OceanBench logo. OceanBench
  • Scores
  • Challengers
  • News
  • About
  • Documentation
    • Evaluation methods
    • Technical documentation
    • FAQ
    • Paper
  • Source code
  • Powered by EDITO logo and EU flag EDITO logo and EU flag

Open challenger datasets

import oceanbench

oceanbench.__version__
'0.5.0'

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

import xarray
import oceanbench

challenger_dataset: xarray.Dataset = oceanbench.datasets.challenger.glo12()

challenger_dataset
<xarray.Dataset> Size: 4TB
Dimensions:             (first_day_datetime: 52, lead_day_index: 10, depth: 50,
                         latitude: 2041, longitude: 4320)
Coordinates:
  * depth               (depth) float32 200B 0.494 1.541 ... 5.275e+03 5.728e+03
  * latitude            (latitude) float32 8kB -80.0 -79.92 ... 89.92 90.0
  * lead_day_index      (lead_day_index) int64 80B 0 1 2 3 4 5 6 7 8 9
  * longitude           (longitude) float32 17kB -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 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
    thetao              (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
    uo                  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
    vo                  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
    zos                 (first_day_datetime, lead_day_index, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray>
Attributes:
    oceanbench_source_kind:  challenger
    oceanbench_source_name:  glo12
xarray.Dataset
    • first_day_datetime: 52
    • lead_day_index: 10
    • depth: 50
    • latitude: 2041
    • longitude: 4320
    • depth
      (depth)
      float32
      0.494 1.541 ... 5.275e+03 5.728e+03
      axis :
      Z
      long_name :
      Depth
      positive :
      down
      standard_name :
      depth
      unit_long :
      Meters
      units :
      m
      valid_max :
      5727.9169921875
      valid_min :
      0.49402499198913574
      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, 6.435668e+02, 7.633331e+02, 9.023393e+02,
             1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,
             2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,
             3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03, 5.727917e+03],
            dtype=float32)
    • latitude
      (latitude)
      float32
      -80.0 -79.92 -79.83 ... 89.92 90.0
      axis :
      Y
      long_name :
      Latitude
      standard_name :
      latitude
      step :
      0.08333587646484375
      unit_long :
      Degrees North
      units :
      degrees_north
      valid_max :
      90.0
      valid_min :
      -80.0
      array([-80.      , -79.916664, -79.833336, ...,  89.83334 ,  89.91667 ,
              90.      ], shape=(2041,), dtype=float32)
    • lead_day_index
      (lead_day_index)
      int64
      0 1 2 3 4 5 6 7 8 9
      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    • longitude
      (longitude)
      float32
      -180.0 -179.9 ... 179.8 179.9
      axis :
      X
      long_name :
      Longitude
      standard_name :
      longitude
      step :
      0.0833282470703125
      unit_long :
      Degrees East
      units :
      degrees_east
      valid_max :
      179.91668701171875
      valid_min :
      -180.0
      array([-180.     , -179.91667, -179.83333, ...,  179.75   ,  179.83334,
              179.91669], shape=(4320,), dtype=float32)
    • 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]')
    • so
      (first_day_datetime, lead_day_index, depth, latitude, longitude)
      float32
      dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
      cell_methods :
      area: mean
      long_name :
      Salinity
      standard_name :
      sea_water_salinity
      unit_long :
      Practical Salinity Unit
      units :
      1e-3
      valid_max :
      50.0
      valid_min :
      0.0
      Array Chunk
      Bytes 854.00 GiB 3.12 MiB
      Shape (52, 10, 50, 2041, 4320) (1, 1, 1, 640, 1280)
      Dask graph 416000 chunks in 157 graph layers
      Data type float32 numpy.ndarray
      10 52 4320 2041 50
    • thetao
      (first_day_datetime, lead_day_index, depth, latitude, longitude)
      float32
      dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
      cell_methods :
      area: mean
      long_name :
      Temperature
