme is used centered on the 34-knot wind radius (R34) of the storm. The R34 value in each storm quadrant is also reported. The algorithm produces global (+/- 40 deg latitude) wind speeds reported on a 0.1x0.1 deg grid every 6 hours for each tropical cyclone, although some 6-hourly increments may be missing if there are an insufficient number of satellite overpasses of the storm during that time interval. The netcdf files are output on a storm-by-storm basis.
The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.",
"license": "proprietary"
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"description": "This dataset contains the Version 1.0 Cyclone Global Navigation Satellite System (CYGNSS) Level 3 Storm Centric Grid (SCG) Science Data Record (SDR) which provides the average wind speed combined from aggregated wind speed measurements made by the entire CYGNSS constellation whose specular points are located near a storm of interest in latitude, longitude and time. Data are provided on both a 0.1x0.1 degree latitude by longitude equirectangular grid and storm centric coordinates obtained from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. Storm centric coordinates are derived from the National Hurricane Center (NHC) Best Track dataset to produce a 6 hourly wind speed averaging window. A single netCDF-4 data file is produced for each storm. Each storm is uniquely identified by the year, storm basin, and a storm number. This dataset is intended for historical storm analysis, and as such, this dataset is periodically updated based on the availability of the NHC Best Track storm center information that is typically made available in April for the previous year's hurricane season. SCG files are produced for named storms, as defined by the NHC, that reach hurricane strength (i.e., having a maximum sustained wind speed of at least 65 knots). Due to the dependency on NHC Best Track data, the SCG files produced in this dataset are confined to storms in the Northern Hemisphere within the North Atlantic and East Pacific ocean regions. Wind speed inputs are provided by the CYGNSS Level 2 SDR Version 3.0 (https://doi.org/10.5067/CYGNS-L2X30 ).",
"license": "proprietary"
},
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"description": "The CYGNSS Level 3 Soil Moisture Product provides volumetric water content estimates for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics. The data were produced by CYGNSS investigators at the University Corporation for Atmospheric Research (UCAR) and the University Colorado at Boulder (CU), and derive from version 2.1 of the CYGNSS L1 SDR. The soil moisture algorithm uses collocated soil moisture retrievals from SMAP to calibrate CYGNSS observations from the same day. For a given location, a linear relationship between the SMAP soil moisture and CYGNSS reflectivity is determined and used to transform the CYGNSS observations into soil moisture. The data are archived in daily files in netCDF-4 format. Two soil moisture variables report the volumetric water content in units of cm3/cm3. The variable SM_subdaily includes up to four soil moisture estimates per day. Another variable SM_daily provides a daily average. The time series covers the period from March 2017 to present.",
"license": "proprietary"
},
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"description": "The CYGNSS Level 3 Soil Moisture V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. It estimates volumetric water content for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics from the V3.2 reflectivity measurements provided in the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.
The soil moisture retrieval algorithm is an update of the previous version developed by UCAR-CU using a linear regression of CYGNSS angle-normalized effective surface reflectivity trained against collocated SMAP soil moisture during the calibration period 8/1/2018 to 11/15/2023. The data are archived in daily files in netCDF-4 format. Volumetric soil moisture water content in units of cm3/cm3 is provided with two gridding resolutions, 9x9 km and 36x36 km. The variable SM_subdaily contains data reported in six hour intervals. The variable SM_daily provides a daily average. The time series covers the period from August 2018 to present.",
"license": "proprietary"
},
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"description": "The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.
This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). This is an update from the previous watermask monthly product (https://doi.org/10.5067/CYGNS-L3W31) which derived from the CYGNSS L1 SDR v3.1 (https://doi.org/10.5067/CYGNS-L1X31). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present.",
"license": "proprietary"
},
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This dataset is derived from version 3.1 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X31), and provides monthly binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with a 1-month latency. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in monthly files in netCDF-4 format and covers the period from August 2018 to present.",
"license": "proprietary"
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"description": "This dataset contains the Version 2.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) first time availability of wind speeds using the Geophysical Model Function (GMF) calibrated for Young Seas with Limited Fetch (YSLF) conditions; 2) inherits all other improvements made to the version 2.1 Level 2 data intended to improve the quality of the wind speed retrievals and uncertainty estimates. For a full list of improvements to the version 2.1 Level 2 data, please refer to the following dataset information page: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L2_V2.1",
"license": "proprietary"
},
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"license": "proprietary"
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"description": "This dataset contains the Version 3.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L3X30. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 3.1 release inherits all improvements made to the version 3.1 Level 2 data intended to improve the quality of the wind speed retrievals. For a full list of improvements to the version 3.1 Level 2 data, please refer to: https://doi.org/10.5067/CYGNS-L2X31.",
"license": "proprietary"
},
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"description": "This dataset contains the version 3.2 CYGNSS level 3 science data record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 3.1; https://doi.org/10.5067/CYGNS-L3X31. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs).
The v3.2 L3 gridded wind speed product inherits the v3.2 L2 FDS data as input at the same temporal and spatial resolution as the Level 2 data, sampled on consistent 0.2 by 0.2 degree latitude by longitude grid cells. The L3 gridding algorithm is unchanged. Range Corrected Gain (RCG) has been added to the L3 netcdf files as a new data field.
The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.",
"license": "proprietary"
},
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"description": "This dataset contains the Version 1.1 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.1 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the first science-quality release produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported sample locations are determined by averaging the specular point locations falling within each 25 km grid cell. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR).",
"license": "proprietary"
},
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"description": "This dataset contains the Version 1.2 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.2 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the second science-quality released through the PO.DAAC, as produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported retrieval locations are determined by averaging the specular point locations falling within each 25 km grid cell. Version 1.2 includes four major updates compared to Version 1.1 ( https://doi.org/10.5067/CYGNN-22511 ), namely: 1) the inclusion of data associated to a spacecraft roll angle exceeding +/- 5 degrees; 2) an improved wind speed performance in the higher wind speed regime; 3) a full revision of the quality flags; 4) the inclusion of a wind speed retrieval error variable. Only one netCDF-4 data file is produced for each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF-4 formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR).",
"license": "proprietary"
},
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"license": "proprietary"
},
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"description": "This dataset provides ancillary data for the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate, and is intended for expert users to reproduce the state estimate. The ancillary data include documentation files, files required to initialize the model, forcing fields, binary input grid files, observational data used to constrain the model, model equivalent of observed profiles, files related to atmospheric flux-forced experiments, and some script files. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds].",
"license": "proprietary"
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"description": "This dataset contains daily-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
"license": "proprietary"
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"description": "This dataset contains monthly-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset provides monthly-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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"description": "This dataset provides geometric parameters for the regular 0.5-degree lat-lon grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides and the horizontal and vertical coordinates of grid cell centers and corners. Additional information related to the global domain geometry (e.g., bathymetry and land/ocean masks) are also included. However, users should note these domain geometry fields are approximations because they have been interpolated from the ECCO lat-lon-cap 90 (llc90) native model grid. Users interested in exact budget closure calculations for volume, heat, salt, or momentum should use ECCO fields provided on the llc90 grid. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
"license": "proprietary"
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"description": "This dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset contains daily-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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"description": "This dataset provides monthly-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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"description": "This dataset provides daily-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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"description": "This dataset provides daily-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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"description": "This dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
"license": "proprietary"
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"description": "This dataset contains monthly-averaged ocean and sea-ice surface stress interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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"license": "proprietary"
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"description": "This dataset provides monthly-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
"license": "proprietary"
},
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"description": "This dataset provides instantaneous ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
"license": "proprietary"
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"description": "This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-1 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP.",
"license": "proprietary"
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"description": "This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-2 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP. For more information, please visit: http://www.scp.byu.edu/docs/ERS_user_notes.html",
"license": "proprietary"
},
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P sea surface temperature produced by The Naval Oceanographic Office (NAVO) from the GOES Imager sensor on the Electro-Optical Infrared Weather System \u2013 Geostationary satellite (EWS-G1). The EWS-G1, formerly GOES-13, is the first Department of Defense owned geostationary weather satellite, which has been repositioned over Indian Ocean (IO) region at 60.0\u00b0 West longitude in January 2018 and fully operational since September 8, 2020, providing timely cloud characterization and theater weather imagery to DoD. The EWS-G1 L2P SST product is calculated based on the 4-micron (band 2) and 11-micron (band 4) channels, providing nighttime and daytime SST. However, daytime SSTs are not produced in areas where the 4-micron channel is strongly affected by Solar radiation, which is defined by solar reflection angle > 50 degree. The L2P data are packaged according to the GHRSST Data Specification version 2 (GDS2) in netCDF4 format at 0.04-degree spatial resolution and stored in 48 half-hour granules per day. The data will be continually updated with 24 hours latency. ",
"license": "proprietary"
},
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"description": "The G18-ABI-L2P-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI onboard the G18 satellite. NOAA\u2019s G18 (aka, GOES-T pre-launch) was launched on March 1, 2022, replacing the G17 as GOES West in Jan'2023. It is the third satellite in the US GOES\u2013R Series, the Western Hemisphere\u2019s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth\u2019s weather, oceans, and environment.
G18/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with a spatial resolution of 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P is produced in netCDF4 GDS2 format, with 24 granules per day, and a total data volume 0.8 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).
Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script available at Documents tab under How-To section. The ACSPO G18 ABI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/G18-ABI-L3C-ACSPO-v2.90 ",
"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2731041317-POCLOUD.html",
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"description": "The G18-ABI-L3C-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI sensor onboard the G18 satellite. NOAA\u2019s G18 (aka GOES-T before launch) was launched on March 1, 2022, replacing G17 as GOES West in Jan'2023. It is the third satellite in the US GOES\u2013R Series, the Western Hemisphere\u2019s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth\u2019s weather, oceans, and environment.
The G18-ABI-L3C-ACSPO-v2.90 dataset is a gridded version of the G18-ABI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/G18-ABI-L2P-ACSPO-v2.90). The L3C (Level 3 Collated) outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST.
The ACSPO G18/ABI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing). ",
"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036881735-POCLOUD.html",
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers with in situ data from ships, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.",
"license": "proprietary"
},
@@ -78840,7 +78866,7 @@
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"description": "These data consist of Geos-3 altimeter measurements produced by NOAA/NODC/Laboratory for Satellite Altimetry. The dataset contains 5,006,956 altimetric sea surface heights and supporting information such as sea state, wind speed, Schwiderski ocean tide height, and Cartwright solid-tide height. Corrections for altimeter bias, wet and dry troposheric delays, and electromagnetic bias are not included. The corrections in this dataset (tides and even orbit height) are old and not very accurate. This dataset should only be used by those with an expertise in altimetry. Measurements are compressed to a rate of 1 per second using a trim mean filter. Data values are written in binary format.",
"license": "proprietary"
},
@@ -79978,26 +80004,26 @@
{
"id": "GLAH02_033",
"title": "GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.html",
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+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDEyXSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxMl0ifQ%3D%3D/GLAH02_033",
"description": "GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.",
"license": "proprietary"
},
{
"id": "GLAH02_033",
"title": "GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.html",
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+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIiwidW1tIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIn0%3D/GLAH02_033",
"description": "GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.",
"license": "proprietary"
},
@@ -80027,19 +80053,6 @@
"description": "Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.",
"license": "proprietary"
},
- {
- "id": "GLAH04_033",
- "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033",
- "catalog": "NSIDC_CPRD STAC Catalog",
- "state_date": "2003-02-20",
- "end_date": "2009-10-11",
- "bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDEyXSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxMl0ifQ%3D%3D/GLAH04_033",
- "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.",
- "license": "proprietary"
- },
{
"id": "GLAH04_033",
"title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033",
@@ -80054,16 +80067,16 @@
"license": "proprietary"
},
{
- "id": "GLAH05_034",
- "title": "GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "id": "GLAH04_033",
+ "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.html",
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- "description": "GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product.",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDEyXSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxMl0ifQ%3D%3D/GLAH04_033",
+ "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.",
"license": "proprietary"
},
{
@@ -80080,16 +80093,16 @@
"license": "proprietary"
},
{
- "id": "GLAH06_034",
- "title": "GLAS/ICESat L1B Global Elevation Data (HDF5) V034",
+ "id": "GLAH05_034",
+ "title": "GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034",
"catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIiwidW1tIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIn0%3D/GLAH06_034",
- "description": "GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIiwidW1tIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIn0%3D/GLAH05_034",
+ "description": "GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product.",
"license": "proprietary"
},
{
@@ -80106,16 +80119,16 @@
"license": "proprietary"
},
{
- "id": "GLAH07_033",
- "title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033",
+ "id": "GLAH06_034",
+ "title": "GLAS/ICESat L1B Global Elevation Data (HDF5) V034",
"catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIiwidW1tIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIn0%3D/GLAH07_033",
- "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIiwidW1tIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIn0%3D/GLAH06_034",
+ "description": "GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.",
"license": "proprietary"
},
{
@@ -80132,16 +80145,16 @@
"license": "proprietary"
},
{
- "id": "GLAH08_033",
- "title": "GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "id": "GLAH07_033",
+ "title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDEyXSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxMl0ifQ%3D%3D/GLAH08_033",
- "description": "GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product.",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIiwidW1tIjoiW1wiYm9vdHN0cmFwIHNlYSBpY2UgY29uY2VudHJhdGlvbnMgZnJvbSBuaW1idXMtNyBzbW1yIGFuZCBkbXNwIHNzbS9pLXNzbWlzIHYwMDRcIixcIk5TSURDX0VDU1wiLFwiTlNJREMtMDA3OVwiLFwiNFwiLDI3OTQ1NDA5MTgsNzRdIn0%3D/GLAH07_033",
+ "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.",
"license": "proprietary"
},
{
@@ -80157,6 +80170,19 @@
"description": "GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product.",
"license": "proprietary"
},
+ {
+ "id": "GLAH08_033",
+ "title": "GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033",
+ "catalog": "NSIDC_CPRD STAC Catalog",
+ "state_date": "2003-02-20",
+ "end_date": "2009-10-11",
+ "bbox": "-180, -86, 180, 86",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDEyXSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxMl0ifQ%3D%3D/GLAH08_033",
+ "description": "GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product.",
+ "license": "proprietary"
+ },
{
"id": "GLAH09_033",
"title": "GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033",
@@ -80186,26 +80212,26 @@
{
"id": "GLAH10_033",
"title": "GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-09-25",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDEyXSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxMl0ifQ%3D%3D/GLAH10_033",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCB3YXZlZm9ybS1iYXNlZCByYW5nZSBjb3JyZWN0aW9ucyBkYXRhIChoZGY1KSB2MDM0XCIsXCJOU0lEQ19FQ1NcIixcIkdMQUgwNVwiLFwiMzRcIiwxMDAwMDAwNDYwLDE3MV0iLCJ1bW0iOiJbXCJnbGFzL2ljZXNhdCBsMWIgZ2xvYmFsIHdhdmVmb3JtLWJhc2VkIHJhbmdlIGNvcnJlY3Rpb25zIGRhdGEgKGhkZjUpIHYwMzRcIixcIk5TSURDX0VDU1wiLFwiR0xBSDA1XCIsXCIzNFwiLDEwMDAwMDA0NjAsMTcxXSJ9/GLAH10_033",
"description": "GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product.",
"license": "proprietary"
},
{
"id": "GLAH10_033",
"title": "GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2003-09-25",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCB3YXZlZm9ybS1iYXNlZCByYW5nZSBjb3JyZWN0aW9ucyBkYXRhIChoZGY1KSB2MDM0XCIsXCJOU0lEQ19FQ1NcIixcIkdMQUgwNVwiLFwiMzRcIiwxMDAwMDAwNDYwLDE3MV0iLCJ1bW0iOiJbXCJnbGFzL2ljZXNhdCBsMWIgZ2xvYmFsIHdhdmVmb3JtLWJhc2VkIHJhbmdlIGNvcnJlY3Rpb25zIGRhdGEgKGhkZjUpIHYwMzRcIixcIk5TSURDX0VDU1wiLFwiR0xBSDA1XCIsXCIzNFwiLDEwMDAwMDA0NjAsMTcxXSJ9/GLAH10_033",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDEyXSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxMl0ifQ%3D%3D/GLAH10_033",
"description": "GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product.",
"license": "proprietary"
},
@@ -80238,26 +80264,26 @@
{
"id": "GLAH12_034",
"title": "GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDEyXSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxMl0ifQ%3D%3D/GLAH12_034",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCB3YXZlZm9ybS1iYXNlZCByYW5nZSBjb3JyZWN0aW9ucyBkYXRhIChoZGY1KSB2MDM0XCIsXCJOU0lEQ19FQ1NcIixcIkdMQUgwNVwiLFwiMzRcIiwxMDAwMDAwNDYwLDE3MV0iLCJ1bW0iOiJbXCJnbGFzL2ljZXNhdCBsMWIgZ2xvYmFsIHdhdmVmb3JtLWJhc2VkIHJhbmdlIGNvcnJlY3Rpb25zIGRhdGEgKGhkZjUpIHYwMzRcIixcIk5TSURDX0VDU1wiLFwiR0xBSDA1XCIsXCIzNFwiLDEwMDAwMDA0NjAsMTcxXSJ9/GLAH12_034",
"description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.",
"license": "proprietary"
},
{
"id": "GLAH12_034",
"title": "GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCB3YXZlZm9ybS1iYXNlZCByYW5nZSBjb3JyZWN0aW9ucyBkYXRhIChoZGY1KSB2MDM0XCIsXCJOU0lEQ19FQ1NcIixcIkdMQUgwNVwiLFwiMzRcIiwxMDAwMDAwNDYwLDE3MV0iLCJ1bW0iOiJbXCJnbGFzL2ljZXNhdCBsMWIgZ2xvYmFsIHdhdmVmb3JtLWJhc2VkIHJhbmdlIGNvcnJlY3Rpb25zIGRhdGEgKGhkZjUpIHYwMzRcIixcIk5TSURDX0VDU1wiLFwiR0xBSDA1XCIsXCIzNFwiLDEwMDAwMDA0NjAsMTcxXSJ9/GLAH12_034",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.html",
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"description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.",
"license": "proprietary"
},
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"description": "The Global Precipitation Measurement (GPM) satellite was launched on February 27th, 2014 with the GPM Microwave Imager (GMI) instrument on board. The GPM mission is a joint effort between NASA, the Japan Aerospace Exploration Agency (JAXA) and other international partners. In march 2005, NASA has chosen the Ball Aerospace and Technologies Corp., Boulder, Colorado to build the GMI instrument on the continued success of the Tropical Rainfall Measuring Mission (TRMM) satellite by expanding current coverage of precipitation from the tropics to the entire world. GMI is a dual-polarization, multi-channel, conical-scanning, passive microwave radiometer with frequent revisit times. One of the primary differences between GPM and other satellites with microwave radiometers is the orbit, which is inclined 65 degrees, allowing a full sampling of all local Earth times repeated approximately every 2 weeks. The GPM platform undergoes yaw maneuvers approximately every 40 days to compensate for the sun's changing position and prevent the side of the spacecraft facing the sun from overheating. Today, the GMI instrument plays an essential role in the worldwide measurement of precipitation and environmental forecasting. Sea Surface Temperature (SST) is one of its major products. The GMI data from the Remote Sensing System (REMSS) have been produced using an updated RTM, Version-8. The V8 brightness temperatures from GMI are slightly different from the V7 brightness temperatures; The SST datasets are available in near-real time (NRT) as they arrive, with a delay of about 3 to 6 hours, including the Daily, 3-Day, Weekly, and Monthly time series products.",
"license": "proprietary"
},
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"description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the America Region (AMERICAS) based on retrievals from the GOES-13 Imager on board GOES-13 satellite. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES 13 in East position. GOES 13 imager level 1 data are acquired at Meteo- France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the GOES 13 infrared channels (3.9 and 10.8 micrometer) using a multispectral algorithm. Due to the lack of 12 micrometer channel in the GOES 13 imager, SST retrieval is not possible in daytime conditions. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 30 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05 degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating 30 minute SST data available in one hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
"license": "proprietary"
},
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"description": "The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-13 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.",
"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
},
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"description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. This GRACE-FO RL06.1 data is an updated version of the initial GRACE-FO RL06 Level-2 data products. RL06.1 differs from RL06 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.1 uses ACH1B RL04, which replaces ACT1B RL04 that was used for Level-2 RL06. All GRACE-FO RL06.1 Level-2 products are fully compatible with the GRACE RL06 Level-2 fields. Refer to the mission page for more information. ",
"license": "proprietary"
},
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"description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. This GRACE-FO RL06.1 data is an updated version of the initial GRACE-FO RL06 Level-2 data products. RL06.1 differs from RL06 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.1 uses ACH1B RL04, which replaces ACT1B RL04 that was used for Level-2 RL06. All GRACE-FO RL06.1 Level-2 products are fully compatible with the GRACE RL06 Level-2 fields. Refer to the mission page for more information. ",
"license": "proprietary"
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"description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission.
