| { |
| "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" |
| ] |
| } |
|
|
