EO-LINCS project: Cube generation for Scientific Case Study (SCS) 3¶

Model-Data Fusion for Understanding Carbon State-Flux Relationships Across Space¶

Objective: SCS3 aims to improve the accuracy of ecosystem carbon cycle models by integrating in-situ observation networks with complementary EO products as inputs. The data cubes will be generated in a manner that allows seamless integration within the SINDBAD ecosystem modelling framework.

Outcomes: Use a terrestrial carbon model structure that can deliver a process understanding of carbon stateflux relationships across space by leveraging and cross-comparing EO data of biomass and vegetation states (fAPAR, LAI, vegetation fraction, etc.) together with ecosystem carbon flux measurements and estimates. Additionally, provide open-source novel MDI tools and workflows for community use paving the way for adoption by other data-assimilation frameworks.

Required Datasets

For this science case, we use small spatial cutouts around flux towers from the following datasets:

• Sentinel-2 L2A
• ERA5-Land
• ESA CCI Biomass

---

Overview of the Cube Generation Pipeline¶

This notebook demonstrates how users can access the required datasets via the xcube Multi-Source Data Store. The full example is available in the GitHub repository. All parameters controlling the cube generation workflow are defined in the configuration file config.yml.

Comprehensive documentation of the configuration schema is available on the documentation webpage. A step-by-step example showing how to set up a configuration file—while extracting information from multiple data stores—is provided here: Setup Config YAML File.

In this notebook, we provide:

• A concise preview of the configuration file (summarized in a table)
• The requested geospatial domain
• A walkthrough of the cube generation process

---

Requirements¶

Before proceeding, ensure that all required dependencies are installed.

Create the conda environment using the following command:

conda env create -f environment.yml

The corresponding file can be found here: environment.yml

After creation, activate the environment:

conda activate eo-lincs-scs3

Next, accessing ERA5 reanalysis data via the Copernicus Data Store (CDS) requires a valid CDS API key. This can be obtained by following the instructions in the xcube-cds documentation.

Once obtained, the credentials must be added to the configuration file. Because the configuration file is generated programmatically while iterating over the flux tower sites, the credentials should be inserted into the Python code shown below, at the end of Cell 5 in the section labeled # add CDS data store.

---

Running the Pipeline¶

Once all dependencies are installed, the final data cubes can be generated by executing this notebook.

The results will be stored locally in the data directory on the top level of the repository, as defined in the configuration YAML file. Note that the user can change the storage data store by specifying a different location, or by selecting an alternative backend such as a "s3" data store to store the final data cubes in object storage. the Initial visualisations of the generated cubes are displayed at the end of the notebook.

---

Imports¶

Let's import everything we need:

In [1]:

import yaml

import pandas as pd
from xcube_multistore.multistore import MultiSourceDataStore

import yaml import pandas as pd from xcube_multistore.multistore import MultiSourceDataStore

---

Configuration of the Cube Generation Process¶

You can explore the available options for the configuration file using the function get_config_schema(). Run it and expand the fields to see all the properties that can be set, in combination with the Configuration Guide.

For guidance on creating your configuration file, you can also refer to the notebook setup_config.ipynb or consult the Configuration Guide in the documentation.

In [2]:

MultiSourceDataStore.get_config_schema()

MultiSourceDataStore.get_config_schema()

Out[2]:

<xcube.util.jsonschema.JsonObjectSchema at 0x797927f8a970>

Since this science case focuses on small spatial cutouts and long time series around flux towers, the configuration for the MultiSourceDataStore is generated programmatically. This enables efficient iteration over individual flux tower sites.

Sentinel-2 access, a time-series mode has been implemented in xcube-stac. It identifies the tiles corresponding to the closest WGS Sentinel-2 grid and performs spatial sub-setting while preserving the native 10 m resolution. Bands at lower resolutions (20 m and 60 m) are resampled to the 10 m grid. This approach preserves the data in its most native form and minimises processing during cube generation.

This mode is available for both Planetary Computer and CDSE. On Planetary Computer, data is stored as GeoTIFF, allowing efficient chunked access, while on CDSE it is stored as JPEG2000, which requires full tile downloads. As a result, Planetary Computer is roughly 10× faster than CDSE when retrieving small cutouts. Switching between the two data stores only requires updating the store configuration. Replace the respective data store in the configuration YAML:

- identifier: stac-pc
  store_id: stac-pc-ardc

with:

- identifier: stac-cdse
  store_id: stac-cdse-ardc
  store_params:
    key: <CDSE S3 key>
    secret: <CDSE S3 secret>

The required CDSE S3 credentials can be generated by following the CDSE S3 documentation.

For ERA5-Land, only a small area around each site is requested. Due to strict request limits from the Copernicus Climate Data Store (CDS), requests are performed sequentially for each site. The retrieved cubes are stacked and interpolated to the exact tower location, allowing time series extraction at a single point rather than from a bounding box. A new dataset, ERA5 hourly time series (reanalysis-era5-single-levels-timeseries), provides point-level access, but currently offers only a limited set of variables, making it unsuitable for this science case (it is used in SCS 1).

The ESA CCI dataset is accessed using xcube-cci. It is loaded lazily and resampled on the fly to match the Sentinel-2 grid. This alignment can be specified in the configuration by setting the grid_mapping field to the Sentinel-2 data ID.

