Open challenger datasets

import oceanbench

oceanbench.__version__

'0.5.1'

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

# Open LangYa forecasts with xarray
import xarray
import oceanbench

challenger_dataset: xarray.Dataset = oceanbench.datasets.challenger.langya()

challenger_dataset

<xarray.Dataset> Size: 2TB
Dimensions:             (first_day_datetime: 52, lead_day_index: 7, depth: 32,
                         latitude: 2041, 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 ... 89.92 90.0
  * 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[ns] 416B 2024-01-03 ....
Data variables:
    so                  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
    thetao              (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
    uo                  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
    vo                  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
    zos                 (first_day_datetime, lead_day_index, latitude, longitude) float32 13GB dask.array<chunksize=(1, 1, 256, 512), 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: 2041
  • 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 ... 89.92 90.0

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-80.      , -79.916667, -79.833333, ...,  89.833333,  89.916667,
            90.      ], shape=(2041,))

  • 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[ns]
    2024-01-03 ... 2024-12-25

    array(['2024-01-03T00:00:00.000000000', '2024-01-10T00:00:00.000000000',
           '2024-01-17T00:00:00.000000000', '2024-01-24T00:00:00.000000000',
           '2024-01-31T00:00:00.000000000', '2024-02-07T00:00:00.000000000',
           '2024-02-14T00:00:00.000000000', '2024-02-21T00:00:00.000000000',
           '2024-02-28T00:00:00.000000000', '2024-03-06T00:00:00.000000000',
           '2024-03-13T00:00:00.000000000', '2024-03-20T00:00:00.000000000',
           '2024-03-27T00:00:00.000000000', '2024-04-03T00:00:00.000000000',
           '2024-04-10T00:00:00.000000000', '2024-04-17T00:00:00.000000000',
           '2024-04-24T00:00:00.000000000', '2024-05-01T00:00:00.000000000',
           '2024-05-08T00:00:00.000000000', '2024-05-15T00:00:00.000000000',
           '2024-05-22T00:00:00.000000000', '2024-05-29T00:00:00.000000000',
           '2024-06-05T00:00:00.000000000', '2024-06-12T00:00:00.000000000',
           '2024-06-19T00:00:00.000000000', '2024-06-26T00:00:00.000000000',
           '2024-07-03T00:00:00.000000000', '2024-07-10T00:00:00.000000000',
           '2024-07-17T00:00:00.000000000', '2024-07-24T00:00:00.000000000',
           '2024-07-31T00:00:00.000000000', '2024-08-07T00:00:00.000000000',
           '2024-08-14T00:00:00.000000000', '2024-08-21T00:00:00.000000000',
           '2024-08-28T00:00:00.000000000', '2024-09-04T00:00:00.000000000',
           '2024-09-11T00:00:00.000000000', '2024-09-18T00:00:00.000000000',
           '2024-09-25T00:00:00.000000000', '2024-10-02T00:00:00.000000000',
           '2024-10-09T00:00:00.000000000', '2024-10-16T00:00:00.000000000',
           '2024-10-23T00:00:00.000000000', '2024-10-30T00:00:00.000000000',
           '2024-11-06T00:00:00.000000000', '2024-11-13T00:00:00.000000000',
           '2024-11-20T00:00:00.000000000', '2024-11-27T00:00:00.000000000',
           '2024-12-04T00:00:00.000000000', '2024-12-11T00:00:00.000000000',
           '2024-12-18T00:00:00.000000000', '2024-12-25T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (5)
  • so
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>

    long_name :

    Salinity

    standard_name :

    sea_water_salinity

    units :

    1e-3

    Array Chunk Bytes 382.59 GiB 512.00 kiB Shape (52, 7, 32, 2041, 4320) (1, 1, 1, 256, 512) Dask graph 838656 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, 256, 512), meta=np.ndarray>

    long_name :

    Temperature

    standard_name :

    sea_water_potential_temperature

    units :

    degrees_C

    Array Chunk Bytes 382.59 GiB 512.00 kiB Shape (52, 7, 32, 2041, 4320) (1, 1, 1, 256, 512) Dask graph 838656 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, 256, 512), meta=np.ndarray>

    long_name :

    Eastward velocity

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    Array Chunk Bytes 382.59 GiB 512.00 kiB Shape (52, 7, 32, 2041, 4320) (1, 1, 1, 256, 512) Dask graph 838656 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, 256, 512), meta=np.ndarray>

    long_name :

    Northward velocity

    standard_name :

    northward_sea_water_velocity

    units :

    m s-1

    Array Chunk Bytes 382.59 GiB 512.00 kiB Shape (52, 7, 32, 2041, 4320) (1, 1, 1, 256, 512) Dask graph 838656 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, 256, 512), meta=np.ndarray>

    long_name :

    Sea surface height

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    Array Chunk Bytes 11.96 GiB 512.00 kiB Shape (52, 7, 2041, 4320) (1, 1, 256, 512) Dask graph 26208 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,
           ...
                        89.25,  89.33333333333334,  89.41666666666666,
                         89.5,  89.58333333333334,  89.66666666666666,
                        89.75,  89.83333333333334,  89.91666666666666,
                         90.0],
          dtype='float64', name='latitude', length=2041))

