Create Atmospheric Cubes CDS

xcube Data Store Framework – Climate Data Store¶

Creating and writing data cubes¶

This notebook demonstrates how to access Climate Data Store (CDS) data via the dedicated xcube store, which provides dynamic data cube views into each gridded data set. Furthermore, an overview will be given on how to write data cubes into a team storage.

Please, also refer to the DeepESDL documentation and visit the platform's website for further information!

Brockmann Consult, 2025

---

This notebook runs with the python environment users-deepesdl-xcube-1.9.1, please checkout the documentation for help on changing the environment.

Please note:

To access data from the Climate Data Store, you need a CDS API key.

Obtain a CDS Personal Access Token¶

You can obtain a CDS Personal Access Token as follows:

1. Create a user account on the CDS Website.
2. Log in to the website with your username and password.
3. Navigate to your user page, where you can find your Personal Access Token.

Configure CDS API access¶

Your CDS Personal Access Token must be made available to the CDS API library. You can do this by creating a file named .cdsapirc in your home directory, with the following format:

url: https://cds.climate.copernicus.eu/api
key: <PERSONAL-ACCESS-TOKEN>

Replace <PERSONAL-ACCESS-TOKEN> with your Personal Access Token.

Then export the CDSAPI_URL and CDSAPI_KEY environment variables:

export CDSAPI_URL=https://cds.climate.copernicus.eu/api
export CDSAPI_KEY=[PERSONAL-ACCESS-TOKEN]

Agree to the terms of use for the datasets you require¶

The datasets available through CDS have associated terms of use. Before accessing a dataset via the API, you must agree to its terms of use, which can only be done via the CDS website, as follows:

1. Log in to the CDS website, and go to 'Datasets' to find the dataset you require.
2. On the dataset's web page, select the ‘Download’ tab.
3. Scroll to the bottom of the page, and you will see a section titled ‘Terms of use’, which will contain either an ‘Accept terms’ button to allow you to accept the terms, or a confirmation that you have already accepted the terms.

Once you have accepted the terms on the website, the dataset will also be made available to you through the API.

In [1]:

import os
os.environ['CDSAPI_URL'] = 'https://cds.climate.copernicus.eu/api'
os.environ['CDSAPI_KEY'] = '[PERSONAL-ACCESS-TOKEN]'

import os os.environ['CDSAPI_URL'] = 'https://cds.climate.copernicus.eu/api' os.environ['CDSAPI_KEY'] = '[PERSONAL-ACCESS-TOKEN]'

In [3]:

# mandatory imports
from xcube.core.store import find_data_store_extensions
from xcube.core.store import get_data_store_params_schema
from xcube.core.store import new_data_store

# Utilities for notebook visualization
import shapely.geometry
import IPython.display
from IPython.display import JSON
import matplotlib as mpl
import matplotlib.pyplot as plt

# mandatory imports from xcube.core.store import find_data_store_extensions from xcube.core.store import get_data_store_params_schema from xcube.core.store import new_data_store # Utilities for notebook visualization import shapely.geometry import IPython.display from IPython.display import JSON import matplotlib as mpl import matplotlib.pyplot as plt

Configure matplotlib to display graphs inline directly in the notebook and set a sensible default figure size.

In [4]:

%matplotlib inline
plt.rcParams["figure.figsize"] = 16,12

%matplotlib inline plt.rcParams["figure.figsize"] = 16,12

Connect to cds store¶

Check whether the cds store is among the available stores, if not please follow the installation information from the top of this notebook.

In [5]:

JSON({e.name: e.metadata for e in find_data_store_extensions()})

JSON({e.name: e.metadata for e in find_data_store_extensions()})

Out[5]:

<IPython.core.display.JSON object>

Usually we need more information to get the actual data store object. Which data store parameters are available for cds?

