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"model_name": "OneForecast",
"model_type": "oneforecast",
"architectures": [
"OneForecast"
],
"framework": "PyTorch",
"domain": "atmosphere",
"task": "global-regional-weather-forecasting",
"implementation": {
"entry_point": "model/oneforecast.py",
"scope": "portable encoder-processor-decoder mesh graph neural network with PyTorch index-tensor graph kernels usable on CPU, CUDA, and DCU; official checkpoint compatible"
},
"architecture": {
"family": "encoder-processor-decoder graph neural network on a refined icosahedral mesh",
"input_grid_shape": [
120,
240
],
"grid_sampling": "ERA5 0.25-degree 721x1440 sampled every sixth point, then cropped to 120x240",
"input_channels": 69,
"output_channels": 69,
"mesh_level": 5,
"mesh_local_refinement_regions": [
{
"description": "South and East Asia",
"lat_range": [
0.0,
30.0
],
"lon_range": [
105.0,
160.0
]
},
{
"description": "Central North America",
"lat_range": [
10.0,
30.0
],
"lon_range": [
-95.0,
-35.0
]
}
],
"hidden_dim": 512,
"processor_layers": 16,
"hidden_layers": 1,
"num_heads_edge": 4,
"num_heads_node": 4,
"aggregation": "sum",
"activation": "SiLU",
"normalization": "LayerNorm"
},
"data": {
"dataset": "ERA5",
"grid_spatial_resolution_degrees": 1.5,
"time_step_hours": 6,
"input_steps": 1,
"output_steps": 1,
"surface_variables": [
"10m_u_component_of_wind",
"10m_v_component_of_wind",
"2m_temperature",
"mean_sea_level_pressure"
],
"atmospheric_variables": [
"geopotential",
"specific_humidity",
"temperature",
"u_component_of_wind",
"v_component_of_wind"
],
"pressure_levels_hpa": [
50,
100,
150,
200,
250,
300,
400,
500,
600,
700,
850,
925,
1000
]
},
"configuration_sources": [
"conf/config.yaml",
"model/oneforecast.py",
"model/era5_adapter.py"
]
}
|