GRACE-SEDA
Model Introduction
GRACE-SEDA is a self-supervised data-assimilation model for global high-resolution total-water-storage anomalies.
Paper: Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms
https://doi.org/10.1038/s44221-024-00194-w
Model Description
The model was proposed by teams including ETH Zurich. It was trained with JPL GRACE, WGHM, GLDAS hydrological variables, and coordinates. A residual encoder-decoder and dual self-supervised constraints support global 0.5-degree TWSA reconstruction, uncertainty estimation, and water-budget analysis.
Use Cases
| Use Case | Description |
|---|---|
| TWSA downscaling | Generate high-resolution water-storage anomalies. |
| Self-supervised assimilation | Balance GRACE aggregates and WGHM structure. |
| Uncertainty | Use a five-model deep ensemble. |
| ModelScope/OneCode execution | Validate data, training, inference, hydrology metrics, and visualization. |
| Multi-GPU training | Start multi-process training through torchrun. |
Usage Instructions
hf download OneScience-Group/GRACE-SEDA --local-dir ./GRACE-SEDA
cd GRACE-SEDA
Environment Dependencies
Hardware Requirements
- A GPU or DCU is recommended.
- A CPU can be used for connectivity validation with the default small-sample configuration.
- DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first.
DCU Environment
# Activate DTK and Conda first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
GPU Environment
# Activate Conda first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
python scripts/fake_data.py
python scripts/train.py
torchrun --standalone --nproc_per_node=2 scripts/train.py
python scripts/inference.py
python scripts/result.py
Training optimizes GRACE aggregate and WGHM structural terms. Inference restores five models and evaluation reports finite correlation, aggregate error, and uncertainty.
Trained Weights
No weights are bundled under weight/. The authors provide core code, trained models, and weights at https://gitlab.ethz.ch/spacegeodesy_public/grace_seda.
Citation and License
This repository is an independent engineering reproduction of the public GRACE-SEDA specifications.
The original paper is licensed under CC BY 4.0; official code, model weights, and related data retain their respective terms.
- Downloads last month
- -