{
  "model_name": "FourCastNet v2",
  "model_type": "fourcastnet_v2",
  "architectures": [
    "FourCastNetV2"
  ],
  "framework": "PyTorch",
  "domain": "atmosphere",
  "task": "global-weather-forecasting",
  "implementation": {
    "entry_point": "model/fourcastnet_v2.py",
    "scope": "adapter for the bundled NVIDIA FourCastNet v2 SFNO network"
  },
  "architecture": {
    "family": "spherical Fourier neural operator",
    "grid_shape": [
      721,
      1440
    ],
    "input_channels": 73,
    "output_channels": 73,
    "spectral_transform": "sht",
    "filter_type": "non-linear",
    "scale_factor": 6,
    "embedding_size": 256,
    "layers": 12,
    "blocks_per_layer": 8,
    "normalization": "instance_norm",
    "mlp_mode": "distributed",
    "spectral_layers": 3,
    "complex_activation": "real",
    "hard_thresholding_fraction": 1.0
  },
  "data": {
    "dataset": "ERA5",
    "spatial_resolution_degrees": 0.25,
    "time_step_hours": 6,
    "input_steps": 1,
    "output_steps": 1,
    "protocol": "synthetic_era5"
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/fourcastnet_v2.py",
    "model/fcnv2"
  ]
}