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efe4fbe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | {
"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"
]
}
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