      standard_name :
      sea_water_potential_temperature
      unit_long :
      Degrees Celsius
      units :
      degrees_C
      valid_max :
      40.0
      valid_min :
      -10.0
      Array Chunk
      Bytes 854.00 GiB 3.12 MiB
      Shape (52, 10, 50, 2041, 4320) (1, 1, 1, 640, 1280)
      Dask graph 416000 chunks in 157 graph layers
      Data type float32 numpy.ndarray
      10 52 4320 2041 50
    • uo
      (first_day_datetime, lead_day_index, depth, latitude, longitude)
      float32
      dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
      cell_methods :
      area: mean
      long_name :
      Eastward velocity
      standard_name :
      eastward_sea_water_velocity
      unit_long :
      Meters per second
      units :
      m s-1
      valid_max :
      5.0
      valid_min :
      -5.0
      Array Chunk
      Bytes 854.00 GiB 3.12 MiB
      Shape (52, 10, 50, 2041, 4320) (1, 1, 1, 640, 1280)
      Dask graph 416000 chunks in 157 graph layers
      Data type float32 numpy.ndarray
      10 52 4320 2041 50
    • vo
      (first_day_datetime, lead_day_index, depth, latitude, longitude)
      float32
      dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
      cell_methods :
      area: mean
      long_name :
      Northward velocity
      standard_name :
      northward_sea_water_velocity
      unit_long :
      Meters per second
      units :
      m s-1
      valid_max :
      5.0
      valid_min :
      -5.0
      Array Chunk
      Bytes 854.00 GiB 3.12 MiB
      Shape (52, 10, 50, 2041, 4320) (1, 1, 1, 640, 1280)
      Dask graph 416000 chunks in 157 graph layers
      Data type float32 numpy.ndarray
      10 52 4320 2041 50
    • zos
      (first_day_datetime, lead_day_index, latitude, longitude)
      float32
      dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray>
      cell_methods :
      area: mean
      long_name :
      Sea surface height
      standard_name :
      sea_surface_height_above_geoid
      unit_long :
      Meters
      units :
      m
      valid_max :
      5.0
      valid_min :
      -5.0
      Array Chunk
      Bytes 17.08 GiB 3.12 MiB
      Shape (52, 10, 2041, 4320) (1, 1, 640, 1280)
      Dask graph 8320 chunks in 157 graph layers
      Data type float32 numpy.ndarray
      52 1 4320 2041 10
    • 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,   643.5667724609375,
               763.3331298828125,   902.3392944335938,    1062.43994140625,
                  1245.291015625,     1452.2509765625,  1684.2840576171875,
              1941.8929443359375,   2225.077880859375,        2533.3359375,
               2865.702880859375,   3220.820068359375,   3597.031982421875,
                3992.48388671875,    4405.22412109375,      4833.291015625,
                 5274.7841796875,     5727.9169921875],
            dtype='float32', name='depth'))
    • latitude
      PandasIndex
      PandasIndex(Index([             -80.0, -79.91666412353516, -79.83333587646484,
                         -79.75, -79.66666412353516, -79.58333587646484,
                          -79.5, -79.41666412353516, -79.33333587646484,
                         -79.25,
             ...
                          89.25,  89.33334350585938,  89.41667175292969,
                           89.5,  89.58334350585938,  89.66667175292969,
                          89.75,  89.83334350585938,  89.91667175292969,
                           90.0],
            dtype='float32', name='latitude', length=2041))
    • lead_day_index
      PandasIndex
      PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64', name='lead_day_index'))
    • longitude
      PandasIndex
      PandasIndex(Index([            -180.0, -179.9166717529297, -179.8333282470703,
                        -179.75, -179.6666717529297, -179.5833282470703,
                         -179.5, -179.4166717529297, -179.3333282470703,
                        -179.25,
             ...
             179.16668701171875,             179.25, 179.33334350585938,
             179.41668701171875,              179.5, 179.58334350585938,
             179.66668701171875,             179.75, 179.83334350585938,
             179.91668701171875],
            dtype='float32', 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))
  • oceanbench_source_kind :
    challenger
    oceanbench_source_name :
    glo12

