{
  "format_version": "1.0",
  "model_name": "FuXi-Ocean",
  "model_type": "fuxi_ocean",
  "architectures": ["FuXiOcean"],
  "framework": "PyTorch",
  "domain": "earth-science-ocean",
  "license": "Apache-2.0",
  "paper": {
    "title": "A deep learning global ocean forecasting model with sub-daily and eddy-resolving resolution",
    "doi": "10.1038/s41612-026-01444-2",
    "paper_license": "CC-BY-4.0"
  },
  "implementation": {
    "entry_point": "model/fuxi_ocean.py",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "scientific_grid": {
    "history": 4,
    "ocean_channels": 105,
    "atmosphere_channels": 5,
    "height": 2160,
    "width": 4320,
    "hours_per_step": 6,
    "forecast_steps": 40
  },
  "execution_protocol": "Tiles retain all 105 channels, physical coordinates, and indices into the unchanged 2160x4320 global grid. Tile dimensions are not the scientific grid dimensions."
}