We will now create the configuration file for the first two flux tower sites as an example.

In [3]:

sites = pd.read_csv("sites.csv")
sites = sites.iloc[:2]
sites

sites = pd.read_csv("sites.csv") sites = sites.iloc[:2] sites

Out[3]:

	Site ID	latitude	longitude	IGBP
0	AU-Dry	-15.2588	132.3706	SAV
1	AU-How	-12.4943	131.1523	WSA

In [4]:

time_range = ["2020-01-01", "2020-06-30"]

time_range = ["2020-01-01", "2020-06-30"]

In [5]:

config = dict(datasets=[])
for index, site in sites.iterrows():    
    # append config for Sentinel-2
    config_ds = dict(
        identifier=f"{site['Site ID']}/sen2",
        store="stac-store",
        data_id="sentinel-2-l2a",
        open_params=dict(
            time_range=time_range,
            point=[site["longitude"], site["latitude"]],
            bbox_width=4000,
            spatial_res=10,
            asset_names=[
                "B01",
                "B02",
                "B03",
                "B04",
                "B05",
                "B06",
                "B07",
                "B08",
                "B8A",
                "B09",
                "B11",
                "B12",
                "SCL",
            ],
        ),
    )
    config["datasets"].append(config_ds)

    # append config for ERA5
    config_ds = dict(
        identifier=f"{site['Site ID']}/era5_land",
        store="cds",
        data_id="reanalysis-era5-land",
        open_params=dict(
            variable_names=[
                "2m_temperature",
                "total_precipitation", 
                "surface_solar_radiation_downwards", 
                "surface_net_solar_radiation", 
                "2m_dewpoint_temperature",    
            ],
            time_range=time_range,
            point=[site["longitude"], site["latitude"]],
            spatial_res=0.1,
        ),
    )
    config["datasets"].append(config_ds)

    # append config for ESA CCI
    config_ds = dict(
        identifier=f"{site['Site ID']}/esa_cci_biomass",
        store="esa-cci-store",
        grid_mapping=f"{site['Site ID']}/sen2",
        data_id="esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.100m",
        open_params=dict(
            time_range=time_range,
        ),
    )
    config["datasets"].append(config_ds)

# define stores
config["data_stores"] = []
# add storage data store
config_store = dict(
    identifier="storage",
    store_id="file",
    store_params=dict(root="../data"),
)
config["data_stores"].append(config_store)
# add ESA CCI data store
config_store = dict(
    identifier="esa-cci-store",
    store_id="cciodp",
)
config["data_stores"].append(config_store)
# add STAC data store
config_store = dict(
    identifier="stac-store",
    store_id="stac-pc-ardc",
)
config["data_stores"].append(config_store)
# add CDS data store
config_store = dict(
    identifier="cds",
    store_id="cds",
    store_params=dict(
        endpoint_url="https://cds.climate.copernicus.eu/api",
        cds_api_key="917c02bf-1a72-468c-a3b3-482affbdfb87",
        normalize_names=True,
    ),
)
config["data_stores"].append(config_store)

config = dict(datasets=[]) for index, site in sites.iterrows(): # append config for Sentinel-2 config_ds = dict( identifier=f"{site['Site ID']}/sen2", store="stac-store", data_id="sentinel-2-l2a", open_params=dict( time_range=time_range, point=[site["longitude"], site["latitude"]], bbox_width=4000, spatial_res=10, asset_names=[ "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B11", "B12", "SCL", ], ), ) config["datasets"].append(config_ds) # append config for ERA5 config_ds = dict( identifier=f"{site['Site ID']}/era5_land", store="cds", data_id="reanalysis-era5-land", open_params=dict( variable_names=[ "2m_temperature", "total_precipitation", "surface_solar_radiation_downwards", "surface_net_solar_radiation", "2m_dewpoint_temperature", ], time_range=time_range, point=[site["longitude"], site["latitude"]], spatial_res=0.1, ), ) config["datasets"].append(config_ds) # append config for ESA CCI config_ds = dict( identifier=f"{site['Site ID']}/esa_cci_biomass", store="esa-cci-store", grid_mapping=f"{site['Site ID']}/sen2", data_id="esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.100m", open_params=dict( time_range=time_range, ), ) config["datasets"].append(config_ds) # define stores config["data_stores"] = [] # add storage data store config_store = dict( identifier="storage", store_id="file", store_params=dict(root="../data"), ) config["data_stores"].append(config_store) # add ESA CCI data store config_store = dict( identifier="esa-cci-store", store_id="cciodp", ) config["data_stores"].append(config_store) # add STAC data store config_store = dict( identifier="stac-store", store_id="stac-pc-ardc", ) config["data_stores"].append(config_store) # add CDS data store config_store = dict( identifier="cds", store_id="cds", store_params=dict( endpoint_url="https://cds.climate.copernicus.eu/api", cds_api_key="917c02bf-1a72-468c-a3b3-482affbdfb87", normalize_names=True, ), ) config["data_stores"].append(config_store)

In [6]:

with open("config.yml", "w") as file:
    yaml.dump(config, file, sort_keys=False)

with open("config.yml", "w") as file: yaml.dump(config, file, sort_keys=False)

Now, we can initialize the MultiSourceDataStore by passing the path to the config.yml which currently is on the same level as this notebook.

