1 degree track

This page documents how OceanBench constructs the 1 degree evaluation track. It is intended to remove ambiguity for both benchmark users and technical reviewers. We will go through how reference datasets are selected for the 1 degree track, the 1 degree GLORYS dataset exposed for training and how challenger datasets are interpolated.

How reference datasets are selected for the 1 degree track

For the 1 degree track, OceanBench uses dedicated 1 degree reference datasets for:

  • GLORYS reanalysis

  • GLO12 analysis

Then, when the challenger resolution is “1_degree”, OceanBench opens the precomputed 1 degree references. The related public dataset helpers are:

1 degree GLORYS dataset for training

For training outside the official evaluation workflow, OceanBench also exposes the 1 degree GLORYS dataset through:

  • oceanbench.datasets.reference.glorys_reanalysis_1_degree()

This public API is documented in:

How challenger datasets are interpolated

The 1 degree challengers exposed in oceanbench.datasets.challenger are:

  • glo12_1_degree()

  • glonet_1_degree()

  • wenhai_1_degree()

  • xihe_1_degree()

Those are base challengers on a higher resolution (1/4 degree for GLONET, 1/12 degree for the others) that are interpolated to the 1 degree resolution. The corresponding public challenger dataset loaders are documented in:

Each of them applies the same interpolation logic:

  1. Rename dataset dimensions and variables to the OceanBench standard names.

  2. Infer the target 1 degree latitude and longitude grid from the native domain bounds.

  3. Build a regular grid with 1.0 degree spacing and cell centres at n + 0.5.

  4. Interpolate the dataset with xarray.Dataset.interp.

  5. Mark the resulting dataset source with resolution=\"one_degree\" so downstream staging and caching logic can distinguish it from the native-resolution challenger.