Generate CMEMS cubes

Generate CMEMS data cubes¶

A DeepESDL example notebook¶

This notebook demonstrates how to access CMEMS data via the dedicated xcube store, which provides dynamic data cube views into each gridded data set. More information on the data sets offered can be found in the Copernicus Marine Data Store.

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.

If you do not have cmems user yet, you can register for an account. For DeepESDL Basic Jupyter Profile default credentials are configured, but due to bandwith limitation by CMEMS they might not be performant when used by several people simultanously.

In [1]:

import os

import os

In [2]:

## replace with your user name and pwd
# os.environ["CMEMS_USERNAME"] = "<your-username>"
# os.environ["CMEMS_PASSWORD"] = "<your-pwd>"

## replace with your user name and pwd # os.environ["CMEMS_USERNAME"] = "" # os.environ["CMEMS_PASSWORD"] = ""

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

In [3]:

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[3]:

<IPython.core.display.JSON object>

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

In [4]:

get_data_store_params_schema("cmems")

get_data_store_params_schema("cmems")

Out[4]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7fa781c211f0>

---

Import the xcube CMEMS store and create store instance¶

If you do not have cmems user yet, you can register for an account. For DeepESDL Basic Jupyter Profile default credentials are configured, but due to bandwith limitation by CMEMS they might not be performant when used by several people simultanously.

In [6]:

# cmems_store = new_data_store('cmems', cmems_username=os.environ["CMEMS_USERNAME"], cmems_password = os.environ["CMEMS_PASSWORD"])
cmems_store = new_data_store("cmems")

# cmems_store = new_data_store('cmems', cmems_username=os.environ["CMEMS_USERNAME"], cmems_password = os.environ["CMEMS_PASSWORD"]) cmems_store = new_data_store("cmems")

---

Get all dataset_ids for CMEMS API and let store describe data set¶

In [17]:

#cmems_store.list_data_ids()

#cmems_store.list_data_ids()

In [8]:

data_des = cmems_store.describe_data("cmems_mod_bal_wav_my_PT1H-i")

data_des = cmems_store.describe_data("cmems_mod_bal_wav_my_PT1H-i")

INFO - 2025-06-10T09:28:41Z - Selected dataset version: "202411"
INFO - 2025-06-10T09:28:41Z - Selected dataset part: "default"
/home/conda/users/635b18b7-1749540690-47-deepesdl-xcube-1.9.1/lib/python3.12/site-packages/xcube/core/store/descriptor.py:247: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  self.dims = dict(dims) if dims else None

In [9]:

data_des.time_range

data_des.time_range

Out[9]:

('1980-01-01', '2025-03-01')

In [10]:

data_des.bbox

data_des.bbox

Out[10]:

(9, 53, 30.221627251342774, 65.91610912890626)

---

Open a dataset without cube parameters¶

In [11]:

ds = cmems_store.open_data("cmems_mod_bal_wav_my_PT1H-i")
ds

ds = cmems_store.open_data("cmems_mod_bal_wav_my_PT1H-i") ds

INFO - 2025-06-10T09:28:50Z - Selected dataset version: "202411"
INFO - 2025-06-10T09:28:50Z - Selected dataset part: "default"

Out[11]:

<xarray.Dataset> Size: 18TB
Dimensions:    (time: 395904, latitude: 775, longitude: 764)
Coordinates:
  * latitude   (latitude) float32 3kB 53.01 53.02 53.04 ... 65.87 65.89 65.91
  * longitude  (longitude) float32 3kB 9.014 9.042 9.069 ... 30.15 30.18 30.21
  * time       (time) datetime64[ns] 3MB 1980-01-01T01:00:00 ... 2025-03-01
Data variables: (12/19)
    VCMX       (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VHM0       (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VHM0_SW1   (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VHM0_SW2   (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VHM0_WW    (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VMDR       (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    ...         ...
    VTM01_SW1  (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VTM01_SW2  (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VTM01_WW   (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VTM02      (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VTM10      (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
    VTPK       (time, latitude, longitude) float32 938GB dask.array<chunksize=(2, 775, 764), meta=np.ndarray>
Attributes:
    institution:  Baltic MFC, PU Finnish Meteorological Institute
    title:        2D - Hourly Instantaneous
    Conventions:  CF-1.0
    source:       FMI-WAM_CMEMS

xarray.Dataset

• Dimensions:
  • time: 395904
  • latitude: 775
  • longitude: 764
• Coordinates: (3)
  • latitude
    (latitude)
    float32
    53.01 53.02 53.04 ... 65.89 65.91

    long_name :

    latitude

    axis :

