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Add an example of ERA5 and GRIB data & visualization to the gallery (#…
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…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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zbruick authored and rabernat committed Sep 23, 2019
1 parent f7c12a9 commit f82d112
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1 change: 1 addition & 0 deletions doc/environment.yml
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Expand Up @@ -29,3 +29,4 @@ dependencies:
- jupyter_client=5.3.1
- ipykernel=5.1.1
- pip
- cfgrib
121 changes: 121 additions & 0 deletions doc/examples/ERA5-GRIB-example.ipynb
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@@ -0,0 +1,121 @@
{
"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
}

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