This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.",
"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"description": "The H09-AHI-L2P-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth\u2019s weather, oceans, and environment with high temporal and spatial resolutions.
The H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km/VZA (view zenith angle) 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P data is produced in GHRSST compliant netCDF4 GDS2 format, with 24 granules per day, and a total data volume 1.2 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).
Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Pixel locations can be obtained using a flat lat/lon file or a Python script available via Documents tab from the dataset landing page. Climate and Forecast (CF) metadata aware software (e.g., Panoply, xarray) can detect and map the data as is via the granule CF projection attributes and variables. The ACSPO H09 HAI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/H09-AHI-L3C-ACSPO-v2.90 ",
"license": "proprietary"
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"description": "The H09-AHI-L3C-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth\u2019s weather, oceans, and environment with high temporal and spatial resolutions.
The H09-AHI-L3C-ACSPO-v2.90 dataset is a gridded version of the ACSPO H09-AHI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/AHI_H09-STAR-L2P-v2.90). The L3C (Level 3 Collated) data is mapped on 0.02-deg lat-lon grid and outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST.
The ACSPO H09/AHI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA for DM instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing). ",
"license": "proprietary"
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"description": "This data set contains the monthly Global Ocean Mass Anomalies (goma) since 04/2002, as measured by the GRACE and GRACE Follow-On (G/GFO) satellite missions. The data are averaged over the global ocean domain, at monthly intervals (note: data gaps exist). This file contains the goma time series based on the spherical harmonic gravity fields provided by the G/GFO SDS centers: JPL, CSR, GFZ. The data are frequently updated as new monthly observations are acquired by the GFO mission. The processing of the spherical harmonics gravity field coefficients is as follows: (1) GAD + GSM: the monthly de-aliasing product GAD is added back to the GSM L2 gravity fields; (2) [GSM + GAD] coefficients are averaged over the global ocean with a coastal buffer of 300 km (to avoid land-ocean leakage); (3) the spatial mean of atmospheric loading of the entire global ocean domain is removed (via the GAA L2 data product). A GIA correction using the ICE-6GD model (Peltier et al., 2018) is applied.",
"license": "proprietary"
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"description": "The [HOMAGE_STERIC_OHC_TIME_SERIES_v01] dataset contains monthly global mean ocean heat content (OHC) anomalies as well as thermosteric, halosteric and total steric sea level anomalies computed from various gridded ocean data sets of sub-temperature and salinity profiles as provided by different institutions: Scripps Institution of Oceanography (SIO); Institute of Atmospheric Physics (IAP); Barnes objective analysis (BOA from CSIO, MNR); Jamstec / Ishii et al. 2017 (I17); and Met Office Hadley Centre: EN4_c13, EN4_c14, EN4_g10, and EN4_I09. The data are averaged over the quasi-global ocean domain (i.e., where valid values are defined; note that gaps exist, in particular towards polar latitudes), at monthly intervals. The input profiling data (i.e, temperature and salinity profiles at depth levels), editing, quality flags and processing schemes vary across the different gridded products, please refer to the documentation for each institution\u2019s data product for details. Since 2005, the profiling data are dominated by the observations from the global Argo network (e.g., https://argo.ucsd.edu/), which comprises nearly 4000 active floats (as of 08/2022). Before 2005, non-Argo data such as XBT profilers were used, and the global ocean coverage was significantly more sparse. Data sets from SIO and BOA are Argo-only, while the others also include other observations, such as expendable bathythermographs (XBTs) and Conductivity-Temperature-Depth (CTD) observations. The data are active forward stream data files and will be frequently updated as new observations are acquired by Argo, and processed by the data centers.",
"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"description": "This is a near real time dataset that provides a GPS based orbit and Sea Surface Height Anomalies (SSHA) from that orbit. It is similar to the Jason-3 Operation Geophysical Data Record (OGDR) that is distributed at NOAA (http://www.nodc.noaa.gov/sog/jason/), but includes the GPS orbit and SSHA as two additional variables. It has a 5 hour time lag due to the time needed to calculate the GPS orbit and SSHA. The GPS orbits have been shown to be more accurate than the DORIS orbits on a near real time scale and therefore produces a more accurate SSHA.",
"license": "proprietary"
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"description": "This dataset provides supplementary wet tropospheric corrections for historical Jason-3 observations (https://www.ncei.noaa.gov/archive/accession/Jason3-xGDR). Recent assessments of the global sea level budget have resulted in increased scrutiny of estimates of global sea level change based on Jason-3. After a careful assessment of the wet tropospheric correction derived from the Advanced Microwave Radiometer (AMR) instrument, it was determined that further improvements to the accuracy of the historical Jason-3 observations could be made. Since this assessment was only completed after Jason-3 data was reprocessed to GDR-F (Geophysical Data Record \u2013 Version F) standards, it was not included in the GDR-F product release. For this reason, this supplementary correction product has been created using the method of Brown et al. (2012) to allow users to correct path delay and sea surface height observations, reducing errors in estimates of global sea level change by 2-3 mm over 8 years.
The correction was computed based on comparison of the AMR-observed brightness temperatures with independent satellite observations from the Special Sensor Microwave Imager Sounder (SSMI), F16, F17 and F18, Fundamental Climate Data Records. SSMI data was obtained from the NOAA Climate Data Record (CDR) of SSMIS Microwave Brightness Temperatures, RSS Version 8 (Wentz et al., 2019, https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C01567/html). The method described in Brown et al. (2012) to map SSMI Brightness Temperatures to AMR equivalent brightness temperatures (TBs) was used. Although it was found that it made little difference to the result, a bias was removed between SSMI equivalent AMR TBs and AMR TBs with respect to latitude for all data prior to computing temporal trends. In addition, only rain free, mostly clear data (TB18.7 GHz < 160K) data were considered.
The correction is supplied on a pass-by-pass basis in a 4-column text file. See the product documentation for guidance on how to apply it to Jason-3 observations.",
"license": "proprietary"
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"description": "NOAA STAR produces two lines of gridded 0.02 degree super-collated L3S LEO sub-skin Sea Surface Temperature (SST) datasets, one from the NOAA afternoon JPSS (L3S_LEO_PM) satellites and the other from the EUMETSAT mid-morning Metop (L3S_LEO_AM) satellites. The L3S_LEO_AM is derived from three Low Earth Orbiting (LEO) Metop-FG satellites: Metop-A, -B and -C . The Metop-FG satellite program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The US National Oceanic and Atmospheric Administration (NOAA) under the joint NOAA/EUMETSAT Initial Joint Polar System Agreement, has contributed three Advanced Very High Resolution Radiometer (AVHRR) sensors capable of collecting and transmitting data in the Full Resolution Area Coverage (FRAC; 1km/nadir) format. The L3S_LEO_AM dataset is produced by aggregating three L3U datasets from MetOp-FG satellites (MetOp-A, -B and -C; all hosted in PO.DAAC) and covers from Dec 2006-present. The L3S_LEO_AM SST dataset is reported in two files per 24-hour interval, daytime and nighttime (nominal Metop local equator crossing times around 09:30/21:30, respectively), in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency, and then replaced by the Re-ANalysis (RAN) files about 2 months later, with identical file names. The dataset is validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014), and monitored in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010). The L3S SST imagery and local coverage are continuously evaluated, and checked for consistency with other Level 2, 3 and 4 datasets in the ACSPO Regional Monitor for SST (ARMS) system. NOAA plans to include data from other mid-morning platforms and sensors, such as MetOp-SG METImage and Terra MODIS, into L3S_LEO_AM. More information about the dataset can be found under the Documentation and Citation tabs.",
"license": "proprietary"
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"description": "The L3S_LEO_DY-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from multiple instruments, including the VIIRS onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites, AVHRR onboard Metop-A, B , C satellites and MODIS onboard the Terra and Aqua satellites. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02\u00ba resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO PM ( https://doi.org/10.5067/GHLPM-3S281 ) and AM ( https://doi.org/10.5067/GHLAM-3SS28 ) data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product.
The L3S-DY-SST combines the both L3S-LEO-PM/AM SSTs into a single daily product. It covers from 2000-02-24 to present and is reported in one file per 24h interval. Data are in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 succeeds the v2.80 dataset (not available from the PO.DAAC) with the following improvements: (1) The L3S-LEO-PM input was updated from v2.80 to v2.81; and (2) ACSPO Terra MODIS SST is included from 2000-02-24 to 2021-12-31. The inclusion of Terra extends the availability of L3S-LEO-DY back to 2000-02-24 (from 2006-12-01 in v2.80). The SST diurnal warming effects from different daily observation times across the series of instruments have been corrected and are described in the publications by Jonasson et al., 2022
The Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names. They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam; https://www.star.nesdis.noaa.gov/socd/sst/iquam), and monitored in another NOAA system, SST Quality Monitor (SQUAM; https://www.star.nesdis.noaa.gov/socd/sst/squam) ",
"license": "proprietary"
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"description": "The L3S_LEO_PM-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from the VIIRSs (Visible Infrared Imaging Radiometer Suite) onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites and MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Aqua satellite. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02\u00ba resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO-PM ( https://doi.org/10.5067/GHLPM-3S281 ) and AM ( https://doi.org/10.5067/GHLAM-3SS28 ) data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product ( https://doi.org/10.5067/GHLDY-3S281 ).
This PM SST product is derived by collating individual satellite ACSPO L3U data ( https://doi.org/10.5067/GHVRS-3UO61, https://doi.org/10.5067/GHV20-3UO61 and https://doi.org/10.5067/GHN21-3U280 ). It covers from 2002-07-04 to present and is reported in 2 files daily, day and night, at 1:30am/pm local time. The SST is in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 is updated from the previous v2.80 ( https://doi.org/10.5067/GHLPM-3SS28 ): (1) v2.81 includes 3 VIIRSs (NPP, N20, and N21 from 2023-03-19 - on); (2) Aqua MODIS SST included from 2002-07-04 to 2022-12-31; (3) Time series in v2.81 extended back to 2002-07-04 (from 2012-02-01 in v2.80); (4) recently uncovered VIIRS daytime SST drifts in NPP and N20 SSTs of approximately -0.1 K/decade mitigated.
The Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names. They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam; https://www.star.nesdis.noaa.gov/socd/sst/iquam), and monitored in another NOAA system, SST Quality Monitor (SQUAM; https://www.star.nesdis.noaa.gov/socd/sst/squam) ",
"license": "proprietary"
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"description": "This dataset contains data from the Lake Observations by Citizen Science and Satellites project, LOCSS which is a lake monitoring network. The data represent the location and main descriptors of the lake gauges and their readings. LOCSS project aims to collaborate with local citizens to monitor small and medium sized lakes (i.e., lakes with an average surface area less than 100 km2). At each location, a lake gauge is installed and provided with a cellphone number. Local citizens read the water level at each lake gauge and sent it in a text message. Data can also be manually collected and uploaded later from the website in remote places where cellphone signal is challenged. The readings are specified in cm, m, or ft, according to the local unit system. This version of the dataset has lakes located in seven (7) countries: Bangladesh, India, Canada, the United States, Pakistan, and Nepal. This product consists of two files in comma-separated values (csv) format : 1) the list of gauges whose attributes include gauge coordinates, installation dates, the height of the gauge, reading units, city, time zone, and installation notes; 2) list of readings by each gauge specified in the local time. To discover more details about LOCSS, please visit https://www.locss.org/.",
"license": "proprietary"
},
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"description": "The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center (GHGC) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and in strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots. The LPJ-EOSIM L2 Global Simulated Monthly Wetland Methane Flux (LPJ_EOSIM_L2_MCH4E) Version 1 data product provides simulated monthly wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The monthly simulation data contains aggregate versions of the daily LPJ-EOSIM L2 Global Simulated Daily Wetland Methane Flux (LPJ_EOSIM_L2_DCH4E) Version 1 data. The monthly data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the aggregated daily forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values calculated from the monthly aggregate data. Due to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated monthly CH4 flux data products, low latency (LPJ_EOSIM_L2_MCH4E_LL) and high latency (LPJ_EOSIM_L2_MCH4E) collections are available. High latency data in this collection will be delivered around May of each year when National Oceanic and Atmospheric Administration\u2019s (NOAA) Global Monitoring Laboratory (GML) publishes the previous year\u2019s CO2 concentration and will have a lag of at least 5 months (January-May), and at most 17 months (January of the current year to May of the next year). Please see Section 2.0.1 of the User Guide for a more detailed explanation of estimated CO2 inputs and timing for scheduled updates to the collections.",
"license": "proprietary"
},
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"description": "The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center (GHGC) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback, and in strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots. The LPJ-EOSIM L2 Global Simulated Monthly Wetland Methane Flux Low Latency (LPJ_EOSIM_L2_MCH4E_LL) Version 1 data product provides simulated monthly wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The monthly simulation data contains aggregate versions of the daily LPJ-EOSIM L2 Global Simulated Daily Wetland Methane Flux Low Latency (LPJ_EOSIM_L2_DCH4E_LL) Version 1 data. The monthly data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the aggregated daily forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values calculated from the monthly aggregate data. Due to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated monthly CH4 flux data products, low latency (LPJ_EOSIM_L2_MCH4E_LL) and high latency (LPJ_EOSIM_L2_MCH4E) collections are available. Low latency data are delivered on a two-month cadence throughout the year. Granules will also be updated as new CO2 input data become available. Please see Section 2.0.1 of the User Guide for a more detailed explanation of estimated CO2 inputs and timing for scheduled updates to the collections. ",
"license": "proprietary"
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"description": "The Integrated Multi-Mission Ocean Altimeter Sea Surface Height (SSH) Version 5.2 dataset provides level 2 along track sea surface height anomalies (SSHA) from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A missions geo-referenced to a mean reference orbit. It is produced by NASA Sea Surface Height (NASA-SSH) project investigators at Goddard Space Flight Center and Jet Propulsion Laboratory with support from NASA\u2019s Physical Oceanography program, and was developed originally as an Earth System Data Record (ESDR) under the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, which supported forward processing and incremental refinements through version 5.1 (released in April 2022).
Geophysical Data Records (GDRs) from each altimetry mission were interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. The entire multi-mission data record covers the period from September 1992 to present; it is extended to include new observations approximately once each quarter. The previous release (version 5.1) integrated Jason-3 data and applied revised internal tides and pole tide across missions (GDR_F standard). The current release (version 5.2) includes the following revisions: a) GSFC std2006_cs21 orbit for all missions, b) GOT5.1 ocean tide model, c) TOPEX/Poseidon GDR_F data, d) Sentinel-6 LR version F08 data, e) Jason-3 re-calibrated radiometer wet troposphere correction. More information about the data content and derivation can be found in the v5.2 User\u2019s Handbook (https://doi.org/10.5067/ALTUG-TJ152).
Please note that this collection is the same data as https://doi.org/10.5067/ALTCY-TJA52 but with all cycles included in one netCDF file.",
"license": "proprietary"
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"description": "The Integrated Multi-Mission Ocean Altimeter Sea Surface Height (SSH) Version 5.2 dataset provides level 2 along track sea surface height anomalies (SSHA) for 10-day cycles from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A missions geo-referenced to a mean reference orbit. It is produced by NASA Sea Surface Height (NASA-SSH) project investigators at Goddard Space Flight Center and Jet Propulsion Laboratory with support from NASA\u2019s Physical Oceanography program, and was developed originally as an Earth System Data Record (ESDR) under the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, which supported forward processing and incremental refinements through version 5.1 (released in April 2022).
Geophysical Data Records (GDRs) from each altimetry mission were interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. The entire multi-mission data record covers the period from September 1992 to present; it is extended to include new observations approximately once each quarter. The previous release (version 5.1) integrated Jason-3 data and applied revised internal tides and pole tide across missions (GDR_F standard). The current release (version 5.2) includes the following revisions: a) GSFC std2006_cs21 orbit for all missions, b) GOT5.1 ocean tide model, c) TOPEX/Poseidon GDR_F data, d) Sentinel-6 LR version F08 data, e) Jason-3 re-calibrated radiometer wet troposphere correction. More information about the data content and derivation can be found in the v5.2 User\u2019s Handbook (https://doi.org/10.5067/ALTUG-TJ152).
Please note that this collection contains the same data as https://doi.org/10.5067/ALTTS-TJA52, re-organized into one netCDF file per cycle for convenience.",
"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"description": "NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 1:30 pm, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDA-2PJ02",
"license": "proprietary"
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"description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-8D4N4",
"license": "proprietary"
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"description": "Day and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be at https://doi.org/10.5067/MODST-MO9N4",
"license": "proprietary"
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"description": "The GHRSST L2P MSG01 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-8 (MSG1) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5\u00b0E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. The full data records stretch from Sept. 18, 2018 to June 1, 2022. After June 1, 2022, the Meteosat-9 (MSG2) took over as the prime geostationary satellite for the Indian Ocean region (MSG02-OSPO-L2P-v1.0). Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors.
The SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977.
This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.",
"license": "proprietary"
},
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The SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977.
This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.",
"license": "proprietary"
},
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"license": "proprietary"
},
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The SST measurements from SEVIRI are parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977.
This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.",
"license": "proprietary"
},
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"description": "Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and its radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis by using Bayesian Cloud Mask algorithm at the Office of Satellite and Product Operations (OSPO). L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0.",
"license": "proprietary"
},
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"license": "proprietary"
},
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"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2269057787-LPCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2269057787-LPCLOUD.html",
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"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794007-LPCLOUD.html",
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"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794018-LPCLOUD.html",
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"description": "The MYD11B2 Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MYD11B2 is a simple average of all the corresponding values from the LST&E values from the MYD11B1 (https://doi.org/10.5067/MODIS/MYD11B1.061) product collected during that 8-day period. Each MYD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 (https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
"license": "proprietary"
},
@@ -135780,7 +135806,7 @@
"bbox": "-180, -90, 180, 90",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794038-LPCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794038-LPCLOUD.html",
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"license": "proprietary"
},
@@ -135793,7 +135819,7 @@
"bbox": "-180, -90, 180, 90",
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794042-LPCLOUD.html",
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"description": "The MYD11C2 Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight MYD11C1 (https://doi.org/10.5067/MODIS/MYD11C1.061) daily files. The MYD11C2 granule consists of 17 layers. Each MYD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
"license": "proprietary"
},
@@ -135806,7 +135832,7 @@
"bbox": "-180, -90, 180, 90",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794044-LPCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794044-LPCLOUD.html",
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"description": "The MYD11C3 Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MYD11C3 product are derived by compositing and averaging the values from the corresponding month of MYD11C1 (https://doi.org/10.5067/MODIS/MYD11C1.061) daily files. Each MYD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
"license": "proprietary"
},
@@ -135845,7 +135871,7 @@
"bbox": "-180, -90, 180, 90",
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2343114808-LPCLOUD.html",
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"description": "The MYD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MYD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MYD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11) Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
"license": "proprietary"
},
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"description": "The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ",
"license": "proprietary"
},
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"description": "The MYD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ",
"license": "proprietary"
},
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"description": "A suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
"license": "proprietary"
},
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"description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MYD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).",
"license": "proprietary"
},
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"description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (httpd://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
"license": "proprietary"
},
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"description": "A new suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C1 Version 6.1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).",
"license": "proprietary"
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"description": "A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (https://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
"license": "proprietary"
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"description": "A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C3 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (http://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).",
"license": "proprietary"
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"description": "The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)). Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
"license": "proprietary"
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"description": "The N21-VIIRS-L2P-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the The Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series.
VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system (Jonasson et al. 2022). Data are reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. In ACSPO products, SSTs are derived using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels with quality level QL=5 are recommended. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5.
