{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRIB Data Example "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GRIB format is commonly used to disseminate 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",
    "\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(\n",
    "    cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={\"shrink\": 0.6}\n",
    ")\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\")"
   ]
  }
 ],
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