Baltic Sea Drift – Preparation

We’ll download a subset of the Baltic Sea Physics Analysis and Forecast dataset dataset which only covers a few days at 3 hour resolution.

import copernicusmarine
import xarray as xr

/miniconda3/envs/jupyter/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

copernicusmarine.login()

INFO - 2025-06-28T15:56:04Z - Downloading Copernicus Marine data requires a Copernicus Marine username and password, sign up for free at: https://data.marine.copernicus.eu/register

Copernicus Marine username:

Copernicus Marine password:

INFO - 2025-06-28T15:56:17Z - Credentials file stored in /home/jovyan/.copernicusmarine/.copernicusmarine-credentials.

True

ds = copernicusmarine.open_dataset(dataset_id="cmems_mod_bal_phy_anfc_PT1H-i")
ds

INFO - 2025-06-28T15:56:19Z - Selected dataset version: "202411"
INFO - 2025-06-28T15:56:19Z - Selected dataset part: "default"

<xarray.Dataset> Size: 15TB
Dimensions:    (depth: 56, latitude: 774, longitude: 763, time: 22668)
Coordinates:
  * depth      (depth) float32 224B 0.5016 1.516 2.548 ... 663.9 688.0 712.0
  * latitude   (latitude) float32 3kB 53.01 53.02 53.04 ... 65.86 65.87 65.89
  * longitude  (longitude) float32 3kB 9.042 9.069 9.097 ... 30.15 30.18 30.21
  * time       (time) datetime64[ns] 181kB 2022-12-02T13:00:00 ... 2025-07-04
Data variables:
    bottomT    (time, latitude, longitude) float32 54GB dask.array<chunksize=(2, 774, 763), meta=np.ndarray>
    mlotst     (time, latitude, longitude) float32 54GB dask.array<chunksize=(2, 774, 763), meta=np.ndarray>
    siconc     (time, latitude, longitude) float32 54GB dask.array<chunksize=(2, 774, 763), meta=np.ndarray>
    sithick    (time, latitude, longitude) float32 54GB dask.array<chunksize=(2, 774, 763), meta=np.ndarray>
    sla        (time, latitude, longitude) float32 54GB dask.array<chunksize=(2, 774, 763), meta=np.ndarray>
    so         (time, depth, latitude, longitude) float32 3TB dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>
    sob        (time, latitude, longitude) float32 54GB dask.array<chunksize=(2, 774, 763), meta=np.ndarray>
    thetao     (time, depth, latitude, longitude) float32 3TB dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>
    uo         (time, depth, latitude, longitude) float32 3TB dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>
    vo         (time, depth, latitude, longitude) float32 3TB dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>
    wo         (time, depth, latitude, longitude) float32 3TB dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>
Attributes:
    references:   https://marine.copernicus.eu/
    comment:      Data on cropped native product grid. Horizontal velocities ...
    title:        CMEMS NEMO hourly model fields
    contact:      servicedesk.cmems@mercator-ocean.eu
    source:       CMEMS BAL MFC NEMO model output converted to NetCDF
    Conventions:  CF-1.0
    institution:  Baltic MFC, PU Swedish Meteorological and Hydrological Inst...

xarray.Dataset

• Dimensions:
  • depth: 56
  • latitude: 774
  • longitude: 763
  • time: 22668
• Coordinates: (4)
  • depth
    (depth)
    float32
    0.5016 1.516 2.548 ... 688.0 712.0

    long_name :

    Depth

    units :

    m

    unit_long :

    meters

    standard_name :

    depth

    axis :

    Z

    positive :

    down

    array([5.016462e-01, 1.515992e+00, 2.548084e+00, 3.602298e+00, 4.684081e+00,
           5.800200e+00, 6.959055e+00, 8.171057e+00, 9.449085e+00, 1.080904e+01,
           1.227047e+01, 1.385737e+01, 1.559901e+01, 1.753092e+01, 1.969594e+01,
           2.214526e+01, 2.493938e+01, 2.814896e+01, 3.185507e+01, 3.614899e+01,
           4.113091e+01, 4.690754e+01, 5.358818e+01, 6.127954e+01, 7.007922e+01,
           8.006874e+01, 9.130695e+01, 1.038247e+02, 1.176220e+02, 1.326680e+02,
           1.489038e+02, 1.662480e+02, 1.846028e+02, 2.038617e+02, 2.239153e+02,
           2.446568e+02, 2.659862e+02, 2.878122e+02, 3.100536e+02, 3.326397e+02,
           3.555102e+02, 3.786138e+02, 4.019077e+02, 4.253563e+02, 4.489304e+02,
           4.726059e+02, 4.963634e+02, 5.201869e+02, 5.440635e+02, 5.679829e+02,
           5.919366e+02, 6.159179e+02, 6.399213e+02, 6.639425e+02, 6.879779e+02,
           7.120247e+02], dtype=float32)