In [6]:

get_data_store_params_schema('cds')

get_data_store_params_schema('cds')

Out[6]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7fa8baff35b0>

Provide mandatory parameters to instantiate the store class:

In [7]:

store = new_data_store('cds')
store

store = new_data_store('cds') store

Out[7]:

<xcube_cds.store.CDSDataStore at 0x7fa8bafddbe0>

Which datasets are provided? (the list may contain both gridded and vector datasets):

In [8]:

#list(store.get_data_ids())

#list(store.get_data_ids())

Get more info about a specific dataset. This includes a description of the possible open formats:

Retrieve and open requested dataset¶

There are 4 required parameters, so we need to provide them to open a dataset:

In [9]:

store.describe_data('reanalysis-era5-single-levels-monthly-means:monthly_averaged_reanalysis')

store.describe_data('reanalysis-era5-single-levels-monthly-means:monthly_averaged_reanalysis')

Out[9]:

<xcube.core.store.descriptor.DatasetDescriptor at 0x7fa8baeb57f0>

Let's set a bbox¶

In [10]:

bbox=[-5, 45, 35, 65]

bbox=[-5, 45, 35, 65]

In [11]:

IPython.display.GeoJSON(shapely.geometry.box(*bbox).__geo_interface__)

IPython.display.GeoJSON(shapely.geometry.box(*bbox).__geo_interface__)

<IPython.display.GeoJSON object>

Now set the other parameters for opening the dataset from the store:¶

In [12]:

dataset = store.open_data('reanalysis-era5-single-levels-monthly-means:monthly_averaged_reanalysis', 
                          variable_names=['2m_temperature'], 
                          bbox=bbox, 
                          spatial_res=0.25, 
                          time_range=['2006-01-01', '2010-12-31'])
dataset

dataset = store.open_data('reanalysis-era5-single-levels-monthly-means:monthly_averaged_reanalysis', variable_names=['2m_temperature'], bbox=bbox, spatial_res=0.25, time_range=['2006-01-01', '2010-12-31']) dataset

xcube-cds version 1.0.0
2025-05-22 10:22:36,340 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.
2025-05-22 10:22:36,341 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the "Show API request code" tool on the dataset Download Form to check you are using the correct syntax for your API request.
2025-05-22 10:22:36,758 INFO Request ID is c8e73bc1-a9f8-40ab-b81c-0817a48eb6d7
2025-05-22 10:22:36,830 INFO status has been updated to accepted
2025-05-22 10:22:51,280 INFO status has been updated to running
2025-05-22 10:22:58,958 WARNING Structural differences in grib fields detected when opening in xarray. Opening the grib file safely, however this may result in files with non-intuitive filenames.
2025-05-22 10:23:10,465 INFO status has been updated to successful

e19485b22e26b2d3794f643c54bfdf0d.nc:   0%|          | 0.00/1.31M [00:00<?, ?B/s]

Out[12]:

<xarray.Dataset> Size: 3MB
Dimensions:  (time: 60, lat: 80, lon: 160)
Coordinates:
    number   int64 8B ...
  * time     (time) datetime64[ns] 480B 2006-01-01 2006-02-01 ... 2010-12-01
  * lat      (lat) float64 640B 64.88 64.62 64.38 64.12 ... 45.62 45.38 45.12
  * lon      (lon) float64 1kB -4.875 -4.625 -4.375 -4.125 ... 34.38 34.62 34.88
    expver   (time) <U4 960B ...
Data variables:
    t2m      (time, lat, lon) float32 3MB ...
Attributes:
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts

xarray.Dataset

• Dimensions:
  • time: 60
  • lat: 80
  • lon: 160
• Coordinates: (5)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2006-01-01 ... 2010-12-01

    long_name :

    time

    standard_name :

