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Add an example of ERA5 and GRIB data & visualization to the gallery (p…
…ydata#3199) * Adds an example of ERA5 and GRIB data to the gallery * Add markdown narrative cells to GRIB example * Update load method to use xr.tutorial * Fix load method
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@@ -6,6 +6,7 @@ dependencies: | |
- python=3.7 | ||
- bottleneck | ||
- cartopy | ||
- cfgrib | ||
- h5netcdf | ||
- ipython | ||
- iris | ||
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# GRIB Data Example " | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"GRIB format is commonly used to disemminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import xarray as xr\n", | ||
"import matplotlib.pyplot as plt" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"To read GRIB data, you can use `xarray.load_dataset`. The only extra code you need is to specify the engine as `cfgrib`." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"ds = xr.tutorial.load_dataset('era5-2mt-2019-03-uk.grib', engine='cfgrib')" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"Let's create a simple plot of 2-m air temperature in degrees Celsius:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"ds = ds - 273.15\n", | ||
"ds.t2m[0].plot(cmap=plt.cm.coolwarm)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"With CartoPy, we can create a more detailed plot, using built-in shapefiles to help provide geographic context:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import cartopy.crs as ccrs\n", | ||
"import cartopy\n", | ||
"fig = plt.figure(figsize=(10,10))\n", | ||
"ax = plt.axes(projection=ccrs.Robinson())\n", | ||
"ax.coastlines(resolution='10m')\n", | ||
"plot = ds.t2m[0].plot(cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={'shrink':0.6})\n", | ||
"plt.title('ERA5 - 2m temperature British Isles March 2019')" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"Finally, we can also pull out a time series for a given location easily:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"ds.t2m.sel(longitude=0,latitude=51.5).plot()\n", | ||
"plt.title('ERA5 - London 2m temperature March 2019')" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.7.3" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 4 | ||
} |