| { |
| "model_name": "Prithvi WxC", |
| "model_type": "prithvi_wxc", |
| "architectures": [ |
| "PrithviWxC" |
| ], |
| "framework": "PyTorch", |
| "domain": "climate-and-atmosphere", |
| "task": "global-weather-forecasting", |
| "implementation": { |
| "entry_point": "model/prithvi_wxc.py", |
| "scope": "YAML-driven wrapper around the vendored official Hiera-MaxViT encoder-decoder, with identity model scalers for the repository's small connectivity configuration" |
| }, |
| "architecture": { |
| "family": "Hiera-MaxViT encoder-decoder Vision Transformer", |
| "attention_mechanism": "alternating local block attention and global grid attention", |
| "input_format": "BTCHW atmospheric history plus BCHW static fields", |
| "output_format": "BCHW next atmospheric state", |
| "tokenization": "Conv2d patch embedding followed by mask-unit grouping and pixel-shuffle reconstruction", |
| "activation": "GELU", |
| "normalization": "LayerNorm", |
| "repository_default_config": { |
| "purpose": "small connectivity validation with synthetic or reduced ERA5 data", |
| "in_channels": 6, |
| "input_size_time": 2, |
| "in_channels_static": 4, |
| "grid_size": [ |
| 32, |
| 64 |
| ], |
| "patch_size_px": [ |
| 2, |
| 2 |
| ], |
| "mask_unit_size_px": [ |
| 8, |
| 8 |
| ], |
| "mask_ratio_inputs": 0.0, |
| "embed_dim": 32, |
| "n_blocks_encoder": 1, |
| "n_blocks_decoder": 1, |
| "n_heads": 4, |
| "mlp_multiplier": 4.0, |
| "dropout": 0.0, |
| "drop_path": 0.0, |
| "parameter_dropout": 0.0, |
| "residual": "none", |
| "masking_mode": "global", |
| "positional_encoding": "absolute", |
| "encoder_shifting": false, |
| "decoder_shifting": false, |
| "normalization_scalers": "identity in the YAML wrapper" |
| }, |
| "paper_reference_config": { |
| "grid_resolution_degrees": [ |
| 0.5, |
| 0.625 |
| ], |
| "grid_size": "approximately 360/361 x 576", |
| "dynamic_channels": 160, |
| "embed_dim": 2560, |
| "encoder_blocks": "13 local plus 12 global blocks", |
| "decoder_blocks": "3 local plus 2 global blocks", |
| "n_heads": 16, |
| "pretraining_mask_ratio_inputs": 0.5, |
| "forecast_finetuning_mask_ratio_inputs": 0.0, |
| "parameter_count": "approximately 2.3 billion", |
| "compatibility_note": "official 2.3B checkpoints do not match the repository default small configuration without aligning channels, grid size, and architecture" |
| } |
| }, |
| "data": { |
| "dataset": "ERA5", |
| "storage": "HDF5 fields with shape TCHW", |
| "temporal_interval_hours": 6, |
| "input_steps": 2, |
| "output_steps": 1, |
| "forecast_lead_time_hours": 6, |
| "dynamic_channels": [ |
| "10m_u_component_of_wind", |
| "10m_v_component_of_wind", |
| "2m_temperature", |
| "mean_sea_level_pressure", |
| "geopotential_500", |
| "temperature_850" |
| ], |
| "static_channels": 4, |
| "default_spatial_size": [ |
| 32, |
| 64 |
| ], |
| "default_year_splits": { |
| "train": [ |
| 1951, |
| 1952 |
| ], |
| "validation": [ |
| 1953 |
| ], |
| "test": [ |
| 1954 |
| ] |
| }, |
| "synthetic_data_generator": "scripts/fake_data.py" |
| }, |
| "configuration_sources": [ |
| "conf/config.yaml", |
| "model/prithvi_wxc.py", |
| "model/prithvi_wxc_official.py", |
| "scripts/train.py", |
| "scripts/inference.py", |
| "scripts/fake_data.py", |
| "README.md", |
| "configuration.json" |
| ] |
| } |
|
|
