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"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"
]
}
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