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add ozone
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@malmans2
malmans2 committed Apr 3, 2024
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{
"cells": [
{
"cell_type": "markdown",
"id": "0",
"metadata": {},
"source": [
"# Ozone - MSR"
]
},
{
"cell_type": "markdown",
"id": "1",
"metadata": {},
"source": [
"## Import Packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2",
"metadata": {},
"outputs": [],
"source": [
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import xarray as xr\n",
"from c3s_eqc_automatic_quality_control import diagnostics, download, plot, utils\n",
"\n",
"plt.style.use(\"seaborn-v0_8-notebook\")"
]
},
{
"cell_type": "markdown",
"id": "3",
"metadata": {},
"source": [
"## Define parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4",
"metadata": {},
"outputs": [],
"source": [
"# Regions\n",
"region_slices = {\n",
" \"global\": {\"lat_slice\": slice(-90, 90), \"lon_slice\": slice(0, 360)},\n",
" \"tropics\": {\"lat_slice\": slice(-25, 25), \"lon_slice\": slice(0, 360)},\n",
" \"NH mid-latitudes\": {\"lat_slice\": slice(30, 60), \"lon_slice\": slice(0, 360)},\n",
" \"SH mid-latitudes\": {\"lat_slice\": slice(-30, -60), \"lon_slice\": slice(0, 360)},\n",
" \"NH polar\": {\"lat_slice\": slice(60, 90), \"lon_slice\": slice(0, 360)},\n",
" \"SH polar\": {\"lat_slice\": slice(-60, -90), \"lon_slice\": slice(0, 360)},\n",
"}\n",
"\n",
"# Periods\n",
"version_timeseries = {\n",
" \"v0020\": {\"start\": \"1979-01\", \"stop\": \"2018-12\"},\n",
" \"v0023\": {\"start\": \"1979-01\", \"stop\": \"2018-12\"},\n",
" \"v0024\": {\"start\": \"2018-12\", \"stop\": \"2021-12\"},\n",
" \"v0025\": {\"start\": \"2022-01\", \"stop\": \"2022-12\"},\n",
"}\n",
"version_maps = {\n",
" \"v0023\": {\"start\": \"1979-01\", \"stop\": \"2018-12\"},\n",
" \"v0024\": {\"start\": \"2019-01\", \"stop\": \"2021-12\"},\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "5",
"metadata": {},
"source": [
"## Define request"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6",
"metadata": {},
"outputs": [],
"source": [
"collection_id = \"satellite-ozone-v1\"\n",
"\n",
"common_request = {\n",
" \"format\": \"zip\",\n",
" \"processing_level\": \"level_4\",\n",
" \"vertical_aggregation\": \"total_column\",\n",
" \"sensor\": \"msr\",\n",
" \"variable\": \"atmosphere_mole_content_of_ozone\",\n",
"}\n",
"\n",
"requests_timeseries = {\n",
" version: download.update_request_date(\n",
" common_request | {\"version\": version},\n",
" stringify_dates=True,\n",
" **update_kwargs,\n",
" )\n",
" for version, update_kwargs in version_timeseries.items()\n",
"}\n",
"\n",
"requests_maps = []\n",
"for version, update_kwargs in version_maps.items():\n",
" requests_maps.extend(\n",
" download.update_request_date(\n",
" common_request | {\"version\": version},\n",
" stringify_dates=True,\n",
" **update_kwargs,\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "7",
"metadata": {},
"source": [
"## Cached functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8",
"metadata": {},
"outputs": [],
"source": [
"def fix_coords(ds):\n",
" ds = ds.swap_dims(\n",
" {\n",
" short: long\n",
" for short, long in zip([\"Lat\", \"Lon\"], [\"latitude\", \"longitude\"])\n",
" if short in ds.dims\n",
" }\n",
" )\n",
" time = ds.attrs[\"Ozone_field_date\"]\n",
" if isinstance(time, np.ndarray):\n",
" time = \"-\".join(ds.attrs[\"Ozone_field_date\"].astype(str))\n",
" ds = ds.assign_coords(time=[pd.to_datetime(time)])\n",
" return ds\n",
"\n",
"\n",
"def spatial_weighted_mean(ds, lon_slice, lat_slice):\n",
" ds = utils.regionalise(ds, lon_slice=lon_slice, lat_slice=lat_slice)\n",
" return diagnostics.spatial_weighted_mean(ds)"
]
},
{
"cell_type": "markdown",
"id": "9",
"metadata": {},
"source": [
"## Download and transform timeseries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10",
"metadata": {},
"outputs": [],
"source": [
"kwargs = {\n",
" \"collection_id\": collection_id,\n",
" \"chunks\": {\"year\": 1},\n",
" \"decode_times\": False,\n",
" \"preprocess\": fix_coords,\n",
"}\n",
"\n",
"datasets = []\n",
"for version, requests in requests_timeseries.items():\n",
" for region, transform_func_kwargs in region_slices.items():\n",
" print(f\"{version=} {region=}\")\n",
" ds = download.download_and_transform(\n",
" requests=requests,\n",
" transform_func=spatial_weighted_mean,\n",
" transform_func_kwargs=transform_func_kwargs,\n",
" **kwargs,\n",
" )\n",
" datasets.append(ds.expand_dims(version=[version], region=[region]))\n",
"ds_timeseries = xr.merge(datasets)"
]
},
{
"cell_type": "markdown",
"id": "11",
"metadata": {},
"source": [
"## Download and transform maps"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12",
"metadata": {},
"outputs": [],
"source": [
"ds_annual = download.download_and_transform(\n",
" requests=requests_maps,\n",
" transform_func=diagnostics.annual_weighted_mean,\n",
" transform_chunks=False,\n",
" **kwargs,\n",
")\n",
"ds_monthly = download.download_and_transform(\n",
" requests=requests_maps,\n",
" transform_func=diagnostics.monthly_weighted_mean,\n",
" transform_chunks=False,\n",
" **kwargs,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "13",
"metadata": {},
"source": [
"## Plot timeseries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14",
"metadata": {},
"outputs": [],
"source": [
"facet = ds_timeseries[\"total_ozone_column\"].plot(\n",
" col=\"region\", hue=\"version\", col_wrap=2\n",
")\n",
"for ax in facet.axs.flatten():\n",
" ax.grid()\n",
"facet.fig.autofmt_xdate(rotation=45)"
]
},
{
"cell_type": "markdown",
"id": "15",
"metadata": {},
"source": [
"## Plot monthly"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16",
"metadata": {},
"outputs": [],
"source": [
"facet = plot.projected_map(\n",
" ds_monthly[\"total_ozone_column\"],\n",
" col=\"month\",\n",
" col_wrap=3,\n",
" projection=ccrs.Robinson(),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "17",
"metadata": {},
"source": [
"## Plot annual"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18",
"metadata": {},
"outputs": [],
"source": [
"facet = plot.projected_map(\n",
" ds_monthly[\"total_ozone_column\"],\n",
" col=\"year\",\n",
" col_wrap=6,\n",
" projection=ccrs.SouthPolarStereo(central_longitude=0),\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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"nbformat": 4,
"nbformat_minor": 5
}

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