In [7]:

msds = MultiSourceDataStore("config.yml")

msds = MultiSourceDataStore("config.yml")

And we can display the overview of the configuration file for each dataset.

In [8]:

msds.display_config()

msds.display_config()

Configuration

User-defined ID	Data Store ID	Data Store Params	Data ID	Open Data Params	Grid-Mapping	Format
AU-Dry/sen2	stac-pc-ardc	-	sentinel-2-l2a	time_range: ['2020-01-01', '2020-06-30']; point: [132.3706, -15.2588]; bbox_width: 4000; spatial_res: 10; asset_names: ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'SCL']	-	Zarr
AU-Dry/era5_land	cds	endpoint_url: https://cds.climate.copernicus.eu/api; cds_api_key: 917c02bf-1a72-468c-a3b3-482affbdfb87; normalize_names: True	reanalysis-era5-land	variable_names: ['2m_temperature', 'total_precipitation', 'surface_solar_radiation_downwards', 'surface_net_solar_radiation', '2m_dewpoint_temperature']; time_range: ['2020-01-01', '2020-06-30']; point: [132.3706, -15.2588]; spatial_res: 0.1	-	Zarr
AU-Dry/esa_cci_biomass	cciodp	-	esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.100m	time_range: ['2020-01-01', '2020-06-30']	Like 'AU-Dry/sen2'	Zarr
AU-How/sen2	stac-pc-ardc	-	sentinel-2-l2a	time_range: ['2020-01-01', '2020-06-30']; point: [131.1523, -12.4943]; bbox_width: 4000; spatial_res: 10; asset_names: ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'SCL']	-	Zarr
AU-How/era5_land	cds	endpoint_url: https://cds.climate.copernicus.eu/api; cds_api_key: 917c02bf-1a72-468c-a3b3-482affbdfb87; normalize_names: True	reanalysis-era5-land	variable_names: ['2m_temperature', 'total_precipitation', 'surface_solar_radiation_downwards', 'surface_net_solar_radiation', '2m_dewpoint_temperature']; time_range: ['2020-01-01', '2020-06-30']; point: [131.1523, -12.4943]; spatial_res: 0.1	-	Zarr
AU-How/esa_cci_biomass	cciodp	-	esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.100m	time_range: ['2020-01-01', '2020-06-30']	Like 'AU-How/sen2'	Zarr

We can display the selected bounding box as shown in the following cell.

In [9]:

msds.display_geolocations()

msds.display_geolocations()

Out[9]:

Make this Notebook Trusted to load map: File -> Trust Notebook

---

Cube Generation¶

Cube generation is triggered with a single command, and each dataset is processed sequentially. The workflow applies the following steps as needed:

• Request the dataset
• Perform dataset-specific preprocessing (e.g. spatio-temporal resampling)
• Writing the final datacube to the data/<site_id>/ output directory

Dataset Notes¶

• sen2 Generates an analysis-ready datacube by stacking multiple Sentinel-2 Level-2A observations along the time dimension and subsetting to the small cutout. The data is kept in the native UTM grid to minimize reprojection errors.

• era5land Provides time series at the flux tower location for the selected variables.

• esa_cci_biomass Accessed lazily via the xcube ESA CCI datastore, with on-the-fly resampling aligned to the Sentinel-2 grid.

Note: Most of the logging is related to the Copernicus Data Store (CDS) when retrieving ERA-5 reanalysis data.

In [10]:

msds.generate()

msds.generate()

Cube Generation

Dataset identifier	Status	Message	Exception
AU-Dry/sen2	COMPLETED	Dataset 'AU-Dry/sen2' already generated.	-
AU-Dry/era5_land	COMPLETED	Dataset 'AU-Dry/era5_land' finished: 0:06:18	-
AU-Dry/esa_cci_biomass	COMPLETED	Dataset 'AU-Dry/esa_cci_biomass' already generated.	-
AU-How/sen2	COMPLETED	Dataset 'AU-How/sen2' already generated.	-
AU-How/era5_land	COMPLETED	Dataset 'AU-How/era5_land' already generated.	-
AU-How/esa_cci_biomass	COMPLETED	Dataset 'AU-How/esa_cci_biomass' already generated.	-

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2026-06-18 10:29:56,908 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-timeseries?tab=overview)
2026-06-18 10:29:56,909 INFO Request ID is 98bf1a55-b296-4747-b3a8-d16ac98c5990
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xcube-cds version 1.4.1
2026-06-18 10:35:13,870 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-timeseries?tab=overview)
2026-06-18 10:35:13,871 INFO Request ID is fdd5c7f2-3b25-474e-9994-6646a57a1e1d
2026-06-18 10:35:13,921 INFO status has been updated to accepted
2026-06-18 10:35:35,159 INFO status has been updated to successful

90e17cb577719ff5e5fff787aaad98b0.zip:   0%|          | 0.00/161k [00:00<?, ?B/s]

xcube-cds version 1.4.1
2026-06-18 10:35:36,128 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-timeseries?tab=overview)
2026-06-18 10:35:36,129 INFO Request ID is f2a46609-33a6-4e0f-ad72-f4965ff1795f
2026-06-18 10:35:36,195 INFO status has been updated to accepted
2026-06-18 10:36:00,681 INFO status has been updated to running
2026-06-18 10:36:12,145 INFO status has been updated to successful

3dd6fec4ab454f2a44ef847d140c4f66.zip:   0%|          | 0.00/164k [00:00<?, ?B/s]

---

Initial Inspection of the Generated Datasets¶

After cube generation has completed, the datasets can be opened using the xcube datastore framework API as usual.

