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 = 'global'

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}	0.072023	0.073918	0.072891	0.073820	0.074834	0.078550	0.081114
Temperature (°C) [sea_water_potential_temperature]{surface}	0.687360	0.670888	0.576500	0.589326	0.602323	0.651077	0.709682
Salinity (PSU) [sea_water_salinity]{surface}	0.631916	0.636078	0.638657	0.643254	0.646737	0.652201	0.657460
Meridional current (m/s) [northward_sea_water_velocity]{surface}	0.134061	0.133371	0.133055	0.133195	0.133054	0.143322	0.148549
Zonal current (m/s) [eastward_sea_water_velocity]{surface}	0.138142	0.138491	0.138966	0.139325	0.139536	0.148618	0.153138
Temperature (°C) [sea_water_potential_temperature]{50m}	0.950404	0.950264	0.919001	0.922108	0.924642	0.968531	1.001719
Salinity (PSU) [sea_water_salinity]{50m}	0.255754	0.256806	0.256516	0.257403	0.258462	0.261613	0.264048
Meridional current (m/s) [northward_sea_water_velocity]{50m}	0.121125	0.120621	0.122133	0.121976	0.121637	0.130144	0.134482
Zonal current (m/s) [eastward_sea_water_velocity]{50m}	0.123707	0.123929	0.125717	0.125674	0.125389	0.132617	0.136603
Temperature (°C) [sea_water_potential_temperature]{100m}	1.109991	1.110378	1.107677	1.113160	1.117255	1.153011	1.178470
Salinity (PSU) [sea_water_salinity]{100m}	0.200461	0.200645	0.200802	0.201295	0.201938	0.203815	0.205066
Meridional current (m/s) [northward_sea_water_velocity]{100m}	0.117668	0.117268	0.118865	0.118585	0.118084	0.125005	0.128605
Zonal current (m/s) [eastward_sea_water_velocity]{100m}	0.123853	0.123834	0.125168	0.124881	0.124522	0.130638	0.133684
Temperature (°C) [sea_water_potential_temperature]{200m}	0.915597	0.915726	0.917669	0.919395	0.921643	0.937164	0.946815
Salinity (PSU) [sea_water_salinity]{200m}	0.163441	0.163482	0.163669	0.163928	0.164310	0.165604	0.166450
Meridional current (m/s) [northward_sea_water_velocity]{200m}	0.114422	0.114321	0.116194	0.115673	0.114889	0.118583	0.119920
Zonal current (m/s) [eastward_sea_water_velocity]{200m}	0.121445	0.121442	0.123211	0.122469	0.121601	0.125303	0.126358
Temperature (°C) [sea_water_potential_temperature]{300m}	0.773085	0.772940	0.775136	0.776386	0.778170	0.791211	0.798832
Salinity (PSU) [sea_water_salinity]{300m}	0.136481	0.136479	0.136693	0.136904	0.137220	0.138365	0.139131
Meridional current (m/s) [northward_sea_water_velocity]{300m}	0.111761	0.111690	0.113587	0.112975	0.112153	0.115396	0.116337
Zonal current (m/s) [eastward_sea_water_velocity]{300m}	0.116901	0.116902	0.118713	0.118014	0.117161	0.120230	0.120948
Temperature (°C) [sea_water_potential_temperature]{500m}	0.593490	0.594044	0.596992	0.598689	0.600963	0.610382	0.616975
Salinity (PSU) [sea_water_salinity]{500m}	0.103536	0.103617	0.103866	0.104105	0.104427	0.105136	0.105770
Meridional current (m/s) [northward_sea_water_velocity]{500m}	0.102574	0.102618	0.104180	0.103518	0.102744	0.105627	0.106327
Zonal current (m/s) [eastward_sea_water_velocity]{500m}	0.107471	0.107505	0.109105	0.108550	0.107916	0.110194	0.110620

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}	41.253272	39.992063	37.18128	38.132036	38.904035	40.362156	41.616102

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}	0.180441	0.183232	0.169623	0.172992	0.175595	0.182338	0.193796
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface}	0.206873	0.209844	0.189610	0.193785	0.196521	0.204235	0.217779

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}	0.915231	0.905541	0.802906	0.835050	0.866614	0.929662	0.963780
Temperature (°C) [sea_water_potential_temperature]{0-5m}	0.876083	0.880324	0.800879	0.841711	0.850585	0.900836	0.966187
Temperature (°C) [sea_water_potential_temperature]{5-100m}	0.949060	0.948322	0.903461	0.933663	0.930487	0.962093	1.003347
Temperature (°C) [sea_water_potential_temperature]{100-300m}	0.825735	0.843423	0.828361	0.831906	0.847287	0.854374	0.882753
Temperature (°C) [sea_water_potential_temperature]{300-600m}	0.571222	0.581156	0.575457	0.572631	0.579814	0.610892	0.617262
Salinity (PSU) [sea_water_salinity]{0-5m}	0.273599	0.294627	0.286761	0.328831	0.288515	0.299874	0.297798
Salinity (PSU) [sea_water_salinity]{5-100m}	0.248641	0.254782	0.257321	0.251758	0.262232	0.261674	0.260704
Salinity (PSU) [sea_water_salinity]{100-300m}	0.136876	0.138122	0.139135	0.137740	0.141230	0.139614	0.145758
Salinity (PSU) [sea_water_salinity]{300-600m}	0.092536	0.092076	0.091227	0.091301	0.092119	0.094917	0.095628
Sea level anomaly (m) [sea_surface_height_above_geoid]{surface}	0.050793	0.052326	0.050767	0.052544	0.053817	0.058929	0.061671
Zonal current (m/s) [eastward_sea_water_velocity]{15m}	0.217414	0.216927	0.218976	0.219425	0.217107	0.219851	0.224268
Meridional current (m/s) [northward_sea_water_velocity]{15m}	0.198994	0.198005	0.198776	0.201059	0.200532	0.204468	0.206431

