{
  "model_name": "W-MAE",
  "model_type": "w_mae",
  "architectures": [
    "MaskedAutoencoderAFNO"
  ],
  "framework": "PyTorch",
  "domain": "atmosphere",
  "task": "masked-weather-pretraining",
  "implementation": {
    "entry_point": "model/w_mae.py",
    "scope": "AFNO-based masked autoencoder for multi-variable weather fields; this metadata describes the local PyTorch implementation and its ERA5 adapter"
  },
  "architecture": {
    "family": "masked autoencoder with Adaptive Fourier Neural Operator (AFNO) blocks",
    "input_format": "BCHW",
    "input_grid_shape": [
      720,
      1440
    ],
    "patch_size": [
      8,
      8
    ],
    "input_channels": 20,
    "encoder": {
      "embed_dim": 768,
      "depth": 12,
      "mlp_ratio": 4.0,
      "afno_num_blocks": 8
    },
    "decoder": {
      "embed_dim": 512,
      "depth": 6,
      "mlp_ratio": 4.0
    },
    "mask_ratio": 0.75,
    "supported_mask_ratios": [
      0.0,
      0.75
    ],
    "normalization": "LayerNorm",
    "activation": "GELU"
  },
  "data": {
    "dataset": "ERA5",
    "source_grid_shape": [
      721,
      1440
    ],
    "preprocessing": "drop the last latitude row to obtain the model grid",
    "time_step_hours": 6,
    "input_steps": 1,
    "output_steps": 1,
    "channel_names": [
      "synthetic_channel_00",
      "synthetic_channel_01",
      "synthetic_channel_02",
      "synthetic_channel_03",
      "synthetic_channel_04",
      "synthetic_channel_05",
      "synthetic_channel_06",
      "synthetic_channel_07",
      "synthetic_channel_08",
      "synthetic_channel_09",
      "synthetic_channel_10",
      "synthetic_channel_11",
      "synthetic_channel_12",
      "synthetic_channel_13",
      "synthetic_channel_14",
      "synthetic_channel_15",
      "synthetic_channel_16",
      "synthetic_channel_17",
      "synthetic_channel_18",
      "synthetic_channel_19"
    ],
    "channel_names_note": "The repository configuration intentionally uses synthetic names; physical ERA5 variable names must be supplied and verified before scientific training."
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/w_mae.py",
    "model/afno.py",
    "scripts/era5_adapter.py"
  ]
}