Note that the multi-source datastore expects a datastore named storage. This datastore is defined in the data_stores section of the config.yml configuration file.

In [11]:

ds = msds.stores.storage.open_data("AU-Dry/sen2.zarr")
ds

ds = msds.stores.storage.open_data("AU-Dry/sen2.zarr") ds

Out[11]:

<xarray.Dataset> Size: 332MB
Dimensions:      (time: 37, y: 400, x: 400)
Coordinates:
  * time         (time) datetime64[ns] 296B 2020-01-02T01:27:09.024000 ... 20...
  * y            (y) float64 3kB 8.312e+06 8.312e+06 ... 8.308e+06 8.308e+06
  * x            (x) float64 3kB 8.601e+05 8.601e+05 ... 8.641e+05 8.641e+05
    spatial_ref  int64 8B ...
Data variables: (12/13)
    B01          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B02          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B03          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B04          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B05          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B06          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    ...           ...
    B08          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B09          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B11          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B12          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B8A          (time, y, x) float32 24MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SCL          (time, y, x) float64 47MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://planetarycomputer.microsoft.com/api/stac/v1
    stac_item_id:        S2B_MSIL2A_20200102T012709_R131_T52LHJ_20201002T215923
    stac_item_ids:       {'2020-01-02T01:27:09.024000': ['S2B_MSIL2A_20200102...
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • time: 37
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-01-02T01:27:09.024000 ... 2...

    array(['2020-01-02T01:27:09.024000000', '2020-01-07T01:27:11.024000000',
           '2020-01-12T01:27:09.024000000', '2020-01-17T01:27:11.024000000',
           '2020-01-22T01:27:09.024000000', '2020-01-27T01:27:11.024000000',
           '2020-02-01T01:27:09.024000000', '2020-02-06T01:27:11.024000000',
           '2020-02-11T01:27:09.024000000', '2020-02-16T01:27:11.024000000',
           '2020-02-21T01:27:09.024000000', '2020-02-26T01:27:11.024000000',
           '2020-03-02T01:27:19.024000000', '2020-03-07T01:27:11.024000000',
           '2020-03-12T01:27:19.024000000', '2020-03-17T01:27:11.024000000',
           '2020-03-22T01:27:19.024000000', '2020-03-27T01:27:11.024000000',
           '2020-04-01T01:27:19.024000000', '2020-04-06T01:27:11.024000000',
           '2020-04-11T01:27:09.024000000', '2020-04-16T01:27:21.024000000',
           '2020-04-21T01:27:09.024000000', '2020-04-26T01:27:21.024000000',
           '2020-05-01T01:27:09.024000000', '2020-05-06T01:27:21.024000000',
           '2020-05-11T01:27:19.024000000', '2020-05-16T01:27:21.024000000',
           '2020-05-21T01:27:19.024000000', '2020-05-26T01:27:21.024000000',
           '2020-05-31T01:27:19.024000000', '2020-06-05T01:27:21.024000000',
           '2020-06-10T01:27:19.024000000', '2020-06-15T01:27:21.024000000',
           '2020-06-20T01:27:19.024000000', '2020-06-25T01:27:21.024000000',
           '2020-06-30T01:27:19.024000000'], dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.312e+06 8.312e+06 ... 8.308e+06

    array([8312235., 8312225., 8312215., ..., 8308265., 8308255., 8308245.],
          shape=(400,))

  • x
    (x)
    float64
    8.601e+05 8.601e+05 ... 8.641e+05

    array([860115., 860125., 860135., ..., 864085., 864095., 864105.], shape=(400,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["WGS 84 / UTM zone 52S",BASEGEOGCRS["WGS 84",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]],CONVERSION["UTM zone 52S",METHOD["Transverse Mercator",ID["EPSG",9807]],PARAMETER["Latitude of natural origin",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",129,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Scale factor at natural origin",0.9996,SCALEUNIT["unity",1],ID["EPSG",8805]],PARAMETER["False easting",500000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",10000000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["easting",east,ORDER[1],LENGTHUNIT["metre",1]],AXIS["northing",north,ORDER[2],LENGTHUNIT["metre",1]],ID["EPSG",32752]]

    false_easting :

    500000.0

    false_northing :

    10000000.0

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    transverse_mercator

    horizontal_datum_name :

    World Geodetic System 1984

    inverse_flattening :

    298.257223563

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    129.0

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    WGS 84 / UTM zone 52S

    reference_ellipsoid_name :

    WGS 84

    scale_factor_at_central_meridian :

    0.9996

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    [1 values with dtype=int64]

• Data variables: (13)
  • B01
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B02
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B03
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B04
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B05
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B06
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B07
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B08
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B09
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B11
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B12
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B8A
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 22.58 MiB 625.00 kiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SCL
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 45.17 MiB 1.22 MiB Shape (37, 400, 400) (1, 400, 400) Dask graph 37 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (4)

  stac_catalog_url :

  https://planetarycomputer.microsoft.com/api/stac/v1

  stac_item_id :