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 Lead day 8 Lead day 9 Lead day 10
Sea surface height (m) [sea_surface_height_above_geoid]{surface} 0.057427 0.057520 0.058417 0.059129 0.061877 0.061762 0.065297 0.067080 0.071805 0.073386
Temperature (°C) [sea_water_potential_temperature]{surface} 0.446993 0.447695 0.450478 0.453363 0.461805 0.471331 0.488180 0.512875 0.539660 0.566718
Salinity (PSU) [sea_water_salinity]{surface} 0.257357 0.256864 0.256924 0.257615 0.259413 0.261248 0.262615 0.265408 0.267387 0.270451
Meridional current (m/s) [northward_sea_water_velocity]{surface} 0.097242 0.098618 0.100275 0.102120 0.104325 0.106922 0.110046 0.112863 0.116846 0.119866
Zonal current (m/s) [eastward_sea_water_velocity]{surface} 0.100805 0.102314 0.104079 0.105972 0.108263 0.111273 0.114812 0.118457 0.123018 0.126177
Temperature (°C) [sea_water_potential_temperature]{50m} 0.717846 0.716808 0.715994 0.715751 0.716693 0.719465 0.723507 0.727680 0.729907 0.732063
Salinity (PSU) [sea_water_salinity]{50m} 0.191857 0.192345 0.192981 0.193693 0.194437 0.195308 0.196409 0.197751 0.198808 0.199891
Meridional current (m/s) [northward_sea_water_velocity]{50m} 0.094895 0.096365 0.097868 0.099426 0.101048 0.102646 0.104485 0.106263 0.107947 0.109408
Zonal current (m/s) [eastward_sea_water_velocity]{50m} 0.098013 0.099437 0.101046 0.102744 0.104382 0.105957 0.107806 0.109734 0.111190 0.112804
Temperature (°C) [sea_water_potential_temperature]{100m} 0.745948 0.746079 0.746521 0.747157 0.748519 0.751460 0.755111 0.758681 0.760764 0.762747
Salinity (PSU) [sea_water_salinity]{100m} 0.197213 0.197860 0.198605 0.199405 0.200292 0.201374 0.202550 0.203786 0.204686 0.205625
Meridional current (m/s) [northward_sea_water_velocity]{100m} 0.091631 0.093061 0.094537 0.096077 0.097610 0.099181 0.100834 0.102488 0.103850 0.105167
Zonal current (m/s) [eastward_sea_water_velocity]{100m} 0.093781 0.095212 0.096778 0.098371 0.099923 0.101514 0.103130 0.104740 0.106073 0.107342
Temperature (°C) [sea_water_potential_temperature]{200m} 0.662606 0.662415 0.662419 0.663063 0.664245 0.666006 0.668209 0.670343 0.671548 0.673096
Salinity (PSU) [sea_water_salinity]{200m} 0.136880 0.136847 0.136847 0.136974 0.137204 0.137522 0.137937 0.138347 0.138616 0.138937
Meridional current (m/s) [northward_sea_water_velocity]{200m} 0.085936 0.087184 0.088553 0.089941 0.091389 0.092844 0.094309 0.095716 0.096897 0.098063
Zonal current (m/s) [eastward_sea_water_velocity]{200m} 0.087198 0.088466 0.089837 0.091204 0.092600 0.094001 0.095412 0.096724 0.097851 0.098912
Temperature (°C) [sea_water_potential_temperature]{300m} 0.542091 0.542001 0.541954 0.542274 0.542988 0.544145 0.545753 0.547409 0.548349 0.549442
Salinity (PSU) [sea_water_salinity]{300m} 0.104989 0.104936 0.104892 0.104915 0.105008 0.105165 0.105396 0.105614 0.105764 0.105937
Meridional current (m/s) [northward_sea_water_velocity]{300m} 0.085360 0.086485 0.087731 0.089018 0.090369 0.091731 0.093072 0.094363 0.095457 0.096520
Zonal current (m/s) [eastward_sea_water_velocity]{300m} 0.086669 0.087796 0.089003 0.090252 0.091540 0.092817 0.094067 0.095260 0.096280 0.097264
Temperature (°C) [sea_water_potential_temperature]{500m} 0.399983 0.399845 0.399795 0.400079 0.400695 0.401731 0.403220 0.404801 0.405818 0.407032
Salinity (PSU) [sea_water_salinity]{500m} 0.091065 0.091070 0.091129 0.091279 0.091506 0.091791 0.092130 0.092442 0.092673 0.092955
Meridional current (m/s) [northward_sea_water_velocity]{500m} 0.088390 0.089239 0.090180 0.091228 0.092330 0.093433 0.094526 0.095579 0.096433 0.097265
Zonal current (m/s) [eastward_sea_water_velocity]{500m} 0.089931 0.090765 0.091679 0.092682 0.093688 0.094697 0.095665 0.096601 0.097415 0.098219

