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cleanup
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@malmans2
malmans2 committed Mar 28, 2024
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Showing 1 changed file with 84 additions and 112 deletions.
196 changes: 84 additions & 112 deletions notebooks/wp5/ozone_merged_uv.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -20,15 +20,13 @@
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"import calendar\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import xarray as xr\n",
"from c3s_eqc_automatic_quality_control import diagnostics, download, plot, utils\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"plt.style.use(\"seaborn-v0_8-notebook\")"
]
},
Expand All @@ -45,26 +43,40 @@
"metadata": {},
"outputs": [],
"source": [
"# Time\n",
"year_start = 1996\n",
"year_stop = 2022\n",
"\n",
"# 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",
" \"northern hemisphere mid-latitudes\": {\n",
" \"global\": {\n",
" \"lat_slice\": slice(-90, 90),\n",
" \"lon_slice\": slice(0, 360),\n",
" },\n",
" \"tropics\": {\n",
" \"lat_slice\": slice(-25, 25),\n",
" \"lon_slice\": slice(0, 360),\n",
" },\n",
" \"NH mid-latitudes\": {\n",
" \"lat_slice\": slice(30, 60),\n",
" \"lon_slice\": slice(0, 360),\n",
" },\n",
" \"southern hemisphere mid-latitudes\": {\n",
" \"SH mid-latitudes\": {\n",
" \"lat_slice\": slice(-30, -60),\n",
" \"lon_slice\": slice(0, 360),\n",
" },\n",
" \"northern hemisphere polar\": {\n",
" \"NH polar\": {\n",
" \"lat_slice\": slice(60, 90),\n",
" \"lon_slice\": slice(0, 360),\n",
" },\n",
" \"southern hemisphere polar\": {\n",
" \"SH polar\": {\n",
" \"lat_slice\": slice(-60, -90),\n",
" \"lon_slice\": slice(0, 360),\n",
" },\n",
"}"
"}\n",
"\n",
"# Variable to show\n",
"varname = \"total_ozone_column\""
]
},
{
Expand All @@ -82,14 +94,6 @@
"outputs": [],
"source": [
"collection_id = \"satellite-ozone-v1\"\n",
"\n",
"chunks = {\"year\": 1}\n",
"\n",
"year_start = 1996\n",
"year_stop = 2022\n",
"\n",
"varname = \"total_ozone_column\"\n",
"\n",
"requests = {\n",
" \"format\": \"zip\",\n",
" \"processing_level\": \"level_3\",\n",
Expand All @@ -115,6 +119,12 @@
"metadata": {},
"outputs": [],
"source": [
"def convert_source_to_time(ds):\n",
" # Naming convention: YYYYMM-*.nc\n",
" ds[\"source\"] = pd.to_datetime(ds[\"source\"].str.slice(None, 6), format=\"%Y%m\")\n",
" return ds.rename(source=\"time\")\n",
"\n",
"\n",
"def spatial_weighted_mean(ds, lon_slice, lat_slice):\n",
" ds = convert_source_to_time(ds)\n",
" ds = utils.regionalise(ds, lon_slice=lon_slice, lat_slice=lat_slice)\n",
Expand All @@ -123,13 +133,7 @@
"\n",
"def time_weighted_mean(ds):\n",
" ds = convert_source_to_time(ds)\n",
" return diagnostics.time_weighted_mean(ds)\n",
"\n",
"\n",
"def convert_source_to_time(ds):\n",
" # Naming convention: YYYYMM-*.nc\n",
" ds[\"source\"] = pd.to_datetime(ds[\"source\"].str.slice(None, 6), format=\"%Y%m\")\n",