    time

    array(['2006-01-01T00:00:00.000000000', '2006-02-01T00:00:00.000000000',
           '2006-03-01T00:00:00.000000000', '2006-04-01T00:00:00.000000000',
           '2006-05-01T00:00:00.000000000', '2006-06-01T00:00:00.000000000',
           '2006-07-01T00:00:00.000000000', '2006-08-01T00:00:00.000000000',
           '2006-09-01T00:00:00.000000000', '2006-10-01T00:00:00.000000000',
           '2006-11-01T00:00:00.000000000', '2006-12-01T00:00:00.000000000',
           '2007-01-01T00:00:00.000000000', '2007-02-01T00:00:00.000000000',
           '2007-03-01T00:00:00.000000000', '2007-04-01T00:00:00.000000000',
           '2007-05-01T00:00:00.000000000', '2007-06-01T00:00:00.000000000',
           '2007-07-01T00:00:00.000000000', '2007-08-01T00:00:00.000000000',
           '2007-09-01T00:00:00.000000000', '2007-10-01T00:00:00.000000000',
           '2007-11-01T00:00:00.000000000', '2007-12-01T00:00:00.000000000',
           '2008-01-01T00:00:00.000000000', '2008-02-01T00:00:00.000000000',
           '2008-03-01T00:00:00.000000000', '2008-04-01T00:00:00.000000000',
           '2008-05-01T00:00:00.000000000', '2008-06-01T00:00:00.000000000',
           '2008-07-01T00:00:00.000000000', '2008-08-01T00:00:00.000000000',
           '2008-09-01T00:00:00.000000000', '2008-10-01T00:00:00.000000000',
           '2008-11-01T00:00:00.000000000', '2008-12-01T00:00:00.000000000',
           '2009-01-01T00:00:00.000000000', '2009-02-01T00:00:00.000000000',
           '2009-03-01T00:00:00.000000000', '2009-04-01T00:00:00.000000000',
           '2009-05-01T00:00:00.000000000', '2009-06-01T00:00:00.000000000',
           '2009-07-01T00:00:00.000000000', '2009-08-01T00:00:00.000000000',
           '2009-09-01T00:00:00.000000000', '2009-10-01T00:00:00.000000000',
           '2009-11-01T00:00:00.000000000', '2009-12-01T00:00:00.000000000',
           '2010-01-01T00:00:00.000000000', '2010-02-01T00:00:00.000000000',
           '2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000',
           '2010-05-01T00:00:00.000000000', '2010-06-01T00:00:00.000000000',
           '2010-07-01T00:00:00.000000000', '2010-08-01T00:00:00.000000000',
           '2010-09-01T00:00:00.000000000', '2010-10-01T00:00:00.000000000',
           '2010-11-01T00:00:00.000000000', '2010-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    64.88 64.62 64.38 ... 45.38 45.12

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([64.875, 64.625, 64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875,
           62.625, 62.375, 62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625,
           60.375, 60.125, 59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375,
           58.125, 57.875, 57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125,
           55.875, 55.625, 55.375, 55.125, 54.875, 54.625, 54.375, 54.125, 53.875,
           53.625, 53.375, 53.125, 52.875, 52.625, 52.375, 52.125, 51.875, 51.625,
           51.375, 51.125, 50.875, 50.625, 50.375, 50.125, 49.875, 49.625, 49.375,
           49.125, 48.875, 48.625, 48.375, 48.125, 47.875, 47.625, 47.375, 47.125,
           46.875, 46.625, 46.375, 46.125, 45.875, 45.625, 45.375, 45.125])

  • lon
    (lon)
    float64
    -4.875 -4.625 ... 34.62 34.88

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375, -3.125, -2.875,
           -2.625, -2.375, -2.125, -1.875, -1.625, -1.375, -1.125, -0.875, -0.625,
           -0.375, -0.125,  0.125,  0.375,  0.625,  0.875,  1.125,  1.375,  1.625,
            1.875,  2.125,  2.375,  2.625,  2.875,  3.125,  3.375,  3.625,  3.875,
            4.125,  4.375,  4.625,  4.875,  5.125,  5.375,  5.625,  5.875,  6.125,
            6.375,  6.625,  6.875,  7.125,  7.375,  7.625,  7.875,  8.125,  8.375,
            8.625,  8.875,  9.125,  9.375,  9.625,  9.875, 10.125, 10.375, 10.625,
           10.875, 11.125, 11.375, 11.625, 11.875, 12.125, 12.375, 12.625, 12.875,
           13.125, 13.375, 13.625, 13.875, 14.125, 14.375, 14.625, 14.875, 15.125,
           15.375, 15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375,
           17.625, 17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625,
           19.875, 20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875,
           22.125, 22.375, 22.625, 22.875, 23.125, 23.375, 23.625, 23.875, 24.125,
           24.375, 24.625, 24.875, 25.125, 25.375, 25.625, 25.875, 26.125, 26.375,
           26.625, 26.875, 27.125, 27.375, 27.625, 27.875, 28.125, 28.375, 28.625,
           28.875, 29.125, 29.375, 29.625, 29.875, 30.125, 30.375, 30.625, 30.875,
           31.125, 31.375, 31.625, 31.875, 32.125, 32.375, 32.625, 32.875, 33.125,
           33.375, 33.625, 33.875, 34.125, 34.375, 34.625, 34.875])

  • expver
    (time)
    <U4
    ...