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 Lead day 8 Lead day 9 Lead day 10
Mixed layer depth (m) [ocean_mixed_layer_thickness]{surface} 24.138102 24.102343 23.445194 23.802013 24.446709 25.427814 26.614634 28.739052 29.352211 29.95063

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 Lead day 8 Lead day 9 Lead day 10
Meridional geostrophic current (m/s) [geostrophic_northward_sea_water_velocity]{surface} 0.091799 0.093232 0.094782 0.096366 0.098062 0.099816 0.101919 0.103814 0.105853 0.10741
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface} 0.092481 0.093884 0.095549 0.097178 0.098979 0.100756 0.102715 0.104484 0.106386 0.10815

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 Lead day 8 Lead day 9 Lead day 10
Temperature (°C) [sea_water_potential_temperature]{surface} 0.490286 0.513988 0.571904 0.463396 0.508202 0.544488 0.767785 0.548021 0.558166 0.669802
Temperature (°C) [sea_water_potential_temperature]{0-5m} 0.424710 0.448153 0.398182 0.425658 0.515173 0.493803 0.612990 0.595604 0.592457 0.567544
Temperature (°C) [sea_water_potential_temperature]{5-100m} 0.657598 0.713165 0.696872 0.681889 0.768103 0.675142 0.791567 0.726775 0.760195 0.778687
Temperature (°C) [sea_water_potential_temperature]{100-300m} 0.298009 0.330635 0.336448 0.319458 0.354257 0.340103 0.356906 0.338214 0.350763 0.380484
Temperature (°C) [sea_water_potential_temperature]{300-600m} 0.255268 0.249996 0.236480 0.235860 0.255569 0.240989 0.246953 0.278521 0.270954 0.247575
Salinity (PSU) [sea_water_salinity]{0-5m} 0.666668 0.865747 0.857370 0.659646 0.633612 0.744267 0.646977 0.707667 0.892080 0.881476
Salinity (PSU) [sea_water_salinity]{5-100m} 0.274088 0.322395 0.343848 0.282922 0.307329 0.309777 0.303775 0.306485 0.363870 0.379873
Salinity (PSU) [sea_water_salinity]{100-300m} 0.116642 0.099614 0.126265 0.100026 0.141694 0.129596 0.140883 0.135565 0.127558 0.162057
Salinity (PSU) [sea_water_salinity]{300-600m} 0.043211 0.045592 0.046734 0.045190 0.052732 0.047129 0.052069 0.049511 0.052280 0.051994
Sea level anomaly (m) [sea_surface_height_above_geoid]{surface} 0.051487 0.055318 0.058807 0.057073 0.059609 0.057478 0.059348 0.059967 0.061995 0.064763
Zonal current (m/s) [eastward_sea_water_velocity]{15m} 0.215723 0.219253 0.220321 0.220543 0.208131 0.221593 0.216471 0.225747 0.230541 0.230732
Meridional current (m/s) [northward_sea_water_velocity]{15m} 0.183486 0.185631 0.183537 0.181886 0.186926 0.184204 0.188207 0.191564 0.193116 0.193821

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 Lead day 7 Lead day 8 Lead day 9
Lagrangian trajectory deviation (km) []{surface} 8.84231 17.177641 24.910484 32.092438 38.821037 45.248356 51.441891 57.458958