" return ds.rename(source=\"time\")"
" return diagnostics.time_weighted_mean(ds)"
]
},
{
Expand All @@ -145,41 +149,33 @@
"metadata": {},
"outputs": [],
"source": [
"ds_map = download.download_and_transform(\n",
" collection_id,\n",
" requests,\n",
" transform_func=convert_source_to_time,\n",
" split_all=True,\n",
")\n",
"kwargs = {\"collection_id\": collection_id, \"requests\": requests, \"chunks\": {\"year\": 1}}\n",
"\n",
"dataarrays = []\n",
"for region, slices in region_slices.items():\n",
"# Timeseries\n",
"datasets = []\n",
"for region, transform_func_kwargs in region_slices.items():\n",
" print(f\"{region=}\")\n",
" ds = download.download_and_transform(\n",
" collection_id,\n",
" requests,\n",
" **kwargs,\n",
" transform_func=spatial_weighted_mean,\n",
" transform_func_kwargs=slices,\n",
" chunks=chunks,\n",
" transform_func_kwargs=transform_func_kwargs,\n",
" drop_variables=\"time_bounds\",\n",
" )\n",
" dataarrays.append(ds[varname].expand_dims(region=[region]))\n",
"da = xr.concat(dataarrays, \"region\")\n",
" datasets.append(ds.expand_dims(region=[region]))\n",
"ds_timeseries = xr.concat(datasets, \"region\")\n",
"\n",
"DU_factor = ds_map[\"total_ozone_column\"].attrs[\"multiplication_factor_to_convert_to_DU\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Results"
"# Original data\n",
"ds = download.download_and_transform(\n",
" **kwargs,\n",
" transform_func=convert_source_to_time,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Coverage"
"## Plot Data Coverage"
]
},
{
Expand All @@ -188,31 +184,22 @@
"metadata": {},
"outputs": [],
"source": [
"nobs_time = ds_map[\"total_ozone_column_number_of_observations\"].sum(dim=\"time\")\n",
"nobs_lat = ds_map[\"total_ozone_column_number_of_observations\"].sum(dim=\"longitude\")\n",
"nobs_lon = ds_map[\"total_ozone_column_number_of_observations\"].sum(dim=\"latitude\")\n",
"\n",
"plot.projected_map(nobs_time, show_stats=False)\n",
"\n",
"fig, axes = plt.subplots(2)\n",
"ax1 = axes[0].contourf(\n",
" nobs_lat[\"time\"], nobs_lat[\"latitude\"], nobs_lat.transpose(), extend=\"max\"\n",
")\n",
"axes[0].set_ylabel(\"Latitude\")\n",
"axes[0].set_xlabel(\"Year\")\n",
"\n",
"ax2 = axes[1].contourf(\n",
" nobs_lon[\"time\"], nobs_lon[\"longitude\"], nobs_lon.transpose(), extend=\"max\"\n",
")\n",
"axes[1].set_ylabel(\"Longitude\")\n",
"axes[1].set_xlabel(\"Year\")\n",
"\n",
"# plt.contourf(ds_obs_lat[\"time\"],ds_obs_lat[\"latitude\"],\n",
"# ds_obs_lat.transpose(),\n",
"# extend='both')\n",
"da_obs = ds[f\"{varname}_number_of_observations\"]\n",
"plot.projected_map(da_obs.sum(dim=\"time\", keep_attrs=True), show_stats=False)\n",
"\n",
"cbar = fig.colorbar(ax1, ax=axes.ravel().tolist(), shrink=0.95)\n",
"cbar.set_label(\"Mean number of observations %\")"
"fig, axs = plt.subplots(2, 1)\n",
"for ax, dim in zip(axs.flatten(), (\"longitude\", \"latitude\")):\n",
" plot_obj = da_obs.sum(dim, keep_attrs=True).plot.contourf(\n",
