Read parquet kerchunk catalog

import fsspec
import xarray as xr

%%time
# references are on an OSN pod (no credentials needed)
url = "s3://gfts-reference-data/CMEMS_v6r1_NWS_PHY_NRT_NL_01hav_AN_2D_combined.parq/"

target_opts = {"anon": False}

# netcdf files are on the AWS public dataset program (no credentials needed)
remote_opts = {"anon": False}

fs = fsspec.filesystem(
    "reference",
    fo=url,
    remote_protocol="s3",
    remote_options=remote_opts,
    target_options=target_opts,
)
m = fs.get_mapper("")

CPU times: user 286 ms, sys: 30.5 ms, total: 316 ms
Wall time: 421 ms

%%time
ds = xr.open_dataset(
    m, engine="zarr", chunks={}, backend_kwargs={"consolidated": False}
)

CPU times: user 2.1 s, sys: 191 ms, total: 2.29 s
Wall time: 2.63 s

ds

<xarray.Dataset> Size: 521GB
Dimensions:    (latitude: 551, longitude: 936, time: 18048)
Coordinates:
  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28
  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977
  * time       (time) datetime64[ns] 144kB 2022-04-02T00:30:00 ... 2024-04-22...
Data variables:
    mlotst     (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>
    thetao     (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>
    ubar       (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>
    uo         (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>
    vbar       (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>
    vo         (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>
    zos        (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>
Attributes: (12/13)
    Conventions:     CF-1.8
    comment:         
    contact:         https://marine.copernicus.eu/contact
    domain_name:     NWS36
    field_date:      20220402
    field_type:      mean
    ...              ...
    forecast_type:   analysis
    institution:     Nologin (Spain)
    licence:         https://marine.copernicus.eu/user-corner/service-commitm...
    references:      http://marine.copernicus.eu/
    source:          NEMO3.6
    title:           Ocean surface hourly mean fields for the North West Shel...

xarray.Dataset

• Dimensions:
  • latitude: 551
  • longitude: 936
  • time: 18048
• Coordinates: (3)
  • latitude
    (latitude)
    float32
    46.0 46.03 46.06 ... 61.25 61.28

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    step :

    0.02777863f

    unit_long :

    Degrees North

    units :

    degrees_north

    array([46.003643, 46.03142 , 46.0592  , ..., 61.22633 , 61.25411 , 61.281887],
          dtype=float32)

  • longitude
    (longitude)
    float32
    -16.0 -15.97 -15.94 ... 9.949 9.977

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    step :

    0.02777863f

    unit_long :

    Degrees East

    units :

    degrees_east

    array([-15.996015, -15.968236, -15.940457, ...,   9.921447,   9.949225,
             9.977004], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2022-04-02T00:30:00 ... 2024-04-...

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array(['2022-04-02T00:30:00.000000000', '2022-04-02T01:30:00.000000000',
           '2022-04-02T02:30:00.000000000', ..., '2024-04-22T21:30:00.000000000',
           '2024-04-22T22:30:00.000000000', '2024-04-22T23:30:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (7)
  • mlotst
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Ocean mixed layer thickness defined by density

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    unit_long :

    Meters

    units :

    m

    valid_max :

    20000

    valid_min :

    -20000

    z_max :

    0.494025f

    z_min :

    0.494025f

    Array Chunk Bytes 69.35 GiB 3.93 MiB Shape (18048, 551, 936) (1, 551, 936) Dask graph 18048 chunks in 2 graph layers Data type float64 numpy.ndarray

  • thetao
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Temperature

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    sea_water_potential_temperature

    unit_long :

    degrees_C

    units :

    degrees_C

    valid_max :

    22000

    valid_min :

    -12000

    z_max :

    0.494025f

    z_min :

    0.494025f

    Array Chunk Bytes 69.35 GiB 3.93 MiB Shape (18048, 551, 936) (1, 551, 936) Dask graph 18048 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ubar
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    zonal barotropic velocity

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    barotropic_eastward_sea_water_velocity

    unit_long :

    Meters per second

    units :

    m s-1

    valid_max :

    9000

    valid_min :

