| { |
| "model_name": "WeatherNext2", |
| "model_type": "fgn", |
| "architectures": [ |
| "FGN" |
| ], |
| "framework": "PyTorch", |
| "domain": "climate-and-atmosphere", |
| "task": "probabilistic-weather-forecasting", |
| "implementation": { |
| "entry_point": "model/fgn.py", |
| "scope": "minimal PyTorch FGN reproduction with regular-grid graph operations, conditional LayerNorm noise injection, autoregressive ensembles, and fair CRPS" |
| }, |
| "architecture": { |
| "family": "probabilistic GNN encoder, graph-Transformer processor, and GNN decoder", |
| "attention_mechanism": "multi-head self-attention over a regular latent mesh with fixed wrap-around 8-neighbor graph message passing", |
| "input_format": "BSTCHW", |
| "output_format": "BMTCHW", |
| "encoder": "per-cell MLP, adaptive pooling from the observation grid to the latent mesh, and GNN message passing", |
| "processor": "graph-Transformer blocks conditioned by a sampled global noise vector", |
| "decoder": "latent-mesh GNN, bilinear upsampling, and per-cell output MLP", |
| "activation": "GELU", |
| "normalization": "ConditionalLayerNorm", |
| "repository_default_config": { |
| "purpose": "small connectivity-validation configuration", |
| "in_channels": 6, |
| "out_channels": 6, |
| "input_steps": 2, |
| "output_steps": 2, |
| "grid_shape": [ |
| 32, |
| 32 |
| ], |
| "mesh_shape": [ |
| 8, |
| 8 |
| ], |
| "latent_dim": 64, |
| "num_encoder_layers": 2, |
| "num_decoder_layers": 2, |
| "num_processor_blocks": 2, |
| "n_heads": 4, |
| "hidden_dim": 64, |
| "noise_dim": 32, |
| "num_ensemble_models": 1, |
| "num_members": 2, |
| "channel_weights": [ |
| 1, |
| 1, |
| 1, |
| 1, |
| 1, |
| 1 |
| ] |
| }, |
| "paper_configuration": { |
| "in_channels": 84, |
| "out_channels": 84, |
| "input_steps": 2, |
| "output_steps": 60, |
| "grid_shape": [ |
| 721, |
| 1440 |
| ], |
| "latent_mesh": "six-times-subdivided icosahedral grid with approximately 40,000 nodes", |
| "latent_dim": 768, |
| "num_processor_blocks": 24, |
| "n_heads": 6, |
| "noise_dim": 32, |
| "num_ensemble_models": 4, |
| "num_members_per_model": 14 |
| } |
| }, |
| "data": { |
| "dataset": "ERA5-format HDF5", |
| "variables": [ |
| "2m_temperature", |
| "10m_u_component_of_wind", |
| "10m_v_component_of_wind", |
| "mean_sea_level_pressure", |
| "sea_surface_temperature", |
| "total_precipitation" |
| ], |
| "frame_interval_hours": 6, |
| "input_length": 2, |
| "output_length": 2, |
| "channels": 6, |
| "default_smoke_spatial_size": [ |
| 32, |
| 32 |
| ], |
| "paper_spatial_size": [ |
| 721, |
| 1440 |
| ], |
| "paper_output_length": 60, |
| "normalization": "per-channel means and standard deviations stored in HDF5" |
| }, |
| "configuration_sources": [ |
| "conf/config.yaml", |
| "model/fgn.py", |
| "scripts/train.py", |
| "scripts/inference.py", |
| "scripts/fake_data.py", |
| "README.md" |
| ] |
| } |
|
|