" x=\"time\",\n",
" ax=ax,\n",
" levels=range(0, 10_500 + 1, 1_500),\n",
" extend=\"max\",\n",
" add_colorbar=False,\n",
" )\n",
"label = da_obs.attrs[\"long_name\"]\n",
"isplit = len(label) // 2\n",
"label = \"\\n\".join([label[:isplit], label[isplit:]])\n",
"_ = fig.colorbar(plot_obj, ax=axs.flatten(), label=label)"
]
},
{
Expand All @@ -228,13 +215,17 @@
"metadata": {},
"outputs": [],
"source": [
"da = ds[varname]\n",
"with xr.set_options(keep_attrs=True):\n",
" da = da * da.attrs[\"multiplication_factor_to_convert_to_DU\"]\n",
"da.attrs[\"units\"] = \"DU\"\n",
"\n",
"contour_kwargs = {\"levels\": range(160, 460, 10), \"extend\": \"both\", \"cmap\": \"RdGy_r\"}\n",
"map = plot.projected_map(\n",
" ds_map[\"total_ozone_column\"].mean(\"time\") * DU_factor,\n",
" cmap=\"RdGy_r\",\n",
" levels=range(160, 460, 10),\n",
" robust=True,\n",
" extend=\"both\",\n",
" da.mean(\"time\", keep_attrs=True),\n",
" plot_func=\"contourf\",\n",
" show_stats=False,\n",
" **contour_kwargs,\n",
")"
]
},
Expand All @@ -251,38 +242,11 @@
"metadata": {},
"outputs": [],
"source": [
"climatology_lon = ds_map.groupby(\"time.month\").mean([\"time\", \"longitude\"])\n",
"\n",
"fig, ax = plt.subplots()\n",
"im = ax.contourf(\n",
" climatology_lon[\"total_ozone_column\"][\"month\"],\n",
" climatology_lon[\"total_ozone_column\"][\"latitude\"],\n",
" climatology_lon[\"total_ozone_column\"].transpose(\"latitude\", \"month\") * DU_factor,\n",
" cmap=\"RdGy_r\",\n",
" extend=\"both\",\n",
" levels=range(160, 460, 10),\n",
")\n",
"ax.set_title(\"TCO [DU]\")\n",
"ax.set_ylabel(\"Latitude\")\n",
"ax.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
"ax.set_xticklabels(\n",
" [\n",
" \"JAN\",\n",
" \"FEB\",\n",
" \"MAR\",\n",
" \"APR\",\n",
" \"MAY\",\n",
" \"JUN\",\n",
" \"JUL\",\n",
" \"AUG\",\n",
" \"SEP\",\n",
" \"OCT\",\n",
" \"NOV\",\n",
" \"DEC\",\n",
" ],\n",
" size=10,\n",
")\n",
"fig.colorbar(im)"
"da_annual_cycle = da.groupby(\"time.month\").mean([\"time\", \"longitude\"], keep_attrs=True)\n",
"da_annual_cycle[\"month\"] = [\n",
" calendar.month_abbr[m] for m in da_annual_cycle[\"month\"].values\n",
"]\n",
"_ = da_annual_cycle.plot.contourf(x=\"month\", **contour_kwargs)"
]
},
{
Expand All @@ -298,9 +262,17 @@
"metadata": {},
"outputs": [],
"source": [
"da_anomaly = DU_factor * (da.groupby(\"time.month\") - da.groupby(\"time.month\").mean())\n",
"\n",
"fig = da_anomaly.plot(row=\"region\", col_wrap=3)"
"with xr.set_options(keep_attrs=True):\n",
" da_anomaly = (\n",
" ds_timeseries[varname].groupby(\"time.month\")\n",
" - ds_timeseries[varname].groupby(\"time.month\").mean()\n",
" )\n",
" da_anomaly *= da_anomaly.attrs[\"multiplication_factor_to_convert_to_DU\"]\n",
"da_anomaly.attrs[\"units\"] = \"DU\"\n",
"facet = da_anomaly.plot(row=\"region\", col_wrap=2)\n",
"for ax in facet.axs.flatten():\n",
" ax.grid()\n",
"facet.fig.autofmt_xdate(rotation=45)"
]
}
],
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