    -9000

    z_max :

    0.494025f

    z_min :

    0.494025f

    Array Chunk Bytes 69.35 GiB 3.93 MiB Shape (18048, 551, 936) (1, 551, 936) Dask graph 18048 chunks in 2 graph layers Data type float64 numpy.ndarray

  • uo
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Eastward velocity

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    eastward_sea_water_velocity

    unit_long :

    Meters per second

    units :

    m s-1

    valid_max :

    9000

    valid_min :

    -9000

    z_max :

    0.494025f

    z_min :

    0.494025f

    Array Chunk Bytes 69.35 GiB 3.93 MiB Shape (18048, 551, 936) (1, 551, 936) Dask graph 18048 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vbar
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    meridional barotropic velocity

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    barotropic_northward_sea_water_velocity

    unit_long :

    Meters per second

    units :

    m s-1

    valid_max :

    9000

    valid_min :

    -9000

    z_max :

    0.494025f

    z_min :

    0.494025f

    Array Chunk Bytes 69.35 GiB 3.93 MiB Shape (18048, 551, 936) (1, 551, 936) Dask graph 18048 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vo
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Northward velocity

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    northward_sea_water_velocity

    unit_long :

    Meters per second

    units :

    m s-1

    valid_max :

    9000

    valid_min :

    -9000

    z_max :

    0.494025f

    z_min :

    0.494025f

    Array Chunk Bytes 69.35 GiB 3.93 MiB Shape (18048, 551, 936) (1, 551, 936) Dask graph 18048 chunks in 2 graph layers Data type float64 numpy.ndarray

  • zos
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Sea surface height

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    sea_surface_height_above_geoid

    unit_long :

    Meters

    units :

    m

    valid_max :

    10000

    valid_min :

    -10000

    z_max :

    0.494025f

    z_min :

    0.494025f

    Array Chunk Bytes 69.35 GiB 3.93 MiB Shape (18048, 551, 936) (1, 551, 936) Dask graph 18048 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 46.00364303588867,  46.03142166137695, 46.059200286865234,
           46.086978912353516,   46.1147575378418,  46.14253616333008,
            46.17031478881836,  46.19809341430664,  46.22587203979492,
             46.2536506652832,
           ...
            61.03187942504883,  61.05965805053711,  61.08743667602539,
            61.11521530151367,  61.14299392700195, 61.170772552490234,
           61.198551177978516,   61.2263298034668,  61.25410842895508,
            61.28188705444336],
          dtype='float32', name='latitude', length=551))

  • longitude
    PandasIndex

    PandasIndex(Index([-15.996014595031738, -15.968235969543457, -15.940457344055176,
           -15.912678718566895, -15.884900093078613, -15.857121467590332,
            -15.82934284210205,  -15.80156421661377, -15.773785591125488,
           -15.746006965637207,
           ...
             9.726996421813965,   9.754775047302246,   9.782553672790527,
             9.810332298278809,    9.83811092376709,   9.865889549255371,
             9.893668174743652,   9.921446800231934,   9.949225425720215,
             9.977004051208496],
          dtype='float32', name='longitude', length=936))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-04-02 00:30:00', '2022-04-02 01:30:00',
                   '2022-04-02 02:30:00', '2022-04-02 03:30:00',
                   '2022-04-02 04:30:00', '2022-04-02 05:30:00',
                   '2022-04-02 06:30:00', '2022-04-02 07:30:00',
                   '2022-04-02 08:30:00', '2022-04-02 09:30:00',
                   ...
                   '2024-04-22 14:30:00', '2024-04-22 15:30:00',
                   '2024-04-22 16:30:00', '2024-04-22 17:30:00',
                   '2024-04-22 18:30:00', '2024-04-22 19:30:00',
                   '2024-04-22 20:30:00', '2024-04-22 21:30:00',
                   '2024-04-22 22:30:00', '2024-04-22 23:30:00'],
                  dtype='datetime64[ns]', name='time', length=18048, freq=None))

• Attributes: (13)

  Conventions :

  CF-1.8

  comment :

  contact :

  https://marine.copernicus.eu/contact

  domain_name :