    Y

    standard_name :

    latitude

    units :

    degrees_north

    array([53.00829 , 53.024956, 53.041622, ..., 65.87444 , 65.891106, 65.907776],
          dtype=float32)

  • longitude
    (longitude)
    float32
    9.014 9.042 9.069 ... 30.18 30.21

    long_name :

    longitude

    axis :

    X

    standard_name :

    longitude

    units :

    degrees_east

    array([ 9.013887,  9.041664,  9.069442, ..., 30.152184, 30.17996 , 30.207739],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1980-01-01T01:00:00 ... 2025-03-01

    long_name :

    Time

    axis :

    T

    unit_long :

    Seconds Since 1970-01-01

    standard_name :

    time

    array(['1980-01-01T01:00:00.000000000', '1980-01-01T02:00:00.000000000',
           '1980-01-01T03:00:00.000000000', ..., '2025-02-28T22:00:00.000000000',
           '2025-02-28T23:00:00.000000000', '2025-03-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (19)
  • VCMX
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Maximum crest trough wave height (Hc,max)

    standard_name :

    sea_surface_wave_maximum_height

    units :

    m

    valid_max :

    50.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VHM0
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral significant wave height (Hm0)

    standard_name :

    sea_surface_wave_significant_height

    units :

    m

    valid_max :

    20.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VHM0_SW1
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral significant primary swell wave height

    standard_name :

    sea_surface_primary_swell_wave_significant_height

    units :

    m

    valid_max :

    20.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VHM0_SW2
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral significant secondary swell wave height

    standard_name :

    sea_surface_secondary_swell_wave_significant_height

    units :

    m

    valid_max :

    20.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VHM0_WW
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral significant wind wave height

    standard_name :

    sea_surface_wind_wave_significant_height

    units :

    m

    valid_max :

    20.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VMDR
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Mean wave direction from (Mdir)

    standard_name :

    sea_surface_wave_from_direction

    units :

    degree

    valid_max :

    360.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VMDR_SW1
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Mean primary swell wave direction from

    standard_name :

    sea_surface_primary_swell_wave_from_direction

    units :

    degree

    valid_max :

    360.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VMDR_SW2
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Mean secondary swell wave direction from

    standard_name :

    sea_surface_secondary_swell_wave_from_direction

    units :

    degree

    valid_max :

    360.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VMDR_WW
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Mean wind wave direction from

    standard_name :

    sea_surface_wind_wave_from_direction

    units :

    degree

    valid_max :

    360.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VMXL
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Height of the highest crest

    standard_name :

    sea_surface_wave_maximum_crest_height

    units :

    m

    valid_max :

    30.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VPED
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Wave principal direction at spectral peak

    standard_name :

    sea_surface_wave_from_direction_at_variance_spectral_density_maximum

    units :

    degree

    valid_max :

    360.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VSDX
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Stokes drift U

    standard_name :

    sea_surface_wave_stokes_drift_x_velocity

    units :

    m s-1

    valid_max :

    999.0

    valid_min :

    -999.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VSDY
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Stokes drift V

    standard_name :

    sea_surface_wave_stokes_drift_y_velocity

    units :

    m s-1

    valid_max :

    999.0

    valid_min :

    -999.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VTM01_SW1
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral moments (0,1) primary swell wave period

    standard_name :

    sea_surface_primary_swell_wave_mean_period

    units :

    s

    valid_max :

    25.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VTM01_SW2
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral moments (0,1) secondary swell wave period

    standard_name :

    sea_surface_secondary_swell_wave_mean_period

    units :

    s

    valid_max :

    25.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VTM01_WW
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral moments (0,1) wind wave period

    standard_name :

    sea_surface_wind_wave_mean_period

    units :

    s

    valid_max :

    20.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VTM02
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral moments (0,2) wave period (Tm02)

    standard_name :

    sea_surface_wave_mean_period_from_variance_spectral_density_second_frequency_moment

    units :

    s

    valid_max :

    20.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VTM10
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Spectral moments (-1,0) wave period (Tm-10)

    standard_name :

    sea_surface_wave_mean_period_from_variance_spectral_density_inverse_frequency_moment

    units :

    s

    valid_max :