  S2B_MSIL2A_20200102T012709_R131_T52LHJ_20201002T215923

  stac_item_ids :

  {'2020-01-02T01:27:09.024000': ['S2B_MSIL2A_20200102T012709_R131_T52LHJ_20201002T215923', 'S2B_MSIL2A_20200102T012709_R131_T53LKD_20201002T215924'], '2020-01-07T01:27:11.024000': ['S2A_MSIL2A_20200107T012711_R131_T52LHJ_20201029T104239', 'S2A_MSIL2A_20200107T012711_R131_T53LKD_20201029T104257'], '2020-01-12T01:27:09.024000': ['S2B_MSIL2A_20200112T012709_R131_T52LHJ_20201009T171852', 'S2B_MSIL2A_20200112T012709_R131_T53LKD_20201002T175001'], '2020-01-17T01:27:11.024000': ['S2A_MSIL2A_20200117T012711_R131_T52LHJ_20201002T194050', 'S2A_MSIL2A_20200117T012711_R131_T53LKD_20201002T194043'], '2020-01-22T01:27:09.024000': ['S2B_MSIL2A_20200122T012709_R131_T52LHJ_20201002T054127', 'S2B_MSIL2A_20200122T012709_R131_T53LKD_20201002T054140'], '2020-01-27T01:27:11.024000': ['S2A_MSIL2A_20200127T012711_R131_T52LHJ_20201002T121225', 'S2A_MSIL2A_20200127T012711_R131_T53LKD_20201002T121233'], '2020-02-01T01:27:09.024000': ['S2B_MSIL2A_20200201T012709_R131_T52LHJ_20200930T085614', 'S2B_MSIL2A_20200201T012709_R131_T53LKD_20200930T085640'], '2020-02-06T01:27:11.024000': ['S2A_MSIL2A_20200206T012711_R131_T52LHJ_20201001T005009', 'S2A_MSIL2A_20200206T012711_R131_T53LKD_20201001T005031'], '2020-02-11T01:27:09.024000': ['S2B_MSIL2A_20200211T012709_R131_T52LHJ_20201001T173744', 'S2B_MSIL2A_20200211T012709_R131_T53LKD_20201001T173756'], '2020-02-16T01:27:11.024000': ['S2A_MSIL2A_20200216T012711_R131_T52LHJ_20200929T085305', 'S2A_MSIL2A_20200216T012711_R131_T53LKD_20200929T085335'], '2020-02-21T01:27:09.024000': ['S2B_MSIL2A_20200221T012709_R131_T52LHJ_20200927T151927', 'S2B_MSIL2A_20200221T012709_R131_T53LKD_20200927T152007'], '2020-02-26T01:27:11.024000': ['S2A_MSIL2A_20200226T012711_R131_T52LHJ_20200928T104224', 'S2A_MSIL2A_20200226T012711_R131_T53LKD_20200928T104240'], '2020-03-02T01:27:19.024000': ['S2B_MSIL2A_20200302T012719_R131_T52LHJ_20200926T025244', 'S2B_MSIL2A_20200302T012719_R131_T53LKD_20200926T025158'], '2020-03-07T01:27:11.024000': ['S2A_MSIL2A_20200307T012711_R131_T52LHJ_20201004T020326', 'S2A_MSIL2A_20200307T012711_R131_T53LKD_20201004T015711'], '2020-03-12T01:27:19.024000': ['S2B_MSIL2A_20200312T012719_R131_T52LHJ_20201007T025651', 'S2B_MSIL2A_20200312T012719_R131_T53LKD_20201007T025223'], '2020-03-17T01:27:11.024000': ['S2A_MSIL2A_20200317T012711_R131_T52LHJ_20201010T115237', 'S2A_MSIL2A_20200317T012711_R131_T53LKD_20201010T115226'], '2020-03-22T01:27:19.024000': ['S2B_MSIL2A_20200322T012719_R131_T52LHJ_20201014T234458', 'S2B_MSIL2A_20200322T012719_R131_T53LKD_20201020T235917'], '2020-03-27T01:27:11.024000': ['S2A_MSIL2A_20200327T012711_R131_T52LHJ_20201027T112006', 'S2A_MSIL2A_20200327T012711_R131_T53LKD_20201027T112009'], '2020-04-01T01:27:19.024000': ['S2B_MSIL2A_20200401T012719_R131_T52LHJ_20200924T140813', 'S2B_MSIL2A_20200401T012719_R131_T53LKD_20200924T140838'], '2020-04-06T01:27:11.024000': ['S2A_MSIL2A_20200406T012711_R131_T52LHJ_20200925T044912', 'S2A_MSIL2A_20200406T012711_R131_T53LKD_20200925T044935'], '2020-04-11T01:27:09.024000': ['S2B_MSIL2A_20200411T012709_R131_T52LHJ_20200923T023145', 'S2B_MSIL2A_20200411T012709_R131_T53LKD_20200923T023159'], '2020-04-16T01:27:21.024000': ['S2A_MSIL2A_20200416T012721_R131_T52LHJ_20200923T170629', 'S2A_MSIL2A_20200416T012721_R131_T53LKD_20200923T170652'], '2020-04-21T01:27:09.024000': ['S2B_MSIL2A_20200421T012709_R131_T52LHJ_20200921T125131', 'S2B_MSIL2A_20200421T012709_R131_T53LKD_20200921T125143'], '2020-04-26T01:27:21.024000': ['S2A_MSIL2A_20200426T012721_R131_T52LHJ_20200922T031828', 'S2A_MSIL2A_20200426T012721_R131_T53LKD_20200922T031853'], '2020-05-01T01:27:09.024000': ['S2B_MSIL2A_20200501T012709_R131_T52LHJ_20200920T020410', 'S2B_MSIL2A_20200501T012709_R131_T53LKD_20200920T020437'], '2020-05-06T01:27:21.024000': ['S2A_MSIL2A_20200506T012721_R131_T52LHJ_20200920T164933', 'S2A_MSIL2A_20200506T012721_R131_T53LKD_20200920T164947'], '2020-05-11T01:27:19.024000': ['S2B_MSIL2A_20200511T012719_R131_T52LHJ_20200917T191056', 'S2B_MSIL2A_20200511T012719_R131_T53LKD_20200917T191127'], '2020-05-16T01:27:21.024000': ['S2A_MSIL2A_20200516T012721_R131_T52LHJ_20200909T203550', 'S2A_MSIL2A_20200516T012721_R131_T53LKD_20200919T163320'], '2020-05-21T01:27:19.024000': ['S2B_MSIL2A_20200521T012719_R131_T52LHJ_20200910T112148', 'S2B_MSIL2A_20200521T012719_R131_T53LKD_20200910T112205'], '2020-05-26T01:27:21.024000': ['S2A_MSIL2A_20200526T012721_R131_T52LHJ_20200911T000802', 'S2A_MSIL2A_20200526T012721_R131_T53LKD_20200911T000829'], '2020-05-31T01:27:19.024000': ['S2B_MSIL2A_20200531T012719_R131_T52LHJ_20200911T154558', 'S2B_MSIL2A_20200531T012719_R131_T53LKD_20200911T154626'], '2020-06-05T01:27:21.024000': ['S2A_MSIL2A_20200605T012721_R131_T52LHJ_20200826T045432', 'S2A_MSIL2A_20200605T012721_R131_T53LKD_20200826T045438'], '2020-06-10T01:27:19.024000': ['S2B_MSIL2A_20200610T012719_R131_T52LHJ_20200826T211252', 'S2B_MSIL2A_20200610T012719_R131_T53LKD_20200826T211306'], '2020-06-15T01:27:21.024000': ['S2A_MSIL2A_20200615T012721_R131_T52LHJ_20200827T103446', 'S2A_MSIL2A_20200615T012721_R131_T53LKD_20200827T103502'], '2020-06-20T01:27:19.024000': ['S2B_MSIL2A_20200620T012719_R131_T52LHJ_20200823T105625', 'S2B_MSIL2A_20200620T012719_R131_T53LKD_20200823T105645'], '2020-06-25T01:27:21.024000': ['S2A_MSIL2A_20200625T012721_R131_T52LHJ_20200824T003304', 'S2A_MSIL2A_20200625T012721_R131_T53LKD_20200824T003320'], '2020-06-30T01:27:19.024000': ['S2B_MSIL2A_20200630T012719_R131_T52LHJ_20200824T171448', 'S2B_MSIL2A_20200630T012719_R131_T53LKD_20200824T171454']}

