Open challenger datasets

import oceanbench

oceanbench.__version__

'0.2.1'

Insert here the code that opens the challenger dataset as challenger_dataset: xarray.Dataset

# SPDX-FileCopyrightText: 2025 Mercator Ocean International <https://www.mercator-ocean.eu/>
#
# SPDX-License-Identifier: EUPL-1.2

from datetime import datetime

import xarray
import dask

# Stage the 35MB-chunk forecasts robustly: read one time-slice at a time, sequentially,
# so to_zarr never bulk-reads the whole variable at once (which hits transient partial-HTTP
# reads -> blosc -1). Pairs with chunks={"time": 1} in the open below.
dask.config.set(scheduler="synchronous")

from oceanbench.core.datetime_utils import generate_dates
from oceanbench.core.remote_http import require_remote_dataset_dimensions, with_remote_http_retries
from oceanbench.core.runtime_configuration import current_runtime_configuration
from oceanbench.core.weekly_stage import maybe_stage_weekly_dataset

_CLOUDFERRO_OCEANBENCH_BASE_URL = "https://s3.waw3-1.cloudferro.com/oceanbench-bucket"
_CLOUDFERRO_ML_FORECASTS_URL = f"{_CLOUDFERRO_OCEANBENCH_BASE_URL}/dev/ml-forecast-outputs"
_LANGYA_LEAD_DAYS_COUNT = 7


def _default_first_day_datetimes() -> list[datetime]:
    return generate_dates("2024-01-03", "2024-12-25", 7)


def _langya_dataset_path(start_datetime: datetime) -> str:
    return f"{_CLOUDFERRO_ML_FORECASTS_URL}/langya/{start_datetime:%Y%m%d}.zarr"


def _prepare_langya_week_dataset(dataset: xarray.Dataset) -> xarray.Dataset:
    week_dataset = require_remote_dataset_dimensions(dataset, ["time"], "langya challenger dataset open")
    week_dataset = week_dataset.isel({"time": slice(0, _LANGYA_LEAD_DAYS_COUNT)})
    week_lead_days_count = week_dataset.sizes["time"]
    return week_dataset.rename({"time": "lead_day_index"}).assign_coords(
        {"lead_day_index": range(week_lead_days_count)}
    )


def _open_langya_week_dataset(first_day_datetime: datetime) -> xarray.Dataset:
    return _prepare_langya_week_dataset(
        xarray.open_dataset(_langya_dataset_path(first_day_datetime), engine="zarr", chunks={"time": 1})
    )


def _remote_langya_dataset(first_day_datetimes: list[datetime]) -> xarray.Dataset:
    return xarray.open_mfdataset(
        list(map(_langya_dataset_path, first_day_datetimes)),
        engine="zarr",
        preprocess=_prepare_langya_week_dataset,
        combine="nested",
        concat_dim="first_day_datetime",
        parallel=False,
    ).assign({"first_day_datetime": first_day_datetimes})


def langya() -> xarray.Dataset:
    first_day_datetimes = _default_first_day_datetimes()

    def open_dataset() -> xarray.Dataset:
        return maybe_stage_weekly_dataset(
            stage_key="challenger",
            dataset_kind="challenger",
            dataset_name="langya",
            first_day_datetimes=first_day_datetimes,
            lead_days_count=_LANGYA_LEAD_DAYS_COUNT,
            open_week_dataset=_open_langya_week_dataset,
            open_remote_dataset=lambda: _remote_langya_dataset(first_day_datetimes),
            attach_source_metadata_when_not_staged=current_runtime_configuration().has_local_stage(),
        )

    return with_remote_http_retries("langya challenger dataset open", open_dataset)


challenger_dataset: xarray.Dataset = langya()
challenger_dataset

<xarray.Dataset> Size: 2TB
Dimensions:             (first_day_datetime: 52, lead_day_index: 7, depth: 32,
                         latitude: 2040, longitude: 4320)
Coordinates:
  * depth               (depth) float32 128B 0.494 1.541 2.646 ... 453.9 541.1
  * latitude            (latitude) float64 16kB -80.0 -79.92 ... 89.83 89.92
  * lead_day_index      (lead_day_index) int64 56B 0 1 2 3 4 5 6
  * longitude           (longitude) float64 35kB -180.0 -179.9 ... 179.8 179.9
  * first_day_datetime  (first_day_datetime) datetime64[us] 416B 2024-01-03 ....
Data variables:
    so                  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 2040, 4320), meta=np.ndarray>
    thetao              (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 2040, 4320), meta=np.ndarray>
    uo                  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 2040, 4320), meta=np.ndarray>
    vo                  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 2040, 4320), meta=np.ndarray>
    zos                 (first_day_datetime, lead_day_index, latitude, longitude) float32 13GB dask.array<chunksize=(1, 1, 2040, 4320), meta=np.ndarray>
Attributes:
    Conventions:              CF-1.8
    challenger:               langya
    forecast_reference_time:  2024-01-02
    oceanbench_source_kind:   challenger
    oceanbench_source_name:   langya