  NWS36

  field_date :

  20220402

  field_type :

  mean

  forecast_range :

  04-day_analysis

  forecast_type :

  analysis

  institution :

  Nologin (Spain)

  licence :

  https://marine.copernicus.eu/user-corner/service-commitments-and-licence

  references :

  http://marine.copernicus.eu/

  source :

  NEMO3.6

  title :

  Ocean surface hourly mean fields for the North West Shelf (NWS) region

ds["thetao"]

<xarray.DataArray 'thetao' (time: 18048, latitude: 551, longitude: 936)> Size: 74GB
dask.array<open_dataset-thetao, shape=(18048, 551, 936), dtype=float64, chunksize=(1, 551, 936), chunktype=numpy.ndarray>
Coordinates:
  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28
  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977
  * time       (time) datetime64[ns] 144kB 2022-04-02T00:30:00 ... 2024-04-22...
Attributes: (12/14)
    easting:        longitude
    latitude_max:   61.2819f
    latitude_min:   46.0036f
    long_name:      Temperature
    longitude_max:  9.977f
    longitude_min:  -15.996f
    ...             ...
    unit_long:      degrees_C
    units:          degrees_C
    valid_max:      22000
    valid_min:      -12000
    z_max:          0.494025f
    z_min:          0.494025f

xarray.DataArray

'thetao'

• time: 18048
• latitude: 551
• longitude: 936

• dask.array<chunksize=(1, 551, 936), meta=np.ndarray>

  Array Chunk Bytes 69.35 GiB 3.93 MiB Shape (18048, 551, 936) (1, 551, 936) Dask graph 18048 chunks in 2 graph layers Data type float64 numpy.ndarray

• Coordinates: (3)
  • latitude
    (latitude)
    float32
    46.0 46.03 46.06 ... 61.25 61.28

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    step :

    0.02777863f

    unit_long :

    Degrees North

    units :

    degrees_north

    array([46.003643, 46.03142 , 46.0592  , ..., 61.22633 , 61.25411 , 61.281887],
          dtype=float32)

  • longitude
    (longitude)
    float32
    -16.0 -15.97 -15.94 ... 9.949 9.977

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    step :

    0.02777863f

    unit_long :

    Degrees East

    units :

    degrees_east

    array([-15.996015, -15.968236, -15.940457, ...,   9.921447,   9.949225,
             9.977004], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2022-04-02T00:30:00 ... 2024-04-...

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array(['2022-04-02T00:30:00.000000000', '2022-04-02T01:30:00.000000000',
           '2022-04-02T02:30:00.000000000', ..., '2024-04-22T21:30:00.000000000',
           '2024-04-22T22:30:00.000000000', '2024-04-22T23:30:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 46.00364303588867,  46.03142166137695, 46.059200286865234,
           46.086978912353516,   46.1147575378418,  46.14253616333008,
            46.17031478881836,  46.19809341430664,  46.22587203979492,
             46.2536506652832,
           ...
            61.03187942504883,  61.05965805053711,  61.08743667602539,
            61.11521530151367,  61.14299392700195, 61.170772552490234,
           61.198551177978516,   61.2263298034668,  61.25410842895508,
            61.28188705444336],
          dtype='float32', name='latitude', length=551))

  • longitude
    PandasIndex

    PandasIndex(Index([-15.996014595031738, -15.968235969543457, -15.940457344055176,
           -15.912678718566895, -15.884900093078613, -15.857121467590332,
            -15.82934284210205,  -15.80156421661377, -15.773785591125488,
           -15.746006965637207,
           ...
             9.726996421813965,   9.754775047302246,   9.782553672790527,
             9.810332298278809,    9.83811092376709,   9.865889549255371,
             9.893668174743652,   9.921446800231934,   9.949225425720215,
             9.977004051208496],
          dtype='float32', name='longitude', length=936))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-04-02 00:30:00', '2022-04-02 01:30:00',
                   '2022-04-02 02:30:00', '2022-04-02 03:30:00',
                   '2022-04-02 04:30:00', '2022-04-02 05:30:00',
                   '2022-04-02 06:30:00', '2022-04-02 07:30:00',
                   '2022-04-02 08:30:00', '2022-04-02 09:30:00',
                   ...
                   '2024-04-22 14:30:00', '2024-04-22 15:30:00',
                   '2024-04-22 16:30:00', '2024-04-22 17:30:00',
                   '2024-04-22 18:30:00', '2024-04-22 19:30:00',
                   '2024-04-22 20:30:00', '2024-04-22 21:30:00',
                   '2024-04-22 22:30:00', '2024-04-22 23:30:00'],
                  dtype='datetime64[ns]', name='time', length=18048, freq=None))