  xcube_stac_version :

  1.3.1

We can now select a variable for one timestep and plot it for a quick preview of the data.

In [12]:

ds.B04.isel(time=-1).plot(vmin=0., vmax=0.2)

ds.B04.isel(time=-1).plot(vmin=0., vmax=0.2)

Out[12]:

<matplotlib.collections.QuadMesh at 0x797905b08ec0>

The same quicklook can be created for any generated dataset.

In [13]:

ds = msds.stores.storage.open_data("AU-Dry/era5_land.zarr")
ds

ds = msds.stores.storage.open_data("AU-Dry/era5_land.zarr") ds

Out[13]:

<xarray.Dataset> Size: 192kB
Dimensions:  (time: 4368)
Coordinates:
  * time     (time) datetime64[ns] 35kB 2020-01-01 ... 2020-06-30T23:00:00
    expver   (time) <U4 70kB dask.array<chunksize=(552,), meta=np.ndarray>
    lat      float64 8B ...
    lon      float64 8B ...
    number   int64 8B ...
Data variables:
    d2m      (time) float32 17kB dask.array<chunksize=(552,), meta=np.ndarray>
    ssr      (time) float32 17kB dask.array<chunksize=(552,), meta=np.ndarray>
    ssrd     (time) float32 17kB dask.array<chunksize=(552,), meta=np.ndarray>
    t2m      (time) float32 17kB dask.array<chunksize=(552,), meta=np.ndarray>
    tp       (time) float32 17kB dask.array<chunksize=(552,), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    history:                 2026-06-12T10:07 GRIB to CDM+CF via cfgrib-0.9.1...
    institution:             European Centre for Medium-Range Weather Forecasts

xarray.Dataset

• Dimensions:
  • time: 4368
• Coordinates: (5)
  • time
    (time)
    datetime64[ns]
    2020-01-01 ... 2020-06-30T23:00:00

    long_name :

    time

    standard_name :

    time

    array(['2020-01-01T00:00:00.000000000', '2020-01-01T01:00:00.000000000',
           '2020-01-01T02:00:00.000000000', ..., '2020-06-30T21:00:00.000000000',
           '2020-06-30T22:00:00.000000000', '2020-06-30T23:00:00.000000000'],
          shape=(4368,), dtype='datetime64[ns]')

  • expver
    (time)
    <U4
    dask.array<chunksize=(552,), meta=np.ndarray>

    Array Chunk Bytes 68.25 kiB 8.62 kiB Shape (4368,) (552,) Dask graph 8 chunks in 2 graph layers Data type

  • lat
    ()
    float64
    ...