xarray.Dataset

• Dimensions:
  • first_day_datetime: 52
  • lead_day_index: 7
  • depth: 32
  • latitude: 2040
  • longitude: 4320
• Coordinates: (5)
  • depth
    (depth)
    float32
    0.494 1.541 2.646 ... 453.9 541.1

    axis :

    Z

    long_name :

    Depth

    positive :

    down

    standard_name :

    depth

    units :

    m

    array([4.940250e-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], dtype=float32)

  • latitude
    (latitude)
    float64
    -80.0 -79.92 -79.83 ... 89.83 89.92

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-80.      , -79.916667, -79.833333, ...,  89.75    ,  89.833333,
            89.916667], shape=(2040,))

  • lead_day_index
    (lead_day_index)
    int64
    0 1 2 3 4 5 6

    array([0, 1, 2, 3, 4, 5, 6])

  • longitude
    (longitude)
    float64
    -180.0 -179.9 ... 179.8 179.9

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-180.      , -179.916667, -179.833333, ...,  179.75    ,  179.833333,
            179.916667], shape=(4320,))

  • first_day_datetime
    (first_day_datetime)
    datetime64[us]
    2024-01-03 ... 2024-12-25

    array(['2024-01-03T00:00:00.000000', '2024-01-10T00:00:00.000000',
           '2024-01-17T00:00:00.000000', '2024-01-24T00:00:00.000000',
           '2024-01-31T00:00:00.000000', '2024-02-07T00:00:00.000000',
           '2024-02-14T00:00:00.000000', '2024-02-21T00:00:00.000000',
           '2024-02-28T00:00:00.000000', '2024-03-06T00:00:00.000000',
           '2024-03-13T00:00:00.000000', '2024-03-20T00:00:00.000000',
           '2024-03-27T00:00:00.000000', '2024-04-03T00:00:00.000000',
           '2024-04-10T00:00:00.000000', '2024-04-17T00:00:00.000000',
           '2024-04-24T00:00:00.000000', '2024-05-01T00:00:00.000000',
           '2024-05-08T00:00:00.000000', '2024-05-15T00:00:00.000000',
           '2024-05-22T00:00:00.000000', '2024-05-29T00:00:00.000000',
           '2024-06-05T00:00:00.000000', '2024-06-12T00:00:00.000000',
           '2024-06-19T00:00:00.000000', '2024-06-26T00:00:00.000000',
           '2024-07-03T00:00:00.000000', '2024-07-10T00:00:00.000000',
           '2024-07-17T00:00:00.000000', '2024-07-24T00:00:00.000000',
           '2024-07-31T00:00:00.000000', '2024-08-07T00:00:00.000000',
           '2024-08-14T00:00:00.000000', '2024-08-21T00:00:00.000000',
           '2024-08-28T00:00:00.000000', '2024-09-04T00:00:00.000000',
           '2024-09-11T00:00:00.000000', '2024-09-18T00:00:00.000000',
           '2024-09-25T00:00:00.000000', '2024-10-02T00:00:00.000000',
           '2024-10-09T00:00:00.000000', '2024-10-16T00:00:00.000000',
           '2024-10-23T00:00:00.000000', '2024-10-30T00:00:00.000000',
           '2024-11-06T00:00:00.000000', '2024-11-13T00:00:00.000000',
           '2024-11-20T00:00:00.000000', '2024-11-27T00:00:00.000000',
           '2024-12-04T00:00:00.000000', '2024-12-11T00:00:00.000000',
           '2024-12-18T00:00:00.000000', '2024-12-25T00:00:00.000000'],
          dtype='datetime64[us]')

• Data variables: (5)
  • so
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 2040, 4320), meta=np.ndarray>

    long_name :

    Salinity

    standard_name :

    sea_water_salinity

    units :

