| # GenFocal — official pretrained climate debiasing and downscaling | |
| [Paper](https://doi.org/10.1038/s42256-026-01308-7) · [Author code](https://github.com/google-research/swirl-dynamics/tree/0beb5d20a5b31969e12069ba7d86fc0537e08528/swirl_dynamics/projects/genfocal) · [Published checkpoints](https://zenodo.org/records/21285802) | |
| `GenFocal` uses the author's unchanged JAX/Flax implementation and published | |
| parameters. `GenFocal_lite` is a separate, untrained PyTorch approximation. | |
| The main entry never falls back to lite or initializes random replacement weights. | |
| ```python | |
| from weatherai import GenFocal, GenFocal_lite | |
| sr = GenFocal.from_pretrained("super_resolution") | |
| flow = GenFocal.from_pretrained("debiasing") | |
| # Author-normalized arrays and conditioning; this is a network evaluation: | |
| y = sr(x, sigma, cond) | |
| # Optional explicitly untrained experimentation: | |
| lite = GenFocal_lite(channels=4) | |
| ``` | |
| Use Linux/Python 3.12 and the pinned environment in | |
| [hf_official/requirements.txt](https://github.com/GISWLH/WeatherAI/blob/main/hf_official/requirements.txt). | |
| Set `WEATHERAI_CACHE` to a large local directory outside synchronized folders. | |
| Author source is fetched at commit `0beb5d20a5b31969e12069ba7d86fc0537e08528` | |
| and checked for modifications. Published OCDBT tensors are fetched directly | |
| from the public `genfocal` GCS bucket via HTTP range reads, with shape, dtype | |
| and SHA-256 records. Optimizer state and duplicate checkpoints are excluded. | |
| Flax module names containing literal dots are restored from Orbax key metadata. | |
| | Stage | Released checkpoint (step 300000) | Parameter tree | | |
| |---|---|---| | |
| | ReFlow debiasing | `debiasing/checkpoints/reflow_uvit` | raw `params`, as in author's sampling code | | |
| | CONUS super-resolution | `super_resolution/checkpoints/conus_7d_t2m_w10m_q1000_msl` | `ema_state.ema`, as in author's inference | | |
| Correction to the earlier documentation: the **released debiasing checkpoint** | |
| uses channels **768, 768, 768, 1024, 1280, 1536**, not the 128-channel example | |
| configuration. Its published run config is loaded from the checkpoint directory. | |
| The raw debiasing tree contains 2,656,553,626 parameters (10.63 GB). | |
| SR uses the author's `conus_sample.yaml` (128/256/384/512 channels, 84 two-hourly | |
| steps, four output variables). This distinction is essential for weight compatibility. | |
| This entry loads/evaluates each official network. Full climate trajectories also | |
| require the author's input normalization, ReFlow integration, diffusion sampling, | |
| regional baseline and residual statistics, overlap handling and physical-unit | |
| reconstruction. A single denoiser/vector-field call is not a completed forecast. | |
| Both complete pretrained networks were executed on HF ZeroGPU: ReFlow 146.13 s | |
| (synthetic normalized input), CONUS SR 52.99 s (real ERA5 conditioning, one denoiser | |
| call). All outputs were finite. See [current validation evidence and limitations](https://github.com/GISWLH/WeatherAI/blob/main/docs/research_20261019.md). | |
| Official code is Apache-2.0; released checkpoints are CC-BY-4.0. Original weights | |
| remain with the authors and are not redistributed in Git. Previous lite loss/RK4 | |
| parity results remain in `docs/results/genfocal_parity.json`; they do not establish | |
| full pretrained model parity or climate skill. | |