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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.

import xarray as xr
import matplotlib.pyplot as plt

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

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

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[2], line 1
----> 1 ds = xr.tutorial.load_dataset("era5-2mt-2019-03-uk.grib", engine="cfgrib")

File ~/checkouts/readthedocs.org/user_builds/xray/checkouts/v2023.05.0/xarray/tutorial.py:206, in load_dataset(*args, **kwargs)
    169 def load_dataset(*args, **kwargs) -> Dataset:
    170     """
    171     Open, load into memory, and close a dataset from the online repository
    172     (requires internet).
   (...)
    204     load_dataset
    205     """
--> 206     with open_dataset(*args, **kwargs) as ds:
    207         return ds.load()

File ~/checkouts/readthedocs.org/user_builds/xray/checkouts/v2023.05.0/xarray/tutorial.py:161, in open_dataset(name, cache, cache_dir, engine, **kws)
    159 # retrieve the file
    160 filepath = pooch.retrieve(url=url, known_hash=None, path=cache_dir)
--> 161 ds = _open_dataset(filepath, engine=engine, **kws)
    162 if not cache:
    163     ds = ds.load()

File ~/checkouts/readthedocs.org/user_builds/xray/checkouts/v2023.05.0/xarray/backends/api.py:552, in open_dataset(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)
    549 if from_array_kwargs is None:
    550     from_array_kwargs = {}
--> 552 backend = plugins.get_backend(engine)
    554 decoders = _resolve_decoders_kwargs(
    555     decode_cf,
    556     open_backend_dataset_parameters=backend.open_dataset_parameters,
   (...)
    562     decode_coords=decode_coords,
    563 )
    565 overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None)

File ~/checkouts/readthedocs.org/user_builds/xray/checkouts/v2023.05.0/xarray/backends/plugins.py:205, in get_backend(engine)
    203     engines = list_engines()
    204     if engine not in engines:
--> 205         raise ValueError(
    206             f"unrecognized engine {engine} must be one of: {list(engines)}"
    207         )
    208     backend = engines[engine]
    209 elif isinstance(engine, type) and issubclass(engine, BackendEntrypoint):

ValueError: unrecognized engine cfgrib must be one of: ['netcdf4', 'h5netcdf', 'scipy', 'store', 'zarr']

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

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

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[3], line 1
----> 1 ds = ds - 273.15
      2 ds.t2m[0].plot(cmap=plt.cm.coolwarm)

NameError: name 'ds' is not defined

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

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")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[4], line 7
      5 ax = plt.axes(projection=ccrs.Robinson())
      6 ax.coastlines(resolution="10m")
----> 7 plot = ds.t2m[0].plot(
      8     cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={"shrink": 0.6}
      9 )
     10 plt.title("ERA5 - 2m temperature British Isles March 2019")

NameError: name 'ds' is not defined

/home/docs/checkouts/readthedocs.org/user_builds/xray/conda/v2023.05.0/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

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

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

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 ds.t2m.sel(longitude=0, latitude=51.5).plot()
      2 plt.title("ERA5 - London 2m temperature March 2019")

NameError: name 'ds' is not defined