    1e-3

    Array Chunk Bytes 382.41 GiB 33.62 MiB Shape (52, 7, 32, 2040, 4320) (1, 1, 1, 2040, 4320) Dask graph 11648 chunks in 157 graph layers Data type float32 numpy.ndarray

  • thetao
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 2040, 4320), meta=np.ndarray>

    long_name :

    Temperature

    standard_name :

    sea_water_potential_temperature

    units :

    degrees_C

    Array Chunk Bytes 382.41 GiB 33.62 MiB Shape (52, 7, 32, 2040, 4320) (1, 1, 1, 2040, 4320) Dask graph 11648 chunks in 157 graph layers Data type float32 numpy.ndarray

  • uo
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 2040, 4320), meta=np.ndarray>

    long_name :

    Eastward velocity

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    Array Chunk Bytes 382.41 GiB 33.62 MiB Shape (52, 7, 32, 2040, 4320) (1, 1, 1, 2040, 4320) Dask graph 11648 chunks in 157 graph layers Data type float32 numpy.ndarray

  • vo
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 2040, 4320), meta=np.ndarray>

    long_name :

    Northward velocity

    standard_name :

    northward_sea_water_velocity

    units :

    m s-1

    Array Chunk Bytes 382.41 GiB 33.62 MiB Shape (52, 7, 32, 2040, 4320) (1, 1, 1, 2040, 4320) Dask graph 11648 chunks in 157 graph layers Data type float32 numpy.ndarray

  • zos
    (first_day_datetime, lead_day_index, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 2040, 4320), meta=np.ndarray>

    long_name :

    Sea surface height

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    Array Chunk Bytes 11.95 GiB 33.62 MiB Shape (52, 7, 2040, 4320) (1, 1, 2040, 4320) Dask graph 364 chunks in 157 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • depth
    PandasIndex

    PandasIndex(Index([0.49402499198913574,  1.5413750410079956,  2.6456689834594727,
            3.8194949626922607,   5.078224182128906,   6.440614223480225,
              7.92956018447876,   9.572997093200684,  11.404999732971191,
            13.467140197753906,  15.810070037841797,  18.495559692382812,
            21.598819732666016,  25.211410522460938,  29.444730758666992,
             34.43415069580078,  40.344051361083984,   47.37369155883789,
             55.76428985595703,   65.80726623535156,   77.85385131835938,
              92.3260726928711,  109.72930145263672,  130.66600036621094,
            155.85069274902344,  186.12559509277344,  222.47520446777344,
             266.0403137207031,   318.1274108886719,   380.2130126953125,
             453.9377136230469,   541.0889282226562],
          dtype='float32', name='depth'))

  • latitude
    PandasIndex

    PandasIndex(Index([             -80.0, -79.91666666666667, -79.83333333333333,
                       -79.75, -79.66666666666667, -79.58333333333333,
                        -79.5, -79.41666666666667, -79.33333333333333,
                       -79.25,
           ...
            89.16666666666666,              89.25,  89.33333333333334,
            89.41666666666666,               89.5,  89.58333333333334,
            89.66666666666666,              89.75,  89.83333333333334,
            89.91666666666666],
          dtype='float64', name='latitude', length=2040))

  • lead_day_index
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6], dtype='int64', name='lead_day_index'))

  • longitude
    PandasIndex

    PandasIndex(Index([             -180.0, -179.91666666666666, -179.83333333333334,
                       -179.75, -179.66666666666666, -179.58333333333334,
                        -179.5, -179.41666666666666, -179.33333333333334,
                       -179.25,
           ...
            179.16666666666669,              179.25,  179.33333333333331,
            179.41666666666669,               179.5,  179.58333333333331,
            179.66666666666669,              179.75,  179.83333333333331,
            179.91666666666669],
          dtype='float64', name='longitude', length=4320))

  • first_day_datetime
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-01-03', '2024-01-10', '2024-01-17', '2024-01-24',
                   '2024-01-31', '2024-02-07', '2024-02-14', '2024-02-21',
                   '2024-02-28', '2024-03-06', '2024-03-13', '2024-03-20',
                   '2024-03-27', '2024-04-03', '2024-04-10', '2024-04-17',
                   '2024-04-24', '2024-05-01', '2024-05-08', '2024-05-15',
                   '2024-05-22', '2024-05-29', '2024-06-05', '2024-06-12',
                   '2024-06-19', '2024-06-26', '2024-07-03', '2024-07-10',
                   '2024-07-17', '2024-07-24', '2024-07-31', '2024-08-07',
                   '2024-08-14', '2024-08-21', '2024-08-28', '2024-09-04',
                   '2024-09-11', '2024-09-18', '2024-09-25', '2024-10-02',
                   '2024-10-09', '2024-10-16', '2024-10-23', '2024-10-30',
                   '2024-11-06', '2024-11-13', '2024-11-20', '2024-11-27',
                   '2024-12-04', '2024-12-11', '2024-12-18', '2024-12-25'],
                  dtype='datetime64[us]', name='first_day_datetime', freq=None))