The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO N21 VIIRS L3U product is also available (10.5067/GHV21-3U280) (Ignatov et al., 2017).",
"license": "proprietary"
},
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"license": "proprietary"
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"description": "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. This NISE Version 3 product contains DMSP-F16, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F16 satellite. For DMSP-F18, SSMIS-derived data, see NISE Version 5. For DMSP-F17, SSMIS-derived data, see NISE Version 4. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2.",
"license": "proprietary"
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"license": "proprietary"
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"description": "The NASA Scatterometer (NSCAT) Level 2.5 high-resolution merged ocean wind vectors and sigma-0 in 25 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0, and sigma-0 values are preserved where measurements are available. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.",
"license": "proprietary"
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"description": "This dataset contains the NASA Scatterometer (NSCAT) Level 2 ocean wind vector ambiguity overlay files for the NSCAT MGDR version 2 dataset, referenced for 25 km wind vector cells (WVC). The dataset is derived from the results of a study which used a 2-D variational analysis method (VAM) to select a wind solution from the NSCAT ambiguous winds (Hoffman et al. 2003). Hoffman et al. chose the ambiguity closest in direction to the VAM surface wind analysis. No ambiguity was chosen for poor quality wind vector cells (WVCs). ECMWF analyses were used as the background field for the VAM. Their choice of ambiguity selection is compared with that of JPL, which used a median filter initialized with NCEP analysis fields. Ambiguity selection is changed in ~5% of the dataset, often improving the depiction of meteorological features where the surface wind is strongly curved or sheared. See Hoffman et al. (2003) for more on the method and results. Additional work by Henderson et al. (2003) compares the results of median filtering (JPL) vs. the 2d-VAR method (Hoffman et al., 2003) using 51 days of NSCAT data, supplemented by the NCEP 1000 hPa wind analyses as background fields.",
"license": "proprietary"
},
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"description": "This NASA Scatterometer (NSCAT) satellite Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 4.45 km pixel resolution stored in SIR files; polar regions are gridded using a polar-stereographic technique. A non-enhanced version is provided at 22.25 km pixel resolution in a format known as GRD files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, polarization, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In additional to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. For more information, please visti: http://www.scp.byu.edu/docs/NSCAT_user_notes.html",
"license": "proprietary"
},
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"description": "The NASA Scatterometer (NSCAT) Level 1.7 ocean sigma-0 referenced to 50 km wind vector cells (WVC) contains daily backscatter (sigma-0) data from ascending and descending passes. Rain flagging information is not included. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; re-processing had only minor impacts on the Level 1.7 data.",
"license": "proprietary"
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"description": "The NASA Scatterometer (NSCAT) Level 2 ocean wind vectors in 50 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea ice. Winds are calculated using the NSCAT-2 model function. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.",
"license": "proprietary"
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"description": "This dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. Information and access to the Level 3 source data used to generate these browse images may be accessed at: http://podaac.jpl.nasa.gov/dataset/NSCAT%20LEVEL%203.",
"license": "proprietary"
},
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"description": "The NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea-ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.",
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"description": "The NASA Scatterometer (NSCAT) Level 2.5 high-resolution reduced MGDR contains only wind vector data (sigma-0 is excluded) in 25 km wind vector cell (WVC) swaths which contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0. Wind vectors are processed using the NSCAT-2 geophysical model function.",
"license": "proprietary"
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"description": "This sea ice concentration data set was derived using measurements from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite and from the Special Sensor Microwave/Imager (SSM/I) sensors on the Defense Meteorological Satellite Program's (DMSP) -F8, -F11, and -F13 satellites. Measurements from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard DMSP-F17 are also included. The data set has been generated using the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) Bootstrap Algorithm with daily varying tie-points. Daily (every other day prior to July 1987) and monthly data are available for both the north and south polar regions. Data are gridded on the SSM/I polar stereographic grid (25 x 25 km) and provided in two-byte integer format. Data coverage began on 01 November 1978 and is ongoing through the most current processing, with updated data processed several times annually.",
"license": "proprietary"
},
@@ -144477,7 +144503,7 @@
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"description": "The Near-Real-Time DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperature product provides near-real-time polar stereographic gridded daily brightness temperatures for both the Northern and Southern Hemispheres.",
"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.1Mv03 dataset, which can be found at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table.",
"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"description": "This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 AERUV product OMAERUV. This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 Aerosol product OMAERUV. OMAERUVG data product is a special Level-2 gridded product where pixel level products are binned into 0.25x0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMAERUVG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits mapped on the Global 0.25x0.25 deg Grids. The maximum file size for the OMAERUVG data product is about 50 Mbytes.",
"license": "proprietary"
},
- {
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- "title": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT",
- "catalog": "OMINRT STAC Catalog",
- "state_date": "2004-07-15",
- "end_date": "",
- "bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMAERUV_003",
- "description": "The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ ",
- "license": "proprietary"
- },
{
"id": "OMAERUV_003",
"title": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 (OMAERUV) at GES DISC",
@@ -149824,6 +149837,19 @@
"description": "The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data are available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The shortname for this Level-2 near-UV Aerosol Product is OMAERUV_V003. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes.",
"license": "proprietary"
},
+ {
+ "id": "OMAERUV_003",
+ "title": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT",
+ "catalog": "OMINRT STAC Catalog",
+ "state_date": "2004-07-15",
+ "end_date": "",
+ "bbox": "-180, -90, 180, 90",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMAERUV_003",
+ "description": "The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ ",
+ "license": "proprietary"
+ },
{
"id": "OMAERUV_004",
"title": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V004 (OMAERUV) at GES DISC",
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"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset contains 50m horizontal resolution gridded digital elevation models (DEMs) of Greenland Ice Sheet outlet glaciers collected during the NASA Oceans Melting Greenland mission. Between 2016 and 2019 the GLacier and Land Ice Surface Topography Interferometer airborne (GLISTIN-A) radar measured surface elevations around the periphery of the Greenland Ice Sheet using Ka-Band (8.4 mm wavelength) single-pass interferometry. Level 2 (L2) GLISTIN-A elevation data, available on the JPL UAVSAR website (uavsar.jpl.nasa.gov), were collected each year in 81 swaths of varying lengths and 10-12km widths and then mapped to 3m horizontal grids. This Level 3 (L3) dataset was created to facilitate analysis of the year-to-year glacier surface elevation changes. Improvements over the L2 dataset include: a consistent swath numbering scheme (1 to 81) corresponding to repeated flight lines; common regular equal-area grids for each swath; filtering and flagging of outliers; an ancillary geoid layer; and UTM map projections corresponding to swath location. The interested user may generate their own L3 DEMs at different horizontal resolutions and projections using the Python 3 resample_GLISTIN_DEMs package available which will be available from https://github.com/NASA/resample_GLISTIN_DEMs",
"license": "proprietary"
},
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+ "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wib2NlYW4gc3VyZmFjZSBjdXJyZW50IGFuYWx5c2VzIHJlYWwtdGltZSAob3NjYXIpIHN1cmZhY2UgY3VycmVudHMgLSBmaW5hbCAwLjI1IGRlZ3JlZSAodmVyc2lvbiAyLjApXCIsXCJQT0NMT1VEXCIsXCJPU0NBUl9MNF9PQ19GSU5BTF9WMi4wXCIsXCIyLjBcIiwyMDk4ODU4NjQyLDE2XSIsInVtbSI6IltcIm9jZWFuIHN1cmZhY2UgY3VycmVudCBhbmFseXNlcyByZWFsLXRpbWUgKG9zY2FyKSBzdXJmYWNlIGN1cnJlbnRzIC0gZmluYWwgMC4yNSBkZWdyZWUgKHZlcnNpb24gMi4wKVwiLFwiUE9DTE9VRFwiLFwiT1NDQVJfTDRfT0NfRklOQUxfVjIuMFwiLFwiMi4wXCIsMjA5ODg1ODY0MiwxNl0ifQ%3D%3D/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0",
"description": "This OMG Narwhals L3 dataset contains daily-averaged temperature and salinity measurements from CTD and temperature loggers from the same mooring.
NASA\u2019s Oceans Melting Greenland (OMG) campaign obtained oceanographic observations around Greenland at an unprecedented spatial scale and confirmed that the ocean plays a key role in Greenland glacier acceleration and retreat. Yet, ocean observations along Greenland\u2019s margins are biased toward summer months with relatively few year-round measurements. OMG Narwhals, a project coupled with NASA\u2019s OMG mission, seeks to understand the ecological importance of glacial habitats to narwhals. Narwhals return to glacial outlets and fjords each summer with high site fidelity but what attracts them to specific glacier fronts remains unclear. Between 2018 and 2020, five bottom-mounted moorings with marine mammal acoustic recorders and oceanographic instruments were deployed year-round near three glacier fronts: Sverdrup Glacier, Kong Oscar Glacier, and Rink Glacier.",
"license": "proprietary"
},
@@ -150210,7 +150236,7 @@
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"description": "This OMG Narwhals dataset contains measurements from the ship based full water column CTD profiles that were obtained during summer mooring deployment/recovery cruises.
NASA\u2019s Oceans Melting Greenland (OMG) campaign obtained oceanographic observations around Greenland at an unprecedented spatial scale and confirmed that the ocean plays a key role in Greenland glacier acceleration and retreat. Yet, ocean observations along Greenland\u2019s margins are biased toward summer months with relatively few year-round measurements. OMG Narwhals, a project coupled with NASA\u2019s OMG mission, seeks to understand the ecological importance of glacial habitats to narwhals. Narwhals return to glacial outlets and fjords each summer with high site fidelity but what attracts them to specific glacier fronts remains unclear. Seafloor-mounted ocean moorings with marine mammal acoustic recorders and oceanographic instruments were deployed near three glacier fronts with known narwhal presence in Melville Bay, northwest Greenland.",
"license": "proprietary"
},
@@ -150994,19 +151020,6 @@
"description": "This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.",
"license": "proprietary"
},
- {
- "id": "OMTO3_003",
- "title": "OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
- "state_date": "2004-10-01",
- "end_date": "",
- "bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections?cursor=eyJqc29uIjoiW1wib21pL2F1cmEgbm8yIGNsb3VkLXNjcmVlbmVkIHRvdGFsIGFuZCB0cm9wb3NwaGVyaWMgY29sdW1uIGwzIGdsb2JhbCBncmlkZGVkIDAuMjUgZGVncmVlIHggMC4yNSBkZWdyZWUgdjMgKG9tbm8yZCkgYXQgZ2VzIGRpc2NcIixcIkdFU19ESVNDXCIsXCJPTU5PMmRcIixcIjNcIiwxMjY2MTM2MTExLDQ1XSIsInVtbSI6IltcIm9taS9hdXJhIG5vMiBjbG91ZC1zY3JlZW5lZCB0b3RhbCBhbmQgdHJvcG9zcGhlcmljIGNvbHVtbiBsMyBnbG9iYWwgZ3JpZGRlZCAwLjI1IGRlZ3JlZSB4IDAuMjUgZGVncmVlIHYzIChvbW5vMmQpIGF0IGdlcyBkaXNjXCIsXCJHRVNfRElTQ1wiLFwiT01OTzJkXCIsXCIzXCIsMTI2NjEzNjExMSw0NV0ifQ%3D%3D/OMTO3_003",
- "description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.",
- "license": "proprietary"
- },
{
"id": "OMTO3_003",
"title": "OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT",
@@ -151020,6 +151033,19 @@
"description": "The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA for the public access. The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ .",
"license": "proprietary"
},
+ {
+ "id": "OMTO3_003",
+ "title": "OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC",
+ "catalog": "GES_DISC STAC Catalog",
+ "state_date": "2004-10-01",
+ "end_date": "",
+ "bbox": "-180, -90, 180, 90",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.html",
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+ "description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.",
+ "license": "proprietary"
+ },
{
"id": "OMTO3_CPR_003",
"title": "OMI/Aura Level 2 Ozone (O3) Total Column 1-Orbit Subset and Collocated Swath along CloudSat track 200-km wide at 13x24 km2 resolution",
@@ -151380,7 +151406,7 @@
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617126679-POCLOUD.html",
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"description": "This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km \u00d7 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer. To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov ",
"license": "proprietary"
},
@@ -151393,7 +151419,7 @@
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"license": "proprietary"
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While OSTIA-NRT is produced to mainly serve as a lower boundary condition in Numerical Weather Prediction (NWP) models, this OSTIA-REP aims to provide a more accurate and consistent record of SST measurements over time, which is crucial for detecting long-term climate trends and variability. Both versions follow GHRSST Data Processing Specification (GDS) version 2 format guidelines.
Data to June 2022 are also distributed through the E.U. Copernicus Marine Service Information (https://marine.copernicus.eu/, DOI: https://doi.org/10.48670/moi-00168 with the following license). Please refer to the user guide for more information. ",
"license": "proprietary"
},
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation (OI) on a global 0.05x0.05 degree grid. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) analysis uses satellite data from over 10 unique sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Geostationary Operational Environmental Satellite (GOES) imager, the Infrared Atmospheric Sounding Interferometer (IASI), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications and is updated daily with 24-hours nominal latency in a Near Real Time (NRT) mode. UKMO also produces the higher quality reprocessed OSTIA L4 SST using more sensors and data with a biannual latency (https://podaac.jpl.nasa.gov/dataset/OSTIA-UKMO-L4-GLOB-REP-v2.0).",
"license": "proprietary"
},
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"license": "proprietary"
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"description": "The Global River Radar Altimeter Time Series (GRRATS) are simulated river gauge data that are derived from ERS-1, ERS-2, TOPEX/Poseidon OSTM/Jason-2, Jason-3 and Envisat altimetric measurements. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.If you are looking for version 1 it can be found at https://podaac.jpl.nasa.gov/dataset/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V1 however this version should be used with caution. Version 2, this page, is the most recent version with the most accurate algorithms used for producing river heights.",
"license": "proprietary"
},
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"description": "The Global Lake/Reservoir Surface Inland Water Height Time Series is derived from the G-REALM10 lake level product https://ipad.fas.usda.gov/cropexplorer/global_reservoir/ The purpose of this dataset is to provide surface water dynamics for several hundred lakes and reservoirs across the globe. These time series potentially span a 25 year time period, from late 1992 to 2017, satisfying the project goal of ESDR creation with a suitable level of quality that supports long-term trend analysis and global water dynamics models. Water level variation is also a key component required for the determination of surface water storages and fluxes. This product is readily accessible and is of direct use to both water managers and the scientific community worldwide, and allows for improved assessment and modeling of the human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.",
"license": "proprietary"
},
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"description": "The Global Lake/Reservoir Surface Inland Water Extent Mask Time Series are derived from the MODIS instruments. The purpose of this dataset is to provide surface water dynamics for several hundred lakes and reservoirs throughout the globe, with a base temporal resolution of 8 days and a spatial resolution of 500 meters. With the exception of periods of low-quality input data, these time series will extend across the lifespan of the MODIS multispectral reflectance products, from roughly 2000 to present. These time series will allow us to satisfy the project goal to produce ESDRs of suitable quality to support long-term trend analysis and global water dynamics models for the longest length possible (in most cases, about 20 years, the length of the altimetry record) of key measures of surface water storages and fluxes. This product should be accessible and of direct use to both water managers and the scientific community worldwide, and will allow for improved assessment and modeling of human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.",
"license": "proprietary"
},
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"description": "The Global Lake/Reservoir Storage Time Series is derived from the Surface Water Height Time Series and Surface Water Extent Mask Time Series products. The purpose of this dataset is to provide surface water storage estimates for several hundred lakes and reservoirs across the globe. These time series potentially span a 25 year time period, from late 1992 to 2017, satisfying the project goal of ESDR creation with a suitable level of quality that supports long-term trend analysis and global water dynamics models. This product is readily accessible and is of direct use to both water managers and the scientific community worldwide, and allows for improved assessment and modeling of the human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.",
"license": "proprietary"
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"description": "This is the PI-produced SMAP sea water salinity, level 2 v1.0 orbital/swath product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Parameterized Rain Impact Model (PRIM) developed at the University of Central Florida (UCF) Central Florida Remote Sensing Lab (CFRSL), Orlando, FL; University of Washington (UW) Applied Physics Lab (APL), Seattle, WA.
The PRIM product range extended from March 31, 2015 to September 30, 2021. It includes data for a range of parameters: derived SMAP sea water salinity at surface, 1m depth and 5m depth, and probability of salinity stratification (PSS), rainfall rate and wind speed data. Each data file covers one 98-minute orbit (15 files per day), and corresponds to a JPL SMAP Level 2B CAP Sea Surface Salinity V5.0 file which corresponds to a single orbit on a given day.
The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Observations are global in extent and provided at 25km swath grid with an approximate spatial resolution of 60 km.",
"license": "proprietary"
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"description": "This dataset contains the first provisional release of the MEaSUREs-funded Earth Science Data Record (ESDR) of ancillary data corresponding to the QuikSCAT Level 2 (L2) data products, interpolated in space and time to the scatterometer observations. These ancillary files include: i) ocean surface wind fields from ERA-5 short-term forecasts (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) collocated in space and time estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobeCurrent project. These auxiliary fields are included to complement the scatterometer observation fields and to help in the evaluation process. The primary purpose of this release is for provisional evaluation to be provided by the NASA International Ocean Vector Winds Science Team (IOVWST). As such, this release is not intended for science-quality research, and is subject to future revision based on feedback provided by the IOVWST. The modeled ocean surface auxiliary fields are provided on a non-uniform grid within the native L2 QuikSCAT sampled locations at 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit.",
"license": "proprietary"
},
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"description": "This dataset is Version 2 of the geo-located and averaged Level 1B Sigma-0 measurements and wind retrievals from the SeaWinds on QuikSCAT platform, initiated in the months following the failure of the rotating antenna motor on 22 November 2009, using the various incidence angles at which QuikSCAT was pointed during the time period from November 2009 until present. Incidence angles were varied in order to cross-calibrate the Oceansat-2 and RapidScat scatterometers and to extend the known Ku-band geophysical model function. The averaging of the L1B input data combined with the wind vector processing results are both contained in this product referred to hereafter as Level 1C (L1C). The fixed and repointed beam processing is relative to either the one corresponding to the vertically polarized \"outer\" beam or the other corresponding to the horizontally polarized \"inner\" beam. The Sigma-0 values from the fixed operating beam for each frame are averaged to a single value representing approximately 50 samples. The data points are land flagged, collocated with ECMWF surface winds, and have climatological nadir attenuations provided for the location and time of the data (not applied to the sigma0). The following enhancements have been applied in the Version 2 re-processing: 1) the GMF has been updated (QNS2016a) to make use of ECMWF nowcast 1x1 degree resolution wind direction information for the entire historical data record; 2) the new QNS2016a GMF leverages a calibration adjustment from Remote Sensing Systems (RSS) resulting in a consistently lower Normalized Radar Cross Section (NRCS or Sigma-0) measurements that establishes a Sigma-0 bias of -0.25 dB (-5.9% linear scale) compared to the L1C Version 1 data; 3) the new QNS2016a GMF also applies an azimuthal modulation that is decreased by several tenths of a dB (for Sigma-0) in variation with wind speed; this results in a more consistent wind speed retrieval comparison between \"non-spinning\" and \"spinning\" modes of the QuikSCAT instrument; 4) spacecraft attitude was re-estimated using slice data over multiple orbits as a replacement for lost echo-tracking capability during the \"non-spinning\" mode of the instrument; this new attitude estimation follows an unpublished manual technique that leverages the echo power of individual slice observations; since only a small subset of slice observations are analyzed, rapid variations in attitude are not captured; 5) continues data production beyond October 2016 through the end of mission on 30 August 2018. Retrieved wind directions are only slightly different from ECMWF values and should not be considered an independent measurement of wind direction. Retrieved wind speeds do not depend significantly on ECMWF speeds as evidenced by the fact that they agree closely with WindSAT polarimetric radiometer speeds whenever WindSAT and ECMWF disagree. The Sigma0 values have also been corrected for scan loss (due to the fact that the antenna does not scan) and for X-factor changes due to repointing.",
"license": "proprietary"
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"description": "The SeaWinds on QuikSCAT Level 1B dataset contains the geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. This dataset represents the second reprocessed version of the Level 1B release. Special note: QuikSCAT went into a \"non-spinning\" mode on 22 November 2009. The final rev number in the nominal Operational \"spinning\" mode is 54296; the \"non-spinning\" mode of the instrument continued predominantly until the end of the time series. There were some brief periods of \"spinning\" in between, which include the following days and rev numbers (identified in parenthesis): 1) 29 January 2013 to 5 February 2013 (7909-71011), 2) 14 March 2013 (71536-71549), 3) 18 March 2013 to 21 March 2013 (71590-71634), and 4) 28 March 2013 to 31 March 2013 (71735-71769). Data during the \"non-spinning\" mode is not consistently calibrated with data from the \"spinning\" mode. Furthermore, incidence angles change periodically during the \"non-spinning\" mode. It is therefore advised that only \"expert\" users attempt using the data during the \"non-spinning\" mode. For standard L1B data users who wish to access consistently calibrated L1B data during the \"non-spinning\" mode, please consider using the L1B Averaged Sigma-0 dataset as alternative, which may be accessed by contacting podaac@podaac.jpl.nasa.gov",
"license": "proprietary"
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"description": "This dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3 processing begins with the same L1B (time-ordered backscatter) data as used in the previous processing. Version 3 has several improvements over the previous JPL processing of the QuikSCAT L2B winds: 1) changes to measurement binning, which was done in order to decrease noise and reduce gaps in the 12.5 km L2B wind retrievals, 2) an improved geophysical model function (GMF) to model the effect of wind on backscatter, 3) a neural network approach to correct rain contaminated winds speeds, 4) cross-track dependent wind speed biases were estimated and removed from the wind retrievals. The 12.5 km binning resolution enables users to obtain wind vector retrievals 10 km closer to shore when compared to the 25 km L2B dataset (only available in versions 1 and 2). More details to the processing changes and improvements are noted by Fore et al. (2014): PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/quikscat/L2B12/docs/fore_et_al_ieee_2014.pdf . Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). The Version 3 User Guide document is accessible from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/quikscat/open/L2B12/docs/qscat_l2b_v3_ug_v1_0.pdf.",
"license": "proprietary"
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"license": "proprietary"
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"description": "This dataset contains the latest reprocessed version 3.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3.1 processing begins with the same L1B (time-ordered backscatter) data as used in the previous Version 3.0 processing. Version 3.1 improves upon the previous Version 3.0 processing by incorporating enhanced coastal processing using a Land Contamination Ratio (LCR) method with a fixed threshold. The 12.5 km binning resolution combined with the LCR processing enables this dataset to provide wind vector retrievals with approximately half the coastal gap as compared to the Version 3.0 12.5 km L2B dataset. The geophysical model function used to produce the wind vector cell retrievals remains unchanged between Version 3.0 and 3.1. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the NASA QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). More details to the processing changes and improvements are to be published in the near future, but for now can be referenced by the following presentation: https://mdc.coaps.fsu.edu/scatterometry/meeting/docs/2016/Thu_AM/coastal-poster.pdf .",
"license": "proprietary"
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"license": "proprietary"
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the BoM Global Weekly 1 degree OI SST analysis, which relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.",
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"description": "The Reconstructed Sea Level dataset contains sea level anomalies derived from satellite altimetry and tide gauges. The satellite altimetric record provides accurate measurements of sea level with near-global coverage, but it has a relatively short time span, since 1993. Tide gauges have measured sea level over the last 200 years, with some records extending back to 1807, but they only provide regional coverage, not global. Combining satellite altimetry with tide gauges, using a technique known as sea level reconstruction, results in a dataset with the record length of the tide gauges and the near-global coverage of satellite altimetry. Cyclostationary empirical orthogonal functions (CSEOFs), derived from satellite altimetry, are combined with historical sea level measurements from tide gauges to create the Reconstructed Sea Level dataset spanning from 1950 through 2009. Combining the altimetric and tide gauge records alleviates the difficulties caused by the short record length and poor spatial distribution of the satellite altimetry and tide gauges, respectively. Previous sea level reconstructions have utilized empirical orthogonal functions (EOFs) as basis functions, but by using CSEOFs and by addressing other aspects of the reconstruction procedure, an alternative sea level reconstruction can be computed. The resulting reconstructed sea level dataset has weekly temporal resolution and half-degree spatial resolution. For specific information on the algorithm and how the CSEOFs are applied to the tide gauge data please see Hamlington et al. 2011.",
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"license": "proprietary"
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"description": "The Smith & Reynolds Extended Reconstructed Sea Surface Temperature (ERSST) Level 4 dataset provides a historical reconstruction of monthly global ocean surface temperatures and temperature anomalies over a 2 degree spatial grid since 1854 from in-situ observations based on a consistent statistical methodology that accounts for uneven sampling distributions over time and related observational biases. Version 5 of this dataset implements release 3.0 of ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and is supplemented by monthly GTS (Global Telecommunications Ship and buoy) system data. As for the prior ERSST version, v5 implements Empirical Orthogonal Teleconnection analysis (EOT) but with an improved tuning method for sparsely sampled regions and periods. ERSST anomalies are computed with respect to a 1971-2000 monthly climatology. The version 5 has been improved from previous version 4. Major improvements in v5 include: 1) Inclusion and use of new sources and new versions of input datasets, such as data from Argo floats (new source), ICOADS R3.0 (from R2.5), HadISST2 (from HadISST1) sea ice concentration, and 2) Improved methodologies, such as inclusion of additional statistical modes, less spatial-temporal smoothing, better quality control method, and bias correction with baseline to modern buoy observations. The new version improves the spatial structures and magnitudes of El Nino and La Nina events. The ERSST v5 in netCDF format contains extended reconstructed sea surface temperature, SST anomaly, and associated estimated SST error standard deviation fields, in compliance with CF1.6 standard metadata.",
"license": "proprietary"
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"description": "The Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.
This Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters. Each netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time.",
"license": "proprietary"
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"description": "This dataset contains the multi-sourced microwave radiometer wind speed, rain and cloud liquid water data collocated to RapidScat Level 2B wind vector cell (WVC) locations. The corresponding NASA mission is officially referred to as ISS-RapidScat. This dataset is produced by Remote Sensing Systems (RSS) with direct funding from the JPL RapidScat project. All of the collocated radiometer data is produced by RSS. The co-located radiometer sources include: 1) DMSP SSM/I (F15) and SSMIS (F16/F17), 2) Coriolis WindSat, 3) GCOM-W1 AMSR2 and 4) GPM Core GMI; more details on these radiometer sources and sensors can be extracted by scrolling down to the \"Platform/Sensor\" section below this description. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-4 file format that follows the netCDF \"classic\" model and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above.",
"license": "proprietary"
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"description": "This dataset contains the ISS-RapidScat Version 2.0 Level 1B geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. Version 2.0 represents a complete historical re-processing of the L1B data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). The Version 2.0 is also the dataset used to derive the Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. This dataset is intended for expert use only. If you must use RapidScat Sigma-0 data but you are unsure about how to use the L1B data record, please consider using either of the following L2A datasets: 1) https://podaac.jpl.nasa.gov/dataset/RSCAT_L2A_25KM_V2.0 or 2) https://podaac.jpl.nasa.gov/dataset/RSCAT_L2A_12KM_V2.0. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the ISS Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. ",
"license": "proprietary"
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"description": "This dataset contains the Version 2.0 ISS-RapidScat on Level 2A 12.5 km science data record, which provides surface-flagged sigma-0 in 12.5 km Wind Vector Cells processed using the pulse \"slice\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the RapidScat instrument the expected number of Sigma-0 cells per WVC is not constant. To minimize the L2A data volume, the Sigma-0 cell data are stored as \"lists\" for each WVC row, with each list indexed by a \"cell_index\" array to indicate the cross-track WVC membership of the data. Each cell is then checked for land or ice and flagged accordingly. Attenuation corrections for each Sigma-0 measurement are also provided. Version 2.0 represents a complete historical re-processing of the L2A data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). It is also derived from the same L1B V2.0 product that was used to generate Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.",
"license": "proprietary"
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"description": "This dataset contains the Version 2.0 ISS-RapidScat Level 2A 25km science data record, which provides surface-flagged sigma-0 in 25km Wind Vector Cells processed using the pulse \"egg\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the SeaWinds instrument the expected number of Sigma-0 cells per WVC is not constant. To minimize the L2A data volume, the Sigma-0 cell data are stored as \"lists\" for each WVC row, with each list indexed by a \"cell_index\" array to indicate the cross-track WVC membership of the data. Each cell is then checked for land or ice and flagged accordingly. Attenuation corrections for each Sigma-0 measurement are also provided. Version 2.0 represents a complete historical re-processing of the L2A data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). It is also derived from the same L1B V2.0 product that was used to generate Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.",
"license": "proprietary"
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"description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a. Sigma-0) from the L1B dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via Direct Download and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This climate quality data set differs from the nominal \"slice\" L2B dataset as follows: 1) it uses full antenna footprint measurements (~20-km) without subdividing by range (~7-km) and 2) the absolute calibration has been modified for the two different low signal-to-noise ratio (SNR) mode data sets: LowSNR1 14 August 2015 to 18 September 2015; LowSNR2 6 October 2015 to 7 February 2016. The above enhancements allow this dataset to provide consistent calibration across all SNR states. Low SNR periods and other key quality control (QC) issues are tracked and kept up-to-date in PO.DAAC Drive at https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/rapidscat/open/L1B/docs/revtime.csv. If you have any questions, please visit our user forums: https://podaac.jpl.nasa.gov/forum/.",
"license": "proprietary"
},
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"description": "This dataset contains the RapidScat Level 2B 12.5km Version 2.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a. Sigma-0) from the L1B dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. The new version has two important improvements over the previous version 1.0. First, an SST-dependent GMF developed by Lucrezia Ricciardulli of Remote Sensing Systems is used in wind retrieval in order to fix persistent speed biases in Ku-band data over cold ocean. Second, flagging is simplified and extra flags are provided. All the previously existing flags are still there and still reflect the same meaning and purpose. A new single bit wind_retrieval_likely_corrupted_flag specifies the approximately 3% of the data which is known to have suboptimal performance due to rain, ice, or a few other rare anomalous cases. Another bit wind_retrieval_possibly_corrupted_flag specifies the approximately 15% of the data near rain, near ice, or near the coast, that is thought to be high quality but may not match up well with numerical wind models due to either remaining rain/ice/land contamination or variability in the winds near ice, rain, and coasts that are not reflected in the NWPs. In addition to these two new bits, copious quality information is provided in the data to allow users to tailor flags to meet their own needs. There is also an added a global attribute called rev_status that specifies whether the RapidScat Instrument was in the original (highest data quality) high SNR mode, or one of the four low SNR time periods, the latter of which indicates the accuracy of winds below 5 m/s is degraded. This attribute also serves to identify MARGINAL orbits in which there are large gaps in the data record due to suboptimal spacecraft attitude. Other than gaps in the data, the accuracy of the winds in the MARGINAL orbits are similar to other orbits. This dataset is provided in netCDF-4 format and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above.",
"license": "proprietary"
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"description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.1 science-quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This Version 1.1 dataset differs from the previous Version 1 dataset as follows: 1) A new neural network approach for high wind speeds provided rain corrections for the \"retrieve_wind_speed\" variable for wind speeds in excess of 15 m/s. 2) The data variables containing the number of measurements of each type for each wind vector cell have been corrected; these variables include \"number_in_aft\", \"number_in_fore\", \"number_out_aft\", and \"number_out_fore\". 3) The \"wind_obj\" data variable has been corrected to include the proper data for the conditional probability for the objective DIRTH function values. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions, please contact podaac@podaac.jpl.nasa.gov",
"license": "proprietary"
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"description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.2 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 data forward from orbital revolution number 5127, corresponding to 19 August 2015; the overlapping time period starting on 19 August 2015 corresponds to the first time period of the recorded low signal-to-noise ratio (SNR). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This Version 1.2 dataset differs from the previous Version 1.1 dataset as follows: 1) L1B sigma-0 has been re-calibrated during the periods of low signal-to-noise ratio (SNR) and 2) during low SNR periods the L1B sigma-0 calibration is determined using re-pointed L1B QuikSCAT data. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.",
"license": "proprietary"
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"description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.3 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 and 1.2 data forward from orbital revolution number 7873, corresponding to 11 February 2016; on 11 Feb 2016, RapidScat entered it's 3rd low signal to noise ratio (SNR) state and the initial calibration of low SNR 3 was preliminary during the Version 1.2 release. The fundamental difference between Version 1.3 and the previous Version 1.2 datasets is that the L1B sigma-0 has been re-calibrated during the periods of low SNR states 3 and 4 using re-pointed QuikSCAT data. The Version 1.1 should still be considered valid up to the first rev of version 1.2 (5127), and similarly version 1.2 shall be considered valid up to the first rev of version 1.3 (7873). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.",
"license": "proprietary"
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"description": "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). The dataset contains the Level 1C WindSat Top of the Atmosphere (TOA) TB processed by RSS. The WindSat radiances are turned into TOA TB after correction for hot and cold calibration anomalies, receiver non-linearities, sensor pointing errors, antenna cross-polarization contamination, spillover, Faraday rotation and polarization alignment. The data are resampled on a fixed regular 0.125 deg Earth grid using Backus-Gilbert Optimum Interpolation. The sampling is done separately for fore and aft looks. The 10.7, 18.7, 23.8, 37.0 GHz channels are resampled to the 10.7 GHz spatial resolution. The 6.8 GHz channels are given at their native spatial resolution. The 10.7, 18.7, 23.8, 37.0 GHz channels are absolutely calibrated using the GMI sensor as calibration reference. The 6.8 GHz channels are calibrated using the open ocean with the RSS ocean emission model and the Amazon rain forest as calibration targets. The Faraday rotation angle (FRA) and geometric polarization basis rotation angle (PRA) were added in the last run.",
"license": "proprietary"
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"description": "The Saildrone Arctic 2019 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2019 Saildrone Arctic campaign featured six Saildrone USVs (jointly funded by NOAA and NASA) deployed during a 150-day cruise in the Bering and Chukchi Seas, spanning from 14 May 2019 to 11 October 2019. The primary mission objective for 2019 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, which could lead to significant improvements in modeling of diurnal warming and understanding of the marginal ice zones. Additionally, these new data will provide additional Arctic SST observations to benefit SST algorithm development and validation, and for studies of air- sea-ice interactions. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2019_Arctic_Cruise_Report.pdf During the Arctic campaign, NASA-funded Saildrones SD-1036 and SD-1037 undertook transects in the Chukchi Sea, approaching the sea ice edge to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.
The Saildrone Arctic 2019 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies. ",
"license": "proprietary"
},
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"description": "The Saildrone Arctic 2021 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2021 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 76-day cruise in the Bering and Chukchi Seas, spanning from 6 July 2021 to 20 September 2021. The primary mission objective for 2021 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, with special emphasis on better understanding the spatial/temporal scales of air-sea covariance in the Chukchi Sea, which was accomplished by running a series of parallel tracks using the two Saildrones at varying horizontal offsets. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/2021_Saildrone_Arctic_Cruise_Report.pdf
During the Arctic campaign, Saildrones SD-1057 and SD-1058 ran transects in the Chukchi Sea, approaching the sea ice edge (up to 50 km away) to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents.\\ The Saildrone Arctic 2021 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses two netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.
The Saildrone Arctic 2021 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses two netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies. ",
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"description": "The Saildrone Arctic 2022 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2022 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 60-day cruise in the Bering and Chukchi Seas, spanning from 18 June 2022 to 17 August 2022. The primary mission objective for 2022 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, specifically in collaboration with the Distributed Biological Observatory (DBO; https://www.pmel.noaa.gov/dbo/; https://dbo.cbl.umces.edu/). Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2022_Arctic_Cruise_Report.pdf
During the Arctic campaign, Saildrones SD-1041 and SD-1046 undertook distinct trajectories to cover designated areas. SD-1041 traversed repeat transects from Point Hope, AK southwestward to near the International Date Line, following DBO line #3 (https://dbo.cbl.umces.edu/images/Frey_DBOmap_IceEdge2022.png). In contrast, SD-1046 ventured northward to DBO line #4 and, upon sea ice retreat, proceeded further north to DBO line #5. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.
The Saildrone Arctic 2022 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.",
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"description": "Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven. The saildrone ATOMIC (Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign) campaign involved the deployment of a fleet of saildrones, jointly funded by NASA and NOAA, in the Atlantic waters offshore of Barbados over a 45 day period from 17 January to 2 March 2020. The goal was to understand the Ocean-Atmosphere interaction particularly over the mesoscale ocean eddies in that region. The saildrones were equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP). Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface. This Saildrone ATOMIC dataset is comprised of two data files for each of the three NASA-funded saildrones deployed, one for the surface observations and one for the ADCP measuements. The surface data files contain saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) spanning the entire cruise at 1 minute temporal resolution. The ADCP files for each saildrone are at 5 minute resolution for the duration of the deployments. All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.",
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"description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. The Surface Wave Instrument Float with Tracking (SWIFT) drifter is a passive Lagrangian wave-following sensor platform. During the SASSIE deployment, five SWIFT drifters were deployed in September 2022, collecting measurements of salinity, sea surface temperature, waves, and meteorological data. SWIFT drifter buoys contain GPS, a pulse-coherent Doppler velocity profiler, an autonomous meteorological station, and a digital video recorder. Level 1 data are available as compressed files containing graphics of the measurements alongside MATLAB and NetCDF files. ",
"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
"license": "proprietary"
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"license": "proprietary"
},
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"description": "This dataset is produced by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument onboard the Meteosat Second Generation (MSG-1), Meteosat-8 satellite (launched on 28 August 2002). The dataset covers the Indian Ocean region with latitude of 60S-60N and longitude of 101.5E-18.5W. Level-3C SST, in the NetCDF format recommended by Group for High Resolution Sea Surface Temperature (GHRSST), is identical to Level-2P GHRSST products, 3 refers to gridded products and C to the fact that hourly products result from compositing 15 minutes (MSG) or 30 minutes (GOES-E) data. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), OSI SAF is producing SST products in near real time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating all 15-minute SST data available in one-hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
"license": "proprietary"
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG-3) satellites (launched 5 July 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05 degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all 15 minute SST data available in one hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
"license": "proprietary"
},
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"description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the MSG satellites (Meteosat-8 and Meteosat-9). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) has reprocessed SST products in (long) delayed-mode from MSG/SEVIRI. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm and the cloud mask (CM) from OSI SAF. Atmospheric profiles of water vapor and temperature from a numerical weather prediction (NWP) model, OSTIA Sea Surface Temperature re-analysis and analysis, together with a radiative transfer model (RTTOV), are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15-minute slot is processed at full satellite resolution. The products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all available 15-minute SST data into hourly files with priority being given to the value closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
"license": "proprietary"
},
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"description": "The SMAP-SSS V5.0, level 2B (NRT CAP) dataset produced by the Jet Propulsion Laboratory Combined Active-Passive (CAP) project , is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) and extreme winds, derived from the NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015. This mission, initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle.
The dataset includes derived SMAP SSS, SSS uncertainty, wind speed and direction data for extreme winds, as well as brightness temperatures for each radiometer polarization. Furthermore, it contains ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. This broad range of parameters stems from the observatory's version 5.0 (V5) CAP retrieval algorithm, initially developed for the Aquarius/SAC-D mission and subsequently extended to SMAP. Datafrom April 1, 2015 to present, is available with a latency of about 6 hours. The observations are global, provided on a 25km swath grid with an approximate spatial resolution of 60 km. Each data file covers one 98-minute orbit, with 15 files generated per day. The data are based on the near-real-time SMAP V5 Level-1 Brightness Temperatures (TB) and benefits from an enhanced calibration methodology, which improves the absolute radiometric calibration and minimizes biases between ascending and descending passes. These improvements also enrich the applicability of SMAP Level-1 data for other uses, such as further sea surface salinity and wind assessments. Due to a malfunction of the SMAP scatterometer on July 7, 2015, collocated wind speed data has been utilized for the necessary surface roughness correction for salinity retrieval.
This JPL SMAP-SSS V5.0 dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature, it achieves global coverage in approximately three days , enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.",
"license": "proprietary"
},
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"description": "This is the PI-produced JPL SMAP-SSS V5.0 CAP, 8-day running mean, level 3 mapped, sea surface salinity (SSS) product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Combined Active-Passive (CAP) retrieval algorithm developed at JPL originally in the context of Aquarius/SAC-D and now extended to SMAP. JPL SMAP V5.0 SSS is based on the newly released SMAP V5 Level-1 Brightness Temperatures (TB). An enhanced calibration methodology has been applied to the brightness temperatures, which improves absolute radiometric calibration and reduces the biases between ascending and descending passes. The improved SMAP TB Level 1 TB will enhance the use of SMAP Level-1 data for other applications, such as sea surface salinity and winds. Daily data files for this L3 product are based on SSS averages spanning an 8-day moving time window. Associated file variables include: derived SSS with associated uncertainties and wind speed data from SMAP, ancillary ice concentration and HYCOM surface salinity data. SMAP data begins on April 1, 2015 and is ongoing, with a 7-day latency in processing and availability. L3 products are global in extent and gridded at 0.25degree x 0.25degree with an approximate spatial resolution of 60km. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed for the surface roughness correction required for the surface salinity retrieval.",
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"description": "This is the PI-produced JPL SMAP-SSS V5.0 CAP, level 3, monthly mapped sea surface salinity (SSS) product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Combined Active-Passive (CAP) retrieval algorithm developed at JPL originally in the context of Aquarius/SAC-D and now extended to SMAP. JPL SMAP V5.0 SSS is based on the newly released SMAP V5 Level-1 Brightness Temperatures (TB). An enhanced calibration methodology has been applied to the brightness temperatures, which improves absolute radiometric calibration and reduces the biases between ascending and descending passes. The improved SMAP TB Level 1 TB will enhance the use of SMAP Level-1 data for other applications, such as sea surface salinity and winds. L3 monthly product file variables include: derived SSS with associated uncertainties and wind speed from SMAP and ancillary surface salinity from HYCOM. SMAP data begins on April 1, 2015 and is ongoing, with a 1 month latency in processing and availability. L3 products are global in extent and gridded at 0.25degree x 0.25degree with an approximate spatial resolution of 60km. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July for the surface roughness correction required for the surface salinity retrieval.",
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"description": "The SMAP-SSS level 2C near real-time (NRT) V5.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. SMAP, launched on January 31, 2015, was initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles, and has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle.