    20.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VTPK
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Wave period at spectral peak / peak period (Tp)

    standard_name :

    sea_surface_wave_period_at_variance_spectral_density_maximum

    units :

    s

    valid_max :

    30.0

    valid_min :

    0.0

    Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([  53.0082893371582,  53.02495574951172, 53.041622161865234,
            53.05828857421875, 53.074954986572266, 53.091617584228516,
            53.10828399658203,  53.12495040893555,  53.14161682128906,
            53.15828323364258,
           ...
            65.75778198242188,  65.77444458007812,   65.7911148071289,
            65.80777740478516,  65.82444763183594,  65.84111022949219,
            65.85777282714844,  65.87444305419922,  65.89110565185547,
            65.90777587890625],
          dtype='float32', name='latitude', length=775))

  • longitude
    PandasIndex

    PandasIndex(Index([ 9.013887405395508,  9.041664123535156,  9.069441795349121,
             9.09721851348877,  9.124995231628418,  9.152771949768066,
            9.180549621582031,   9.20832633972168,  9.236103057861328,
            9.263880729675293,
           ...
           29.957744598388672,  29.98552131652832,  30.01329803466797,
            30.04107666015625,   30.0688533782959, 30.096630096435547,
           30.124406814575195, 30.152183532714844, 30.179960250854492,
           30.207738876342773],
          dtype='float32', name='longitude', length=764))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1980-01-01 01:00:00', '1980-01-01 02:00:00',
                   '1980-01-01 03:00:00', '1980-01-01 04:00:00',
                   '1980-01-01 05:00:00', '1980-01-01 06:00:00',
                   '1980-01-01 07:00:00', '1980-01-01 08:00:00',
                   '1980-01-01 09:00:00', '1980-01-01 10:00:00',
                   ...
                   '2025-02-28 15:00:00', '2025-02-28 16:00:00',
                   '2025-02-28 17:00:00', '2025-02-28 18:00:00',
                   '2025-02-28 19:00:00', '2025-02-28 20:00:00',
                   '2025-02-28 21:00:00', '2025-02-28 22:00:00',
                   '2025-02-28 23:00:00', '2025-03-01 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=395904, freq=None))

• Attributes: (4)

  institution :

  Baltic MFC, PU Finnish Meteorological Institute

  title :

  2D - Hourly Instantaneous

  Conventions :

  CF-1.0

  source :

  FMI-WAM_CMEMS

In [12]:

ds.VHM0

ds.VHM0

Out[12]:

<xarray.DataArray 'VHM0' (time: 395904, latitude: 775, longitude: 764)> Size: 938GB
dask.array<xarray-VHM0, shape=(395904, 775, 764), dtype=float32, chunksize=(2, 775, 764), chunktype=numpy.ndarray>
Coordinates:
  * latitude   (latitude) float32 3kB 53.01 53.02 53.04 ... 65.87 65.89 65.91
  * longitude  (longitude) float32 3kB 9.014 9.042 9.069 ... 30.15 30.18 30.21
  * time       (time) datetime64[ns] 3MB 1980-01-01T01:00:00 ... 2025-03-01
Attributes:
    cell_methods:   time: mean
    long_name:      Spectral significant wave height (Hm0)
    standard_name:  sea_surface_wave_significant_height
    units:          m
    valid_max:      20.0
    valid_min:      0.0

xarray.DataArray

'VHM0'

• time: 395904
• latitude: 775
• longitude: 764

• dask.array<chunksize=(2, 775, 764), meta=np.ndarray>

  Array Chunk Bytes 873.26 GiB 4.52 MiB Shape (395904, 775, 764) (2, 775, 764) Dask graph 197952 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • latitude
    (latitude)
    float32
    53.01 53.02 53.04 ... 65.89 65.91

    long_name :

    latitude

    axis :

    Y

    standard_name :

    latitude

    units :

    degrees_north

    array([53.00829 , 53.024956, 53.041622, ..., 65.87444 , 65.891106, 65.907776],
          dtype=float32)

  • longitude
    (longitude)
    float32
    9.014 9.042 9.069 ... 30.18 30.21

    long_name :

    longitude

    axis :

    X

    standard_name :

    longitude

    units :

    degrees_east

    array([ 9.013887,  9.041664,  9.069442, ..., 30.152184, 30.17996 , 30.207739],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1980-01-01T01:00:00 ... 2025-03-01

    long_name :

