esmvalcore.io.xarray#

Access data using xarray.open_dataset().

To use this module, copy one of the following configuration files to your configuration directory:

data-cds-era5.yml

Enable access to ERA5 data stored in the Copernicus Climate Data Store (CDS):

esmvaltool config copy data-cds-era5.yml

The file content is:

Listing 21 Contents of data-cds-era5.yml#

# Read ERA5 data in zarr format on Climate Data Store.
# Go to
# https://github.com/ecmwf-training/dss-notebooks/blob/main/datasets%2Freanalysis-era5-single-levels%2Farco-access.ipynb
# for further information.
projects:
  native6:
    data:
      cds-era5-zarr:
        type: esmvalcore.io.xarray.XarrayDataSource
        # Available stores:
        # - Geo-chunked surface levels data (optimised for time-series at a single location):
        #   https://arco.datastores.ecmwf.int/cadl-arco-geo-002/arco/reanalysis_era5_single_levels/sfc/geoChunked.zarr
        # - Time-chunked surface levels data (optimised for global map at a single time step):
        #   https://arco.datastores.ecmwf.int/cadl-arco-time-002/arco/reanalysis_era5_single_levels/sfc/timeChunked.zarr
        # - Geo-chunked wave data (optimised for time-series at a single location):
        #   https://arco.datastores.ecmwf.int/cadl-arco-geo-003/arco/reanalysis_era5_single_levels/wav/geoChunked.zarr
        # - Time-chunked wave data (optimised for global map at a single time step):
        #   https://arco.datastores.ecmwf.int/cadl-arco-time-003/arco/reanalysis_era5_single_levels/wav/timeChunked.zarr
        store: "https://arco.datastores.ecmwf.int/cadl-arco-time-002/arco/reanalysis_era5_single_levels/sfc/timeChunked.zarr"
        options:
          chunks: "auto"
          consolidated: true
          engine: "zarr"
          storage_options:
            headers:
              Authorization: "Bearer <INSERT YOUR CDS API KEY HERE>"
        variables:
          t2m: tas
        facets:
          dataset: "ERA5"
          frequency: "1hr"

Then edit the copied file and replace the text <INSERT YOUR CDS API KEY HERE> with your personal CDS API key.

data-gcs-era5.yml
Enable access to ERA5 data stored in Google Cloud Storage:
esmvaltool config copy data-gcs-era5.yml
Install gcsfs alongside ESMValCore to use this data source.

Classes:

`XarrayDataSource`(name, project, priority, ...)	Data source that can find data using `xarray`.
`XarrayDataset`(name, facets, store, options, ...)	A dataset that can load data using `xarray`.

class esmvalcore.io.xarray.XarrayDataSource(name: str, project: str, priority: int, variables: dict[str, str], facets: Facets, store: str, options: dict[str, Any] = <factory>)[source]#

Bases: DataSource

Data source that can find data using xarray.

Attributes:

`debug_info`	A string containing debug information when no data is found.
`facets`	Facets that will be added to the data elements found using this data source.
`name`	A name identifying the data source.
`options`	Options to use when opening the data store.
`priority`	The priority of the data source.
`project`	The project that the data source provides data for.
`store`	The store used to load the data.
`variables`	Mapping between the dataset variable name and ESMValCore `short_name`.

Methods:

find_data(**facets)

Find data.

Parameters:

name (str)
project (str)
priority (int)
variables (dict[str, str])
facets (Facets)
store (str)
options (dict[str, Any])

debug_info: str = ''#: A string containing debug information when no data is found.

facets: Facets#: Facets that will be added to the data elements found using this data source.

find_data(**facets: FacetValue) → list[XarrayDataset][source]#

Find data.

Parameters:: **facets (FacetValue) – Find data matching these facets.
Returns:: A list of data elements that have been found.
Return type:: list[XarrayDataset]

name: str#: A name identifying the data source.

options: dict[str, Any]#: Options to use when opening the data store.

priority: int#: The priority of the data source. Lower values have priority.

project: str#: The project that the data source provides data for.

store: str#: The store used to load the data.

variables: dict[str, str]#: Mapping between the dataset variable name and ESMValCore short_name.

class esmvalcore.io.xarray.XarrayDataset(name: str, facets: Facets, store: str, options: dict[str, Any] = <factory>)[source]#

Bases: DataElement

A dataset that can load data using xarray.

Attributes:

`attributes`	Attributes are key-value pairs describing the data.
`facets`	Facets are key-value pairs that were used to find this data.
`name`	A unique name identifying the dataset.
`options`	Options to use when opening the data.
`store`	The data store containing the data.

Methods:

`prepare`()	Prepare the data for access.
`to_iris`()	Load the data as Iris cubes.

Parameters:

name (str)
facets (Facets)
store (str)
options (dict[str, Any])

property attributes: dict[str, Any]#: Attributes are key-value pairs describing the data.

facets: Facets#: Facets are key-value pairs that were used to find this data.

name: str#: A unique name identifying the dataset.

options: dict[str, Any]#: Options to use when opening the data.

prepare() → None[source]#

Prepare the data for access.

Return type:: None

store: str#: The data store containing the data.

to_iris() → iris.cube.CubeList[source]#

Load the data as Iris cubes.

Returns:: The loaded data.
Return type:: iris.cube.CubeList