DAWIS pretrained checkpoints
Pretrained models for the paper DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants. Code: github.com/Erik-Wikingsson/DAWIS.
The folder layout matches what the code expects under MODELS_ROOT, so download the repository and point
MODELS_ROOT at it:
hf download Erik-Wikingsson/dawis-checkpoints --local-dir /path/to/models
# then in .env: MODELS_ROOT="/path/to/models"
| File | Dataset | Used by |
|---|---|---|
DAWIS/models/eta_channel_init_6-FMW-224-06_28_18-3280/last.ckpt | SQG, 64×64 | DAWIS window model, --init_states 6 |
DAWIS/models_SEVIR/SEVIR_FLOWDAS_SPLIT_lr_vil_eta_channel_init_6_flowdas-FMW-224-09_12_05-1441/last.ckpt | SEVIR VIL, 128×128 | DAWIS window model, --init_states 6 |
SQG/models/daisi/daisi_64.pth | SQG, 64×64 | DAISI prior |
SQG/models/daisi/daisi_sevir_128.pth | SEVIR VIL, 128×128 | DAISI prior |
SQG/models/flowdas/flowdas_sqg_3hrly.pt | SQG, 64×64 | FlowDAS forecaster (FlowDAS baseline, --forward_model flowdas) |
The window models are PyTorch Lightning checkpoints reduced to state_dict and hyper_parameters (the training
arguments, from which the code reads the architecture); optimizer state is not included. The DAISI priors are plain
state dicts. The SQG FlowDAS forecaster is the backbone from DAISI, stored as
{"model": state_dict, "step", "val_loss"} without optimizer state.
The SEVIR FlowDAS forecaster is the pretrained checkpoint released by FlowDAS (Chen et al., 2025) and is not redistributed here. Download it into the same layout:
curl -L -o /path/to/models/SQG/models/flowdas/flowdas_sevir.pt \
"https://www.dropbox.com/scl/fi/5z1bwfdvbztnums9deqhe/latest.pt?rlkey=o5izt721am3hzkcwjmmn7joym&dl=1"
or set FLOWDAS_SEVIR_MODEL_PATH to wherever you saved it.
Citation
@article{wikingsson2026dawis,
title = {{DAWIS}: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants},
author = {Wikingsson, Erik and Andrae, Martin and Landelius, Tomas and Lindsten, Fredrik},
journal = {arXiv preprint arXiv:2610.03314},
year = {2026},
eprint = {2610.03314},
archivePrefix = {arXiv},
primaryClass = {stat.ML},
url = {https://arxiv.org/abs/2610.03314}
}