The dataset includes derived SMAP SSS, SSS uncertainty using the NRT SMAP Salinity Retrieval Algorithm, top of atmosphere brightness temperature (TB), wind speed and direction data for extreme winds, and other all necessary ancillary data and the results of all intermediate steps. Data from July 28, 2022 to present, is available with a latency of about 6 hours. The observations are global, provided on a 0.25° fixed Earth grid with an approximate spatial resolution of 70 km. The major differences to the standard version 5.0 data products are: (1) the NRT version of the L1B SMAP antenna temperatures is used, (2) the latest 6-hourly 0.25° wind speed and direction are used for the ancillary wind speed and direction input, (3) the CMC SST from 2 days earlier is used for the ancillary SST input, (4) the sea-ice mask of the 3-day aggregate RSS AMSR-2 Air-Sea Essential Climate Variables (AS-ECV) data set from 2-days earlier is used for the sea-ice flag, (5) no correction for sea-ice contamination is performed, it is recommended to use only SMAP data that are classified to be within sea-ice zone 0 for open ocean scene and no sea-ice contamination.
This RSS SMAP-SSS V5.0 NRT dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature with its 1000km swath, it achieves global coverage in approximately three days, enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.",
"license": "proprietary"
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"description": "The SMAP-SSS level 2C near real-time (NRT) V6.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. SMAP, launched on January 31, 2015, was initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles, and has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle.
The dataset includes derived SMAP SSS, SSS uncertainty using the NRT SMAP Salinity Retrieval Algorithm, top of atmosphere brightness temperature (TB), wind speed and direction data for extreme winds, and other all necessary ancillary data and the results of all intermediate steps. The observations are global, provided on a 0.25° fixed Earth grid with an approximate spatial resolution of 70 km. The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. Each data file covers one 98-minute orbit (15 files per day), is available in netCDF-4 file format with about 5 hours l atency.
This RSS SMAP-SSS V6.0 NRT dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature with its 1000km swath, it achieves global coverage in approximately three days, enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.",
"license": "proprietary"
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"description": "The version 4.0 SMAP-SSS, level 2C product contains the fourth release of the validated sea surface salinity orbital/swath data from the NASA Soil Moisture Active Passive (SMAP) observatory, and is produced operationally by Remote Sensing Systems (RSS). Enhancements with this release include: use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields in a single data product. The SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data. Each data file covers one 98-minute orbit (15 files per day). Data begins on April 1,2015 and is ongoing. Observations are global in extent and provided at a 0.25 degree x 0.25 degree grid with an approximate spatial feature resolution of 40KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data.The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
"license": "proprietary"
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"description": "The RSS SMAP level 2C sea surface salinity V5.3 evaluation dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.
The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file covers one 98-minute orbit (15 files per day) and is available in netCDF-4 file format. The SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
"license": "proprietary"
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"description": "The version 5.0 SMAP-SSS, level 2C product contains the fourth release of the validated sea surface salinity orbital/swath data from the NASA Soil Moisture Active Passive (SMAP) observatory, and is produced operationally by Remote Sensing Systems (RSS). The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. The SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data. Each data file covers one 98-minute orbit (15 files per day). Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
"license": "proprietary"
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"description": "The RSS SMAP level 2C sea surface salinity V6.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.
The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. The SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data. Each data file covers one 98-minute orbit (15 files per day), is available in netCDF-4 file format with about 4 days l atency. Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
"license": "proprietary"
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"description": "The version 4.0 SMAP-SSS level 3, 8-Day running mean gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS). Enhancements with this release include: use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields in a single data product. Daily data files for this product are based on SSS averages spanning an 8-day moving time window. SMAP data begins on April 1,2015 and is ongoing. L3 products are global in extent and gridded at 0.25degree x 0.25degree with a default spatial feature resolution of approximately 70KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
"license": "proprietary"
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"description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Evaluation Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.
The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
"license": "proprietary"
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"description": "The version 5.0 SMAP-SSS level 3, 8-Day running mean gridded product is based on the fifth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS). The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. Daily data files for this product are based on SSS averages spanning an 8-day moving time window. SMAP data begins on April 1,2015 and is ongoing. L3 products are global in extent with a default spatial resolution of approximately 70KM. The datasets are gridded at 0.25degree x 0.25degree. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
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The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. The RSS SMAP 8-Day running mean product is based on SSS averages spanning an 8-day moving time window, it includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM. Each data file is available in netCDF-4 file format with about 7-day latency (after the end of the averaging period). Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
"license": "proprietary"
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"description": "The version 4.0 SMAP-SSS level 3, monthly gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS) with a one-month latency. Enhancements with this release include: use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields in a single data product. Monthly data files for this product are averages over one-month time intervals. SMAP data begins on April 1,2015 and is ongoing, with a one-month latency in processing and availability. L3 products are global in extent and gridded at 0.25degree x 0.25degree with a default spatial feature resolution of approximately 70KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
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The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
"license": "proprietary"
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"license": "proprietary"
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"description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.
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"license": "proprietary"
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"description": "This data set includes tower-based Ka-band ocean surface backscatter measurements (cross section, incidence angle, radial velocity from radar, pulse-pair correlation) located offshore of Martha\u2019s Vineyard (41\u00b019.5\u2032N, 70\u00b034\u2032W), Massachusetts (USA) over a period of three months, from October 2019 to January 2020. Data from the Ka-band radar are collected at multiple distances from the tower (up to ~32 m) at several incidence angles and at sub-second resolution. The measurements are provided as hourly files in netCDF format.
Ka-band backscatter data are often utilized to derived ocean surface vector winds. The instrument used for this dataset was a Ka-Band Ocean continuous wave Doppler Scatterometer (KaBODS) built by the University of Massachusetts, Amherst, which was installed on the Woods Hole Oceanographic Institution Air-Sea Interaction Tower (ASIT). The tower is located in 15 m deep water and extends 76 feet into the marine atmosphere. Data were collected as part of a pre-pilot campaign for the S-MODE (Submesoscale Ocean Dynamics Experiment) project. The measurements provided the opportunity to develop Ka-band backscatter models as well as study backscattering mechanisms under different wind, wave, and weather conditions in order to support operation of the airborne Ka-band Doppler scatterometer used during the main S-MODE intensive observation periods. ",
"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2162104652-POCLOUD.html",
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"description": "These wave slope data from polarimetry described below are considered preliminary and should not be used for any purpose without consulting Chris Zappa (zappa@ldeo.columbia.edu).
This data set includes tower-based measurements of ocean wave slope fields from visible-band polarimetry using a Polaris Pyxis Mono VIS polarimetric camera. The data here include wave slope fields at 30 frames per second temporal resolution and mm-scale spatial resolution over a ~2 m x 2 m area of ocean surface observed off the Air-Sea Interaction Tower (ASIT; 41\u00b020.1950'N, 70\u00b033.3865'W). Measurements were taken over the period from October 2019 through January 2020. Surface slopes are along two dimensions: along-look and cross-look orientations of the camera. Data was acquired for 10 minutes per hour, 8 hours per day, and each data file (netCDF-4) captures one of the 10-minute segments. Note that data files are large, 142 GB each.
Data were collected as part of a pre-pilot campaign for the S-MODE (Submesoscale Ocean Dynamics Experiment) project. The polarimetric slope sensing (PSS) technique of Zappa et al. [2008] allows one to reconstruct the water surface slope field by measuring the polarization state of reflected light at each image pixel, allowing for surface resolutions of order 1 mm with no in-water measurement component. From these data one is able to compute water surface slope variance, wave directional spreading, and the near-surface current profile. ",
"license": "proprietary"
},
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"description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 1 data includes geolocated physical measurements for a measurement footprint, which are the basis for the DopplerScatt L2 surface winds and currents estimates. Data are available in netCDF format and are ordered by measurement acquisition time and radar range, and are not on a geospatial grid. ",
"license": "proprietary"
},
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"description": "This dataset contains airborne DoppVis imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a Nikon D850 camera with a 14mm lens mounted with a 90 degree rotation and a 30 degree positive pitch angle during flight. The camera was synchronized to a coupled GPS/IMU system with images taken at 2hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at 50cm resolution. Level 1 DoppVis images are available as GZIP flightlines containing individual TIFF images.",
"license": "proprietary"
},
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"description": "This dataset contains airborne DoppVis imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a Nikon D850 camera with a 14mm lens mounted with a 90 degree rotation and a 30 degree positive pitch angle during flight. The camera was synchronized to a coupled GPS/IMU system with images taken at 2hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at 50cm resolution. Level 1 DoppVis images are available in TIFF format.",
"license": "proprietary"
},
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"description": "This dataset contains airborne hyperspectral imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a hyperspectral camera operating in the visible to near-IR range (400-990 nm). Hyperspectral data are used by S-MODE to provide visible imagery of the kinematics of whitecaps and ocean color measurements. Level 1 data are available as zip files containing data in ENVI format and text files containing location and timing information.",
"license": "proprietary"
},
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"description": "This dataset contains geolocated airborne LiDAR point cloud measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a high resolution LiDAR, used to characterize the properties of ocean surface topography. The sensor has a maximum pulse repetition rate of 400 kHz, with a +/- 30\u00b0 cross-heading raster scan rate of 200 Hz. Level 1 LiDAR point clouds are available in .laz format.",
"license": "proprietary"
},
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"description": "NOTICE: This dataset is currently undergoing maintenance to be repackaged as zip files of flight lines. The file count will decrease dramatically when new zip files are available.
This dataset contains airborne longwave infrared (LWIR) imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a FLIR SC6700 camera with 13mm lens was mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the flight track. The camera was synchronized to a coupled GPS/IMU system with images collected at 50hz. Raw images were calibrated for lens distortion, vignetting, and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to the WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent resolution. Level 1 images are available in TIFF format.",
"license": "proprietary"
},
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"description": "This dataset contains airborne visible imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes an IO Industries Flare 12M125-CL camera with 14mm lens mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the aircraft heading. The camera was synchronized to a coupled GPS/IMU system with images taken at 5hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the post-processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent spatial resolution. Level 1 images are available in TIFF format.",
"license": "proprietary"
},
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"license": "proprietary"
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"description": "This dataset contains a suite of Saildrone in-situ measurements (including but not limited to temperature, salinity, currents, biochemistry, and meteorology) taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign spanning two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples are available in their raw 1 Hz sampling frequency as well as 5 minute averages, the latter available with navigation data. Other measurements are available as raw files (1Hz or 20 Hz where applicable), as well as 1 minute averages. L1 data are available as a zip file. ",
"license": "proprietary"
},
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"description": "This dataset contains APEX float in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. Data was collected approximately 300 km offshore of San Francisco, during Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. US Naval Oceanographic Office (NAVO) APEX floats measure subsurface properties including temperature and salinity. Data are available in netCDF format.",
"license": "proprietary"
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"description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 2 data includes estimates of surface winds and currents. The V1 data have been cross-calibrated against SIO-DopVis leading to the 'dopvis_2021' current geophysical model function. It is expected that additional DopVis data will lead to a reprocessing of this data set and it should be regarded as provisional, to be refined after future S-MODE deployments. Data are available in netCDF format.",
"license": "proprietary"
},
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"description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 2 data includes estimates of surface winds and currents. The V2 data have been cross-calibrated against ADCPs, surface drifters, and the SIO-DopVis instrument collected during the Pilot and IOP1 campaigns. Additional DopVis data collected during IOP1 and IOP2, in addition to IOP2 ADCP and surface drifter data will lead to a reprocessing of this dataset, and it should be regarded as provisional. Data are available in netCDF format.",
"license": "proprietary"
},
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"description": "This dataset contains in-situ position data from surface drifters from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Drifting buoys were deployed from the research vessels and configured to nominally report positions every five minutes. Drifter position as a function of time is used by S-MODE to measure the average horizontal velocity of currents in the upper 60 cm of the ocean. Tracking and telemetry of the drifters is done by Pacific Gyre, Inc. The data are available in netCDF format with a dimension of time. ",
"license": "proprietary"
},
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"description": "This dataset contains in-situ measurements of temperature, salinity, and velocity from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco, during an intensive observation period in the fall of 2022. The data are available in netCDF format with a dimension of time. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The target in-situ quantities were measured by Lagrangian floats, which were deployed from research vessels and retrieved 3-5 days later. The floats follow the 3D motion of water parcels at depths within or just below the mixed layer and carried a CTD instrument to measure temperature, salinity, and pressure, in addition to an ADCP instrument to measure velocity. ",
"license": "proprietary"
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"description": "This dataset contains airborne sea surface temperature (SST) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Multiscale Observing System of the Ocean Surface (MOSES) is an aerial observing system that primarily uses a longwave infrared (LWIR) camera to record SST at a resolution of several meters. Individual images are mosaiced together to provide a synoptic map of the sample domain covering approximately 200 km. MOSES is mounted on the B200 aircraft which flies daily surveys of the field domain during deployments. Data are available in netCDF format.",
"license": "proprietary"
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"description": "This dataset contains estimated chlorophyll-a and particulate organic carbon concentration data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which produced high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and is used to provide accurate atmospheric correction of the ocean color measurements. Level 2 chlorophyll-a data are available in netCDF format. ",
"license": "proprietary"
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"description": "This dataset contains Saildrone in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples originally measured at 1 Hz frequency are averaged into 5 minute bins, along with navigation data. Non-ADCP data from IOP1 contain additional bio-optical measurements. All data are available in netCDF format. ",
"license": "proprietary"
},
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"description": "This dataset contains profiles of temperature, dissolved oxygen, salinity, and other observations collected by Seagliders during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Seagliders are autonomous underwater vehicles (AUVs) designed to glide from the ocean surface to as deep as 1000 m and back while collecting profiles of oceanic variables. Data are available in netCDF format.",
"license": "proprietary"
},
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"description": "This dataset contains shipboard Acoustic Doppler Current Profiler (ADCP) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign and two intensive operating periods (IOPs) conducted approximately 300 km offshore of San Francisco during Fall 2021, 2022, and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The ADCP was mounted to the bottom of the hulls of the research vessels deployed during each campaign, measuring horizontal and vertical currents, as well as acoustic backscatter from approximately 3 m to 50 m depth along the ship\u2019s track. The data are available in netCDF format with dimensions of time and depth. ",
"license": "proprietary"
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"description": "This dataset contains shipboard bio-optical measurements collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data are available in netCDF format. ",
"license": "proprietary"
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"description": "This dataset contains in-situ seawater samples taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Water samples collected in Niskin bottles mounted on the ship\u2019s rosette sampler were taken of chlorophyll (\u00b5g/L), phaeopigments (\u00b5g/L), and nutrient concentrations (\u00b5M or \u00b5mol/L) of particulate organic carbon, particulate organic nitrogen, silicate, nitrate, nitrite, and phosphate. Samples analyzed with fluorometry contain chlorophyll concentrations in \u00b5g/L and phaeopigment concentrations in \u00b5g/L. Samples analyzed with elemental analysis contain POC molarity in \u00b5M and PON molarity in \u00b5M. Samples analyzed via ion analysis contain silicate concentrations in \u00b5M, total nitrate+nitrite in \u00b5M, phosphate in \u00b5M, nitrite in \u00b5M, and nitrate in \u00b5M. These data are mainly used by S-MODE for validating the PRISM-derived products and calibrating the in-situ sensors on the autonomous platforms. Data are available in netCDF format. ",
"license": "proprietary"
},
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"description": "This dataset contains shipboard conductivity, temperature, and depth (CTD) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The shipboard CTD rosette is cast from the R/V Oceanus where it records ocean temperature, conductivity, and pressure as it descends to depth and then returns to the surface. IOP1 and IOP2 measurements also contain biological data. Data are available in netCDF format.",
"license": "proprietary"
},
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"description": "This dataset contains shipboard radiometer measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Air-Sea Interaction METeorology (ASIMET) sensors mounted onboard the R/V Oceanus record shortwave and longwave radiation fluxes. These are used by S-MODE to compare with DopplerScatt retrievals. Data are available in netCDF format. ",
"license": "proprietary"
},
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"description": "This dataset contains atmospheric sounding measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Sounding profiles were collected using shipboard Windsond S1H3-S radiosondes launched from the R/V Oceanus cruise OC2108A, to a maximum elevation of at least 5 km above ground level (ABL). These measurements are used to understand the vertical structure of atmospheric temperature, winds, and moisture. The original 1Hz observations were gridded onto a uniform 20 m vertical grid. The data are available in netCDF format with dimensions of altitude and profile number. ",
"license": "proprietary"
},
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"description": "This dataset contains Submersible Ultraviolet Nitrate Analyzer (SUNA) nitrate measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. SUNA is a standalone optical nitrate sensor that mounts onto the shipboard CTD rosette cast from the R/V Oceanus. The SUNA measurements are calibrated against bottle nutrient samples taken from the underway flow-through system on the ship and later analyzed with a Lachat Nutrient Analyzer. From the Lachat data, the average concentration of nitrate+nitrite are used for each sample. Data are available in netCDF format.",
"license": "proprietary"
},
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"description": "This dataset contains shipboard thermosalinograph (TSG), meteorology, and bio-optics measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The TSG instrument measures the temperature and conductivity of seawater passing through a port in the hull of the ship. TSG data is calibrated using water samples compared to standard seawater and a laboratory salinometer onboard the ship. This dataset also contains chlorophyll and meteorology measurements including air temperature, barometric pressure, wind speed and direction, relative humidity, and radiative fluxes. Data are available in netCDF format, with separate dimensions for time, time of bio-optics measurements, and time of radiometer measurements.",
"license": "proprietary"
},
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"description": "This dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Underway CTD system contains a standard UCDT probe measuring conductivity, temperature, and pressure, as well as an augmented EcoCDT probe that concurrently measures both hydrographic and bio-optical data including conductivity, temperature, pressure, dissolved oxygen concentration, chlorophyll-fluorescence, and particulate backscatter at two different wavelengths. The level 2 data herein combines measurements from both the UCDT and EcoCDT into a single dataset, where for each variable, all profiles are binned onto a 5m vertical grid and merged into a 2-D matrix. Additional computed variables include backscatter baseline signal and backscatter spike signal. Data are available in netCDF format.",
"license": "proprietary"
},
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"description": "This dataset contains Slocum glider in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. US Naval Oceanographic Office (NAVOCEANO) Slocum gliders measure subsurface properties including temperature and salinity by profiling to a depth of 1000m at a fixed location every 4 hours. Data are available in netCDF format.",
"license": "proprietary"
},
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"description": "This dataset contains waveglider observations from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Three wave gliders were deployed as part of the S-MODE pilot campaign, equipped with a suite of sensors including sonic anemometers, shortwave and longwave radiometers, CTD profilers, and ADCPs. All wave gliders include an IMU that records platform orientation and motion at 20Hz. Data are available in netCDF format.",
"license": "proprietary"
},
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"description": "This dataset contains orthocorrected and atmospherically corrected water-leaving reflectance data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which produced high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and is used to provide accurate atmospheric correction of the ocean color measurements. Level 2a reflectance data is available in ENVI format. ",
"license": "proprietary"
},
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"description": "This dataset contains profiles of temperature, dissolved oxygen, salinity, and other observations collected by Seagliders during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Seagliders are autonomous underwater vehicles (AUVs) designed to glide from the ocean surface to as deep as 1000 m and back while collecting profiles of oceanic variables. Data are available in netCDF format.",
"license": "proprietary"
},
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"license": "proprietary"
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"description": "This dataset contains model output from the Navy Coastal Ocean Model (NCOM) run during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. NCOM model output consists of daily files during the deployment dates of the pilot campaign in Fall 2021, IOP1 in Fall 2022, and IOP2 in Spring 2023. Data consists of ocean variables such as salinity, sea water temperature, water depth, and surface wind stress, and are available in netCDF format. ",
"license": "proprietary"
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@@ -166481,7 +166507,7 @@
"id": "SPL1BTB_NRT_105",
"title": "Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105",
"catalog": "NSIDC_ECS STAC Catalog",
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"catalog": "NSIDC_ECS STAC Catalog",
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"description": "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4\u00b0S to 86.4\u00b0N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0).",
"license": "proprietary"
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"description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. Acoustic Doppler Current Profilers (ADCP) provide water column current velocity profile observations. Shipborne ADCP data were collected during the 3 US cruises, using the Knorr and Endeavor 300 kHz Workhorse, 75 khz broadband and 75 khz narrowband instruments, and during the Sarmiento cruise using a 76.8 khz broadband ADCP. Corresponding ruise dates were as follows: Knorr: 6 Sept-9 Oct 2012; Sarmiento: 14 Mar-10 Apr 2013, Endeavor: 15 Mar-15 Apr 2013 and 19 Sep-13 Oct 2013. Additionally, lowered ADCP (L-ADCP) measurements were made during the Knorr cruise on every CTD cast and during the Sarmiento cruise. The ADCP data files here (1 per cruise) are for the shipborne ADCP measurements only.",
"license": "proprietary"
},
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"license": "proprietary"
},
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"description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. Approximately 83 drifters were deployed during the SPURS-1 campaign. A drifter is a passive Lagrangian sensor platform consisting of a surface buoy and tethered subsurface drogue. Drifter buoys contain GPS/ARGOS and satellite data transmitters, with sensors measuring temperature and other properties. For SPURS-1, these were standard Surface Velocity Program (SVP) drifters with salinity sensors added (SVP/S). Data for both US and European drifter deployments during SPURS-1 are available here. For each series, drifter data have been aggregrated within single netCDF data filea with their corresponding drifter-IDs and associaciated near-surface salinity, temperaure georeferenced (GPS and ARGOS) trajectory series data.",
"license": "proprietary"
},
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"license": "proprietary"
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"description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The Seasoar is a towed vehicle equipped with impeller-forced wings that can be rotated on command to allow the vehicle to undulate in the upper ocean. Generally, Seasoar operates between the surface and about 400 meters depth while being towed on faired cable at about eight knots. A typical dive cycle takes about 12 minutes to complete, providing an up and down profile every 3 km. For SPURS-1, a Seasoar was deployed exclusively during the Sarmiento cruise over the period 22 Mar-8 Apr, 2013 and to a maximum depth of 312m. The Seasoar towed sensor system was equipped with dual pumped temperature/conductivity sensors. The Seasoar data in netCDF form here contains a highly processed 1-meter gridded version of the original source dataset, which is comprised of temperature, conductivity, salinity, pressure observations from 1144 casts during 2013 Spring SPURS Cruise.",
"license": "proprietary"
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"description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The Tenuse (Slocum) glider is an autonomous undulating profiler measuring salinity and temperature. It was deployed from the Thalassa on 21-August and recovered by the Knorr on 4-October-2012. It made a total of about 1400 profiles during that period (1-2 profiles/hour), going from the surface to 200 m. Resulting trajectory profile data from the Tenuse glider include georeferenced CTD observations on salinity, temperature, pressure, and depth.",
"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
},
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"description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. A Waveglider is an autonomous platform propelled by the conversion of ocean wave energy into forward thrust and employing solar panels to power instrumentation. During SPURS-1, three wavegliders (ASL2, ASL3 and ASL4) were deployed from the Knorr in September 2012, redeployed in April 2013 (ASL22, ASL32 and ASL42) with final recovery in September. Waveglider trajectories followed a square loop or butterfly pattern around the central SPURS mooring. Sensors included a CTD at the near-surface and another at 6 m depth, a surface current meter, air temperature, atmospheric pressure and wind speed sensors providing continuous along-track observations. NetCDF waveglider data files here contain hour averaged, georeferenced trajectory data for those parameters and depths.",
"license": "proprietary"
},
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"description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D,SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Shipborne ADCP observations were made during both SPURS-2 R/V Revelle cruises. Acoustic Doppler Current Profilers (ADCP) provide water column current velocity profile observations. The resulting data files available here are for narrowband 75 and 150khz ADCP measurements made during the first cruise, plus narrowband (NB) 75khz and both 75khz and 150khz broadband (BB) ADCP measurements obtained during the second R/V Revelle cruise.",
"license": "proprietary"
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"license": "proprietary"
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"description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The Controlled Flux Technique (CFT) is a system for measuring the net heat transfer velocity and turbulent kinetic energy (TKE) dissipation at the ocean surface, and is a useful tool for studying the turbulence generated at the ocean surface by the impact of raindrops. CFT was employed during both SPURS-2 Revelle cruises. It involves a laser heating a small patch of water on the ocean surface, and an infrared imaging camera then tracking the resulting thermal decay. This decay is known to be proportional to the dissipation of TKE at the water surface, which in turn can be used to scale the transfer velocity for the net heat flux. SPURS2 CFT data take the form of a series of .raw video files each with corresponding .met text header files containing the associated file metadata. The CFT data was recorded at 15 frames per second (fps) during the first Revelle cruise in 2016, and at 25 fps during the second in 2017. Matlab CFT reader software are provided by UW/APL and distributed here with the CFT data files.",
"license": "proprietary"
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"license": "proprietary"
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"description": "The SPURS-2 raindrop ODM-470 disdrometer dataset was collected from the ship during both the 2016 and 2017 cruises. Please see file global attributes and Klepp et al. (2015, 2018) for information on the disdrometer: http://dx.doi.org/10.1016/j.atmosres.2014.12.014, https://doi.org/10.1038/sdata.2018.122 . As explained in the references and global attributes, small drops that cause voltage drops < 0.12 V (i.e. drops with diameters < 0.44 mm) cannot be distinguished from noise by this instrument, and are thus missed. This undercounting of small drops cannot be corrected, and prevents accurate estimation of DSD parameters such as Nw, D0, Dm with any confidence or precision since the minimum detectable drop size is close to the median drop size of tropical oceanic rain (Thompson et al. 2015, https://doi.org/10.1175/JAS-D-14-0206.1). Nonetheless, this dataset provides estimates of drop counts as a function of drop size for the remaining rain drops > 0.44 mm in diameter, and their associated rain rates and liquid water contents. The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. ",
"license": "proprietary"
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"license": "proprietary"
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"description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Neutrally buoyant floats (also known as Mixed Layer Floats - MLF) drift and move through the water column providing continuous CTD temperature and salinity profiles and GPS surface position location data. One float was deployed in SPURS-2 during the first Revelle cruise in August 2016 and recovered in December 2016 after 3.5 months about 1800 km east of the central mooring. The MLF data are provided in netCDF file format with standards compliant metadata.",
"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
},
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"description": "Cycle average and aggregation of lake pass data within predefined hydrological basins. Basin for each cycle. Available in Shapefile file format.",
"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2758162595-POCLOUD.html",
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"license": "proprietary"
},
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Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a PLD-oriented feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
Please note that this collection contains SWOT Version C science data products.
