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"model_name": "PPNN",
"model_type": "ppnn",
"architectures": [
"PPNN"
],
"framework": "PyTorch",
"domain": "weather forecasting",
"task": "probabilistic ensemble forecast postprocessing",
"implementation": {
"entry_point": "model/ppnn.py",
"scope": "Core-method and full-data-dimension reduced-scale end-to-end 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": {
"variant": "NN-aux-emb",
"continuous_features": 40,
"station_embedding_dim": 2,
"network_input_features": 42,
"engineering_hidden_size": 32,
"paper_long_training_hidden_size": 512,
"activation": "ReLU",
"outputs": [
"mu",
"raw_sigma"
],
"scale_transform": "abs(raw_sigma) + epsilon"
},
"data": {
"datasets": [
"ECMWF TIGGE ensemble forecasts",
"DWD station observations"
],
"protocol": "Daily 00 UTC initialization at a fixed 48-hour lead; one sample is a valid date-station pair",
"format_version": "ppnn_nn_aux_emb_v1",
"raw_ensemble_shape": [
"N",
50,
18
],
"continuous_input_shape": [
"N",
40
],
"output_shape": [
"N",
2
],
"ensemble_members": 50,
"forecast_variables": 18,
"station_count": 537,
"lead_time_hours": 48,
"target": "2 m temperature",
"target_unit": "degree Celsius",
"ensemble_statistics": [
"mean",
"sample standard deviation (ddof=1)"
]
},
"configuration_sources": [
"conf/config.yaml",
"model/ppnn.py",
"scripts/fake_data.py",
"scripts/train.py",
"scripts/inference.py",
"scripts/result.py"
]
}
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