| license: mit | |
| library_name: pytorch | |
| tags: | |
| - weather | |
| - data-assimilation | |
| - diffusion-models | |
| - aurora | |
| # SBDA Resources | |
| This repository contains the model checkpoints, normalization statistics, and | |
| sparse-observation mask required to reproduce the SBDA experiments. | |
| ## Files | |
| | Path | Purpose | | |
| | --- | --- | | |
| | `model_ckpt/sbda_full.pt` | Full SBDA checkpoint | | |
| | `model_ckpt/sbda_wo_oi_train.pt` | SBDA checkpoint trained without observation refinement | | |
| | `model_ckpt/diffda.pt` | Baseline DiffDA checkpoint | | |
| | `aifs_ckpt/aurora-1.5-finetuned.pt` | Fine-tuned Aurora checkpoint for background generation and forecast evaluation | | |
| | `stats/mean_std_121x240.npz` | ERA5 normalization statistics used by all experiments | | |
| | `stats/mean_std_diff_120x240.npz` | ERA5–Aurora difference statistics used by baseline DiffDA | | |
| | `mask/mask_120x240_1pct.pt` | Fixed 1% sparse-observation mask | | |
| Download all resources into the `resource/` directory of the code release: | |
| ```bash | |
| hf download strayee/SBDA --local-dir resource | |
| ``` | |
| File integrity can be checked with: | |
| ```bash | |
| sha256sum -c SHA256SUMS | |
| ``` | |
| ## License | |
| The distributed checkpoints are released under the MIT terms in `LICENSE.md`. | |
| The fine-tuned Aurora checkpoint retains the Microsoft Aurora MIT notice. The | |
| license of these files does not relicense the software required to run them or | |
| the ERA5-derived source data; see `LICENSE.md` for the precise scope. | |
| PyTorch `.pt` files may use Python pickle internally. Load checkpoint files only | |
| from a source you trust. | |