  • 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[ns]', 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}	0.060361	0.062578	0.065082	0.068331	0.069573	0.071352	0.066516
Temperature (°C) [sea_water_potential_temperature]{surface}	0.619591	0.568462	0.505240	0.528274	0.549111	0.612853	0.632734
Salinity (PSU) [sea_water_salinity]{surface}	0.258264	0.261542	0.265463	0.267390	0.270939	0.274698	0.276671
Meridional current (m/s) [northward_sea_water_velocity]{surface}	0.105598	0.105737	0.102859	0.103951	0.102178	0.104436	0.109229
Zonal current (m/s) [eastward_sea_water_velocity]{surface}	0.113686	0.111701	0.110121	0.109988	0.109937	0.112354	0.113514
Temperature (°C) [sea_water_potential_temperature]{50m}	0.693266	0.699209	0.710640	0.713620	0.718136	0.747845	0.744935
Salinity (PSU) [sea_water_salinity]{50m}	0.191046	0.191350	0.191892	0.192168	0.192565	0.193710	0.194280
Meridional current (m/s) [northward_sea_water_velocity]{50m}	0.089183	0.089773	0.092430	0.092289	0.091322	0.092166	0.092957
Zonal current (m/s) [eastward_sea_water_velocity]{50m}	0.093540	0.094244	0.096792	0.096684	0.095976	0.097110	0.097916
Temperature (°C) [sea_water_potential_temperature]{100m}	0.735946	0.732490	0.741752	0.742779	0.744096	0.749086	0.750119
Salinity (PSU) [sea_water_salinity]{100m}	0.197044	0.197021	0.197392	0.197953	0.198622	0.199508	0.200189
Meridional current (m/s) [northward_sea_water_velocity]{100m}	0.086291	0.086592	0.089129	0.089019	0.088257	0.088798	0.089508
Zonal current (m/s) [eastward_sea_water_velocity]{100m}	0.089196	0.089467	0.091843	0.091647	0.090944	0.091864	0.092593
Temperature (°C) [sea_water_potential_temperature]{200m}	0.651387	0.652174	0.664423	0.664613	0.664834	0.663207	0.663016
Salinity (PSU) [sea_water_salinity]{200m}	0.136729	0.136728	0.137104	0.137246	0.137462	0.137519	0.137627
Meridional current (m/s) [northward_sea_water_velocity]{200m}	0.081161	0.081074	0.083007	0.082794	0.082310	0.082437	0.083105
Zonal current (m/s) [eastward_sea_water_velocity]{200m}	0.082564	0.082385	0.084336	0.084006	0.083443	0.083845	0.084531
Temperature (°C) [sea_water_potential_temperature]{300m}	0.535452	0.535896	0.544263	0.544470	0.545047	0.542135	0.541994
Salinity (PSU) [sea_water_salinity]{300m}	0.104837	0.104819	0.105079	0.105125	0.105259	0.105151	0.105182
Meridional current (m/s) [northward_sea_water_velocity]{300m}	0.080341	0.080196	0.082214	0.081860	0.081293	0.081590	0.082362
Zonal current (m/s) [eastward_sea_water_velocity]{300m}	0.081833	0.081639	0.083668	0.083238	0.082589	0.083023	0.083738
Temperature (°C) [sea_water_potential_temperature]{500m}	0.397534	0.397231	0.401003	0.401662	0.402973	0.401047	0.401161
Salinity (PSU) [sea_water_salinity]{500m}	0.091117	0.091135	0.090943	0.091076	0.091236	0.091314	0.091462
Meridional current (m/s) [northward_sea_water_velocity]{500m}	0.083489	0.083185	0.085235	0.084612	0.083823	0.084656	0.085556
Zonal current (m/s) [eastward_sea_water_velocity]{500m}	0.085435	0.085063	0.087108	0.086460	0.085616	0.086447	0.087204

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}	46.275248	37.511662	30.935132	32.834266	34.092312	38.74794	39.862172

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}	0.089477	0.091299	0.092991	0.093787	0.093865	0.094921	0.096213
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface}	0.091021	0.092417	0.094114	0.094603	0.094661	0.096045	0.097078

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}	0.695292	0.633569	0.630924	0.557584	0.584486	0.667529	0.859473
Temperature (°C) [sea_water_potential_temperature]{0-5m}	0.682071	0.609013	0.456690	0.572447	0.604897	0.646114	0.701758
Temperature (°C) [sea_water_potential_temperature]{5-100m}	0.716803	0.696524	0.680253	0.690964	0.778317	0.734166	0.750677
Temperature (°C) [sea_water_potential_temperature]{100-300m}	0.300960	0.326613	0.320029	0.319125	0.332471	0.332792	0.338086
Temperature (°C) [sea_water_potential_temperature]{300-600m}	0.256846	0.250014	0.238363	0.238176	0.241380	0.252914	0.248764
Salinity (PSU) [sea_water_salinity]{0-5m}	0.665095	0.865429	0.849486	0.656230	0.632459	0.738958	0.639721
Salinity (PSU) [sea_water_salinity]{5-100m}	0.268923	0.322581	0.326464	0.272210	0.297687	0.307152	0.293127
Salinity (PSU) [sea_water_salinity]{100-300m}	0.110056	0.094990	0.116685	0.087616	0.131195	0.115879	0.125396
Salinity (PSU) [sea_water_salinity]{300-600m}	0.041557	0.043327	0.043931	0.042518	0.044602	0.047315	0.050872
Sea level anomaly (m) [sea_surface_height_above_geoid]{surface}	0.048665	0.048311	0.049110	0.046917	0.047700	0.051046	0.053610
Zonal current (m/s) [eastward_sea_water_velocity]{15m}	0.219735	0.221985	0.222494	0.220487	0.206839	0.218924	0.215025
Meridional current (m/s) [northward_sea_water_velocity]{15m}	0.184722	0.184772	0.183999	0.181374	0.183578	0.182098	0.185245