• Attributes: (5)

  Conventions :

  CF-1.8

  challenger :

  langya

  forecast_reference_time :

  2024-01-02

  oceanbench_source_kind :

  challenger

  oceanbench_source_name :

  langya

Evaluation configuration

region = 'ibi'

Evaluation of challenger dataset using OceanBench

Root Mean Square Deviation (RMSD) of variables compared to GLORYS reanalysis

oceanbench.metrics.rmsd_of_variables_compared_to_glorys_reanalysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Sea surface height (m) [sea_surface_height_above_geoid]{surface}	0.062100	0.064186	0.066888	0.070458	0.071757	0.073726	0.068189
Temperature (°C) [sea_water_potential_temperature]{surface}	0.599001	0.557495	0.506480	0.529116	0.550291	0.622110	0.638899
Salinity (PSU) [sea_water_salinity]{surface}	0.250811	0.254787	0.257992	0.258713	0.261274	0.264511	0.266274
Meridional current (m/s) [northward_sea_water_velocity]{surface}	0.105150	0.105171	0.102609	0.103835	0.102053	0.104102	0.108655
Zonal current (m/s) [eastward_sea_water_velocity]{surface}	0.113798	0.111742	0.110483	0.110000	0.110023	0.112445	0.113353
Temperature (°C) [sea_water_potential_temperature]{50m}	0.686278	0.695008	0.707614	0.710540	0.714922	0.747854	0.742091
Salinity (PSU) [sea_water_salinity]{50m}	0.186609	0.186863	0.187231	0.187431	0.187746	0.188736	0.189090
Meridional current (m/s) [northward_sea_water_velocity]{50m}	0.088807	0.089294	0.091939	0.091833	0.090864	0.091655	0.092332
Zonal current (m/s) [eastward_sea_water_velocity]{50m}	0.094412	0.094744	0.097552	0.097318	0.096560	0.097624	0.098314
Temperature (°C) [sea_water_potential_temperature]{100m}	0.721498	0.718538	0.727306	0.728108	0.729342	0.735404	0.735570
Salinity (PSU) [sea_water_salinity]{100m}	0.192460	0.192374	0.192545	0.192962	0.193562	0.194368	0.194906
Meridional current (m/s) [northward_sea_water_velocity]{100m}	0.086471	0.086670	0.089314	0.089203	0.088416	0.088940	0.089537
Zonal current (m/s) [eastward_sea_water_velocity]{100m}	0.090262	0.090225	0.092917	0.092592	0.091856	0.092758	0.093378
Temperature (°C) [sea_water_potential_temperature]{200m}	0.634146	0.634946	0.647402	0.647441	0.647399	0.646235	0.646034
Salinity (PSU) [sea_water_salinity]{200m}	0.132178	0.132172	0.132530	0.132651	0.132829	0.132898	0.132974
Meridional current (m/s) [northward_sea_water_velocity]{200m}	0.081990	0.081801	0.083843	0.083597	0.083097	0.083280	0.083871
Zonal current (m/s) [eastward_sea_water_velocity]{200m}	0.084011	0.083581	0.085967	0.085608	0.084996	0.085464	0.086003
Temperature (°C) [sea_water_potential_temperature]{300m}	0.524302	0.524666	0.533575	0.533580	0.533965	0.531556	0.531646
Salinity (PSU) [sea_water_salinity]{300m}	0.101495	0.101483	0.101766	0.101810	0.101950	0.101883	0.101906
Meridional current (m/s) [northward_sea_water_velocity]{300m}	0.081033	0.080790	0.082924	0.082557	0.081988	0.082356	0.083044
Zonal current (m/s) [eastward_sea_water_velocity]{300m}	0.082896	0.082478	0.084947	0.084513	0.083821	0.084344	0.084925
Temperature (°C) [sea_water_potential_temperature]{500m}	0.400599	0.400643	0.404746	0.405386	0.406779	0.406030	0.406681
Salinity (PSU) [sea_water_salinity]{500m}	0.090088	0.090122	0.089940	0.090067	0.090215	0.090327	0.090473
Meridional current (m/s) [northward_sea_water_velocity]{500m}	0.083887	0.083504	0.085663	0.085026	0.084250	0.085155	0.085969
Zonal current (m/s) [eastward_sea_water_velocity]{500m}	0.086244	0.085722	0.088094	0.087468	0.086600	0.087491	0.088121

