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The ARCO data is stored as Zarr datacubes to provide efficient access to time-chunked and geo-chunked ERA5 pressure levels 6-hourly data:


Info
titleTarget audience

Access to ARCO data is programmatic; therefore, users of these resources and this documentation are expected to have some relevant programming experience.

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Code Block
titlePlug and play xarray access
collapsetrue
import xarray as xr

# Geo-chunked data for access optimised along the time dimension (e.g. for time-series at a single point)
# Surface
geo_url = "https://arco.datastores.ecmwf.int/cadl-arco-geo-045/arco/reanalysis_cerra_land/surface/geoChunked.zarr"

# Time-chunked data for access optimised along the time dimension (e.g. for short-time period for large areas)
# Surface
time_url = "https://arco.datastores.ecmwf.int/cadl-arco-time-045/arco/reanalysis_cerra_land/surface/timeChunked.zarr"

# Open one of the Zarr objects with xarray, the default example opens the geo-chunked surface variables
ds = xr.open_zarr(
    surface_geo_url,
    consolidated=True,
    storage_options={
        "headers": {"Authorization": f"Bearer <CDS-API-KEY>"}
    }
)

# Inspect the variables
print(ds)
Expand
titleJupyter Notebook Example

Jupyter Viewer
notebookUrlhttps://github.com/ecmwf-training/dss-notebooks/blob/main/datasets/reanalysis-era5-pressure-levels/arco-access.ipynb
applicationLinka675ea11-b2c4-336c-bfb6-077e786ef5b2

Time-series access via the CDS

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Table 1: List of available parameters


NameUnitsVariable nameRemarks
1

Evaporation

kg m-2
evaporation
type: forecast
2Surface latent heat fluxJ m-2
surface_latent_heat_flux
type: forecast
3Surface sensible heat fluxJ m-2
surface_sensible_heat_flux
type: forecast
4Total precipitationkg m-2
total_precipitation
type: analysis
5Volumetric soil moisturem3 m-3
volumetric_soil_moisture
type: forecast

Known issues

Please refer to the CERRA-Land documentation.

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