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}	10.325733	19.372841	27.442385	35.009399	42.015175

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}	0.029432	0.035548	0.037690	0.044180	0.045998	0.048296	0.045289
Temperature (°C) [sea_water_potential_temperature]{surface}	0.513924	0.431846	0.349182	0.403144	0.443882	0.513787	0.559750
Salinity (PSU) [sea_water_salinity]{surface}	0.059625	0.088356	0.107840	0.122619	0.134953	0.144234	0.152166
Meridional current (m/s) [northward_sea_water_velocity]{surface}	0.070947	0.072757	0.063752	0.070001	0.072912	0.083437	0.094977
Zonal current (m/s) [eastward_sea_water_velocity]{surface}	0.073848	0.073745	0.068712	0.073388	0.078423	0.089197	0.096121
Temperature (°C) [sea_water_potential_temperature]{50m}	0.265159	0.308992	0.329606	0.380024	0.423351	0.482153	0.518711
Salinity (PSU) [sea_water_salinity]{50m}	0.039856	0.058050	0.072688	0.084577	0.094163	0.103000	0.110349
Meridional current (m/s) [northward_sea_water_velocity]{50m}	0.042598	0.045904	0.046258	0.052861	0.057976	0.068213	0.076080
Zonal current (m/s) [eastward_sea_water_velocity]{50m}	0.042424	0.046830	0.047724	0.054650	0.059989	0.070692	0.078637
Temperature (°C) [sea_water_potential_temperature]{100m}	0.193311	0.236163	0.270666	0.315054	0.353224	0.398639	0.434438
Salinity (PSU) [sea_water_salinity]{100m}	0.039247	0.057849	0.073395	0.086429	0.096965	0.105854	0.113302
Meridional current (m/s) [northward_sea_water_velocity]{100m}	0.038083	0.041316	0.042425	0.048973	0.054195	0.064726	0.072558
Zonal current (m/s) [eastward_sea_water_velocity]{100m}	0.036707	0.041512	0.043434	0.050015	0.055370	0.066078	0.073650
Temperature (°C) [sea_water_potential_temperature]{200m}	0.148926	0.181349	0.208638	0.241613	0.270211	0.303881	0.333470
Salinity (PSU) [sea_water_salinity]{200m}	0.026568	0.037465	0.046588	0.054286	0.060649	0.066100	0.070987
Meridional current (m/s) [northward_sea_water_velocity]{200m}	0.032762	0.035734	0.036159	0.042135	0.047507	0.057407	0.064895
Zonal current (m/s) [eastward_sea_water_velocity]{200m}	0.031501	0.035616	0.036769	0.042628	0.047949	0.057595	0.064903
Temperature (°C) [sea_water_potential_temperature]{300m}	0.121590	0.155369	0.182048	0.211546	0.236988	0.265121	0.289934
Salinity (PSU) [sea_water_salinity]{300m}	0.021189	0.030520	0.037890	0.044181	0.049366	0.053730	0.057661
Meridional current (m/s) [northward_sea_water_velocity]{300m}	0.030324	0.033885	0.035105	0.040989	0.046291	0.055626	0.062897
Zonal current (m/s) [eastward_sea_water_velocity]{300m}	0.029560	0.034123	0.035601	0.041406	0.046679	0.055691	0.062792
Temperature (°C) [sea_water_potential_temperature]{500m}	0.091490	0.128905	0.155986	0.183065	0.206339	0.228589	0.247528
Salinity (PSU) [sea_water_salinity]{500m}	0.018414	0.027539	0.034690	0.040661	0.045585	0.049662	0.053248
Meridional current (m/s) [northward_sea_water_velocity]{500m}	0.026224	0.032321	0.036940	0.043036	0.048312	0.056819	0.063556
Zonal current (m/s) [eastward_sea_water_velocity]{500m}	0.025348	0.032371	0.037552	0.043760	0.049094	0.057345	0.063898

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}	43.551025	33.943386	26.734418	29.485549	31.316682	36.968545	38.643863

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}	0.040558	0.047826	0.049284	0.056759	0.062385	0.072252	0.079978
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface}	0.037728	0.044863	0.046857	0.053982	0.059875	0.070098	0.077642

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}	6.939085	12.581882	17.534216	22.869259	28.461174