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"model_name": "Spherical Fourier Neural Operators",
"model_type": "sfno",
"architectures": [
"SFNO"
],
"framework": "PyTorch",
"domain": "climate-and-atmosphere",
"task": "global-weather-forecasting",
"implementation": {
"entry_point": "model/sfno.py",
"scope": "configurable wrapper around torch_harmonics.examples.models.sfno.SphericalFourierNeuralOperator for deterministic single-state 6-hour forecasting"
},
"architecture": {
"family": "Spherical Fourier Neural Operator",
"spectral_operator": "spherical harmonic transform",
"input_format": "B C H W",
"prediction": "one atmospheric state to the next 6-hour state; longer forecasts use autoregressive rollout",
"activation": "GELU",
"normalization": "InstanceNorm",
"repository_default_config": {
"purpose": "connectivity validation with synthetic ERA5 data",
"img_size": [
32,
64
],
"scale_factor": 2,
"in_channels": 6,
"out_channels": 6,
"embed_dim": 16,
"num_layers": 2,
"use_mlp": true,
"mlp_ratio": 2.0,
"drop_rate": 0.0,
"drop_path_rate": 0.0,
"hard_thresholding_fraction": 1.0,
"residual_prediction": false,
"positional_embedding": "none",
"bias": false
},
"paper_reference_config": {
"grid_resolution_degrees": 0.25,
"grid_size": [
721,
1440
],
"channels": "26 or 73",
"embed_dim": 256,
"num_layers": "approximately 8",
"note": "the weather model's exact internal spectral downsampling factor is not disclosed"
}
},
"data": {
"dataset": "ERA5",
"variables": [
"10m_u_component_of_wind",
"10m_v_component_of_wind",
"2m_temperature",
"mean_sea_level_pressure",
"geopotential_500",
"temperature_850"
],
"time_step_hours": 6,
"input_length": 1,
"output_length": 1,
"channels": 6,
"spatial_size": [
32,
64
],
"storage_format": "HDF5 fields with T C H W layout",
"train_years": [
1951,
1952
],
"validation_years": [
1953
],
"test_years": [
1954
]
},
"configuration_sources": [
"README.md",
"conf/config.yaml",
"model/sfno.py",
"scripts/train.py",
"scripts/inference.py",
"scripts/fake_data.py",
"configuration.json"
]
}
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