Add English model card

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- TWSA
+- GRACE
+- Self-Supervised Assimilation
+- Hydrology
+frameworks: PyTorch
+---
+
+<p align="center"><strong><span style="font-size: 30px;">GRACE-SEDA</span></strong></p>
+
+# 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
+
+```bash
+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**
+
+```bash
+# 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**
+
+```bash
+# 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
+```
+
+```bash
+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.