# GRIB Data Example

GRIB format is commonly used to disseminate atmospheric model data. With xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized.

```ipython3
import xarray as xr
import matplotlib.pyplot as plt
%matplotlib inline
```

To read GRIB data, you can use `xarray.load_dataset`. The only extra code you need is to specify the engine as `cfgrib`.

```ipython3
ds = xr.tutorial.load_dataset("era5-2mt-2019-03-uk.grib", engine="cfgrib")
```

Let’s create a simple plot of 2-m air temperature in degrees Celsius:

```ipython3
ds = ds - 273.15
ds.t2m[0].plot(cmap=plt.cm.coolwarm)
```

With CartoPy, we can create a more detailed plot, using built-in shapefiles to help provide geographic context:

```ipython3
import cartopy.crs as ccrs
import cartopy

fig = plt.figure(figsize=(10, 10))
ax = plt.axes(projection=ccrs.Robinson())
ax.coastlines(resolution="10m")
plot = ds.t2m[0].plot(
    cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={"shrink": 0.6}
)
plt.title("ERA5 - 2m temperature British Isles March 2019")
```

Finally, we can also pull out a time series for a given location easily:

```ipython3
ds.t2m.sel(longitude=0, latitude=51.5).plot()
plt.title("ERA5 - London 2m temperature March 2019")
```