    [60 values with dtype=<U4]

• Data variables: (1)
  • t2m
    (time, lat, lon)
    float32
    ...

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    12800

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    avgua

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    160

    GRIB_Ny :

    80

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    64.875

    GRIB_latitudeOfLastGridPointInDegrees :

    45.125

    GRIB_longitudeOfFirstGridPointInDegrees :

    -4.875

    GRIB_longitudeOfLastGridPointInDegrees :

    34.875

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    GRIB_surface :

    0.0

    [768000 values with dtype=float32]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2006-01-01', '2006-02-01', '2006-03-01', '2006-04-01',
                   '2006-05-01', '2006-06-01', '2006-07-01', '2006-08-01',
                   '2006-09-01', '2006-10-01', '2006-11-01', '2006-12-01',
                   '2007-01-01', '2007-02-01', '2007-03-01', '2007-04-01',
                   '2007-05-01', '2007-06-01', '2007-07-01', '2007-08-01',
                   '2007-09-01', '2007-10-01', '2007-11-01', '2007-12-01',
                   '2008-01-01', '2008-02-01', '2008-03-01', '2008-04-01',
                   '2008-05-01', '2008-06-01', '2008-07-01', '2008-08-01',
                   '2008-09-01', '2008-10-01', '2008-11-01', '2008-12-01',
                   '2009-01-01', '2009-02-01', '2009-03-01', '2009-04-01',
                   '2009-05-01', '2009-06-01', '2009-07-01', '2009-08-01',
                   '2009-09-01', '2009-10-01', '2009-11-01', '2009-12-01',
                   '2010-01-01', '2010-02-01', '2010-03-01', '2010-04-01',
                   '2010-05-01', '2010-06-01', '2010-07-01', '2010-08-01',
                   '2010-09-01', '2010-10-01', '2010-11-01', '2010-12-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([64.875, 64.625, 64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875,
           62.625, 62.375, 62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625,
           60.375, 60.125, 59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375,
           58.125, 57.875, 57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125,
           55.875, 55.625, 55.375, 55.125, 54.875, 54.625, 54.375, 54.125, 53.875,
           53.625, 53.375, 53.125, 52.875, 52.625, 52.375, 52.125, 51.875, 51.625,
           51.375, 51.125, 50.875, 50.625, 50.375, 50.125, 49.875, 49.625, 49.375,
           49.125, 48.875, 48.625, 48.375, 48.125, 47.875, 47.625, 47.375, 47.125,
           46.875, 46.625, 46.375, 46.125, 45.875, 45.625, 45.375, 45.125],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([-4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375, -3.125, -2.875,
           -2.625,
           ...
           32.625, 32.875, 33.125, 33.375, 33.625, 33.875, 34.125, 34.375, 34.625,
           34.875],
          dtype='float64', name='lon', length=160))

• Attributes: (5)

  GRIB_centre :

  ecmf

  GRIB_centreDescription :

  European Centre for Medium-Range Weather Forecasts

  GRIB_subCentre :

  0

  Conventions :

  CF-1.7

  institution :

  European Centre for Medium-Range Weather Forecasts

We can explore this dataset by plotting a temperature map for selected time points. First, we select January 2001. Land areas – and mountain ranges in particular – show up on the map as colder regions.

