{
  "model_name": "DGMR",
  "model_type": "dgmr",
  "architectures": [
    "DGMR"
  ],
  "framework": "PyTorch",
  "domain": "climate-and-atmosphere",
  "task": "precipitation-nowcasting",
  "implementation": {
    "entry_point": "model/dgmr.py",
    "scope": "YAML-configured DGMR conditional GAN wrapper with vendored Open Climate Fix generator, spatial and temporal discriminators, and training losses"
  },
  "architecture": {
    "family": "conditional generative adversarial network for probabilistic radar nowcasting",
    "generator": "context conditioning stack plus latent conditioning stack and a four-scale ConvGRU autoregressive sampler",
    "discriminator": "combined spatial and temporal discriminators with spectrally normalized residual blocks",
    "training_objective": "hinge GAN loss plus weighted grid-cell regularizer",
    "input_format": "BTCHW",
    "output_format": "BTCHW",
    "activation": "ReLU",
    "normalization": "BatchNorm",
    "repository_default_config": {
      "purpose": "small connectivity-validation configuration",
      "num_context": 4,
      "forecast_steps": 6,
      "input_channels": 1,
      "output_shape": 128,
      "conv_type": "standard",
      "latent_channels": 384,
      "context_channels": 192,
      "generation_steps": 6,
      "grid_lambda": 20.0,
      "precip_weight_cap": 24.0
    },
    "paper_configuration": {
      "num_context": 4,
      "forecast_steps": 18,
      "input_channels": 1,
      "output_shape": 256,
      "latent_channels": 768,
      "context_channels": 384,
      "generation_steps": 6,
      "grid_lambda": 20.0,
      "precip_weight_cap": 24.0
    }
  },
  "data": {
    "dataset": "ERA5Datapipe-compatible HDF5 placeholder radar sequences",
    "variable": "rain_radar",
    "input_length": 4,
    "output_length": 6,
    "channels": 1,
    "default_smoke_spatial_size": [
      128,
      128
    ],
    "paper_frame_interval_minutes": 5,
    "paper_output_length": 18,
    "paper_spatial_size": [
      256,
      256
    ],
    "normalization": "identity statistics in synthetic data; real-data statistics are read from HDF5"
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/dgmr.py",
    "model/dgmr_official/",
    "scripts/train.py",
    "scripts/fake_data.py",
    "README.md"
  ]
}