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 Lead day 8 Lead day 9 Lead day 10
Sea surface height (m) [sea_surface_height_above_geoid]{surface} 0.007259 0.008933 0.012043 0.015556 0.021598 0.025086 0.031662 0.034650 0.041951 0.045142
Temperature (°C) [sea_water_potential_temperature]{surface} 0.090916 0.109327 0.136970 0.169607 0.209324 0.251095 0.292122 0.340692 0.389452 0.436729
Salinity (PSU) [sea_water_salinity]{surface} 0.028149 0.033634 0.040957 0.050270 0.061282 0.072113 0.084454 0.098442 0.110666 0.123360
Meridional current (m/s) [northward_sea_water_velocity]{surface} 0.018346 0.022817 0.028673 0.035810 0.044299 0.053431 0.062937 0.071497 0.080586 0.087271
Zonal current (m/s) [eastward_sea_water_velocity]{surface} 0.018342 0.023104 0.029082 0.036446 0.044809 0.054933 0.065432 0.075284 0.085021 0.091686
Temperature (°C) [sea_water_potential_temperature]{50m} 0.130396 0.148908 0.176466 0.211922 0.251929 0.295011 0.339685 0.383223 0.410674 0.437778
Salinity (PSU) [sea_water_salinity]{50m} 0.026180 0.030699 0.036601 0.043767 0.051864 0.060656 0.069731 0.078500 0.085503 0.092291
Meridional current (m/s) [northward_sea_water_velocity]{50m} 0.017558 0.021123 0.026146 0.032274 0.039391 0.046698 0.054702 0.061988 0.068084 0.073195
Zonal current (m/s) [eastward_sea_water_velocity]{50m} 0.017367 0.021123 0.026250 0.032531 0.039609 0.047271 0.055444 0.062919 0.068727 0.074222
Temperature (°C) [sea_water_potential_temperature]{100m} 0.110808 0.127255 0.150754 0.180827 0.215672 0.253473 0.292485 0.329116 0.355218 0.380415
Salinity (PSU) [sea_water_salinity]{100m} 0.026277 0.030815 0.036885 0.044263 0.052618 0.061568 0.070693 0.079398 0.086176 0.092782
Meridional current (m/s) [northward_sea_water_velocity]{100m} 0.016781 0.020144 0.024877 0.030689 0.037382 0.044521 0.051960 0.058749 0.064109 0.068976
Zonal current (m/s) [eastward_sea_water_velocity]{100m} 0.016506 0.020039 0.024854 0.030797 0.037560 0.044842 0.052271 0.059205 0.064563 0.069567
Temperature (°C) [sea_water_potential_temperature]{200m} 0.091416 0.104114 0.122727 0.146723 0.174533 0.204588 0.235479 0.263885 0.283476 0.301973
Salinity (PSU) [sea_water_salinity]{200m} 0.018674 0.021264 0.024971 0.029681 0.035093 0.040910 0.046908 0.052408 0.056306 0.059978
Meridional current (m/s) [northward_sea_water_velocity]{200m} 0.014673 0.017583 0.021717 0.026909 0.032858 0.039308 0.045892 0.051995 0.056717 0.061148
Zonal current (m/s) [eastward_sea_water_velocity]{200m} 0.014256 0.017352 0.021612 0.026888 0.032928 0.039402 0.046023 0.052169 0.056953 0.061474
Temperature (°C) [sea_water_potential_temperature]{300m} 0.074593 0.085336 0.101098 0.121424 0.145020 0.170604 0.196947 0.221446 0.238660 0.254725
Salinity (PSU) [sea_water_salinity]{300m} 0.014108 0.016143 0.019056 0.022753 0.027028 0.031671 0.036457 0.040922 0.044151 0.047137
Meridional current (m/s) [northward_sea_water_velocity]{300m} 0.013829 0.016546 0.020447 0.025347 0.030961 0.037074 0.043321 0.049165 0.053712 0.058029
Zonal current (m/s) [eastward_sea_water_velocity]{300m} 0.013393 0.016273 0.020295 0.025281 0.030998 0.037150 0.043475 0.049375 0.054014 0.058401
Temperature (°C) [sea_water_potential_temperature]{500m} 0.058307 0.067448 0.081080 0.098562 0.118718 0.140557 0.163068 0.183965 0.198751 0.212785
Salinity (PSU) [sea_water_salinity]{500m} 0.012362 0.014255 0.016865 0.020187 0.024038 0.028210 0.032500 0.036489 0.039476 0.042351
Meridional current (m/s) [northward_sea_water_velocity]{500m} 0.013006 0.015535 0.019101 0.023589 0.028774 0.034396 0.040185 0.045674 0.050035 0.054258
Zonal current (m/s) [eastward_sea_water_velocity]{500m} 0.012652 0.015309 0.018989 0.023544 0.028801 0.034525 0.040440 0.046017 0.050501 0.054748

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 Lead day 8 Lead day 9 Lead day 10
Mixed layer depth (m) [ocean_mixed_layer_thickness]{surface} 6.858749 9.431922 11.653698 12.970525 15.007453 17.657921 20.091002 23.166401 24.135216 26.097441

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 Lead day 8 Lead day 9 Lead day 10
Meridional geostrophic current (m/s) [geostrophic_northward_sea_water_velocity]{surface} 0.016696 0.020085 0.024890 0.030805 0.037538 0.045006 0.052997 0.060012 0.066268 0.071188
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface} 0.015974 0.019359 0.024177 0.030057 0.036799 0.044186 0.052013 0.058991 0.065246 0.070384

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 Lead day 7 Lead day 8 Lead day 9
Lagrangian trajectory deviation (km) []{surface} 1.445626 3.224747 5.448678 8.179537 11.512771 15.497852 19.994965 24.992002
 

EDITO, the European Digital Twin Ocean EDITO, the European Digital Twin Ocean