• Attributes: (14)

  easting :

  longitude

  latitude_max :

  61.2819f

  latitude_min :

  46.0036f

  long_name :

  Temperature

  longitude_max :

  9.977f

  longitude_min :

  -15.996f

  northing :

  latitude

  standard_name :

  sea_water_potential_temperature

  unit_long :

  degrees_C

  units :

  degrees_C

  valid_max :

  22000

  valid_min :

  -12000

  z_max :

  0.494025f

  z_min :

  0.494025f

da = ds["thetao"].sel(time="2022-04-02 00:00", method="nearest").load()

da

<xarray.DataArray 'thetao' (latitude: 551, longitude: 936)> Size: 4MB
array([[13.31200016, 13.32400016, 13.33900016, ...,         nan,
                nan,         nan],
       [13.32600016, 13.34100016, 13.34700016, ...,         nan,
                nan,         nan],
       [13.34300016, 13.35300016, 13.34600016, ...,         nan,
                nan,         nan],
       ...,
       [ 8.45599993,  8.49499993,  8.53999993, ...,         nan,
                nan,         nan],
       [ 8.44899993,  8.47899993,  8.53999993, ...,         nan,
                nan,         nan],
       [ 8.45999993,  8.47099993,  8.53399993, ...,         nan,
                nan,         nan]])
Coordinates:
  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28
  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977
    time       datetime64[ns] 8B 2022-04-02T00:30:00
Attributes: (12/14)
    easting:        longitude
    latitude_max:   61.2819f
    latitude_min:   46.0036f
    long_name:      Temperature
    longitude_max:  9.977f
    longitude_min:  -15.996f
    ...             ...
    unit_long:      degrees_C
    units:          degrees_C
    valid_max:      22000
    valid_min:      -12000
    z_max:          0.494025f
    z_min:          0.494025f

xarray.DataArray

'thetao'

• latitude: 551
• longitude: 936

• 13.31 13.32 13.34 13.34 13.31 13.27 13.23 ... nan nan nan nan nan nan

  array([[13.31200016, 13.32400016, 13.33900016, ...,         nan,
                  nan,         nan],
         [13.32600016, 13.34100016, 13.34700016, ...,         nan,
                  nan,         nan],
         [13.34300016, 13.35300016, 13.34600016, ...,         nan,
                  nan,         nan],
         ...,
         [ 8.45599993,  8.49499993,  8.53999993, ...,         nan,
                  nan,         nan],
         [ 8.44899993,  8.47899993,  8.53999993, ...,         nan,
                  nan,         nan],
         [ 8.45999993,  8.47099993,  8.53399993, ...,         nan,
                  nan,         nan]])

• Coordinates: (3)
  • latitude
    (latitude)
    float32
    46.0 46.03 46.06 ... 61.25 61.28

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    step :

    0.02777863f

    unit_long :

    Degrees North

    units :

    degrees_north

    array([46.003643, 46.03142 , 46.0592  , ..., 61.22633 , 61.25411 , 61.281887],
          dtype=float32)

  • longitude
    (longitude)
    float32
    -16.0 -15.97 -15.94 ... 9.949 9.977

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    step :

    0.02777863f

    unit_long :

    Degrees East

    units :

    degrees_east

    array([-15.996015, -15.968236, -15.940457, ...,   9.921447,   9.949225,
             9.977004], dtype=float32)

  • time
    ()
    datetime64[ns]
    2022-04-02T00:30:00

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array('2022-04-02T00:30:00.000000000', dtype='datetime64[ns]')

• Indexes: (2)
  • latitude
    PandasIndex

    PandasIndex(Index([ 46.00364303588867,  46.03142166137695, 46.059200286865234,
           46.086978912353516,   46.1147575378418,  46.14253616333008,
            46.17031478881836,  46.19809341430664,  46.22587203979492,
             46.2536506652832,
           ...
            61.03187942504883,  61.05965805053711,  61.08743667602539,
            61.11521530151367,  61.14299392700195, 61.170772552490234,
           61.198551177978516,   61.2263298034668,  61.25410842895508,
            61.28188705444336],
          dtype='float32', name='latitude', length=551))

  • longitude
    PandasIndex

    PandasIndex(Index([-15.996014595031738, -15.968235969543457, -15.940457344055176,
           -15.912678718566895, -15.884900093078613, -15.857121467590332,
            -15.82934284210205,  -15.80156421661377, -15.773785591125488,
           -15.746006965637207,
           ...
             9.726996421813965,   9.754775047302246,   9.782553672790527,
             9.810332298278809,    9.83811092376709,   9.865889549255371,
             9.893668174743652,   9.921446800231934,   9.949225425720215,
             9.977004051208496],
          dtype='float32', name='longitude', length=936))

• Attributes: (14)

  easting :

  longitude

  latitude_max :

  61.2819f

  latitude_min :

  46.0036f

  long_name :

  Temperature

  longitude_max :

  9.977f

  longitude_min :

  -15.996f

  northing :

  latitude

  standard_name :

  sea_water_potential_temperature

  unit_long :

  degrees_C

  units :

  degrees_C

  valid_max :

  22000

  valid_min :

  -12000

  z_max :

  0.494025f

  z_min :

  0.494025f

da.hvplot.quadmesh(x="longitude", y="latitude", rasterize=True, data_aspect=1)

Read 3D data

%%time
# references are on an OSN pod (no credentials needed)
url = "s3://gfts-reference-data/CMEMS_v6r1_NWS_PHY_NRT_NL_3D_combined.parq/"

target_opts = {"anon": False}

# netcdf files are on the AWS public dataset program (no credentials needed)
remote_opts = {"anon": False}

fs3D = fsspec.filesystem(
    "reference",
    fo=url,
    remote_protocol="s3",
    remote_options=remote_opts,
    target_options=target_opts,
)
m3D = fs3D.get_mapper("")

CPU times: user 20.6 ms, sys: 1.34 ms, total: 21.9 ms
Wall time: 67.7 ms

%%time
ds3D = xr.open_dataset(
    m3D, engine="zarr", chunks={}, backend_kwargs={"consolidated": False}
)

CPU times: user 102 ms, sys: 620 µs, total: 102 ms
Wall time: 413 ms

ds3D

<xarray.Dataset> Size: 6TB
Dimensions:    (depth: 50, latitude: 551, longitude: 936, time: 7104)
Coordinates:
  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03
  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28
  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977
  * time       (time) datetime64[ns] 57kB 2023-07-02T00:30:00 ... 2024-04-22T...
Data variables:
    so         (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>
    thetao     (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>
    uo         (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>
    vo         (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>
Attributes: (12/13)
    Conventions:     CF-1.8
    comment:         
    contact:         https://marine.copernicus.eu/contact
    domain_name:     NWS36
    field_date:      20230702
    field_type:      mean
    ...              ...
    forecast_type:   hindcast
    institution:     Nologin (Spain)
    licence:         https://marine.copernicus.eu/user-corner/service-commitm...
    references:      http://marine.copernicus.eu/
    source:          NEMO3.6
    title:           Ocean 3D hourly mean fields for the North West Shelf (NW...

xarray.Dataset

• Dimensions:
  • depth: 50
  • latitude: 551
  • longitude: 936
  • time: 7104
• Coordinates: (4)
  • depth
    (depth)
    float32
    0.494 1.541 ... 5.275e+03 5.728e+03

    axis :

    Z

    long_name :

    Depth

    positive :

    down

    standard_name :

    depth

    unit_long :

    Meters

    units :

    m

    array([4.940254e-01, 1.541375e+00, 2.645669e+00, 3.819495e+00, 5.078224e+00,
           6.440614e+00, 7.929560e+00, 9.572997e+00, 1.140500e+01, 1.346714e+01,
           1.581007e+01, 1.849556e+01, 2.159882e+01, 2.521141e+01, 2.944473e+01,
           3.443415e+01, 4.034405e+01, 4.737369e+01, 5.576429e+01, 6.580727e+01,
           7.785385e+01, 9.232607e+01, 1.097293e+02, 1.306660e+02, 1.558507e+02,
           1.861256e+02, 2.224752e+02, 2.660403e+02, 3.181274e+02, 3.802130e+02,
           4.539377e+02, 5.410889e+02, 6.435668e+02, 7.633331e+02, 9.023393e+02,
           1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,
           2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,
           3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03, 5.727917e+03],
          dtype=float32)

  • latitude
    (latitude)
    float32
    46.0 46.03 46.06 ... 61.25 61.28

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    step :

    0.02777863f

    unit_long :

    Degrees North

    units :

    degrees_north

    array([46.003643, 46.03142 , 46.0592  , ..., 61.22633 , 61.25411 , 61.281887],
          dtype=float32)

  • longitude
    (longitude)
    float32
    -16.0 -15.97 -15.94 ... 9.949 9.977

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    step :

    0.02777863f

    unit_long :

    Degrees East

    units :

    degrees_east

    array([-15.996015, -15.968236, -15.940457, ...,   9.921447,   9.949225,
             9.977004], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2023-07-02T00:30:00 ... 2024-04-...

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array(['2023-07-02T00:30:00.000000000', '2023-07-02T01:30:00.000000000',
           '2023-07-02T02:30:00.000000000', ..., '2024-04-22T21:30:00.000000000',
           '2024-04-22T22:30:00.000000000', '2024-04-22T23:30:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (4)
  • so
    (time, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Salinity

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    sea_water_salinity

    unit_long :

    Practical Salinity Unit

    units :

    1e-3

    valid_max :

    20000

    valid_min :

    -20000

    z_max :

    5727.917f

    z_min :

    0.49402f

    Array Chunk Bytes 1.33 TiB 3.93 MiB Shape (7104, 50, 551, 936) (1, 1, 551, 936) Dask graph 355200 chunks in 2 graph layers Data type float64 numpy.ndarray

  • thetao
    (time, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Temperature

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    sea_water_potential_temperature

    unit_long :

    degrees_C

    units :

    degrees_C

    valid_max :

    22000

    valid_min :

    -12000

    z_max :

    5727.917f

    z_min :

    0.49402f

    Array Chunk Bytes 1.33 TiB 3.93 MiB Shape (7104, 50, 551, 936) (1, 1, 551, 936) Dask graph 355200 chunks in 2 graph layers Data type float64 numpy.ndarray

  • uo
    (time, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Eastward velocity

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    eastward_sea_water_velocity

    unit_long :

    Meters per second

    units :

    m s-1

    valid_max :

    9000

    valid_min :

    -9000

    z_max :

    5727.917f

    z_min :

    0.49402f

    Array Chunk Bytes 1.33 TiB 3.93 MiB Shape (7104, 50, 551, 936) (1, 1, 551, 936) Dask graph 355200 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vo
    (time, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>

    easting :

    longitude

    latitude_max :

    61.2819f

    latitude_min :

    46.0036f

    long_name :

    Northward velocity

    longitude_max :

    9.977f

    longitude_min :

    -15.996f

    northing :

    latitude

    standard_name :

    northward_sea_water_velocity

    unit_long :

    Meters per second

    units :

    m s-1

    valid_max :

    9000

    valid_min :

    -9000

    z_max :

    5727.917f

    z_min :

    0.49402f

    Array Chunk Bytes 1.33 TiB 3.93 MiB Shape (7104, 50, 551, 936) (1, 1, 551, 936) Dask graph 355200 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • depth
    PandasIndex

    PandasIndex(Index([0.4940253794193268, 1.5413753986358643, 2.6456685066223145,
           3.8194947242736816,  5.078223705291748,  6.440614223480225,
             7.92956018447876,  9.572997093200684,  11.40500259399414,
           13.467138290405273, 15.810072898864746, 18.495559692382812,
            21.59881591796875, 25.211408615112305,  29.44472885131836,
            34.43415451049805, 40.344051361083984, 47.373687744140625,
            55.76428985595703,   65.8072738647461,  77.85385131835938,
             92.3260726928711, 109.72927856445312, 130.66598510742188,
           155.85072326660156,  186.1255645751953,  222.4751739501953,
            266.0402526855469,    318.12744140625,  380.2130126953125,
             453.937744140625,  541.0889282226562,  643.5668334960938,
            763.3330688476562,  902.3392944335938,  1062.439697265625,
           1245.2911376953125,    1452.2509765625,  1684.284423828125,
           1941.8934326171875,  2225.077880859375,  2533.336181640625,
             2865.70263671875,       3220.8203125,  3597.031982421875,
             3992.48388671875,   4405.22412109375,   4833.29052734375,
              5274.7841796875,   5727.91650390625],
          dtype='float32', name='depth'))

  • latitude
    PandasIndex

    PandasIndex(Index([ 46.00364303588867,  46.03142166137695, 46.059200286865234,
           46.086978912353516,   46.1147575378418,  46.14253616333008,
            46.17031478881836,  46.19809341430664,  46.22587203979492,
             46.2536506652832,
           ...
            61.03187942504883,  61.05965805053711,  61.08743667602539,
            61.11521530151367,  61.14299392700195, 61.170772552490234,
           61.198551177978516,   61.2263298034668,  61.25410842895508,
            61.28188705444336],
          dtype='float32', name='latitude', length=551))

  • longitude
    PandasIndex

    PandasIndex(Index([-15.996014595031738, -15.968235969543457, -15.940457344055176,
           -15.912678718566895, -15.884900093078613, -15.857121467590332,
            -15.82934284210205,  -15.80156421661377, -15.773785591125488,
           -15.746006965637207,
           ...
             9.726996421813965,   9.754775047302246,   9.782553672790527,
             9.810332298278809,    9.83811092376709,   9.865889549255371,
             9.893668174743652,   9.921446800231934,   9.949225425720215,
             9.977004051208496],
          dtype='float32', name='longitude', length=936))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-07-02 00:30:00', '2023-07-02 01:30:00',
                   '2023-07-02 02:30:00', '2023-07-02 03:30:00',
                   '2023-07-02 04:30:00', '2023-07-02 05:30:00',
                   '2023-07-02 06:30:00', '2023-07-02 07:30:00',
                   '2023-07-02 08:30:00', '2023-07-02 09:30:00',
                   ...
                   '2024-04-22 14:30:00', '2024-04-22 15:30:00',
                   '2024-04-22 16:30:00', '2024-04-22 17:30:00',
                   '2024-04-22 18:30:00', '2024-04-22 19:30:00',
                   '2024-04-22 20:30:00', '2024-04-22 21:30:00',
                   '2024-04-22 22:30:00', '2024-04-22 23:30:00'],
                  dtype='datetime64[ns]', name='time', length=7104, freq=None))

• Attributes: (13)

  Conventions :

  CF-1.8

  comment :

  contact :

  https://marine.copernicus.eu/contact

  domain_name :

  NWS36

  field_date :

  20230702

  field_type :

  mean

  forecast_range :

  01-day_hindcast

  forecast_type :

  hindcast

  institution :

  Nologin (Spain)

  licence :

  https://marine.copernicus.eu/user-corner/service-commitments-and-licence

  references :

  http://marine.copernicus.eu/

  source :

  NEMO3.6

  title :

  Ocean 3D hourly mean fields for the North West Shelf (NWS) region

da3D = (
    ds3D["thetao"].isel(depth=0).sel(time="2022-04-02 00:00", method="nearest").load()
)

da3D.hvplot.quadmesh(x="longitude", y="latitude", rasterize=True, data_aspect=1)