metadata
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:
hf download strayee/SBDA --local-dir resource
File integrity can be checked with:
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.