Front Finder! #15
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New repository started by OHW Australia participants: https://github.com/oceanhackweek/OHW22_proj_front_finder.git |
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Hi, @sophieclayton. I am interested in the project. I discussed with @mayajakes and we thought of using the peaks in |SSH gradients| to set a region where fronts are found. It works well for regions where there is a surface-to-bottom current (e.g., the ACC) and the altimeter SSH gradient captures the surface-to-bottom density gradients. Then, we thought of using the in situ observations (Saildrones?, ARGO?) to evaluate the hydrographic information within the area of high |SSH gradients|. In this case, we would not use the profiles out of the regions of strong |SSH gradients|. If the hydrography structure is consistent in different coordinates of peaks in |SSH gradients|, it could return subsurface characteristics of fronts. What do you think? @NickMortimer suggested us to start a repository with these ideas. @lidefi87 helped us with that. |
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We have created a slack channel called #frontfinder-project within the Oceanhackweek slack, so if you are interested in this project feel free to join! |
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frontfinder - a package to detect fronts in ocean data
Summary
Develop an easy-to-use package that will ingest ocean data and find fronts (e.g. large gradients in density, temperature, chlorophyl...).
Personnel
Sophie Clayton/TBD
Data sets and infrastructure support
This could be tested out using glider/AUV data as a first step? Would also be useful to use
The problem
Although there are some handy off-the-shelf remote sensing products that can be used to locate fronts in SST and Chl (NOAA Chl front magnitude product), it is a recurring problem for oceanographers to identify fronts from in situ data (e.g. underway TSG, glider sections, SailDrone, shipboard ADCP...). The goal of this project would be to provide a solution for better identification of fronts from in situ ocean data (possibly also making use of remote sensing products). There has also been a focus on finding fronts in sea surface data, but with depth-resolved sections we can get a better view of the structure of fronts.
Maybe combine surface and depth-resolved data?
Existing methods
There are many existing algorithms for front detection, particularly from satellite data.
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