  • latitude
    (latitude)
    float32
    53.01 53.02 53.04 ... 65.87 65.89

    long_name :

    Latitude of each location

    units :

    degrees_north

    unit_long :

    degrees North

    standard_name :

    latitude

    axis :

    Y

    array([53.008297, 53.024963, 53.04163 , ..., 65.857666, 65.87433 , 65.89099 ],
          shape=(774,), dtype=float32)

  • longitude
    (longitude)
    float32
    9.042 9.069 9.097 ... 30.18 30.21

    long_name :

    Longitude of each location

    units :

    degrees_east

    unit_long :

    degrees East

    standard_name :

    longitude

    axis :

    X

    array([ 9.041582,  9.06936 ,  9.097137, ..., 30.1531  , 30.180878, 30.208656],
          shape=(763,), dtype=float32)

  • time
    (time)
    datetime64[ns]
    2022-12-02T13:00:00 ... 2025-07-04

    unit_long :

    Days Since 1900-01-01

    long_name :

    Time

    standard_name :

    time

    axis :

    T

    array(['2022-12-02T13:00:00.000000000', '2022-12-02T14:00:00.000000000',
           '2022-12-02T15:00:00.000000000', ..., '2025-07-03T22:00:00.000000000',
           '2025-07-03T23:00:00.000000000', '2025-07-04T00:00:00.000000000'],
          shape=(22668,), dtype='datetime64[ns]')

• Data variables: (11)
  • bottomT
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 774, 763), meta=np.ndarray>

    long_name :

    Sea water potential temperature at sea floor (given for depth comprise between 0 and 500m)

    standard_name :

    sea_water_potential_temperature_at_sea_floor

    unit_long :

    degree Celsius

    units :

    degrees_C

    valid_range :

    [-3.0, 40.0]

    Array Chunk Bytes 49.87 GiB 4.51 MiB Shape (22668, 774, 763) (2, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mlotst
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 774, 763), meta=np.ndarray>

    long_name :

    Ocean mixed layer thickness defined by density (as in de Boyer Montegut, 2004)

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    unit_long :

    meters

    units :

    m

    Array Chunk Bytes 49.87 GiB 4.51 MiB Shape (22668, 774, 763) (2, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • siconc
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 774, 763), meta=np.ndarray>

    long_name :

    Sea ice cover

    standard_name :

    sea_ice_area_fraction

    units :

    1

    valid_range :

    [0.0, 1.0]

    Array Chunk Bytes 49.87 GiB 4.51 MiB Shape (22668, 774, 763) (2, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sithick
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 774, 763), meta=np.ndarray>

    long_name :

    Sea ice thickness

    standard_name :

    sea_ice_thickness

    unit_long :

    meters

    units :

    m

    Array Chunk Bytes 49.87 GiB 4.51 MiB Shape (22668, 774, 763) (2, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sla
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 774, 763), meta=np.ndarray>

    long_name :

    Sea level elevation

    standard_name :

    sea_surface_height_above_sea_level

    unit_long :

    meters

    units :

    m

    valid_range :

    [-10.0, 10.0]

    Array Chunk Bytes 49.87 GiB 4.51 MiB Shape (22668, 774, 763) (2, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • so
    (time, depth, latitude, longitude)
    float32
    dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>

    long_name :

    salinity

    standard_name :

    sea_water_salinity

    unit_long :

    1e-3

    units :

    1e-3

    Array Chunk Bytes 2.73 TiB 252.32 MiB Shape (22668, 56, 774, 763) (2, 56, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sob
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(2, 774, 763), meta=np.ndarray>

    long_name :

    Sea water salinity at sea floor

    standard_name :

    sea_water_salinity_at_sea_floor

    unit_long :

    0.001

    units :

    0.001

    valid_range :

    [0.0, 40.0]

    Array Chunk Bytes 49.87 GiB 4.51 MiB Shape (22668, 774, 763) (2, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • thetao
    (time, depth, latitude, longitude)
    float32
    dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>

    long_name :

    potential temperature

    standard_name :

    sea_water_potential_temperature

    unit_long :

    degree Celsius

    units :

    degree_Celsius

    Array Chunk Bytes 2.73 TiB 252.32 MiB Shape (22668, 56, 774, 763) (2, 56, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uo
    (time, depth, latitude, longitude)
    float32
    dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>

    long_name :

    Eastward current

    standard_name :

    eastward_sea_water_velocity

    unit_long :

    meters per second

    units :

    m s-1

    Array Chunk Bytes 2.73 TiB 252.32 MiB Shape (22668, 56, 774, 763) (2, 56, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vo
    (time, depth, latitude, longitude)
    float32
    dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>

    long_name :

    Northward current

    standard_name :

    northward_sea_water_velocity

    unit_long :

    meters per second

    units :

    m s-1

    Array Chunk Bytes 2.73 TiB 252.32 MiB Shape (22668, 56, 774, 763) (2, 56, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wo
    (time, depth, latitude, longitude)
    float32
    dask.array<chunksize=(2, 56, 774, 763), meta=np.ndarray>

    long_name :

    Upward current

    standard_name :

    upward_sea_water_velocity

    unit_long :

    meters per second

    units :

    m s-1

    Array Chunk Bytes 2.73 TiB 252.32 MiB Shape (22668, 56, 774, 763) (2, 56, 774, 763) Dask graph 11334 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • depth
    PandasIndex

    PandasIndex(Index([0.5016462206840515, 1.5159924030303955,  2.548084020614624,
           3.6022984981536865,  4.684081077575684,   5.80019998550415,
            6.959054946899414,  8.171056747436523,  9.449085235595703,
           10.809036254882812, 12.270469665527344, 13.857369422912598,
            15.59900951385498,  17.53092384338379,  19.69594383239746,
            22.14525604248047,  24.93938446044922, 28.148958206176758,
           31.855070114135742,  36.14898681640625,  41.13091278076172,
           46.907535552978516,  53.58818435668945,    61.279541015625,
            70.07921600341797,  80.06874084472656,  91.30695343017578,
              103.82470703125, 117.62200164794922,       132.66796875,
              148.90380859375, 166.24795532226562, 184.60281372070312,
           203.86167907714844, 223.91526794433594, 244.65684509277344,
            265.9862365722656,  287.8121643066406,  310.0535583496094,
            332.6397399902344,  355.5102233886719,  378.6138000488281,
            401.9076843261719,  425.3562927246094, 448.93035888671875,
           472.60589599609375,  496.3633728027344,  520.1868896484375,
            544.0635375976562,    567.98291015625,  591.9366455078125,
            615.9179077148438,  639.9213256835938,  663.9425048828125,
            687.9779052734375,   712.024658203125],
          dtype='float32', name='depth'))

  • latitude
    PandasIndex

    PandasIndex(Index([53.008296966552734,  53.02496337890625, 53.041629791259766,
            53.05829620361328,   53.0749626159668,  53.09162902832031,
            53.10829544067383, 53.124961853027344,  53.14162826538086,
           53.158294677734375,
           ...
            65.74102783203125,   65.7576904296875,  65.77435302734375,
                 65.791015625,  65.80767822265625,   65.8243408203125,
            65.84100341796875,    65.857666015625,  65.87432861328125,
             65.8909912109375],
          dtype='float32', name='latitude', length=774))

  • longitude
    PandasIndex

    PandasIndex(Index([ 9.041582107543945,   9.06935977935791,  9.097137451171875,
             9.12491512298584,  9.152692794799805,   9.18047046661377,
            9.208248138427734,    9.2360258102417,  9.263803482055664,
            9.291581153869629,
           ...
           29.958648681640625, 29.986427307128906, 30.014205932617188,
            30.04198455810547,  30.06976318359375,  30.09754180908203,
           30.125320434570312, 30.153099060058594, 30.180877685546875,
           30.208656311035156],
          dtype='float32', name='longitude', length=763))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-12-02 13:00:00', '2022-12-02 14:00:00',
                   '2022-12-02 15:00:00', '2022-12-02 16:00:00',
                   '2022-12-02 17:00:00', '2022-12-02 18:00:00',
                   '2022-12-02 19:00:00', '2022-12-02 20:00:00',
                   '2022-12-02 21:00:00', '2022-12-02 22:00:00',
                   ...
                   '2025-07-03 15:00:00', '2025-07-03 16:00:00',
                   '2025-07-03 17:00:00', '2025-07-03 18:00:00',
                   '2025-07-03 19:00:00', '2025-07-03 20:00:00',
                   '2025-07-03 21:00:00', '2025-07-03 22:00:00',
                   '2025-07-03 23:00:00', '2025-07-04 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=22668, freq=None))

• Attributes: (7)

  references :

  https://marine.copernicus.eu/

  comment :

  Data on cropped native product grid. Horizontal velocities destaggered

  title :

  CMEMS NEMO hourly model fields

  contact :

  servicedesk.cmems@mercator-ocean.eu

  source :

  CMEMS BAL MFC NEMO model output converted to NetCDF

  Conventions :

  CF-1.0

  institution :

  Baltic MFC, PU Swedish Meteorological and Hydrological Institute

ds_uv = (
    ds
    [["uo", "vo"]]
    .sel(time=slice("2023-01-01T00:00:00", "2023-01-10T00:00:00"))
    .isel(time=slice(None, None, 3))
    .sel(depth=5, method="nearest")
    .rename({"uo": "u", "vo": "v"})
)
ds_uv

<xarray.Dataset> Size: 345MB
Dimensions:    (time: 73, latitude: 774, longitude: 763)
Coordinates:
    depth      float32 4B 4.684
  * latitude   (latitude) float32 3kB 53.01 53.02 53.04 ... 65.86 65.87 65.89
  * longitude  (longitude) float32 3kB 9.042 9.069 9.097 ... 30.15 30.18 30.21
  * time       (time) datetime64[ns] 584B 2023-01-01 ... 2023-01-10
Data variables:
    u          (time, latitude, longitude) float32 172MB dask.array<chunksize=(1, 774, 763), meta=np.ndarray>
    v          (time, latitude, longitude) float32 172MB dask.array<chunksize=(1, 774, 763), meta=np.ndarray>
Attributes:
    references:   https://marine.copernicus.eu/
    comment:      Data on cropped native product grid. Horizontal velocities ...
    title:        CMEMS NEMO hourly model fields
    contact:      servicedesk.cmems@mercator-ocean.eu
    source:       CMEMS BAL MFC NEMO model output converted to NetCDF
    Conventions:  CF-1.0
    institution:  Baltic MFC, PU Swedish Meteorological and Hydrological Inst...

xarray.Dataset

• Dimensions:
  • time: 73
  • latitude: 774
  • longitude: 763
• Coordinates: (4)
  • depth
    ()
    float32
    4.684

    long_name :

    Depth

    units :

    m

    unit_long :

    meters

    standard_name :

    depth

    axis :

    Z

    positive :

    down

    array(4.684081, dtype=float32)

  • latitude
    (latitude)
    float32
    53.01 53.02 53.04 ... 65.87 65.89

    long_name :

    Latitude of each location

    units :

    degrees_north

    unit_long :

    degrees North

    standard_name :

    latitude

    axis :

    Y

    array([53.008297, 53.024963, 53.04163 , ..., 65.857666, 65.87433 , 65.89099 ],
          shape=(774,), dtype=float32)

  • longitude
    (longitude)
    float32
    9.042 9.069 9.097 ... 30.18 30.21

    long_name :

    Longitude of each location

    units :

    degrees_east

    unit_long :

    degrees East

    standard_name :

    longitude

    axis :

    X

    array([ 9.041582,  9.06936 ,  9.097137, ..., 30.1531  , 30.180878, 30.208656],
          shape=(763,), dtype=float32)

  • time
    (time)
    datetime64[ns]
    2023-01-01 ... 2023-01-10

    unit_long :

    Days Since 1900-01-01

    long_name :

    Time

    standard_name :

    time

    axis :

    T

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T03:00:00.000000000',
           '2023-01-01T06:00:00.000000000', '2023-01-01T09:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T15:00:00.000000000',
           '2023-01-01T18:00:00.000000000', '2023-01-01T21:00:00.000000000',
           '2023-01-02T00:00:00.000000000', '2023-01-02T03:00:00.000000000',
           '2023-01-02T06:00:00.000000000', '2023-01-02T09:00:00.000000000',
           '2023-01-02T12:00:00.000000000', '2023-01-02T15:00:00.000000000',
           '2023-01-02T18:00:00.000000000', '2023-01-02T21:00:00.000000000',
           '2023-01-03T00:00:00.000000000', '2023-01-03T03:00:00.000000000',
           '2023-01-03T06:00:00.000000000', '2023-01-03T09:00:00.000000000',
           '2023-01-03T12:00:00.000000000', '2023-01-03T15:00:00.000000000',
           '2023-01-03T18:00:00.000000000', '2023-01-03T21:00:00.000000000',
           '2023-01-04T00:00:00.000000000', '2023-01-04T03:00:00.000000000',
           '2023-01-04T06:00:00.000000000', '2023-01-04T09:00:00.000000000',
           '2023-01-04T12:00:00.000000000', '2023-01-04T15:00:00.000000000',
           '2023-01-04T18:00:00.000000000', '2023-01-04T21:00:00.000000000',
           '2023-01-05T00:00:00.000000000', '2023-01-05T03:00:00.000000000',
           '2023-01-05T06:00:00.000000000', '2023-01-05T09:00:00.000000000',
           '2023-01-05T12:00:00.000000000', '2023-01-05T15:00:00.000000000',
           '2023-01-05T18:00:00.000000000', '2023-01-05T21:00:00.000000000',
           '2023-01-06T00:00:00.000000000', '2023-01-06T03:00:00.000000000',
           '2023-01-06T06:00:00.000000000', '2023-01-06T09:00:00.000000000',
           '2023-01-06T12:00:00.000000000', '2023-01-06T15:00:00.000000000',
           '2023-01-06T18:00:00.000000000', '2023-01-06T21:00:00.000000000',
           '2023-01-07T00:00:00.000000000', '2023-01-07T03:00:00.000000000',
           '2023-01-07T06:00:00.000000000', '2023-01-07T09:00:00.000000000',
           '2023-01-07T12:00:00.000000000', '2023-01-07T15:00:00.000000000',
           '2023-01-07T18:00:00.000000000', '2023-01-07T21:00:00.000000000',
           '2023-01-08T00:00:00.000000000', '2023-01-08T03:00:00.000000000',
           '2023-01-08T06:00:00.000000000', '2023-01-08T09:00:00.000000000',
           '2023-01-08T12:00:00.000000000', '2023-01-08T15:00:00.000000000',
           '2023-01-08T18:00:00.000000000', '2023-01-08T21:00:00.000000000',
           '2023-01-09T00:00:00.000000000', '2023-01-09T03:00:00.000000000',
           '2023-01-09T06:00:00.000000000', '2023-01-09T09:00:00.000000000',
           '2023-01-09T12:00:00.000000000', '2023-01-09T15:00:00.000000000',
           '2023-01-09T18:00:00.000000000', '2023-01-09T21:00:00.000000000',
           '2023-01-10T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (2)
  • u
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 774, 763), meta=np.ndarray>

    long_name :

    Eastward current

    standard_name :

    eastward_sea_water_velocity

    unit_long :

    meters per second

    units :

    m s-1

    Array Chunk Bytes 164.46 MiB 2.25 MiB Shape (73, 774, 763) (1, 774, 763) Dask graph 73 chunks in 5 graph layers Data type float32 numpy.ndarray

  • v
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 774, 763), meta=np.ndarray>

    long_name :

    Northward current

    standard_name :

    northward_sea_water_velocity

    unit_long :

    meters per second

    units :

    m s-1

    Array Chunk Bytes 164.46 MiB 2.25 MiB Shape (73, 774, 763) (1, 774, 763) Dask graph 73 chunks in 5 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([53.008296966552734,  53.02496337890625, 53.041629791259766,
            53.05829620361328,   53.0749626159668,  53.09162902832031,
            53.10829544067383, 53.124961853027344,  53.14162826538086,
           53.158294677734375,
           ...
            65.74102783203125,   65.7576904296875,  65.77435302734375,
                 65.791015625,  65.80767822265625,   65.8243408203125,
            65.84100341796875,    65.857666015625,  65.87432861328125,
             65.8909912109375],
          dtype='float32', name='latitude', length=774))

  • longitude
    PandasIndex

    PandasIndex(Index([ 9.041582107543945,   9.06935977935791,  9.097137451171875,
             9.12491512298584,  9.152692794799805,   9.18047046661377,
            9.208248138427734,    9.2360258102417,  9.263803482055664,
            9.291581153869629,
           ...
           29.958648681640625, 29.986427307128906, 30.014205932617188,
            30.04198455810547,  30.06976318359375,  30.09754180908203,
           30.125320434570312, 30.153099060058594, 30.180877685546875,
           30.208656311035156],
          dtype='float32', name='longitude', length=763))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 03:00:00',
                   '2023-01-01 06:00:00', '2023-01-01 09:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 15:00:00',
                   '2023-01-01 18:00:00', '2023-01-01 21:00:00',
                   '2023-01-02 00:00:00', '2023-01-02 03:00:00',
                   '2023-01-02 06:00:00', '2023-01-02 09:00:00',
                   '2023-01-02 12:00:00', '2023-01-02 15:00:00',
                   '2023-01-02 18:00:00', '2023-01-02 21:00:00',
                   '2023-01-03 00:00:00', '2023-01-03 03:00:00',
                   '2023-01-03 06:00:00', '2023-01-03 09:00:00',
                   '2023-01-03 12:00:00', '2023-01-03 15:00:00',
                   '2023-01-03 18:00:00', '2023-01-03 21:00:00',
                   '2023-01-04 00:00:00', '2023-01-04 03:00:00',
                   '2023-01-04 06:00:00', '2023-01-04 09:00:00',
                   '2023-01-04 12:00:00', '2023-01-04 15:00:00',
                   '2023-01-04 18:00:00', '2023-01-04 21:00:00',
                   '2023-01-05 00:00:00', '2023-01-05 03:00:00',
                   '2023-01-05 06:00:00', '2023-01-05 09:00:00',
                   '2023-01-05 12:00:00', '2023-01-05 15:00:00',
                   '2023-01-05 18:00:00', '2023-01-05 21:00:00',
                   '2023-01-06 00:00:00', '2023-01-06 03:00:00',
                   '2023-01-06 06:00:00', '2023-01-06 09:00:00',
                   '2023-01-06 12:00:00', '2023-01-06 15:00:00',
                   '2023-01-06 18:00:00', '2023-01-06 21:00:00',
                   '2023-01-07 00:00:00', '2023-01-07 03:00:00',
                   '2023-01-07 06:00:00', '2023-01-07 09:00:00',
                   '2023-01-07 12:00:00', '2023-01-07 15:00:00',
                   '2023-01-07 18:00:00', '2023-01-07 21:00:00',
                   '2023-01-08 00:00:00', '2023-01-08 03:00:00',
                   '2023-01-08 06:00:00', '2023-01-08 09:00:00',
                   '2023-01-08 12:00:00', '2023-01-08 15:00:00',
                   '2023-01-08 18:00:00', '2023-01-08 21:00:00',
                   '2023-01-09 00:00:00', '2023-01-09 03:00:00',
                   '2023-01-09 06:00:00', '2023-01-09 09:00:00',
                   '2023-01-09 12:00:00', '2023-01-09 15:00:00',
                   '2023-01-09 18:00:00', '2023-01-09 21:00:00',
                   '2023-01-10 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (7)

  references :

  https://marine.copernicus.eu/

  comment :

  Data on cropped native product grid. Horizontal velocities destaggered

  title :

  CMEMS NEMO hourly model fields

  contact :

  servicedesk.cmems@mercator-ocean.eu

  source :

  CMEMS BAL MFC NEMO model output converted to NetCDF

  Conventions :

  CF-1.0

  institution :

  Baltic MFC, PU Swedish Meteorological and Hydrological Institute

ds_uv.nbytes / 1e6

344.894944

%%time

ds_uv = ds_uv.compute()

CPU times: user 2min 44s, sys: 19.8 s, total: 3min 4s
Wall time: 4min 48s

!mkdir -p /home/jovyan/shared_materials/projects_data/

ds_uv.drop_encoding().to_netcdf("/home/jovyan/shared_materials/projects_data/01_baltic_sea_particle_drift_flow.nc")
ds_uv = xr.open_dataset("/home/jovyan/shared_materials/projects_data/01_baltic_sea_particle_drift_flow.nc")
ds_uv

<xarray.Dataset> Size: 345MB
Dimensions:    (time: 73, latitude: 774, longitude: 763)
Coordinates:
    depth      float32 4B ...
  * latitude   (latitude) float32 3kB 53.01 53.02 53.04 ... 65.86 65.87 65.89
  * longitude  (longitude) float32 3kB 9.042 9.069 9.097 ... 30.15 30.18 30.21
  * time       (time) datetime64[ns] 584B 2023-01-01 ... 2023-01-10
Data variables:
    u          (time, latitude, longitude) float32 172MB ...
    v          (time, latitude, longitude) float32 172MB ...
Attributes:
    references:   https://marine.copernicus.eu/
    comment:      Data on cropped native product grid. Horizontal velocities ...
    title:        CMEMS NEMO hourly model fields
    contact:      servicedesk.cmems@mercator-ocean.eu
    source:       CMEMS BAL MFC NEMO model output converted to NetCDF
    Conventions:  CF-1.0
    institution:  Baltic MFC, PU Swedish Meteorological and Hydrological Inst...

xarray.Dataset

• Dimensions:
  • time: 73
  • latitude: 774
  • longitude: 763
• Coordinates: (4)
  • depth
    ()
    float32
    ...

    long_name :

    Depth

    units :

    m

    unit_long :

    meters

    standard_name :

    depth

    axis :

    Z

    positive :

    down

    [1 values with dtype=float32]

  • latitude
    (latitude)
    float32
    53.01 53.02 53.04 ... 65.87 65.89

    long_name :

    Latitude of each location

    units :

    degrees_north

    unit_long :

    degrees North

    standard_name :

    latitude

    axis :

    Y

    array([53.008297, 53.024963, 53.04163 , ..., 65.857666, 65.87433 , 65.89099 ],
          shape=(774,), dtype=float32)

  • longitude
    (longitude)
    float32
    9.042 9.069 9.097 ... 30.18 30.21

    long_name :

    Longitude of each location

    units :

    degrees_east

    unit_long :

    degrees East

    standard_name :

    longitude

    axis :

    X

    array([ 9.041582,  9.06936 ,  9.097137, ..., 30.1531  , 30.180878, 30.208656],
          shape=(763,), dtype=float32)

  • time
    (time)
    datetime64[ns]
    2023-01-01 ... 2023-01-10

    unit_long :

    Days Since 1900-01-01

    long_name :

    Time

    standard_name :

    time

    axis :

    T

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T03:00:00.000000000',
           '2023-01-01T06:00:00.000000000', '2023-01-01T09:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T15:00:00.000000000',
           '2023-01-01T18:00:00.000000000', '2023-01-01T21:00:00.000000000',
           '2023-01-02T00:00:00.000000000', '2023-01-02T03:00:00.000000000',
           '2023-01-02T06:00:00.000000000', '2023-01-02T09:00:00.000000000',
           '2023-01-02T12:00:00.000000000', '2023-01-02T15:00:00.000000000',
           '2023-01-02T18:00:00.000000000', '2023-01-02T21:00:00.000000000',
           '2023-01-03T00:00:00.000000000', '2023-01-03T03:00:00.000000000',
           '2023-01-03T06:00:00.000000000', '2023-01-03T09:00:00.000000000',
           '2023-01-03T12:00:00.000000000', '2023-01-03T15:00:00.000000000',
           '2023-01-03T18:00:00.000000000', '2023-01-03T21:00:00.000000000',
           '2023-01-04T00:00:00.000000000', '2023-01-04T03:00:00.000000000',
           '2023-01-04T06:00:00.000000000', '2023-01-04T09:00:00.000000000',
           '2023-01-04T12:00:00.000000000', '2023-01-04T15:00:00.000000000',
           '2023-01-04T18:00:00.000000000', '2023-01-04T21:00:00.000000000',
           '2023-01-05T00:00:00.000000000', '2023-01-05T03:00:00.000000000',
           '2023-01-05T06:00:00.000000000', '2023-01-05T09:00:00.000000000',
           '2023-01-05T12:00:00.000000000', '2023-01-05T15:00:00.000000000',
           '2023-01-05T18:00:00.000000000', '2023-01-05T21:00:00.000000000',
           '2023-01-06T00:00:00.000000000', '2023-01-06T03:00:00.000000000',
           '2023-01-06T06:00:00.000000000', '2023-01-06T09:00:00.000000000',
           '2023-01-06T12:00:00.000000000', '2023-01-06T15:00:00.000000000',
           '2023-01-06T18:00:00.000000000', '2023-01-06T21:00:00.000000000',
           '2023-01-07T00:00:00.000000000', '2023-01-07T03:00:00.000000000',
           '2023-01-07T06:00:00.000000000', '2023-01-07T09:00:00.000000000',
           '2023-01-07T12:00:00.000000000', '2023-01-07T15:00:00.000000000',
           '2023-01-07T18:00:00.000000000', '2023-01-07T21:00:00.000000000',
           '2023-01-08T00:00:00.000000000', '2023-01-08T03:00:00.000000000',
           '2023-01-08T06:00:00.000000000', '2023-01-08T09:00:00.000000000',
           '2023-01-08T12:00:00.000000000', '2023-01-08T15:00:00.000000000',
           '2023-01-08T18:00:00.000000000', '2023-01-08T21:00:00.000000000',
           '2023-01-09T00:00:00.000000000', '2023-01-09T03:00:00.000000000',
           '2023-01-09T06:00:00.000000000', '2023-01-09T09:00:00.000000000',
           '2023-01-09T12:00:00.000000000', '2023-01-09T15:00:00.000000000',
           '2023-01-09T18:00:00.000000000', '2023-01-09T21:00:00.000000000',
           '2023-01-10T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (2)
  • u
    (time, latitude, longitude)
    float32
    ...

    long_name :

    Eastward current

    standard_name :

    eastward_sea_water_velocity

    unit_long :

    meters per second

    units :

    m s-1

    [43111026 values with dtype=float32]

  • v
    (time, latitude, longitude)
    float32
    ...

    long_name :

    Northward current

    standard_name :

    northward_sea_water_velocity

    unit_long :

    meters per second

    units :

    m s-1

    [43111026 values with dtype=float32]