This dataset is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_obs_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_prior_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_unassigned_2.0
",
"license": "proprietary"
},
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Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains observation-oriented feature datasets of lakes identified in the prior lake database (PLD).",
"license": "proprietary"
},
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Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains feature datasets of lakes identified in the PLD.",
"license": "proprietary"
},
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Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains feature datasets of unassigned water features that were not identified in the PLD or PRD.",
"license": "proprietary"
},
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2762949418-POCLOUD.html",
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"license": "proprietary"
},
@@ -169437,7 +169463,7 @@
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"license": "proprietary"
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"license": "proprietary"
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Please note that this collection contains SWOT Version C science data products.",
"license": "proprietary"
},
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"license": "proprietary"
},
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"description": "The SWOT Level 2 Water Mask Raster Image 100m Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.\\r\\n
Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at 100 meter horizontal resolution in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats.
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_2.0 ",
"license": "proprietary"
},
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"description": "The SWOT Level 2 Water Mask Raster Image Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
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Please note that this collection contains SWOT Version C science data products.
This dataset is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_2.0
",
"license": "proprietary"
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"description": "The SWOT Level 2 Water Mask Raster Image 250m Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.\\r\\n
Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at 250 meter horizontal resolution in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats.
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_2.0 ",
"license": "proprietary"
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Please note that this collection contains SWOT Version C science data products.",
"license": "proprietary"
},
@@ -169541,7 +169567,7 @@
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"license": "proprietary"
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Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
Please note that this collection contains SWOT Version C science data products.
This dataset is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_node_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_reach_2.0
",
"license": "proprietary"
},
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Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_2.0 It contains only river nodes. ",
"license": "proprietary"
},
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"description": "The SWOT Level 2 River Single-Pass Vector Reach Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_2.0 It contains only river reaches. ",
"license": "proprietary"
},
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"license": "proprietary"
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"license": "proprietary"
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"description": "The SWOT Level 2 KaRIn Low Rate Sea Surface Height Data Product from the Surface Water Ocean Topography (SWOT) mission provides global sea surface height and significant wave height observations derived from low rate (LR) measurements from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.
The L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
Please note that this collection contains SWOT Version C science data products.
This dataset is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_Basic_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_WindWave_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_Expert_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_Unsmoothed_2.0
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"license": "proprietary"
},
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The L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_2.0 It provides the \"Basic\" file from each L2 SSH product, which contains a limited set of variables and is aimed at the general user.",
"license": "proprietary"
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The L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_2.0 It provides the \"Expert\" file from each L2 SSH product, which contain all related variables and is intended for expert users.",
"license": "proprietary"
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The L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_2.0 It provides the \"Unsmoothed\" file from each L2 SSH product, which includes all related variables on the finer resolution \"native\" grid with minimal smoothing applied.",
"license": "proprietary"
},
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The L2 sea surface height data product is distributed in one netCDF-4 file per pass (half-orbit) covering the full KaRIn swath width, which spans 10-60km on each side of the nadir track. Sea surface height, sea surface height anomaly, wind speed, significant waveheight, and related parameters are provided on a geographically fixed, swath-aligned 2x2 km2 grid (Basic, Expert, Windwave). The sea surface height data are also provided on a finer 250x250 m2 \"native\" grid with minimal smoothing applied (Unsmoothed).
Please note that this collection contains SWOT Version C science data products.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_LR_SSH_2.0 It provides the \"Windwave\" file from each L2 SSH product, which includes significant wave height (SWH), normalized radar cross section (NRCS or backscatter cross section or sigma0), wind speed derived from sigma0 and SWH, model information on wind and waves, and quality flags.",
"license": "proprietary"
},
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"description": "The SWOT Level 2 Nadir Altimeter Geophysical Data Record (GDR) with Waveforms dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally. The GDR dataset consists of discrete measurements for each half orbit along the ground track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using restituted auxiliary data and the Precise Orbit Ephemeris (POE). The data are available with latency of < 90 days and distributed in netCDF-4 file format.
This collection is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_SSHA_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_GDR_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_SGDR_2.0
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This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_2.0 ",
"license": "proprietary"
},
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"description": "The SWOT Level 2 Nadir Altimeter Geophysical Data Record (GDR) with Waveforms dataset produced by the Surface Water and Ocean Topography (SWOT) mission provides sea surface height, significant wave height and wind speed measurements from the Poseidon-3C nadir altimeter, a Jason-class dual frequency (Ku/C) altimeter. SWOT is a joint mission between NASA and CNES that launched on December 16, 2022. It aims to measure ocean surface topography with unprecedented resolution and accuracy, as well as map inland water bodies globally. The GDR dataset consists of discrete measurements for each half orbit along the ground track with sampling resolutions of approximately 6-km and 300-m at 1Hz and 20Hz, respectively. The data were processed using restituted auxiliary data and the Precise Orbit Ephemeris (POE). The data are available with latency of < 90 days and distributed in netCDF-4 file format.
This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_2.0 ",
"license": "proprietary"
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This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_GDR_2.0 ",
"license": "proprietary"
},
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"license": "proprietary"
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2799438335-POCLOUD.html",
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This collection is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_SSHA_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_GDR_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_IGDR_SGDR_2.0
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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This collection is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_OGDR_SSHA_2.0
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_NALT_OGDR_GDR_2.0
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
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"license": "proprietary"
},
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"description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data stream from the Poseidon 3C nadir altimeter. The simulated data were generated by the \"ECCO LLC4320\" global ocean simulation. ECCO, which means \"Estimating the Circulation and Climate of the Ocean\", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.",
"license": "proprietary"
},
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"description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data from the Poseidon 3C nadir altimeter. The simulated data are from the Global Ocean Reanalysis and Simulations (GLORYS). SSH data from GLORYS were rendered from their native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.",
"license": "proprietary"
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"description": "This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data from the Poseidon 3C nadir altimeter. The simulated data are from the Global Ocean Reanalysis and Simulations (GLORYS). SSH data from GLORYS were rendered from their native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.",
"license": "proprietary"
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"description": "This dataset contains a simulated lake product to be provided by the Surface Water and Ocean Topography (SWOT) mission with a focus on the North America continent. The product is derived from the high-rate (HR) measurements produced by the SWOT main instrument, a Ka-band Radar Interferometer. These data are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product consists of three shapefiles: 1) an observation-oriented shapefile of lakes identified in the Prior Lake Database (PLD); 2) a PLD-oriented shapefile of lakes identified in the PLD; 3) a shapefile of unassigned features that have not been identified as a lake in the PLD nor as a river in the Prior River Database (PRD). Lake attributes include water surface elevation, area, and uncertainty estimates. The identified lake shapes inherit the SWOT swath width that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.",
"license": "proprietary"
},
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"description": "This dataset provides a simulated water surface elevation product that resembles the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. This product is complementary to the L2_HR_PIXC_V1 product. It provides a less noisy, height-constrained geolocation (latitude, longitude, and height) of the L2_HR_PIXC_V1 pixels. In addition, this product provides an identifier associated with each pixel. The identifier contains the information of the river and/or lake features pulled from the Prior River Database (PRD) or in the Prior Lake Database (PLD). Note that this is a simulated SWOT product and not suited for any scientific exploration.",
"license": "proprietary"
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"description": "This dataset includes simulated water surface elevations that resemble the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. The primary product contains the following: 1. Geolocated elevations (latitude, longitude, and height) 2. Classification mask (water/land flags, and water fraction) 3. Surface areas (projected pixel area on the ground) 4. Relevant data needed to compute and aggregate height and area uncertainties. Additional information includes: 1. Meta data (global instrument parameters) 2. Time varying parameters (TVP), which include sensor position, velocity, altitude, and time 3. Noise power estimates 4. Quality flags 5. Interferogram measurements (power and phase) and range and azimuth indices 6. Geophysical and crossover-calibration correction values. These additional fields are provided to improve the utility of the product and to facilitate generation of downstream products. Note that this is a simulated SWOT product and not suited for any scientific exploration.",
"license": "proprietary"
},
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"description": "This dataset contains a simulated rasterized water surface elevation and inundation-extent product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. This is a derived product through resampling the upstream dataset L2_HR_PIXC_V1 and L2_HR_PIXCVEC_V1 onto a uniform grid over the North America continent. A uniform grid is superimposed onto the pixel cloud from the source products, and all pixel-cloud samples within each grid cell are aggregated to produce a single value per raster cell. The raster data are produced geographically fixed tiles at resolutions of 100 m and 250 m in a Universal Transverse Mercator projection grid. Note that this is a simulated SWOT product and not suited for any scientific exploration. ",
"license": "proprietary"
},
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"description": "This dataset contains a simulated river data product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this dataset is a subset for the North America continent. This product is derived from the measurements produced by the main SWOT instrument, the Ka-band Interferometer. They are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product contains two shapefiles: 1) river reaches (approximately 10 km long) identified in the prior river database (PRD); and 2) river nodes (approximately 200 m spacing) identified in prior river database (PRD). Each river reach is divided into a number of nodes. Attributes include water surface elevation, slope, width, and uncertainty estimates. As they are derived from SWOT KaRIn measurements, each granule covers an area that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.",
"license": "proprietary"
},
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"license": "proprietary"
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The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. A subset of these individual mascons span coastlines, and contain mixed land and ocean mass change signals. In a post-processing step, the CRI filter is applied to those mixed land/ocean Mascons to separate land and ocean mass. The land mask used to perform this separation is provided in the same directory as this dataset, as are uncertainty values, and the gridded mascon-ID number to enable further analysis. Since the individual mascons act as an inherent smoother on the gravity field, a set of optional gain factors (for continental hydrology applications) that can be applied to the solution to study mass change signals at sub-mascon resolution is also provided within the same data directory as the Mascon data. For use-case examples and further background on the gain factors, please see Wiese, Landerer & Watkins, 2016, https://doi.org/10.1002/2016WR019344.
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"license": "proprietary"
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"description": "This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.1Mv03). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. The water storage/height anomalies are given in equivalent water thickness units (cm). The solution provided here is derived from solving for monthly gravity field variations in terms of geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the solution inversion to intrinsically remove correlated error. Thus, these Mascon grids do not need to be destriped or smoothed, like traditional spherical harmonic gravity solutions. The complete Mascon solution consists of 4,551 relatively independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. It should be noted that this dataset does not correct for leakage errors across coastlines; it is therefore recommended only for users who want to apply their own algorithm to separate between land and ocean mass very near coastlines. This RL06.1Mv03 is an updated version of the previous Tellus JPL Mascon RL06Mv02 (DOI, 10.5067/TEMSC-3JC62). RL06.1Mv03 differs from RL06Mv02 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; RL06.1Mv03 uses the ACH data product. For more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/. For a detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. This product is intended for expert use only; other users are encouraged to use the CRI-filtered Mascon dataset, which is available here: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.1_V3 ",
"license": "proprietary"
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The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. Please note that this dataset does not correct for leakage errors across coastlines; it is therefore recommended only for users who want to apply their own algorithm to separate between land and ocean mass very near coastlines.
This RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03. For more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/. For a detailed description on the Mascon processing, including the mathematical derivation, implementation of geophysical constraints, and validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. This product is intended for expert use only; other users are encouraged to use the CRI-filtered Mascon dataset, which is available here: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4.",
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"license": "proprietary"
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"license": "proprietary"
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"description": "This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.
GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.",
"license": "proprietary"
},
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"description": "This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.
GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.",
"license": "proprietary"
},
+ {
+ "id": "TEMPEST_STPH8_L1_TSDR_V10.0_10.0",
+ "title": "TEMPEST STP-H8 Antenna and Microwave Brightness Temperatures Version 10.0",
+ "catalog": "POCLOUD STAC Catalog",
+ "state_date": "2022-01-08",
+ "end_date": "2024-12-31",
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+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3237795822-POCLOUD.html",
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+ "description": "!!!Temporary notice posted Sept. 27th, 2024!!! These data are in the process of being ingested and not all files are available yet. The data were made public early to allow assessment by early science users. Accordingly, not all data set resources may be available yet. Please check over the next 2-3 weeks for finalization of this data set and PO.DAAC's release announcement.
This dataset includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 87, 164, 174, 178 and 181 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the TEMPEST (Temporal Experiment for Storms and Tropical Systems) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. TEMPEST swath width is 1400 kilometers and resolution at nadir is 25 km for the 87 GHz channel and 13 km for the 180 GHz channels. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release.
The TEMPEST instrument is a microwave radiometer deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission, with the primary objective of tropical cyclone intensity tracking. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. A successful mission will demonstrate a lower-cost, lighter-weight sensor architecture for providing microwave data. TEMPEST was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.",
+ "license": "proprietary"
+ },
{
"id": "TEMPO_CLDO4_L2_V03",
"title": "TEMPO cloud pressure and fraction (O2-O2 dimer) V03 (BETA)",
@@ -175066,7 +175105,7 @@
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"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617197672-POCLOUD.html",
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"license": "proprietary"
},
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"description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Continuous Fields (VCF) Version 1 data product (VCF5KYR) provides global fractional vegetation cover at 0.05 degree (5,600 meter) spatial resolution at yearly intervals from 1982 to 2016. The VCF5KYR data product is derived from a bagged linear model algorithm using Long Term Data Record Version 4 (LTDR V4) data compiled from Advanced Very High Resolution Radiometer (AVHRR) observations. Fractional vegetation cover (FVC) is the ratio of the area of the vertical projection of green vegetation above ground to the total area, capturing the horizontal distribution and density of vegetation on the Earth\u2019s surface. FVC is a primary means for measuring global forest cover change and is a key parameter for a variety of environmental and climate-related applications, including carbon land surface models and biomass measurements. The three bands included in each VCF5KYR Version 1 GeoTIFF are: percent of tree cover, non-tree vegetation, and bare ground. A water mask was applied with all pure water pixels (defined as \u2265 95% water coverage) set to zero. Data from years 1994 and 2000 were excluded due to lack of data in the LTDR V4.",
"license": "proprietary"
},
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"description": "The VIIRS_N20-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-1 satellite, renamed as NOAA-20 (N20). N20 was launched on November 18, 2017, the 2nd satellite in the US NOAA JPSS series.
VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 20 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.
The product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N21 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N21-NAVO-L2P-v3.0) datasets. It also has similar coverage and quality as the NOAA ACSPO VIIRS L2P SST (https://podaac.jpl.nasa.gov/dataset/N20-VIIRS-L2P-ACSPO-v2.80). ",
"license": "proprietary"
},
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"description": "NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available at https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L3U-v2.80, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask. ",
"license": "proprietary"
},
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"description": "NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L2P-v2.80. The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://www.doi.org/10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.",
"license": "proprietary"
},
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"description": "The VIIRS_N21-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on November 10, 2022, the 3rd satellite in the US NOAA JPSS series.
VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 21 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.