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    [1 values with dtype=float64]

  • lon
    ()
    float64
    ...

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    [1 values with dtype=float64]

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    [1 values with dtype=int64]

• Data variables: (5)
  • d2m
    (time)
    float32
    dask.array<chunksize=(552,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    4

    GRIB_Ny :

    4

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    d2m

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.1

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.1

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    -15.108

    GRIB_latitudeOfLastGridPointInDegrees :

    -15.409

    GRIB_longitudeOfFirstGridPointInDegrees :

    132.22

    GRIB_longitudeOfLastGridPointInDegrees :

    132.521

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre dewpoint temperature

    GRIB_numberOfPoints :

    16

    GRIB_paramId :

    168

    GRIB_shortName :

    2d

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_surface :

    0.0

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    GRIB_uvRelativeToGrid :

    0

    long_name :

    2 metre dewpoint temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 17.06 kiB 2.16 kiB Shape (4368,) (552,) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ssr
    (time)
    float32
    dask.array<chunksize=(552,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    4

    GRIB_Ny :

    4

    GRIB_cfName :

    surface_net_downward_shortwave_flux

    GRIB_cfVarName :

    ssr

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.1

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.1

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    -15.108

    GRIB_latitudeOfLastGridPointInDegrees :

    -15.409

    GRIB_longitudeOfFirstGridPointInDegrees :

    132.22

    GRIB_longitudeOfLastGridPointInDegrees :

    132.521

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Surface net short-wave (solar) radiation

    GRIB_numberOfPoints :

    16

    GRIB_paramId :

    176

    GRIB_shortName :

    ssr

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_surface :

    0.0

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    GRIB_uvRelativeToGrid :

    0

    long_name :

    Surface net short-wave (solar) radiation

    standard_name :

    surface_net_downward_shortwave_flux

    units :

    J m**-2

    Array Chunk Bytes 17.06 kiB 2.16 kiB Shape (4368,) (552,) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ssrd
    (time)
    float32
    dask.array<chunksize=(552,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    4

    GRIB_Ny :

    4

    GRIB_cfName :

    surface_downwelling_shortwave_flux_in_air

    GRIB_cfVarName :

    ssrd

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.1

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.1

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    -15.108

    GRIB_latitudeOfLastGridPointInDegrees :

    -15.409

    GRIB_longitudeOfFirstGridPointInDegrees :

    132.22

    GRIB_longitudeOfLastGridPointInDegrees :

    132.521

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Surface short-wave (solar) radiation downwards

    GRIB_numberOfPoints :

    16

    GRIB_paramId :

    169

    GRIB_shortName :

    ssrd

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_surface :

    0.0

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    GRIB_uvRelativeToGrid :

    0

    long_name :

    Surface short-wave (solar) radiation downwards

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    units :

    J m**-2

    Array Chunk Bytes 17.06 kiB 2.16 kiB Shape (4368,) (552,) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

  • t2m
    (time)
    float32
    dask.array<chunksize=(552,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    4

    GRIB_Ny :

    4

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.1

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.1

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    -15.108

    GRIB_latitudeOfLastGridPointInDegrees :

    -15.409

    GRIB_longitudeOfFirstGridPointInDegrees :

    132.22

    GRIB_longitudeOfLastGridPointInDegrees :

    132.521

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_numberOfPoints :

    16

    GRIB_paramId :

    167

    GRIB_shortName :

    2t

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_surface :

    0.0

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    GRIB_uvRelativeToGrid :

    0

    long_name :

    2 metre temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 17.06 kiB 2.16 kiB Shape (4368,) (552,) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tp
    (time)
    float32
    dask.array<chunksize=(552,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    4

    GRIB_Ny :

    4

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    tp

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.1

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.1

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    -15.108

    GRIB_latitudeOfLastGridPointInDegrees :

    -15.409

    GRIB_longitudeOfFirstGridPointInDegrees :

    132.22

    GRIB_longitudeOfLastGridPointInDegrees :

    132.521

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Total precipitation

    GRIB_numberOfPoints :

    16

    GRIB_paramId :

    228

    GRIB_shortName :

    tp

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_surface :

    0.0

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    GRIB_uvRelativeToGrid :

    0

    long_name :

    Total precipitation

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 17.06 kiB 2.16 kiB Shape (4368,) (552,) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (6)

  Conventions :

  CF-1.7

  GRIB_centre :

  ecmf

  GRIB_centreDescription :

  European Centre for Medium-Range Weather Forecasts

  GRIB_subCentre :

  0

  history :

  2026-06-12T10:07 GRIB to CDM+CF via cfgrib-0.9.15.1/ecCodes-2.42.0 with {"source": "data.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

  institution :

  European Centre for Medium-Range Weather Forecasts

In [14]:

ds.t2m.plot()

ds.t2m.plot()

Out[14]:

[<matplotlib.lines.Line2D at 0x797905cf9f90>]

In [15]:

ds = msds.stores.storage.open_data("AU-Dry/esa_cci_biomass.zarr")
ds

ds = msds.stores.storage.open_data("AU-Dry/esa_cci_biomass.zarr") ds

Out[15]:

<xarray.Dataset> Size: 1MB
Dimensions:      (time: 1, y: 400, x: 400)
Coordinates:
  * time         (time) datetime64[ns] 8B 2020-07-01T23:59:59
  * y            (y) float64 3kB 8.312e+06 8.312e+06 ... 8.308e+06 8.308e+06
  * x            (x) float64 3kB 8.601e+05 8.601e+05 ... 8.641e+05 8.641e+05
    spatial_ref  int64 8B ...
Data variables:
    agb          (time, y, x) float32 640kB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    agb_sd       (time, y, x) float32 640kB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    date_created:            2026-06-16T13:39:26.361311
    history:                 [{'cube_params': {'time_range': ['2020-01-01T00:...
    processing_level:        L4
    time_coverage_duration:  P365DT23H59M59S
    time_coverage_end:       2020-12-31T23:59:59
    time_coverage_start:     2020-01-01T00:00:00
    title:                   esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-plat...

xarray.Dataset

• Dimensions:
  • time: 1
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-07-01T23:59:59

    bounds :

    time_bnds

    standard_name :

    time

    array(['2020-07-01T23:59:59.000000000'], dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.312e+06 8.312e+06 ... 8.308e+06

    array([8312235., 8312225., 8312215., ..., 8308265., 8308255., 8308245.],
          shape=(400,))

  • x
    (x)
    float64
    8.601e+05 8.601e+05 ... 8.641e+05

    array([860115., 860125., 860135., ..., 864085., 864095., 864105.], shape=(400,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["WGS 84 / UTM zone 52S",BASEGEOGCRS["WGS 84",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]],CONVERSION["UTM zone 52S",METHOD["Transverse Mercator",ID["EPSG",9807]],PARAMETER["Latitude of natural origin",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",129,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Scale factor at natural origin",0.9996,SCALEUNIT["unity",1],ID["EPSG",8805]],PARAMETER["False easting",500000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",10000000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["easting",east,ORDER[1],LENGTHUNIT["metre",1]],AXIS["northing",north,ORDER[2],LENGTHUNIT["metre",1]],ID["EPSG",32752]]

    false_easting :

    500000.0

    false_northing :

    10000000.0

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    transverse_mercator

    horizontal_datum_name :

    World Geodetic System 1984

    inverse_flattening :

    298.257223563

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    129.0

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    WGS 84 / UTM zone 52S

    reference_ellipsoid_name :

    WGS 84

    scale_factor_at_central_meridian :

    0.9996

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    [1 values with dtype=int64]

• Data variables: (2)
  • agb
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    ancillary_variables :

    agb_se

    chunk_sizes :

    [1, 1875, 1875]

    data_type :

    int16

    dimensions :

    ['time', 'lat', 'lon']

    file_chunk_sizes :

    [1, 1875, 1875]

    file_dimensions :

    ['time', 'lat', 'lon']

    fill_value :

    -32768

    grid_mapping :

    spatial_ref

    long_name :

    Above-ground biomass

    orig_data_type :

    int16

    shape :

    [1, 157500, 405000]

    size :

    63787500000

    units :

    Mg/ha

    valid_max :

    10000.0

    valid_min :

    0.0

    Array Chunk Bytes 625.00 kiB 625.00 kiB Shape (1, 400, 400) (1, 400, 400) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • agb_sd
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    chunk_sizes :

    [1, 1875, 1875]

    data_type :

    int16

    dimensions :

    ['time', 'lat', 'lon']

    file_chunk_sizes :

    [1, 1875, 1875]

    file_dimensions :

    ['time', 'lat', 'lon']

    fill_value :

    -32768

    grid_mapping :

    spatial_ref

    long_name :

    Above-ground biomass standard_deviation

    orig_data_type :

    int16

    shape :

    [1, 157500, 405000]

    size :

    63787500000

    units :

    Mg/ha

    valid_max :

    10000.0

    valid_min :

    0.0

    Array Chunk Bytes 625.00 kiB 625.00 kiB Shape (1, 400, 400) (1, 400, 400) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (8)

  Conventions :

  CF-1.7

  date_created :

  2026-06-16T13:39:26.361311

  history :

  [{'cube_params': {'time_range': ['2020-01-01T00:00:00', '2020-06-30T00:00:00'], 'variable_names': ['agb', 'agb_sd', 'crs']}, 'program': 'xcube_cci.chunkstore.CciChunkStore'}]

  processing_level :

  L4

  time_coverage_duration :

  P365DT23H59M59S

  time_coverage_end :

  2020-12-31T23:59:59

  time_coverage_start :

  2020-01-01T00:00:00

  title :

  esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.100m

In [16]:

ds["agb"].plot()

ds["agb"].plot()

Out[16]:

<matplotlib.collections.QuadMesh at 0x797905cab890>