In [13]:

t2m_2001_jan = dataset.t2m.sel(time='2010-01-01 00:00:00', method='nearest')
t2m_2001_jan.plot.imshow(vmin=260, vmax=285, figsize=(14, 8), cmap='plasma')

t2m_2001_jan = dataset.t2m.sel(time='2010-01-01 00:00:00', method='nearest') t2m_2001_jan.plot.imshow(vmin=260, vmax=285, figsize=(14, 8), cmap='plasma')

Out[13]:

<matplotlib.image.AxesImage at 0x7fa8babec830>

Write cube in team storage¶

To store the cube in your teams user space, please first retrieve the details from your environment variables as the following:

In [14]:

S3_USER_STORAGE_KEY = os.environ["S3_USER_STORAGE_KEY"]
S3_USER_STORAGE_SECRET = os.environ["S3_USER_STORAGE_SECRET"]
S3_USER_STORAGE_BUCKET = os.environ["S3_USER_STORAGE_BUCKET"]

S3_USER_STORAGE_KEY = os.environ["S3_USER_STORAGE_KEY"] S3_USER_STORAGE_SECRET = os.environ["S3_USER_STORAGE_SECRET"] S3_USER_STORAGE_BUCKET = os.environ["S3_USER_STORAGE_BUCKET"]

You need to instantiate a s3 datastore pointing to the team bucket:

In [15]:

from xcube.core.store import new_data_store
team_store = new_data_store("s3", 
                       root=S3_USER_STORAGE_BUCKET, 
                       storage_options=dict(anon=False, 
                                            key=S3_USER_STORAGE_KEY, 
                                            secret=S3_USER_STORAGE_SECRET))

from xcube.core.store import new_data_store team_store = new_data_store("s3", root=S3_USER_STORAGE_BUCKET, storage_options=dict(anon=False, key=S3_USER_STORAGE_KEY, secret=S3_USER_STORAGE_SECRET))

If you have stored no data to your user space, the returned list will be empty:

In [16]:

list(team_store.get_data_ids())

list(team_store.get_data_ids())

Out[16]:

['ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.rechunked.zarr',
 'LC-1x720x1440-0.25deg-2.0.0-v1.zarr',
 'LC-1x720x1440-0.25deg-2.0.0-v2.zarr',
 'SST.levels',
 'SeasFireCube-8D-0.25deg-1x720x1440-3.0.0.zarr',
 'amazonas_v8.zarr',
 'amazonas_v9.zarr',
 'analysed_sst.zarr',
 'analysed_sst_2.zarr',
 'analysed_sst_3.zarr',
 'analysed_sst_4.zarr',
 'esa-cci-permafrost-1x1151x1641-0.1.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.4.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.5.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.6.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.7.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.8.0.zarr',
 'esa-cci-permafrost-1x1151x1641-1.0.0.zarr',
 'esa_gda-health_pakistan_ERA5_precipitation_and_temperature_testdata.zarr',
 'noise_trajectory.zarr']

In [17]:

team_store.write_data(dataset,'reanalysis-era5-single-levels-monthly-means-subset-2006-2010_TMH.zarr', replace=True)

team_store.write_data(dataset,'reanalysis-era5-single-levels-monthly-means-subset-2006-2010_TMH.zarr', replace=True)

Out[17]:

'reanalysis-era5-single-levels-monthly-means-subset-2006-2010_TMH.zarr'

In [18]:

list(team_store.get_data_ids())

list(team_store.get_data_ids())

Out[18]:

['ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.rechunked.zarr',
 'LC-1x720x1440-0.25deg-2.0.0-v1.zarr',
 'LC-1x720x1440-0.25deg-2.0.0-v2.zarr',
 'SST.levels',
 'SeasFireCube-8D-0.25deg-1x720x1440-3.0.0.zarr',
 'amazonas_v8.zarr',
 'amazonas_v9.zarr',
 'analysed_sst.zarr',
 'analysed_sst_2.zarr',
 'analysed_sst_3.zarr',
 'analysed_sst_4.zarr',
 'esa-cci-permafrost-1x1151x1641-0.1.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.4.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.5.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.6.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.7.0.zarr',
 'esa-cci-permafrost-1x1151x1641-0.8.0.zarr',
 'esa-cci-permafrost-1x1151x1641-1.0.0.zarr',
 'esa_gda-health_pakistan_ERA5_precipitation_and_temperature_testdata.zarr',
 'noise_trajectory.zarr',
 'reanalysis-era5-single-levels-monthly-means-subset-2006-2010_TMH.zarr']