The product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N20 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-NAVO-L2P-v3.0). It also has similar coverage and quality as the NOAA ACSPO VIIRS L2P SST (https://podaac.jpl.nasa.gov/dataset/N21-VIIRS-L2P-ACSPO-v2.80). ",
"license": "proprietary"
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"description": "These files contain NASA produced skin sea surface temperature (SST) products from the Infrared (IR) channels of the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. VIIRS is a multi-disciplinary instrument that is also being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, of which NOAA-20 is the first. JPSS is a multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). Suomi-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data. VIIRS has 22 spectral bands ranging from 412 nm to 12 micron . There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375 m), and one day-night band (DNB). VIIRS uses on-board pixel aggregation to reduce the growth in size of pixels away from nadir. Two SST products are contained in these files. The first is a skin SST produced separately for day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST products from heritage and current NASA sensors. At night, a second triple channel SST product is generated using the 3.7 , 11 and 12 micron IR channels, identified as SST_triple. Due to the sun glint in the 3.7 micron SST_triple can only be used at night. VIIRS L2P SST data have a 750 spatial resolution at nadir and are stored in ~288 five minute granules per day. Full global coverage is obtained each day. The production of VIIRS NASA L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS were responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of VIIRS ocean products. JPL acquires VIIRS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. In mid-August, 2018, the RSMAS involvement in the VIIRS SST project ceased, and the subsequent fields are not maintained.The R2016.2 supersedes the previous v2016.0 datasets which can be found at https://doi.org/10.5067/GHVRS-2PN16",
"license": "proprietary"
},
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"description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite launched on 28 October 2011. The VIIRS instrument is a a 22-band, multi-spectral scanning radiometer with a 3040-km swath width that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 740 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. However, the processing of this dataset aggregates two pixels into one so the resolution is 1500 meters at nadir. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
"license": "proprietary"
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"description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011. VIIRS is a whiskbroom scanning radiometer which takes measurements in the cross-track direction within a field of regard of 112.56 degrees using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3060 km, providing full daily coverage both on the day and night side of the Earth. The VIIRS instrument is a 22-band, multi-spectral scanning radiometer that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 750 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. This L2P SST v3.0 is upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades. It contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
"license": "proprietary"
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"description": "The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available at https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-STAR-L3U-v2.80, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask.",
"license": "proprietary"
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"license": "proprietary"
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"description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the North Atlantic Region (NAR) based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
"license": "proprietary"
},
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"description": "Contains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath. Each record contains data binned in 100 km cells. No wind vectors are computed for the cells along the left and right edges of the swath. Wind direction ambiguities are resolved using a global weather prediction model. This complete dataset is the result of the reprocessing efforts on behalf of Frank Wentz, Robert Atlas, and Michael Freilich.",
"license": "proprietary"
},
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"description": "Contains three parameters: ocean near-surface wind speed, columnar water vapor, and columnar liquid water. Product is produced by Frank Wentz at Remote Sensing Systems using data obtained from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR). Observations within 100 km of land are excluded; ice flags are also utilized. Data is obtained from all 10 individual SMMR channels, which closely correspond to the SMM/I channels and utilizing the same processing algorithms that were used to produce similar products derived from SSM/I observations (see PO.DAAC products 33 and 34).",
"license": "proprietary"
},
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"description": "Contains Seasat-A Scatterometer (SASS) Sigma-0 measurements for the entire Seasat mission, from July 1978 until October 1978, produced by Frank Wentz at Remote Sensing Systems. The data are presented chronologically by swath and consist of the forward and aft values, binned in 50 km cells. For each cell there are 17 parameters including time, location, incidence angle, sigma-0, instrument corrections, and data quality.",
"license": "proprietary"
},
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"description": "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). In addition to wind speed and direction, the instrument can also measure sea surface temperature, soil moisture, ice and snow characteristics, water vapor, cloud liquid water, and rain rate. Unlike previous radiometers, the WindSat sensor takes observations during both the forward and aft looking scans. This makes the WindSat geometry of the earth view swath quite different and significantly more complicated to work with than the other passive microwave sensors. The Remote Sensing Systems (RSS, or REMSS) WindSat products are the only dataset available that uses both the fore and aft look directions. By using both directions, a wider swath and more complicated swath geometry is obtained. RSS providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of WindSat instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7.0.1a\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v7.0.1a\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 7 days. The version with letter \"a\" refers to the file incompliance with GHRSST format.",
"license": "proprietary"
},
@@ -205473,7 +205512,7 @@
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"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814580-ENVIDAT.umm_json",
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"description": "snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).",
"license": "proprietary"
},
@@ -205486,7 +205525,7 @@
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"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814580-ENVIDAT.umm_json",
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"description": "snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).",
"license": "proprietary"
},
diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv
index fbe7eca..b5624bb 100644
--- a/nasa_cmr_catalog.tsv
+++ b/nasa_cmr_catalog.tsv
@@ -1472,8 +1472,8 @@ AERDT_L2_VIIRS_NOAA20_NRT_2 VIIRS/NOAA-20 Dark Target Aerosol L2 6-Min Swath (v2
AERDT_L2_VIIRS_SNPP_2 VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath 6 km V2 LAADS STAC Catalog 2012-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2771506686-LAADS.umm_json The VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath 6 km product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) incarnation of the dark target (DT) aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible). This orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.0 constitutes the latest collection of the L2 Dark Target Aerosol product and contains improvements over its previous collection (v1.1). For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDT_L2_VIIRS_SNPP Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
AERDT_L2_VIIRS_SNPP_NRT_1.1 VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath ASIPS STAC Catalog 2020-06-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1976333380-ASIPS.umm_json The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible). proprietary
AERDT_L2_VIIRS_SNPP_NRT_2 VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath (v2.0) ASIPS STAC Catalog 2023-11-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2812412751-ASIPS.umm_json The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible). This orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP_NRT) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.0 constitutes the latest collection of the L2 Dark Target Aerosol product and contains improvements over its previous collection (v1.1). proprietary
-AERIALDIGI_Not provided Aircraft Scanners - AERIALDIGI CEOS_EXTRA STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
AERIALDIGI_Not provided Aircraft Scanners USGS_LTA STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
+AERIALDIGI_Not provided Aircraft Scanners - AERIALDIGI CEOS_EXTRA STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
AERONET_aerosol_706_1 SAFARI 2000 AERONET Ground-based Aerosol Data, Dry Season 2000 ORNL_CLOUD STAC Catalog 1999-01-01 2001-12-31 28.03, -26.19, 28.03, -26.19 https://cmr.earthdata.nasa.gov/search/concepts/C2788355135-ORNL_CLOUD.umm_json AERONET (AErosol RObotic NETwork) is an optical ground-based aerosol monitoring network and data archive system. AERONET measurements of the column-integrated aerosol optical properties in the southern Africa region were made by sun-sky radiometers at several sites in August-September 2000 as a part of the SAFARI 2000 dry season aircraft campaign. AERONET is supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions. The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities. Data from this collaboration provides globally-distributed near-real-time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes. proprietary
AEROSE_0 Saharan Dust AERosols and Ocean Science Expeditions OB_DAAC STAC Catalog 2004-03-02 2017-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108358203-OB_DAAC.umm_json AEROSE is an internationally recognized series of trans-Atlantic field campaigns conducted onboard the NOAA Ship Ronald H. Brown designed to explore African air mass outflows and their impacts on climate, weather, and environmental health. proprietary
AE_5DSno_2 AMSR-E/Aqua 5-Day L3 Global Snow Water Equivalent EASE-Grids V002 NSIDC_ECS STAC Catalog 2002-06-20 2011-10-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179014698-NSIDC_ECS.umm_json These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids). proprietary
@@ -2420,46 +2420,46 @@ AST_L1B_003 ASTER L1B Registered Radiance at the Sensor V003 LPDAAC_ECS STAC Cat
AST_L1T_003 ASTER Level 1 precision terrain corrected registered at-sensor radiance V003 LPDAAC_ECS STAC Catalog 2000-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000320-LPDAAC_ECS.umm_json The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B) (https://doi.org/10.5067/ASTER/AST_L1B.003), that has been geometrically corrected, and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A (AST_L1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both as a text file and a single band browse images with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding (AST_L1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) dataset. proprietary
AST_L1T_031 ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance V031 LPDAAC_ECS STAC Catalog 2000-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2052604735-LPDAAC_ECS.umm_json The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Version 3.1 data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B AST_L1B (https://doi.org/10.5067/ASTER/AST_L1B.003), that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T V3.1 is created from a single resampling of the corresponding ASTER L1A AST_L1A (https://doi.org/10.5067/ASTER/AST_L1A.003) product. Radiometric calibration coefficients Version 5 (RCC V5) are applied to this product to improve the degradation curve derived from vicarious and lunar calibrations. The bands available in the AST_L1T V3.1 depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T V3.1 dataset does not include the aft-looking VNIR band 3. The 3.1 version uses a precision terrain correction process that incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both a text file and a single band browse image with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depend on the bands available in the corresponding AST_L1A dataset. The AST_L1T V3.1 data product is only available through NASA’s Earthdata Search. The ASTER L1T V3.1 Order Instructions provide step-by-step directions for ordering this product. proprietary
ATCS_0 The A-Train Cloud Segmentation Dataset OB_DAAC STAC Catalog 2007-11-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2172083412-OB_DAAC.umm_json ATCS is a dataset designed to train deep learning models to volumetrically segment clouds from multi-angle satellite imagery. The dataset consists of spatiotemporally aligned patches of multi-angle polarimetry from the POLDER sensor aboard the PARASOL mission and vertical cloud profiles from the 2B-CLDCLASS product using the cloud profiling radar (CPR) aboard CloudSat. proprietary
-ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary
ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary
+ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary
ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary
ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary
ATL03_ANC_MASKS_1 ATLAS/ICESat-2 ATL03 Ancillary Masks, Version 1 NSIDCV0 STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2278879612-NSIDCV0.umm_json This ancillary ICESat-2 data set contains four static surface masks (land ice, sea ice, land, and ocean) provided by ATL03 to reduce the volume of data that each surface-specific along-track data product is required to process. For example, the land ice surface mask directs the ATL06 land ice algorithm to consider data from only those areas of interest to the land ice community. Similarly, the sea ice, land, and ocean masks direct ATL07, ATL08, and ATL12 algorithms, respectively. A detailed description of all four masks can be found in section 4 of the Algorithm Theoretical Basis Document (ATBD) for ATL03 linked under technical references. proprietary
-ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2670138092-NSIDC_CPRD.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564427300-NSIDC_ECS.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2670138092-NSIDC_CPRD.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL07QL_006 ATLAS/ICESat-2 L3A Sea Ice Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-06-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548344839-NSIDC_ECS.umm_json ATL07QL is the quick look version of ATL07. Once final ATL07 files are available, the corresponding ATL07QL files will be removed. ATL07 contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL08QL_006 ATLAS/ICESat-2 L3A Land and Vegetation Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-05-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548345108-NSIDC_ECS.umm_json ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed. ATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL09QL_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics Quick Look V006 NSIDC_ECS STAC Catalog 2024-05-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551528419-NSIDC_ECS.umm_json ATL09QL is the quick look version of ATL09. Once final ATL09 files are available the corresponding ATL09QL files will be removed. ATL09 contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL10QL_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard Quick Look V006 NSIDC_ECS STAC Catalog 2024-06-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551529078-NSIDC_ECS.umm_json ATL10QL is the quick look version of ATL10. Once final ATL10 files are available the corresponding ATL10QL files will be removed. ATL10 contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553243-NSIDC_CPRD.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2567856357-NSIDC_ECS.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary
+ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553243-NSIDC_CPRD.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary
-ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary
ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL13QL_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006 NSIDC_ECS STAC Catalog 2024-06-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650092501-NSIDC_ECS.umm_json ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary
ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary
ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary
-ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
+ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_ECS STAC Catalog 2019-01-01 2023-12-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary
ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_CPRD STAC Catalog 2019-01-01 2023-12-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary
-ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
-ATL15_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162334027-NSIDC_CPRD.umm_json This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary
+ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
ATL15_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004 NSIDC_ECS STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159684532-NSIDC_ECS.umm_json This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary
-ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
+ATL15_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162334027-NSIDC_CPRD.umm_json This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary
ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
-ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
+ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
+ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_ECS STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary
ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary
ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary
@@ -4341,6 +4341,8 @@ CORAL_0 CORAL Experiment OB_DAAC STAC Catalog 2014-07-21 -180, -90, 180, 90 htt
COROAS-AVHRR_Not provided AVHRR Sea Surface Temperature for Southwestern Atlantic CEOS_EXTRA STAC Catalog 1992-09-01 -60, -38, -38, -20 https://cmr.earthdata.nasa.gov/search/concepts/C2227456149-CEOS_EXTRA.umm_json Data consisting of AVHRR five channels from satellites NOAA-11 and NOAA-12 and Sea Surface Temperature derived from brightness temperature files through NOAA algorithms. Exchange of data after January 1995. Due to system limitation, files are 512 lines x 512 pixels per line, 8 bits resolution. proprietary
CORONA_SATELLITE_PHOTOS_Not provided CORONA Satellite Photographs from the U.S. Geological Survey USGS_LTA STAC Catalog 1960-08-01 1972-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566377-USGS_LTA.umm_json The first generation of U.S. photo intelligence satellites collected more than 860,000 images of the Earth’s surface between 1960 and 1972. The classified military satellite systems code-named CORONA, ARGON, and LANYARD acquired photographic images from space and returned the film to Earth for processing and analysis. The images were originally used for reconnaissance and to produce maps for U.S. intelligence agencies. In 1992, an Environmental Task Force evaluated the application of early satellite data for environmental studies. Since the CORONA, ARGON, and LANYARD data were no longer critical to national security and could be of historical value for global change research, the images were declassified by Executive Order 12951 in 1995. The first successful CORONA mission was launched from Vandenberg Air Force Base in 1960. The satellite acquired photographs with a telescopic camera system and loaded the exposed film into recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications. The intelligence community used Keyhole (KH) designators to describe system characteristics and accomplishments. The CORONA systems were designated KH-1, KH-2, KH-3, KH-4, KH-4A, and KH-4B. The ARGON systems used the designator KH-5 and the LANYARD systems used KH-6. Mission numbers were a means for indexing the imagery and associated collateral data. A variety of camera systems were used with the satellites. Early systems (KH-1, KH-2, KH-3, and KH-6) carried a single panoramic camera or a single frame camera (KH-5). The later systems (KH-4, KH-4A, and KH-4B) carried two panoramic cameras with a separation angle of 30° with one camera looking forward and the other looking aft. The original film and technical mission-related documents are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery. Mathematical calculations based on camera operation and satellite path were used to approximate image coordinates. Since the accuracy of the coordinates varies according to the precision of information used for the derivation, users should inspect the preview image to verify that the area of interest is contained in the selected frame. Users should also note that the images have not been georeferenced. proprietary
COSMO-SkyMed.full.archive.and.tasking_NA COSMO-SkyMed full archive and tasking ESA STAC Catalog 2008-09-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336822-ESA.umm_json "The archive and new tasking X-band SAR products are available from COSMO-Skymed (CSK) and COSMO-SkyMed Second Generation (CSG) missions in ScanSAR and Stripmap modes, right and left looking acquisition (20 to 60° incidence angle). COSMO-SkyMed modes: Acquisition Mode / Single look Resolution [Az. X. Rg, SCS] (m) / Scene size [Az. X. Rg] (km) / Polarisation / Scene duration (seconds) / Number of looks / Multilook resolution (m) / Geolocation accuracy ±3 s (m) // / / / / / [DGM, GEC, GTC] // Stripmap Himage / 2.6 x 3 / 40 x 40 / Single: HH, HV, VH, VV / 7 / 3 / 5 / 25 // Stripmap PingPong / 9.7 x 11 / 30 x 30 / Alternate: HH/VV, HH/HV, VV/VH / 6 / 3 / 20 / 25 // ScanSAR Wide / 23 x 13.5 / 100 x 100 / Single: HH, HV, VH, VV / 15 / 4 - 9 / 30 / 30 // ScanSAR Huge / 38 x 13.5 / 200 x 200 / Single: HH, HV, VH, VV / 30 / 25 - 66 / 100 / 100 // COSMO-Skymed Second Generation Modes: Acquisition Mode / Single look Resolution [Az. X. Rg, SCS] (m) / Scene size [Az. X. Rg] (km) / Polarisation / Scene duration (seconds) / Number of looks / Multilook resolution (m)10/05/2021 10:28 / Geolocation accuracy ±3 s (m) // / / / / / [DGM, GEC, GTC] // Stripmap / 3 x 3 / 40 x 40 / Single (HH, VV, HV, VH) or Dual (HH+HV, VV+VH) / 7 / 2 x 2; 4 x 4 / 5 x 5; 11 x 11 / 3.75 // Stripmap PingPong / 12 x 5 / 30 x 30 / Alternate (HH/VV, HH/VH-HV/VV) / 6 / 1 x 2; 2 x 5 / 12 x 10; 22 x 25/ 12 // ScanSAR 1 / 20 x 4 / 100 x 100 / Single (HH, VV, HV, VH) or Dual (HH+HV, VV+VH) / 15 / 1 x 3; 1 x 5 / 20 x 13; 23 x 27; 35 x 40 / 12 // ScanSAR 2 / 40 x 6 / 200 x 200 / Single (HH, VV, HV, VH) or Dual (HH+HV, VV+VH) / 30 / 1 x 4; 1 x 7; 3 x 16 / 40 x 27; 47 x 54; 115 x 135 / 12 // Following Processing Levels are available, for both CSK and CSG: - SCS (Level 1A, Single-look Complex Slant): data in complex format, in slant range projection (the sensor's natural acquisition projection) and zero doppler projection, weighted and radiometrically equalised; the coverage corresponds to the full resolution area illuminated by the SAR instrument - DGM (Level 1B, Detected Ground Multi-look): product obtained detecting, multi-looking and projecting the Single-look Complex Slant data onto a grid regular in ground: it contains focused data, amplitude detected, optionally despeckled by multi-looking approach, radiometrically equalised and represented in ground/azimuth projection - GEC (Level 1C, Geocoded Ellipsoid Corrected): focused data, amplitude detected, optionally despeckled by multi-looking approach, geolocated on the reference ellipsoid and represented in a uniform preselected cartographic presentation. Any geometric correction derived by usage of terrain model isn't applied to this product by default - GTC (Level 1D, Geocoded Terrain Corrected): focused data, fully calibrated with the usage of terrain model, amplitude detected, optionally despeckled by multi-looking approach, geolocated on a DEM and represented in a uniform preselected cartographic presentation. The image scene is located and accurately rectified onto a map projection, through the use of Ground Control Points (GCPs) and Digital Elevation Model (DEM); it differs from GEC for the use of the DEM (instead of reference ellipsoid) for the accurate conversion from slant to ground range and to approximate the real earth surface The list of available data can be retrieved using the _$$CLEOS COSMO-SkyMed products catalogue$$ https://www.cleos.earth/ . User registration is requested to navigate the catalogue." proprietary
+COWVR_STPH8_L1_TSDR_V10.0_10.0 COWVR STP-H8 Antenna and Microwave Brightness Temperatures Version 10.0 POCLOUD STAC Catalog 2022-01-08 2024-12-31 -180, -61, 180, 61 https://cmr.earthdata.nasa.gov/search/concepts/C3237785963-POCLOUD.umm_json !!!Temporary notice posted Sept. 27th, 2024!!! These data are in the process of being ingested and not all files are available yet. The data were made public early to allow assessment by early science users. Accordingly, not all data set resources may be available yet. Please check over the next 2-3 weeks for finalization of this data set and PO.DAAC's release announcement.
This data set includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 18.7 GHz, 23.8 GHz, and 34.5 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. Its swath width is 1012 km and spatial resolution is <35 km. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbers of the project team prior to release
The COWVR sensor is a fully polarimetric, conically imaging microwave radiometer for measuring ocean surface wind vectors. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. It was deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. A successful COWVR mission will demonstrate a lower-cost sensor architecture (e.g. in comparison to WindSat) for providing imaging passive microwave data, including ocean surface vector wind products for the Department of Defense (DoD). COWVR was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping. proprietary
+COWVR_STPH8_L2_EDR_V10.0_10.0 COWVR STP-H8 Surface Wind Vector and Column-Integrated Atmospheric Water Measurements Version 10.0 POCLOUD STAC Catalog 2022-01-08 -180, -61, 180, 61 https://cmr.earthdata.nasa.gov/search/concepts/C3237678855-POCLOUD.umm_json !!!Temporary notice posted Sept. 27th, 2024!!! These data are in the process of being ingested and not all files are available yet. The data were made public early to allow assessment by early science users. Accordingly, not all data set resources may be available yet. Please check over the next 2-3 weeks for finalization of this data set and PO.DAAC's release announcement.
This dataset includes satellite-based observations of geolocated surface wind vectors, precipitable water vapor, and integrated cloud liquid water, as well as the microwave brightness temperatures used to derive them. Theses measurements make up the environmental data record (EDR) from the COWVR (Compact Ocean Wind Vector Radiometer) sensor aboard the international space station (ISS), beginning in January 2022 with forward-streaming to PO.DAAC. Data over the satellite swath are available in HDF5 format with roughly one file per hour (the ISS orbit period is ~90 minutes), and coverage shown in the thumbnail is for a full day. The file metadata formats may be different than what an average user is familiar with – please see the User Guide to learn more. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release.