Root Mean Square Deviation (RMSD) of Mixed Layer Depth (MLD) compared to GLORYS reanalysis

oceanbench.metrics.rmsd_of_mixed_layer_depth_compared_to_glorys_reanalysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Mixed layer depth (m) [ocean_mixed_layer_thickness]{surface}	48.922356	39.905499	32.438881	34.254089	35.604412	40.550808	41.796471

Root Mean Square Deviation (RMSD) of geostrophic currents compared to GLORYS reanalysis

oceanbench.metrics.rmsd_of_geostrophic_currents_compared_to_glorys_reanalysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Meridional geostrophic current (m/s) [geostrophic_northward_sea_water_velocity]{surface}	0.089132	0.091004	0.092589	0.093463	0.093632	0.094741	0.096031
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface}	0.091689	0.092695	0.094522	0.094728	0.094726	0.096291	0.097311

Root Mean Square Deviation (RMSD) of variables compared to observations

oceanbench.metrics.rmsd_of_variables_compared_to_observations(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Temperature (°C) [sea_water_potential_temperature]{surface}	0.770993	0.661069	0.718360	0.715718	0.693699	0.769072	0.944062
Temperature (°C) [sea_water_potential_temperature]{0-5m}	0.631508	0.566354	0.508483	0.598180	0.612680	0.674602	0.702842
Temperature (°C) [sea_water_potential_temperature]{5-100m}	0.707035	0.710896	0.695521	0.711983	0.805308	0.761474	0.756023
Temperature (°C) [sea_water_potential_temperature]{100-300m}	0.300613	0.315809	0.332118	0.335119	0.324359	0.343311	0.339647
Temperature (°C) [sea_water_potential_temperature]{300-600m}	0.256294	0.249651	0.242777	0.245255	0.239802	0.264513	0.247248
Salinity (PSU) [sea_water_salinity]{0-5m}	0.654134	0.853800	0.837935	0.652831	0.625067	0.736924	0.631822
Salinity (PSU) [sea_water_salinity]{5-100m}	0.265386	0.321050	0.327803	0.274161	0.293020	0.306346	0.291096
Salinity (PSU) [sea_water_salinity]{100-300m}	0.109454	0.100698	0.117241	0.094039	0.122124	0.124723	0.126719
Salinity (PSU) [sea_water_salinity]{300-600m}	0.043430	0.045118	0.044631	0.044311	0.045890	0.048614	0.050479
Sea level anomaly (m) [sea_surface_height_above_geoid]{surface}	0.116369	0.116850	0.115066	0.113197	0.114278	0.114554	0.116812
Zonal current (m/s) [eastward_sea_water_velocity]{15m}	0.219563	0.222801	0.222899	0.220794	0.207182	0.220185	0.214736
Meridional current (m/s) [northward_sea_water_velocity]{15m}	0.185119	0.184961	0.184538	0.181305	0.183252	0.181841	0.185170

Deviation of Lagrangian trajectories compared to GLORYS reanalysis

oceanbench.metrics.deviation_of_lagrangian_trajectories_compared_to_glorys_reanalysis(
    challenger_dataset,
    region=region,
)

	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6
Lagrangian trajectory deviation (km) []{surface}	10.193719	19.080271	26.98807	34.464176	41.402603