Open data from team storage¶

In [19]:

cds_subset = team_store.open_data('reanalysis-era5-single-levels-monthly-means-subset-2006-2010_TMH.zarr')
cds_subset

cds_subset = team_store.open_data('reanalysis-era5-single-levels-monthly-means-subset-2006-2010_TMH.zarr') cds_subset

Out[19]:

<xarray.Dataset> Size: 3MB
Dimensions:  (time: 60, lat: 80, lon: 160)
Coordinates:
    expver   (time) <U4 960B dask.array<chunksize=(60,), meta=np.ndarray>
  * lat      (lat) float64 640B 64.88 64.62 64.38 64.12 ... 45.62 45.38 45.12
  * lon      (lon) float64 1kB -4.875 -4.625 -4.375 -4.125 ... 34.38 34.62 34.88
    number   int64 8B ...
  * time     (time) datetime64[ns] 480B 2006-01-01 2006-02-01 ... 2010-12-01
Data variables:
    t2m      (time, lat, lon) float32 3MB dask.array<chunksize=(30, 40, 80), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    institution:             European Centre for Medium-Range Weather Forecasts

xarray.Dataset

• Dimensions:
  • time: 60
  • lat: 80
  • lon: 160
• Coordinates: (5)
  • expver
    (time)
    <U4
    dask.array<chunksize=(60,), meta=np.ndarray>

    Array Chunk Bytes 0.94 kiB 0.94 kiB Shape (60,) (60,) Dask graph 1 chunks in 2 graph layers Data type

  • lat
    (lat)
    float64
    64.88 64.62 64.38 ... 45.38 45.12

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    array([64.875, 64.625, 64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875,
           62.625, 62.375, 62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625,
           60.375, 60.125, 59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375,
           58.125, 57.875, 57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125,
           55.875, 55.625, 55.375, 55.125, 54.875, 54.625, 54.375, 54.125, 53.875,
           53.625, 53.375, 53.125, 52.875, 52.625, 52.375, 52.125, 51.875, 51.625,
           51.375, 51.125, 50.875, 50.625, 50.375, 50.125, 49.875, 49.625, 49.375,
           49.125, 48.875, 48.625, 48.375, 48.125, 47.875, 47.625, 47.375, 47.125,
           46.875, 46.625, 46.375, 46.125, 45.875, 45.625, 45.375, 45.125])

  • lon
    (lon)
    float64
    -4.875 -4.625 ... 34.62 34.88

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375, -3.125, -2.875,
           -2.625, -2.375, -2.125, -1.875, -1.625, -1.375, -1.125, -0.875, -0.625,
           -0.375, -0.125,  0.125,  0.375,  0.625,  0.875,  1.125,  1.375,  1.625,
            1.875,  2.125,  2.375,  2.625,  2.875,  3.125,  3.375,  3.625,  3.875,
            4.125,  4.375,  4.625,  4.875,  5.125,  5.375,  5.625,  5.875,  6.125,
            6.375,  6.625,  6.875,  7.125,  7.375,  7.625,  7.875,  8.125,  8.375,
            8.625,  8.875,  9.125,  9.375,  9.625,  9.875, 10.125, 10.375, 10.625,
           10.875, 11.125, 11.375, 11.625, 11.875, 12.125, 12.375, 12.625, 12.875,
           13.125, 13.375, 13.625, 13.875, 14.125, 14.375, 14.625, 14.875, 15.125,
           15.375, 15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375,
           17.625, 17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625,
           19.875, 20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875,
           22.125, 22.375, 22.625, 22.875, 23.125, 23.375, 23.625, 23.875, 24.125,
           24.375, 24.625, 24.875, 25.125, 25.375, 25.625, 25.875, 26.125, 26.375,
           26.625, 26.875, 27.125, 27.375, 27.625, 27.875, 28.125, 28.375, 28.625,
           28.875, 29.125, 29.375, 29.625, 29.875, 30.125, 30.375, 30.625, 30.875,
           31.125, 31.375, 31.625, 31.875, 32.125, 32.375, 32.625, 32.875, 33.125,
           33.375, 33.625, 33.875, 34.125, 34.375, 34.625, 34.875])