The COWVR sensor is a fully polarimetric, conically imaging microwave radiometer for measuring ocean surface wind vectors. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. It was deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. A successful COWVR mission will demonstrate a lower-cost sensor architecture (e.g. in comparison to WindSat) for providing imaging passive microwave data, including ocean surface vector wind products for the Department of Defense (DoD). COWVR was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping. proprietary
CPEXAW-ADM-Aeolus_1 CPEX-AW ADM-Aeolus Datasets LARC_ASDC STAC Catalog 2021-08-06 2021-09-17 -125, 11, -45, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2404262719-LARC_ASDC.umm_json CPEXAW-ADM-Aeolus_1 is the ESA ADM-Aeolus Datasets for the Convective Processes Experiment - Aerosols & Winds (CPEX-AW) sub-orbital campaign. Data collection for this product is complete. The Convective Processes Experiment – Aerosols & Winds (CPEX-AW) campaign was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. CPEX-AW is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in the summer of 2017. In addition to joint calibration/validation of ADM-AEOLUS, CPEX-AW studied the dynamics related to the Saharan Air Layer, African Easterly Waves and Jets, Tropical Easterly Jet, and deep convection in the InterTropical Convergence Zone (ITCZ). CPEX-AW science goals include: • Better understanding interactions of convective cloud systems and tropospheric winds as part of the joint NASA-ESA Aeolus Cal/Val effort over the tropical Atlantic; • Observing the vertical structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems, convective processes (e.g., cold pools), and environmental conditions within and across the ITCZ; • Investigating how the African easterly waves and dry air and dust associated with Sahara Air Layer control the convectively suppressed and active periods of the ITCZ; • Investigating interactions of wind, aerosol, clouds, and precipitation and effects on long range dust transport and air quality over the western Atlantic. In order to successfully achieve the objectives of the campaign, NASA deployed its DC-8 aircraft equipped with an Airborne Third Generation Precipitation Radar (APR-3), Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. This campaign aims to provide useful material to atmospheric scientists, meteorologists, lidar experts, air quality experts, professors, and students. The Atmospheric Science Data Center (ASDC) archives the dropsonde, HALO, and DAWN data products for CPEX-AW. For additional datasets please visit the Global Hydrometeorology Resource Center (GHRC). proprietary
CPEXAW-DAWN_DC8_1 CPEX-AW DAWN Doppler Aerosol WiNd Lidar LARC_ASDC STAC Catalog 2020-08-20 2021-09-04 -81, 11, -45, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2260262728-LARC_ASDC.umm_json CPEXAW-DAWN_DC8_1 are the Doppler Aerosol WiNd lidar (DAWN) image and NetCDF data files collected during the Convective Processes Experiment - Aerosols & Winds (CPEX-AW) onboard the DC-8 aircraft. Data collection for this product is complete. The Convective Processes Experiment – Aerosols & Winds (CPEX-AW) campaign was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. CPEX-AW is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in the summer of 2017. In addition to joint calibration/validation of ADM-AEOLUS, CPEX-AW studied the dynamics related to the Saharan Air Layer, African Easterly Waves and Jets, Tropical Easterly Jet, and deep convection in the InterTropical Convergence Zone (ITCZ). CPEX-AW science goals include: • Better understanding interactions of convective cloud systems and tropospheric winds as part of the joint NASA-ESA Aeolus Cal/Val effort over the tropical Atlantic; • Observing the vertical structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems, convective processes (e.g., cold pools), and environmental conditions within and across the ITCZ; • Investigating how the African easterly waves and dry air and dust associated with Sahara Air Layer control the convectively suppressed and active periods of the ITCZ; • Investigating interactions of wind, aerosol, clouds, and precipitation and effects on long range dust transport and air quality over the western Atlantic. In order to successfully achieve the objectives of the campaign, NASA deployed its DC-8 aircraft equipped with an Airborne Third Generation Precipitation Radar (APR-3), Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. This campaign aims to provide useful material to atmospheric scientists, meteorologists, lidar experts, air quality experts, professors, and students. The Atmospheric Science Data Center (ASDC) archives the dropsonde, HALO, and DAWN data products for CPEX-AW. For additional datasets please visit the Global Hydrometeorology Resource Center (GHRC). proprietary
CPEXAW-Dropsondes_1 CPEX-AW Dropsonde Data LARC_ASDC STAC Catalog 2021-08-05 2021-09-17 -127, 9, -35, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2299858387-LARC_ASDC.umm_json CPEXAW-Dropsondes_1 is the dropsonde data files collected during the Convective Processes Experiment - Aerosols & Winds (CPEX-AW). Data collection for this product is complete. The Convective Processes Experiment – Aerosols & Winds (CPEX-AW) campaign was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. CPEX-AW is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in the summer of 2017. In addition to joint calibration/validation of ADM-AEOLUS, CPEX-AW studied the dynamics related to the Saharan Air Layer, African Easterly Waves and Jets, Tropical Easterly Jet, and deep convection in the InterTropical Convergence Zone (ITCZ). CPEX-AW science goals include: • Better understanding interactions of convective cloud systems and tropospheric winds as part of the joint NASA-ESA Aeolus Cal/Val effort over the tropical Atlantic; • Observing the vertical structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems, convective processes (e.g., cold pools), and environmental conditions within and across the ITCZ; • Investigating how the African easterly waves and dry air and dust associated with Sahara Air Layer control the convectively suppressed and active periods of the ITCZ; • Investigating interactions of wind, aerosol, clouds, and precipitation and effects on long range dust transport and air quality over the western Atlantic. In order to successfully achieve the objectives of the campaign, NASA deployed its DC-8 aircraft equipped with an Airborne Third Generation Precipitation Radar (APR-3), Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. This campaign aims to provide useful material to atmospheric scientists, meteorologists, lidar experts, air quality experts, professors, and students. The Atmospheric Science Data Center (ASDC) archives the dropsonde, HALO, and DAWN data products for CPEX-AW. For additional datasets please visit the Global Hydrometeorology Resource Center (GHRC). proprietary
@@ -6154,28 +6156,28 @@ GISS-CMIP5_1 GISS ModelE2 contributions to the CMIP5 archive NCCS STAC Catalog 0
GIS_EastAngliaClimateMonthly_551_1 Global Monthly Climatology for the Twentieth Century (New et al.) ORNL_CLOUD STAC Catalog 1900-01-01 1998-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2780535151-ORNL_CLOUD.umm_json A 0.5 degree lat/lon data set of monthly surface climate over global land areas, excluding Antarctica. Primary variables are interpolated directly from station time-series: precipitation, mean temperature and diurnal temperature range. proprietary
GLAH01_033 GLAS/ICESat L1A Global Altimetry Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.umm_json Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product. proprietary
GLAH01_033 GLAS/ICESat L1A Global Altimetry Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.umm_json Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter. Each data granule has an associated browse product. proprietary
-GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary
GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary
+GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary
GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary
GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary
-GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary
GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary
-GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary
+GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary
GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549166-NSIDC_CPRD.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary
-GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary
+GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary
GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary
-GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary
+GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary
GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary
-GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary
+GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary
GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary
+GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary
GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary
GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary
-GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary
GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary
+GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary
GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary
GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary
-GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
+GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH14_034 GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153551318-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
@@ -11528,8 +11530,8 @@ OMAEROZ_003 OMI/Aura Aerosol product Multi-wavelength Algorithm Zoomed 1-Orbit L
OMAERO_003 OMI/Aura Multi-wavelength Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 (OMAERO) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966755-GES_DISC.umm_json The Level-2 Aura Ozone Monitoring Instrument (OMI) Aerosol Product (OMAERO) is now available from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. This is the second public release of version 003. The data was re-processed in late 2011 using an improved algorithm (processing version 1.2.3.1). After some quick validation the reprocessed data was released to the public in March 2012. The shortname for this Level-2 Aerosol Product is OMAERO_V003. There are two Level-2 Aura OMI aerosol products OMAERUV and OMAERO. The OMAERUV product uses the near-UV algorithm. The OMAERO product is based on the multi-wavelength algorithm and that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAERO retrieval algorithm is developed by the KNMI OMI Team Scientists. Drs. Deborah Stein-Zweers, Martin Sneep and Pepijn Veefkind are now the key investigators of this product. The OMAERO product contains Aerosol Optical Depths, Single Scattering Albedo, and other ancillary and geolocation information. The OMAERO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERO data product is about 6 Mbytes. proprietary
OMAEROe_003 OMI/Aura Multi-wavelength Aerosol Optical Depth and Single Scattering Albedo L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 (OMAEROe) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136062-GES_DISC.umm_json The OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The selection criteria is based on the shortest optical path length (secant of solar zenith angle + secant of viewing zenith angle). The OMAEROe files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROe data product is about 7 Mbytes. (The shortname for this Level-3 Global Gridded Aerosol Product is OMAEROe) proprietary
OMAERUVG_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMAERUVG) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136097-GES_DISC.umm_json This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 AERUV product OMAERUV. This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 Aerosol product OMAERUV. OMAERUVG data product is a special Level-2 gridded product where pixel level products are binned into 0.25x0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMAERUVG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits mapped on the Global 0.25x0.25 deg Grids. The maximum file size for the OMAERUVG data product is about 50 Mbytes. proprietary
-OMAERUV_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.umm_json The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ proprietary
OMAERUV_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 (OMAERUV) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966768-GES_DISC.umm_json The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data are available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The shortname for this Level-2 near-UV Aerosol Product is OMAERUV_V003. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. proprietary
+OMAERUV_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.umm_json The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ proprietary
OMAERUV_004 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V004 (OMAERUV) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3185856256-GES_DISC.umm_json The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product OMAERUV (Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Optical Depth, Aerosol Single Scattering Albedo, Absorption Optical Depth, UV Aerosol Index, and Aerosol Optical Depth over clouds at three wavelengths (354, 388, and 500 nm), and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 4.0 Network Common Data Form (NetCDF). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 17 Mbytes. proprietary
OMAERUV_CPR_003 OMI/Aura Level 2 Near UV Aerosol Optical Depth and Single Scattering Albedo 200-m swath subset along CloudSat track V003 (OMAERUV_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2017-05-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350969-GES_DISC.umm_json This is a CloudSat-collocated subset of the original OMI product OMAERUV, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 near-UV aerosol subset is OMAERUV_CPR_003) proprietary
OMAERUVd_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo L3 1 day 1.0 degree x 1.0 degree V3 (OMAERUVd) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136096-GES_DISC.umm_json The OMI science team produces this Level-3 daily global gridded product OMAERUVd (1 deg Lat/Lon grids). The OMAERUVd product is produced with all data pixels that fall in a grid box with quality filtered and then averaged, based on the pixel level OMI Level-2 Aerosol data product OMAERUV. The OMAERUV data product is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data. The OMAERUVd data product contains extinction and absorption optical depths at three wavelenghts (355 nm, 388 nm and 500 nm). The OMAERUVd files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAERUVd data product is about 0.2 Mbytes. proprietary
@@ -11620,8 +11622,8 @@ OMSO2_003 OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km
OMSO2_CPR_003 OMI/Aura Level 2 Sulphur Dioxide (SO2) Trace Gas Column Data 1-Orbit Subset and Collocated Swath along CloudSat V003 (OMSO2_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350970-GES_DISC.umm_json "This is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated subset of the original product OMSO2 Product is OMSO2_CPR_V003) This document describes the original OMI SO2 product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimum SO2 mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO2 data (1978-2005) [Krueger et al 1995]. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O3 from SO2. The product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath containing 60 pixels per viewing line. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the ""zoom mode"" for one day every 452 orbits (~32 days). For each OMI pixel we provide 4 different estimates of the column density of SO2 in Dobson Units (1DU=2.69x10^16 molecules/cm2) obtained by making different assumptions about the vertical distribution of the SO2. However, it is important to note that in most cases the precise vertical distribution of SO2 is unimportant. The users can use either the SO2 plume height, or the center of mass altitude (CMA) derived from SO2 vertical distribution, to interpolate between the 4 values: 1)Planetary Boundary Layer (PBL) SO2 column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km. 2)Lower tropospheric SO2 column (ColumnAmountSO2_TRL), corresponding to CMA of 2.5 km. 3)Middle tropospheric SO2 column, (ColumnAmountSO2_TRM), usually produced by volcanic degassing, corresponding to CMA of 7.5 km, 4)Upper tropospheric and Stratospheric SO2 column (ColumnAmountSO2_STL), usually produced by explosive volcanic eruption, corresponding to CMA of 17 km. The accuracy and precision of the derived SO2 columns vary significantly with the SO2 CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO2 above clouds and snow/ice, and less sensitive to SO2 below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 9 Mbytes." proprietary
OMSO2e_003 OMI/Aura Sulfur Dioxide (SO2) Total Column Daily L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 (OMSO2e) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136112-GES_DISC.umm_json "The OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm. This single observation is the ""best pixel"", selected from all ""good"" L2 pixels of OMSO2 that overlap this grid and have UTC time between UTC times of 00:00:00 and 23:59:59.999. In addition to the SO2 Vertical column value some ancillary parameters, e.g., cloud fraction, terrain height, scene number, solar and satellite viewing angles, row anomaly flags, and quality flags have been also made available corresponding to the best selected SO2 data pixel in each grid. The OMSO2e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the grid model." proprietary
OMTO3G_003 OMI/Aura Ozone (O3) Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMTO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136114-GES_DISC.umm_json This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes. proprietary
-OMTO3_003 OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB. proprietary
OMTO3_003 OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA for the public access. The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ . proprietary
+OMTO3_003 OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB. proprietary
OMTO3_CPR_003 OMI/Aura Level 2 Ozone (O3) Total Column 1-Orbit Subset and Collocated Swath along CloudSat track 200-km wide at 13x24 km2 resolution GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350982-GES_DISC.umm_json This is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 Total Ozone Column subset is OMTO3_CPR_V003) proprietary
OMTO3d_003 OMI/Aura TOMS-Like Ozone, Aerosol Index, Cloud Radiance Fraction L3 1 day 1 degree x 1 degree V3 (OMTO3d) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136070-GES_DISC.umm_json The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids. The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes. proprietary
OMTO3e_003 OMI/Aura TOMS-Like Ozone and Radiative Cloud Fraction L3 1 day 0.25 degree x 0.25 degree V3 (OMTO3e) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136071-GES_DISC.umm_json The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. proprietary
@@ -12811,7 +12813,7 @@ SPL1A_RO_QA_002_2 SMAP_L1A_RADAR_RECEIVE_ONLY_QA_V002 ASF STAC Catalog 2015-02-1
SPL1A_RO_QA_003_3 SMAP_L1A_RADAR_RECEIVE_ONLY_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243124139-ASF.umm_json SMAP Level 1A Radar Receive Only Data Quality Information Version 3 proprietary
SPL1BTB_005 SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V005 NSIDC_ECS STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C1931655418-NSIDC_ECS.umm_json This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed. proprietary
SPL1BTB_006 SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.umm_json This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed. proprietary
-SPL1BTB_NRT_105 Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105 NSIDC_ECS STAC Catalog 2024-09-05 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2257958430-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product, SPL1BTB (https://doi.org/10.5067/ZHHBN1KQLI20)." proprietary
+SPL1BTB_NRT_105 Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105 NSIDC_ECS STAC Catalog 2024-09-11 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2257958430-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product, SPL1BTB (https://doi.org/10.5067/ZHHBN1KQLI20)." proprietary
SPL1B_SO_LoRes_001_1 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473308-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product proprietary
SPL1B_SO_LoRes_002_2 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243253631-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product Version 2 proprietary
SPL1B_SO_LoRes_003_3 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243133445-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product Version 3 proprietary
@@ -12841,7 +12843,7 @@ SPL2SMP_008 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V008 NSI
SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary
SPL2SMP_E_005 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V005 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2136471686-NSIDC_ECS.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary
SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary
-SPL2SMP_NRT_107 Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107 NSIDC_ECS STAC Catalog 2024-09-05 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0)." proprietary
+SPL2SMP_NRT_107 Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107 NSIDC_ECS STAC Catalog 2024-09-11 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0)." proprietary
SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL3FTP_003 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V003 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1931660632-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary
SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary
@@ -13270,6 +13272,7 @@ TELLUS_GRFO_L3_JPL_RL06.1_LND_v04_RL06.1v04 JPL TELLUS GRACE-FO Level-3 Monthly
TELLUS_GRFO_L3_JPL_RL06.1_OCN_v04_RL06.1v04 JPL TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.1 version 04 POCLOUD STAC Catalog 2018-05-22 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2537030370-POCLOUD.umm_json The monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). The Level-2 GAD product has been added back, a glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters (i.e., de-striping and spatial smoothing) have been applied to reduce correlated errors. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. This RL06.1 is an updated release version of the previous RL06. The difference is only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; RL06.1 uses the ACH data product. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version and beyond, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices. proprietary
TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04 JPL TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.3 version 04 POCLOUD STAC Catalog 2018-05-22 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C3193302127-POCLOUD.umm_json This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.
GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data. proprietary
TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04_RL06.3v04 JPL TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.3 version 04 POCLOUD STAC Catalog 2018-05-22 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C3193304376-POCLOUD.umm_json This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.
GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data. proprietary
+TEMPEST_STPH8_L1_TSDR_V10.0_10.0 TEMPEST STP-H8 Antenna and Microwave Brightness Temperatures Version 10.0 POCLOUD STAC Catalog 2022-01-08 2024-12-31 -180, -61, 180, 61 https://cmr.earthdata.nasa.gov/search/concepts/C3237795822-POCLOUD.umm_json !!!Temporary notice posted Sept. 27th, 2024!!! These data are in the process of being ingested and not all files are available yet. The data were made public early to allow assessment by early science users. Accordingly, not all data set resources may be available yet. Please check over the next 2-3 weeks for finalization of this data set and PO.DAAC's release announcement.
This dataset includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 87, 164, 174, 178 and 181 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the TEMPEST (Temporal Experiment for Storms and Tropical Systems) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. TEMPEST swath width is 1400 kilometers and resolution at nadir is 25 km for the 87 GHz channel and 13 km for the 180 GHz channels. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release.
The TEMPEST instrument is a microwave radiometer deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission, with the primary objective of tropical cyclone intensity tracking. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. A successful mission will demonstrate a lower-cost, lighter-weight sensor architecture for providing microwave data. TEMPEST was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping. proprietary
TEMPO_CLDO4_L2_V03 TEMPO cloud pressure and fraction (O2-O2 dimer) V03 (BETA) LARC_CLOUD STAC Catalog 2023-08-01 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2930760329-LARC_CLOUD.umm_json O2-O2 cloud Level 2 files provide cloud information at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on effective cloud fraction (ECF), cloud optical centroid pressure (OCP), ancillary data, processing quality flags, etc. The ECF is derived from reflectance at 466 nm. The OCP is derived from O2-O2 slant column density. The cloud retrieval uses Look Up Tables (LUTs) of reflectance and air mass factors, GEOS-CF forecast meteorology, and GLER surface albedo. proprietary
TEMPO_CLDO4_L3_V03 TEMPO gridded cloud fraction and pressure (O2-O2 dimer) V03 (BETA) LARC_CLOUD STAC Catalog 2023-08-01 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2930727817-LARC_CLOUD.umm_json O2-O2 cloud Level 3 files provide cloud information on a regular grid covering the TEMPO field of regard for nominal TEMPO observations. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle. The files are provided in netCDF4 format, and contain information on effective cloud fraction, cloud optical centroid pressure, and ancillary data. The re-gridding algorithm uses an area-weighted approach. proprietary
TEMPO_DRK_L1_V02 TEMPO dark exposure V02 (BETA) LARC_CLOUD STAC Catalog 2023-06-06 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2842836142-LARC_CLOUD.umm_json Level 1 dark files provide the processed dark currents, corresponding to either solar irradiance measurements or radiance measurements. Each file includes the measured dark currents for all the North-South cross-track pixels. The files are provided in netCDF4 format, and contain information on dark current rates of all frames and their average for the UV and visible bands, pixel quality flags and other ancillary information. The product is produced using the image processing of L0-1b processor. Please refer to the ATBD for details. These data are beta. Beta maturity is defined as: the product is minimally validated but may still contain significant errors; it is based on product quick looks using the initial calibration parameters. Because the products at this stage have minimal validation, users should refrain from making conclusive public statements regarding science and applications of the data products until a product is designated at the provisional validation status. The TEMPO Level 1 ATBD is still being finalized. For access to Version 1.0 ATBD, please contact the ASDC at larc-dl-asdc-tempo@mail.nasa.gov. proprietary