4.2.3. ESA CCI Toolbox Data Access with Parameters

This notebook shows how to open data cubes from the ESA Open Data Portal for a given time range and region:

A temporarily regular cube with aggregated CCI data that fall into equal-size time periods;

We start with necessary imports.

from xcube.core.store import new_data_store
import IPython.display
import shapely

Now we can create the store.

cci_store = new_data_store('esa-cci')

For this demo, we are using the dataset with the id ‘esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1’. Here is its metadata:

cci_store.describe_data('esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1')

<xcube.core.store.descriptor.DatasetDescriptor at 0x7f1837f9a0d0>

We are focussing on Africa, so we define a bounding box accordingly.

x1 = -23.40  # degree
y1 = -40.40  # degree
x2 = 57.40  # degree
y2 = 40.40  # degree

bbox = (x1, y1, x2, y2)

Visualize the bounding box. If you don’t see anything, please refer to Ex0-DCFS-Setup.

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

<IPython.display.GeoJSON object>

Our time range covers data from 1997: 1997-01-01 to 1997-12-01

---

Now to open the dataset. You may check its metadata first to make sure that your parameters are valid.

cci_store.get_open_data_params_schema(data_id='esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1', opener_id='dataset:zarr:esa-cdc')

<xcube.util.jsonschema.JsonObjectSchema at 0x7f183779f490>

Select any valid combination from the paramater in properties above. The only mandatory parameter is the data_id. If you leave any parameter out, all available values will be considered.

cube = cci_store.open_data('esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1',
                           variable_names=['absorbing_aerosol_index'],
                           bbox=bbox,
                           time_range=['2005-01-01', '2005-12-31'])
cube

<xarray.Dataset>
Dimensions:                  (time: 12, lat: 80, lon: 80, bnds: 2)
Coordinates:
  * lat                      (lat) float32 -39.5 -38.5 -37.5 ... 37.5 38.5 39.5
  * lon                      (lon) float32 -22.5 -21.5 -20.5 ... 54.5 55.5 56.5
  * time                     (time) datetime64[ns] 2005-01-16T12:00:00 ... 20...
    time_bnds                (time, bnds) datetime64[ns] dask.array<chunksize=(12, 2), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
    absorbing_aerosol_index  (time, lat, lon) float32 dask.array<chunksize=(1, 80, 80), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    title:                   esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-pla...
    date_created:            2024-02-29T21:53:26.841148
    processing_level:        L3
    time_coverage_start:     2005-01-01T00:00:00
    time_coverage_end:       2006-01-01T00:00:00
    time_coverage_duration:  P365DT0H0M0S
    history:                 [{'program': 'esa_climate_toolbox.ds.chunkstore....

xarray.Dataset

• Dimensions:
  • time: 12
  • lat: 80
  • lon: 80
  • bnds: 2
• Coordinates: (4)
  • lat
    (lat)
    float32
    -39.5 -38.5 -37.5 ... 38.5 39.5

    long_name :

    Latitudes of the centre of the grid cells

    units :

    degrees

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    80

    shape :

    [80]

    chunk_sizes :

    [80]

    file_chunk_sizes :

    [80]

    data_type :

    float32

    dimensions :

    ['latitude']

    file_dimensions :

    ['latitude']

    array([-39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5],
          dtype=float32)

  • lon
    (lon)
    float32
    -22.5 -21.5 -20.5 ... 55.5 56.5

    long_name :

    Longitudes of the centre of the grid cells

    units :

    degrees

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    80

    shape :

    [80]

    chunk_sizes :

    [80]

    file_chunk_sizes :

    [80]

    data_type :

    float32

    dimensions :

    ['longitude']

    file_dimensions :

    ['longitude']

    array([-22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5,
           -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,
            -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,
             7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,
            17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,
            27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,
            37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,
            47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2005-01-16T12:00:00 ... 2005-12-...

    standard_name :

    time

    bounds :

    time_bnds

    array(['2005-01-16T12:00:00.000000000', '2005-02-15T00:00:00.000000000',
           '2005-03-16T12:00:00.000000000', '2005-04-16T00:00:00.000000000',
           '2005-05-16T12:00:00.000000000', '2005-06-16T00:00:00.000000000',
           '2005-07-16T12:00:00.000000000', '2005-08-16T12:00:00.000000000',
           '2005-09-16T00:00:00.000000000', '2005-10-16T12:00:00.000000000',
           '2005-11-16T00:00:00.000000000', '2005-12-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • time_bnds
    (time, bnds)
    datetime64[ns]
    dask.array<chunksize=(12, 2), meta=np.ndarray>

    standard_name :

    time_bnds

    Array Chunk Bytes 192 B 192 B Shape (12, 2) (12, 2) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

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

    long_name :

    Absorbing aerosol index averaged for each grid cell

    units :

    1

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    76800

    shape :

    [12, 80, 80]

    chunk_sizes :

    [1, 80, 80]

    file_chunk_sizes :

    [80, 80]

    data_type :

    float32

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    Array Chunk Bytes 300.00 kiB 25.00 kiB Shape (12, 80, 80) (1, 80, 80) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Float64Index([-39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5,
                  -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5,
                  -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5,
                  -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,
                   -3.5,  -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,
                    5.5,   6.5,   7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,
                   14.5,  15.5,  16.5,  17.5,  18.5,  19.5,  20.5,  21.5,  22.5,
                   23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,  30.5,  31.5,
                   32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5],
                 dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Float64Index([-22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5,
                  -13.5, -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,
                   -4.5,  -3.5,  -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,
                    4.5,   5.5,   6.5,   7.5,   8.5,   9.5,  10.5,  11.5,  12.5,
                   13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,  20.5,  21.5,
                   22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,  30.5,
                   31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
                   40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,
                   49.5,  50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5],
                 dtype='float64', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2005-01-16 12:00:00', '2005-02-15 00:00:00',
                   '2005-03-16 12:00:00', '2005-04-16 00:00:00',
                   '2005-05-16 12:00:00', '2005-06-16 00:00:00',
                   '2005-07-16 12:00:00', '2005-08-16 12:00:00',
                   '2005-09-16 00:00:00', '2005-10-16 12:00:00',
                   '2005-11-16 00:00:00', '2005-12-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (8)

  Conventions :

  CF-1.7

  title :

  esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1

  date_created :

  2024-02-29T21:53:26.841148

  processing_level :

  L3

  time_coverage_start :

  2005-01-01T00:00:00

  time_coverage_end :

  2006-01-01T00:00:00

  time_coverage_duration :

  P365DT0H0M0S

  history :

  [{'program': 'esa_climate_toolbox.ds.chunkstore.CciChunkStore', 'cube_params': {'variable_names': ['absorbing_aerosol_index'], 'time_range': ['2005-01-01T00:00:00', '2005-12-31T00:00:00'], 'bbox': [-23.4, -40.4, 57.4, 40.4]}}]

We may visualize the cube’s time coordinates:

cube.time.diff(dim='time').plot.line()

[<matplotlib.lines.Line2D at 0x7f183610fed0>]

And finally, display the results:

cube.absorbing_aerosol_index.sel(time='2005-04-15 12:00:00', method='nearest').plot.imshow(cmap='Greys_r', figsize=(8, 8))

<matplotlib.image.AxesImage at 0x7f18361b19d0>