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2006-01-01 ... 2010-12-01

    long_name :

    time

    standard_name :

    time

    array(['2006-01-01T00:00:00.000000000', '2006-02-01T00:00:00.000000000',
           '2006-03-01T00:00:00.000000000', '2006-04-01T00:00:00.000000000',
           '2006-05-01T00:00:00.000000000', '2006-06-01T00:00:00.000000000',
           '2006-07-01T00:00:00.000000000', '2006-08-01T00:00:00.000000000',
           '2006-09-01T00:00:00.000000000', '2006-10-01T00:00:00.000000000',
           '2006-11-01T00:00:00.000000000', '2006-12-01T00:00:00.000000000',
           '2007-01-01T00:00:00.000000000', '2007-02-01T00:00:00.000000000',
           '2007-03-01T00:00:00.000000000', '2007-04-01T00:00:00.000000000',
           '2007-05-01T00:00:00.000000000', '2007-06-01T00:00:00.000000000',
           '2007-07-01T00:00:00.000000000', '2007-08-01T00:00:00.000000000',
           '2007-09-01T00:00:00.000000000', '2007-10-01T00:00:00.000000000',
           '2007-11-01T00:00:00.000000000', '2007-12-01T00:00:00.000000000',
           '2008-01-01T00:00:00.000000000', '2008-02-01T00:00:00.000000000',
           '2008-03-01T00:00:00.000000000', '2008-04-01T00:00:00.000000000',
           '2008-05-01T00:00:00.000000000', '2008-06-01T00:00:00.000000000',
           '2008-07-01T00:00:00.000000000', '2008-08-01T00:00:00.000000000',
           '2008-09-01T00:00:00.000000000', '2008-10-01T00:00:00.000000000',
           '2008-11-01T00:00:00.000000000', '2008-12-01T00:00:00.000000000',
           '2009-01-01T00:00:00.000000000', '2009-02-01T00:00:00.000000000',
           '2009-03-01T00:00:00.000000000', '2009-04-01T00:00:00.000000000',
           '2009-05-01T00:00:00.000000000', '2009-06-01T00:00:00.000000000',
           '2009-07-01T00:00:00.000000000', '2009-08-01T00:00:00.000000000',
           '2009-09-01T00:00:00.000000000', '2009-10-01T00:00:00.000000000',
           '2009-11-01T00:00:00.000000000', '2009-12-01T00:00:00.000000000',
           '2010-01-01T00:00:00.000000000', '2010-02-01T00:00:00.000000000',
           '2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000',
           '2010-05-01T00:00:00.000000000', '2010-06-01T00:00:00.000000000',
           '2010-07-01T00:00:00.000000000', '2010-08-01T00:00:00.000000000',
           '2010-09-01T00:00:00.000000000', '2010-10-01T00:00:00.000000000',
           '2010-11-01T00:00:00.000000000', '2010-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • t2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(30, 40, 80), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    160

    GRIB_Ny :

    80

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    64.875

    GRIB_latitudeOfLastGridPointInDegrees :

    45.125

    GRIB_longitudeOfFirstGridPointInDegrees :

    -4.875

    GRIB_longitudeOfLastGridPointInDegrees :

    34.875

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_numberOfPoints :

    12800

    GRIB_paramId :

    167

    GRIB_shortName :

    2t

    GRIB_stepType :

