diff --git a/doc/environment.yml b/doc/environment.yml
index 9aadfa745a1..596a8df6a7f 100644
--- a/doc/environment.yml
+++ b/doc/environment.yml
@@ -29,3 +29,4 @@ dependencies:
   - jupyter_client=5.3.1
   - ipykernel=5.1.1
   - pip
+  - cfgrib
diff --git a/doc/examples/ERA5-GRIB-example.ipynb b/doc/examples/ERA5-GRIB-example.ipynb
new file mode 100644
index 00000000000..b82a07a64e6
--- /dev/null
+++ b/doc/examples/ERA5-GRIB-example.ipynb
@@ -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
+}