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([53.008296966552734,  53.02496337890625, 53.041629791259766,
            53.05829620361328,   53.0749626159668,  53.09162902832031,
            53.10829544067383, 53.124961853027344,  53.14162826538086,
           53.158294677734375,
           ...
            65.74102783203125,   65.7576904296875,  65.77435302734375,
                 65.791015625,  65.80767822265625,   65.8243408203125,
            65.84100341796875,    65.857666015625,  65.87432861328125,
             65.8909912109375],
          dtype='float32', name='latitude', length=774))

  • longitude
    PandasIndex

    PandasIndex(Index([ 9.041582107543945,   9.06935977935791,  9.097137451171875,
             9.12491512298584,  9.152692794799805,   9.18047046661377,
            9.208248138427734,    9.2360258102417,  9.263803482055664,
            9.291581153869629,
           ...
           29.958648681640625, 29.986427307128906, 30.014205932617188,
            30.04198455810547,  30.06976318359375,  30.09754180908203,
           30.125320434570312, 30.153099060058594, 30.180877685546875,
           30.208656311035156],
          dtype='float32', name='longitude', length=763))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 03:00:00',
                   '2023-01-01 06:00:00', '2023-01-01 09:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 15:00:00',
                   '2023-01-01 18:00:00', '2023-01-01 21:00:00',
                   '2023-01-02 00:00:00', '2023-01-02 03:00:00',
                   '2023-01-02 06:00:00', '2023-01-02 09:00:00',
                   '2023-01-02 12:00:00', '2023-01-02 15:00:00',
                   '2023-01-02 18:00:00', '2023-01-02 21:00:00',
                   '2023-01-03 00:00:00', '2023-01-03 03:00:00',
                   '2023-01-03 06:00:00', '2023-01-03 09:00:00',
                   '2023-01-03 12:00:00', '2023-01-03 15:00:00',
                   '2023-01-03 18:00:00', '2023-01-03 21:00:00',
                   '2023-01-04 00:00:00', '2023-01-04 03:00:00',
                   '2023-01-04 06:00:00', '2023-01-04 09:00:00',
                   '2023-01-04 12:00:00', '2023-01-04 15:00:00',
                   '2023-01-04 18:00:00', '2023-01-04 21:00:00',
                   '2023-01-05 00:00:00', '2023-01-05 03:00:00',
                   '2023-01-05 06:00:00', '2023-01-05 09:00:00',
                   '2023-01-05 12:00:00', '2023-01-05 15:00:00',
                   '2023-01-05 18:00:00', '2023-01-05 21:00:00',
                   '2023-01-06 00:00:00', '2023-01-06 03:00:00',
                   '2023-01-06 06:00:00', '2023-01-06 09:00:00',
                   '2023-01-06 12:00:00', '2023-01-06 15:00:00',
                   '2023-01-06 18:00:00', '2023-01-06 21:00:00',
                   '2023-01-07 00:00:00', '2023-01-07 03:00:00',
                   '2023-01-07 06:00:00', '2023-01-07 09:00:00',
                   '2023-01-07 12:00:00', '2023-01-07 15:00:00',
                   '2023-01-07 18:00:00', '2023-01-07 21:00:00',
                   '2023-01-08 00:00:00', '2023-01-08 03:00:00',
                   '2023-01-08 06:00:00', '2023-01-08 09:00:00',
                   '2023-01-08 12:00:00', '2023-01-08 15:00:00',
                   '2023-01-08 18:00:00', '2023-01-08 21:00:00',
                   '2023-01-09 00:00:00', '2023-01-09 03:00:00',
                   '2023-01-09 06:00:00', '2023-01-09 09:00:00',
                   '2023-01-09 12:00:00', '2023-01-09 15:00:00',
                   '2023-01-09 18:00:00', '2023-01-09 21:00:00',
                   '2023-01-10 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (7)

  references :

  https://marine.copernicus.eu/

  comment :

  Data on cropped native product grid. Horizontal velocities destaggered

  title :

  CMEMS NEMO hourly model fields

  contact :

  servicedesk.cmems@mercator-ocean.eu

  source :

  CMEMS BAL MFC NEMO model output converted to NetCDF

  Conventions :

  CF-1.0

  institution :

  Baltic MFC, PU Swedish Meteorological and Hydrological Institute

!ls -lah /home/jovyan/shared_materials/projects_data/01_baltic_sea_particle_drift_flow.nc

-rw-r--r-- 1 jovyan jovyan 329M Jun 28 18:01 /home/jovyan/shared_materials/projects_data/01_baltic_sea_particle_drift_flow.nc