    avgua

    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 2.93 MiB 375.00 kiB Shape (60, 80, 160) (30, 40, 80) Dask graph 8 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([64.875, 64.625, 64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875,
           62.625, 62.375, 62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625,
           60.375, 60.125, 59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375,
           58.125, 57.875, 57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125,
           55.875, 55.625, 55.375, 55.125, 54.875, 54.625, 54.375, 54.125, 53.875,
           53.625, 53.375, 53.125, 52.875, 52.625, 52.375, 52.125, 51.875, 51.625,
           51.375, 51.125, 50.875, 50.625, 50.375, 50.125, 49.875, 49.625, 49.375,
           49.125, 48.875, 48.625, 48.375, 48.125, 47.875, 47.625, 47.375, 47.125,
           46.875, 46.625, 46.375, 46.125, 45.875, 45.625, 45.375, 45.125],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([-4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375, -3.125, -2.875,
           -2.625,
           ...
           32.625, 32.875, 33.125, 33.375, 33.625, 33.875, 34.125, 34.375, 34.625,
           34.875],
          dtype='float64', name='lon', length=160))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2006-01-01', '2006-02-01', '2006-03-01', '2006-04-01',
                   '2006-05-01', '2006-06-01', '2006-07-01', '2006-08-01',
                   '2006-09-01', '2006-10-01', '2006-11-01', '2006-12-01',
                   '2007-01-01', '2007-02-01', '2007-03-01', '2007-04-01',
                   '2007-05-01', '2007-06-01', '2007-07-01', '2007-08-01',
                   '2007-09-01', '2007-10-01', '2007-11-01', '2007-12-01',
                   '2008-01-01', '2008-02-01', '2008-03-01', '2008-04-01',
                   '2008-05-01', '2008-06-01', '2008-07-01', '2008-08-01',
                   '2008-09-01', '2008-10-01', '2008-11-01', '2008-12-01',
                   '2009-01-01', '2009-02-01', '2009-03-01', '2009-04-01',
                   '2009-05-01', '2009-06-01', '2009-07-01', '2009-08-01',
                   '2009-09-01', '2009-10-01', '2009-11-01', '2009-12-01',
                   '2010-01-01', '2010-02-01', '2010-03-01', '2010-04-01',
                   '2010-05-01', '2010-06-01', '2010-07-01', '2010-08-01',
                   '2010-09-01', '2010-10-01', '2010-11-01', '2010-12-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (5)

  Conventions :

  CF-1.7

  GRIB_centre :

  ecmf

  GRIB_centreDescription :

  European Centre for Medium-Range Weather Forecasts

  GRIB_subCentre :

  0

  institution :

  European Centre for Medium-Range Weather Forecasts

Visualize the dataset using xcube viewer¶

In [20]:

from xcube.webapi.viewer import Viewer

from xcube.webapi.viewer import Viewer

Let's set some attributes of the variable, so the colormapping does not fall back on the default

In [21]:

cds_subset.t2m.attrs["color_value_min"] = 270
cds_subset.t2m.attrs["color_value_max"] = 310
cds_subset.t2m.attrs["color_bar_name"] = "plasma"

cds_subset.t2m.attrs["color_value_min"] = 270 cds_subset.t2m.attrs["color_value_max"] = 310 cds_subset.t2m.attrs["color_bar_name"] = "plasma"

In [22]:

cds_subset.attrs["title"] = "ERA5 2m Temperature"

cds_subset.attrs["title"] = "ERA5 2m Temperature"

In [23]:

viewer = Viewer()

viewer = Viewer()

404 GET /viewer/assets/index-CHdlbXuI.js (127.0.0.1) 1.06ms

In [24]:

viewer.add_dataset(cds_subset)

viewer.add_dataset(cds_subset)

Out[24]:

'0514f29e-6944-44f4-9eea-5756dfad83a8'

You can click on the viewer link to open xcube Viewer in a new browser tab:

In [25]:

viewer.info()

viewer.info()

Server: https://deep.earthsystemdatalab.net/user/alicebalfanz/proxy/8000
Viewer: https://deep.earthsystemdatalab.net/user/alicebalfanz/proxy/8000/viewer/?serverUrl=https://deep.earthsystemdatalab.net/user/alicebalfanz/proxy/8000

You can also open xcube Viewer inlined here:

In [26]:

viewer.show()

viewer.show()

Out[26]:

In [27]:

viewer.stop_server()

viewer.stop_server()

In case you wish to delete data:

In [28]:

team_store.delete_data('reanalysis-era5-single-levels-monthly-means-subset-2006-2010_TMH.zarr')

team_store.delete_data('reanalysis-era5-single-levels-monthly-means-subset-2006-2010_TMH.zarr')

In [ ]: