{
  "model_name": "MetNet-2",
  "model_type": "metnet_2",
  "architectures": ["MetNet2"],
  "framework": "PyTorch",
  "domain": "weather",
  "task": "probabilistic-precipitation-forecasting",
  "implementation": {
    "entry_point": "model/metnet_2.py",
    "scope": "core-method and logical full-dimension sampled-window engineering reproduction",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "logical_input_shape": ["B", 641, 512, 512],
    "logical_output_shape": ["B", 512, 512, 512],
    "engineering_window": [32, 32],
    "classes": 512,
    "lead_minutes": [2, 720, 2],
    "core": ["ConvLSTM", "lead-time FiLM", "dilated residual blocks", "spatial and class chunking"]
  },
  "data": {
    "datasets": ["MRMS", "HRRR", "GOES"],
    "format_version": "metnet2_selected_windows_v1",
    "input_channels": 641,
    "precipitation_range_mm_h": [0.0, 102.4],
    "coverage": "selected 32x32 target windows",
    "is_complete_global": false,
    "synthetic": true
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/metnet_2.py",
    "scripts/fake_data.py",
    "scripts/train.py",
    "scripts/inference.py",
    "scripts/result.py"
  ]
}