config.json

{
  "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"
  ]
}