Root Mean Square Deviation (RMSD) of variables compared to GLO12 analysis

oceanbench.metrics.rmsd_of_variables_compared_to_glo12_analysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Sea surface height (m) [sea_surface_height_above_geoid]{surface}	0.032357	0.038089	0.040569	0.047262	0.048923	0.051086	0.047401
Temperature (°C) [sea_water_potential_temperature]{surface}	0.503589	0.433514	0.364156	0.412809	0.450987	0.526542	0.565944
Salinity (PSU) [sea_water_salinity]{surface}	0.113590	0.127532	0.139366	0.149441	0.157639	0.163957	0.170461
Meridional current (m/s) [northward_sea_water_velocity]{surface}	0.074522	0.075699	0.067841	0.073326	0.075665	0.085058	0.095610
Zonal current (m/s) [eastward_sea_water_velocity]{surface}	0.083973	0.082718	0.079067	0.081537	0.084861	0.093730	0.099020
Temperature (°C) [sea_water_potential_temperature]{50m}	0.313655	0.342942	0.357509	0.397218	0.434174	0.493606	0.522276
Salinity (PSU) [sea_water_salinity]{50m}	0.060178	0.069668	0.079337	0.088241	0.096038	0.103344	0.109677
Meridional current (m/s) [northward_sea_water_velocity]{50m}	0.047964	0.050304	0.050951	0.056464	0.060790	0.069860	0.076827
Zonal current (m/s) [eastward_sea_water_velocity]{50m}	0.058440	0.059773	0.061653	0.065469	0.068558	0.076631	0.082720
Temperature (°C) [sea_water_potential_temperature]{100m}	0.251810	0.274731	0.298806	0.332060	0.362938	0.403945	0.434546
Salinity (PSU) [sea_water_salinity]{100m}	0.063060	0.072839	0.083015	0.092638	0.100892	0.108147	0.114394
Meridional current (m/s) [northward_sea_water_velocity]{100m}	0.044576	0.046499	0.047628	0.053058	0.057517	0.066978	0.074028
Zonal current (m/s) [eastward_sea_water_velocity]{100m}	0.054203	0.055229	0.057439	0.060935	0.064112	0.072237	0.078158
Temperature (°C) [sea_water_potential_temperature]{200m}	0.186949	0.205806	0.225906	0.252104	0.276110	0.305760	0.332157
Salinity (PSU) [sea_water_salinity]{200m}	0.037355	0.043446	0.049862	0.055990	0.061315	0.065959	0.070285
Meridional current (m/s) [northward_sea_water_velocity]{200m}	0.039711	0.041298	0.041649	0.046571	0.051232	0.060295	0.067121
Zonal current (m/s) [eastward_sea_water_velocity]{200m}	0.049124	0.049670	0.051270	0.054444	0.057857	0.065124	0.070732
Temperature (°C) [sea_water_potential_temperature]{300m}	0.159752	0.179760	0.199407	0.222935	0.244491	0.269572	0.291810
Salinity (PSU) [sea_water_salinity]{300m}	0.030086	0.035413	0.040655	0.045714	0.050103	0.053905	0.057363
Meridional current (m/s) [northward_sea_water_velocity]{300m}	0.037476	0.039529	0.040662	0.045519	0.050096	0.058659	0.065252
Zonal current (m/s) [eastward_sea_water_velocity]{300m}	0.048003	0.048851	0.050601	0.053807	0.057206	0.063906	0.069294
Temperature (°C) [sea_water_potential_temperature]{500m}	0.138078	0.161307	0.181659	0.203553	0.223659	0.245354	0.263395
Salinity (PSU) [sea_water_salinity]{500m}	0.031045	0.035738	0.040551	0.045087	0.049135	0.052773	0.055992
Meridional current (m/s) [northward_sea_water_velocity]{500m}	0.034682	0.038485	0.042267	0.047280	0.051869	0.059732	0.065825
Zonal current (m/s) [eastward_sea_water_velocity]{500m}	0.047269	0.048924	0.052447	0.055915	0.059381	0.065565	0.070592

Root Mean Square Deviation (RMSD) of Mixed Layer Depth (MLD) compared to GLO12 analysis

oceanbench.metrics.rmsd_of_mixed_layer_depth_compared_to_glo12_analysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Mixed layer depth (m) [ocean_mixed_layer_thickness]{surface}	46.447136	36.800579	28.768095	31.286652	33.181797	39.050426	40.993469

Root Mean Square Deviation (RMSD) of geostrophic currents compared to GLO12 analysis

oceanbench.metrics.rmsd_of_geostrophic_currents_compared_to_glo12_analysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Meridional geostrophic current (m/s) [geostrophic_northward_sea_water_velocity]{surface}	0.046561	0.052435	0.053750	0.060170	0.065039	0.074163	0.081311
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface}	0.052186	0.055989	0.058381	0.062614	0.066586	0.075126	0.081223

Deviation of Lagrangian trajectories compared to GLO12 analysis

oceanbench.metrics.deviation_of_lagrangian_trajectories_compared_to_glo12_analysis(
    challenger_dataset,
    region=region,
)

	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6
Lagrangian trajectory deviation (km) []{surface}	7.329322	13.055861	18.035519	23.313465	28.836548