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}	12.387727	23.575798	33.953323	43.980129	53.528202

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}	0.029689	0.034530	0.033617	0.037379	0.041130	0.049836	0.054226
Temperature (°C) [sea_water_potential_temperature]{surface}	0.457759	0.460383	0.352981	0.399455	0.439768	0.526843	0.602856
Salinity (PSU) [sea_water_salinity]{surface}	0.120910	0.173950	0.205135	0.230620	0.250193	0.270741	0.288437
Meridional current (m/s) [northward_sea_water_velocity]{surface}	0.103623	0.101192	0.089492	0.096429	0.103086	0.126961	0.139002
Zonal current (m/s) [eastward_sea_water_velocity]{surface}	0.102009	0.102854	0.092702	0.099850	0.107074	0.129164	0.141547
Temperature (°C) [sea_water_potential_temperature]{50m}	0.416372	0.475870	0.475827	0.524641	0.570101	0.665562	0.734679
Salinity (PSU) [sea_water_salinity]{50m}	0.066531	0.091376	0.107585	0.121648	0.133412	0.147152	0.157158
Meridional current (m/s) [northward_sea_water_velocity]{50m}	0.081139	0.079218	0.071373	0.077992	0.084433	0.108282	0.119949
Zonal current (m/s) [eastward_sea_water_velocity]{50m}	0.076389	0.077818	0.073127	0.079857	0.086546	0.107916	0.119260
Temperature (°C) [sea_water_potential_temperature]{100m}	0.403552	0.474883	0.500418	0.559480	0.614294	0.722796	0.799875
Salinity (PSU) [sea_water_salinity]{100m}	0.053370	0.073813	0.088801	0.101466	0.111872	0.123279	0.131257
Meridional current (m/s) [northward_sea_water_velocity]{100m}	0.071731	0.071172	0.065260	0.071757	0.078234	0.100167	0.111220
Zonal current (m/s) [eastward_sea_water_velocity]{100m}	0.067112	0.070528	0.068754	0.075550	0.082075	0.101109	0.111101
Temperature (°C) [sea_water_potential_temperature]{200m}	0.275573	0.332782	0.371491	0.424290	0.471415	0.550603	0.601560
Salinity (PSU) [sea_water_salinity]{200m}	0.040560	0.057610	0.070658	0.081568	0.090582	0.100258	0.107290
Meridional current (m/s) [northward_sea_water_velocity]{200m}	0.057463	0.057670	0.053646	0.059832	0.065866	0.084335	0.093891
Zonal current (m/s) [eastward_sea_water_velocity]{200m}	0.051429	0.053787	0.053271	0.059640	0.065764	0.082226	0.090604
Temperature (°C) [sea_water_potential_temperature]{300m}	0.225147	0.278015	0.318053	0.365785	0.407789	0.475586	0.518079
Salinity (PSU) [sea_water_salinity]{300m}	0.033343	0.048113	0.059481	0.068848	0.076556	0.084778	0.090850
Meridional current (m/s) [northward_sea_water_velocity]{300m}	0.052874	0.053418	0.050198	0.056310	0.062210	0.079381	0.088446
Zonal current (m/s) [eastward_sea_water_velocity]{300m}	0.047151	0.049325	0.048776	0.054932	0.060786	0.076203	0.084286
Temperature (°C) [sea_water_potential_temperature]{500m}	0.160492	0.210484	0.250233	0.290097	0.324742	0.375465	0.409905
Salinity (PSU) [sea_water_salinity]{500m}	0.023488	0.035477	0.044680	0.051959	0.057894	0.063651	0.068296
Meridional current (m/s) [northward_sea_water_velocity]{500m}	0.044352	0.046791	0.046384	0.052379	0.057908	0.071388	0.079136
Zonal current (m/s) [eastward_sea_water_velocity]{500m}	0.038852	0.042913	0.044919	0.051030	0.056590	0.068448	0.075234

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}	32.056032	31.488696	27.17607	29.365048	31.077645	33.51047	36.310015

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}	0.147676	0.156357	0.135592	0.144168	0.151702	0.167649	0.184562
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface}	0.179308	0.191259	0.164299	0.172068	0.179878	0.193877	0.213773

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}	8.558249	15.251342	21.140297	27.600624	34.652981