{
  "model_name": "CorrDiff",
  "model_type": "corrdiff",
  "architectures": [
    "CorrDiff",
    "RegressionUNet",
    "ResidualDenoiser"
  ],
  "framework": "PyTorch",
  "domain": "atmosphere",
  "task": "probabilistic-weather-downscaling",
  "implementation": {
    "entry_point": "model/corrdiff.py",
    "scope": "conditional-regression mean prediction followed by residual EDM ensemble generation for kilometer-scale atmospheric downscaling",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "family": "conditional regression plus residual corrective diffusion",
    "in_channels": 12,
    "out_channels": 4,
    "base_channels": 8,
    "feature_size": 28,
    "output_size": 448,
    "sigma_data": 0.5,
    "regression_component": "RegressionUNet",
    "diffusion_component": "ResidualDenoiser",
    "diffusion_preconditioning": "EDM",
    "noise_schedule": "Karras",
    "sampling": {
      "ensemble_size": 2,
      "steps": 3,
      "sigma_min": 0.002,
      "sigma_max": 5.0,
      "rho": 7.0,
      "solver": "heun"
    }
  },
  "data": {
    "datasets": [
      "ERA5",
      "CWA-WRF"
    ],
    "protocol": "corrdiff_npz_v1",
    "format": "NPZ",
    "default_file": "data/corrdiff.npz",
    "input_key": "input",
    "input_shape": [
      "N",
      12,
      36,
      36
    ],
    "target_key": "target",
    "target_shape": [
      "N",
      4,
      448,
      448
    ],
    "input_variables": [
      "tcwv",
      "t2m",
      "u10m",
      "v10m",
      "t500",
      "z500",
      "u500",
      "v500",
      "t850",
      "z850",
      "u850",
      "v850"
    ],
    "target_variables": [
      "t2m",
      "u10m",
      "v10m",
      "maximum_radar_reflectivity"
    ],
    "required_metadata": [
      "protocol",
      "data_source"
    ]
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/corrdiff.py",
    "scripts/fake_data.py",
    "scripts/train.py",
    "scripts/inference.py",
    "scripts/result.py"
  ]
}