Lazily access ECMWF's ERA5 data via xarray from Planetary Computer's SpatioTemporal Asset Catalog (STAC) API. The data is physically stored in Zarr format on Azure Blob Filesystem (ABFS).

example_era5_planetary.py

"""
About
=====
Lazily access ECMWF's ERA5 data via xarray from Planetary Computer's
SpatioTemporal Asset Catalog (STAC) API. The data is physically stored in Zarr
format on Azure Blob Filesystem (ABFS).

Setup
=====

    pip install adlfs dask planetary-computer pystac-client xarray zarr

References
==========
- https://github.com/earthobservations/wetterdienst/issues/871#issuecomment-1447045874
- https://planetarycomputer.microsoft.com/dataset/era5-pds
- https://github.com/davemlz/cubo
"""
import pystac_client
import planetary_computer
import xarray as xr
from pystac import ItemCollection, Item


def stac_open_collection(name: str, **kwargs) -> ItemCollection:
    catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1/")
    search = catalog.search(collections=[name], **kwargs)
    return search.item_collection()


def stacitem_to_dataset(item: Item) -> xr.Dataset:
    signed_item = planetary_computer.sign(item)
    datasets = [
        xr.open_dataset(asset.href, **asset.extra_fields["xarray:open_kwargs"]) for asset in signed_item.assets.values()
    ]
    return xr.combine_by_coords(datasets, join="exact")


if __name__ == "__main__":
    collection = stac_open_collection("era5-pds", datetime="1980-01")
    ds = stacitem_to_dataset(collection[0])
    print(ds)

The shape of the xarray.Dataset looks like this:

<xarray.Dataset>
Dimensions:                                                                                   (
                                                                                               time: 744,
                                                                                               lat: 721,
                                                                                               lon: 1440,
                                                                                               nv: 2)
Coordinates:
  * lat                                                                                       (lat) float32 ...
  * lon                                                                                       (lon) float32 ...
  * time                                                                                      (time) datetime64[ns] ...
Dimensions without coordinates: nv
Data variables:
    air_temperature_at_2_metres_1hour_Maximum                                                 (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>
    time1_bounds                                                                              (time, nv) datetime64[ns] dask.array<chunksize=(744, 2), meta=np.ndarray>
    air_temperature_at_2_metres_1hour_Minimum                                                 (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>
    integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation  (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>
    precipitation_amount_1hour_Accumulation                                                   (time, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>
Attributes:
    institution:  ECMWF
    source:       Reanalysis
    tilte:        ERA5 forecasts
    title:        ERA5 forecasts