    Time

    axis :

    T

    unit_long :

    Seconds Since 1970-01-01

    standard_name :

    time

    array(['1980-01-01T01:00:00.000000000', '1980-01-01T02:00:00.000000000',
           '1980-01-01T03:00:00.000000000', ..., '2025-02-28T22:00:00.000000000',
           '2025-02-28T23:00:00.000000000', '2025-03-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([  53.0082893371582,  53.02495574951172, 53.041622161865234,
            53.05828857421875, 53.074954986572266, 53.091617584228516,
            53.10828399658203,  53.12495040893555,  53.14161682128906,
            53.15828323364258,
           ...
            65.75778198242188,  65.77444458007812,   65.7911148071289,
            65.80777740478516,  65.82444763183594,  65.84111022949219,
            65.85777282714844,  65.87444305419922,  65.89110565185547,
            65.90777587890625],
          dtype='float32', name='latitude', length=775))

  • longitude
    PandasIndex

    PandasIndex(Index([ 9.013887405395508,  9.041664123535156,  9.069441795349121,
             9.09721851348877,  9.124995231628418,  9.152771949768066,
            9.180549621582031,   9.20832633972168,  9.236103057861328,
            9.263880729675293,
           ...
           29.957744598388672,  29.98552131652832,  30.01329803466797,
            30.04107666015625,   30.0688533782959, 30.096630096435547,
           30.124406814575195, 30.152183532714844, 30.179960250854492,
           30.207738876342773],
          dtype='float32', name='longitude', length=764))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1980-01-01 01:00:00', '1980-01-01 02:00:00',
                   '1980-01-01 03:00:00', '1980-01-01 04:00:00',
                   '1980-01-01 05:00:00', '1980-01-01 06:00:00',
                   '1980-01-01 07:00:00', '1980-01-01 08:00:00',
                   '1980-01-01 09:00:00', '1980-01-01 10:00:00',
                   ...
                   '2025-02-28 15:00:00', '2025-02-28 16:00:00',
                   '2025-02-28 17:00:00', '2025-02-28 18:00:00',
                   '2025-02-28 19:00:00', '2025-02-28 20:00:00',
                   '2025-02-28 21:00:00', '2025-02-28 22:00:00',
                   '2025-02-28 23:00:00', '2025-03-01 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=395904, freq=None))

• Attributes: (6)

  cell_methods :

  time: mean

  long_name :

  Spectral significant wave height (Hm0)

  standard_name :

  sea_surface_wave_significant_height

  units :

  m

  valid_max :

  20.0

  valid_min :

  0.0

In [13]:

ds.VHM0.isel(time=0).plot.imshow()

ds.VHM0.isel(time=0).plot.imshow()

Out[13]:

<matplotlib.image.AxesImage at 0x7fa733288320>

---

Open a dataset with cube parameters¶

In [14]:

cmems_store.get_open_data_params_schema(
    "cmems_mod_bal_wav_my_PT1H-i"
)

cmems_store.get_open_data_params_schema( "cmems_mod_bal_wav_my_PT1H-i" )

Out[14]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7fa72fe22630>

In [15]:

ds = cmems_store.open_data(
    "cmems_mod_bal_wav_my_PT1H-i",
    variable_names=["VHM0"],
    time_range=("2021-11-01", "2021-12-13"),
    bbox=[10, 54, 20, 62]
)
ds

ds = cmems_store.open_data( "cmems_mod_bal_wav_my_PT1H-i", variable_names=["VHM0"], time_range=("2021-11-01", "2021-12-13"), bbox=[10, 54, 20, 62] ) ds

INFO - 2025-06-10T09:29:06Z - Selected dataset version: "202411"
INFO - 2025-06-10T09:29:06Z - Selected dataset part: "default"

Out[15]:

<xarray.Dataset> Size: 697MB
Dimensions:    (time: 1009, latitude: 480, longitude: 360)
Coordinates:
  * latitude   (latitude) float32 2kB 54.01 54.02 54.04 ... 61.96 61.97 61.99
  * longitude  (longitude) float32 1kB 10.01 10.04 10.07 ... 19.93 19.96 19.99
  * time       (time) datetime64[ns] 8kB 2021-11-01 ... 2021-12-13
Data variables:
    VHM0       (time, latitude, longitude) float32 697MB dask.array<chunksize=(2, 480, 360), meta=np.ndarray>
Attributes:
    institution:  Baltic MFC, PU Finnish Meteorological Institute
    title:        2D - Hourly Instantaneous
    Conventions:  CF-1.0
    source:       FMI-WAM_CMEMS

xarray.Dataset

• Dimensions:
  • time: 1009
  • latitude: 480
  • longitude: 360
• Coordinates: (3)
  • latitude
    (latitude)
    float32
    54.01 54.02 54.04 ... 61.97 61.99

    long_name :

    latitude

    axis :

    Y

    standard_name :

    latitude

    units :

    degrees_north

    array([54.00825 , 54.024914, 54.04158 , ..., 61.95793 , 61.974598, 61.991264],
          dtype=float32)

  • longitude
    (longitude)
    float32
    10.01 10.04 10.07 ... 19.96 19.99

    long_name :

    longitude

    axis :

    X

    standard_name :

    longitude

    units :

    degrees_east

    array([10.01386 , 10.041636, 10.069413, ..., 19.930248, 19.958025, 19.985802],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2021-11-01 ... 2021-12-13

    long_name :

    Time

    axis :

    T

    unit_long :

    Seconds Since 1970-01-01

    standard_name :

    time

    array(['2021-11-01T00:00:00.000000000', '2021-11-01T01:00:00.000000000',
           '2021-11-01T02:00:00.000000000', ..., '2021-12-12T22:00:00.000000000',
           '2021-12-12T23:00:00.000000000', '2021-12-13T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • VHM0
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 480, 360), meta=np.ndarray>

    long_name :

    Spectral significant wave height (Hm0)

    valid_max :

    20.0

    units :

    m

    standard_name :

    sea_surface_wave_significant_height

    valid_min :

    0.0

    Array Chunk Bytes 665.11 MiB 1.32 MiB Shape (1009, 480, 360) (2, 480, 360) Dask graph 505 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 54.00825119018555,   54.0249137878418,  54.04158020019531,
            54.05824661254883, 54.074913024902344,  54.09157943725586,
           54.108245849609375,  54.12491226196289, 54.141578674316406,
            54.15824508666992,
           ...
           61.841270446777344,  61.85793685913086, 61.874603271484375,
           61.891265869140625,  61.90793228149414, 61.924598693847656,
            61.94126510620117,  61.95793151855469,   61.9745979309082,
            61.99126434326172],
          dtype='float32', name='latitude', length=480))

  • longitude
    PandasIndex

    PandasIndex(Index([10.013859748840332,  10.04163646697998, 10.069413185119629,
           10.097190856933594, 10.124967575073242,  10.15274429321289,
           10.180521011352539, 10.208298683166504, 10.236075401306152,
             10.2638521194458,
           ...
           19.735809326171875, 19.763586044311523, 19.791362762451172,
            19.81913948059082,   19.8469181060791,  19.87469482421875,
             19.9024715423584, 19.930248260498047, 19.958024978637695,
           19.985801696777344],
          dtype='float32', name='longitude', length=360))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-11-01 00:00:00', '2021-11-01 01:00:00',
                   '2021-11-01 02:00:00', '2021-11-01 03:00:00',
                   '2021-11-01 04:00:00', '2021-11-01 05:00:00',
                   '2021-11-01 06:00:00', '2021-11-01 07:00:00',
                   '2021-11-01 08:00:00', '2021-11-01 09:00:00',
                   ...
                   '2021-12-12 15:00:00', '2021-12-12 16:00:00',
                   '2021-12-12 17:00:00', '2021-12-12 18:00:00',
                   '2021-12-12 19:00:00', '2021-12-12 20:00:00',
                   '2021-12-12 21:00:00', '2021-12-12 22:00:00',
                   '2021-12-12 23:00:00', '2021-12-13 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=1009, freq=None))

• Attributes: (4)

  institution :

  Baltic MFC, PU Finnish Meteorological Institute

  title :

  2D - Hourly Instantaneous

  Conventions :

  CF-1.0

  source :

  FMI-WAM_CMEMS

In [16]:

ds.VHM0.sel(time="2021-12-13T12:12.00", method="nearest").plot.imshow()

ds.VHM0.sel(time="2021-12-13T12:12.00", method="nearest").plot.imshow()

Out[16]:

<matplotlib.image.AxesImage at 0x7fa72c042600>

In [ ]: