Add English model card

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- Soil Moisture
+- Drought Forecasting
+- Subseasonal Forecasting
+- Ensemble Forecasting
+frameworks: PyTorch
+---
+
+<p align="center"><strong><span style="font-size: 30px;">RISE-UNet</span></strong></p>
+
+# Model Introduction
+
+RISE-UNet combines deep learning and dynamical forecasts for subseasonal root-zone soil-moisture prediction. It recursively predicts five weekly anomalies and evaluates drought probabilities through ensembles.
+
+Paper: Skillful subseasonal soil moisture drought forecasts with deep learning-dynamic models  
+https://doi.org/10.1038/s41467-025-62761-3
+
+# Model Description
+
+The model was proposed by researchers at Auburn University. It was trained with GLEAM root-zone soil moisture, ERA5 reanalysis, and GEFSv12 and ECMWF S2S reforecasts. By combining residual, inception, squeeze-and-excitation, and UNet++ operations with recursive predictions, it supports weekly root-zone soil-moisture and flash-drought forecasting over the contiguous United States, China, and Australia.
+
+# Use Cases
+
+| Use Case | Description |
+| :---: | :--- |
+| Subseasonal soil moisture | Predict root-zone soil-moisture anomalies for weeks 1–5. |
+| Drought forecasting | Identify events below the twentieth percentile. |
+| Ensemble forecasting | Use 11 dynamical members and stochastic inference dropout. |
+| Hybrid modeling | Fuse reanalysis and dynamical reforecasts. |
+| ModelScope/OneCode execution | Validate structured data, training, inference, probabilistic precipitation metrics, and visualization in ModelScope or OneCode. |
+| Multi-GPU training | Start multi-process training through `torchrun`. |
+
+# Usage Instructions
+
+## Download
+
+```bash
+hf download OneScience-Group/RISE-UNet --local-dir ./RISE-UNet
+cd RISE-UNet
+```
+
+### Environment Dependencies
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- A CPU can run the default small-sample connectivity 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
+```
+
+## Synthetic Data
+
+The paper uses a `48x96` 0.5-degree regional grid, 11 ensemble members, weekly historical and forecast variables, and GLEAM 0–100 cm root-zone soil-moisture anomalies as targets. Synthetic data preserve the grid, member count, recursive five-week protocol, and RISE operators while reducing initialization count, width, and epochs. Results verify the workflow only and do not represent paper performance.
+
+```bash
+python scripts/fake_data.py
+```
+
+## Training
+
+```bash
+python scripts/train.py
+torchrun --nproc_per_node=2 --nnodes=1 --master_addr="localhost" --master_port=29500 scripts/train.py
+```
+
+The default synthetic run completes five-week recursive optimization, deep-supervision losses, and the ensemble-spread constraint, and both single-process and two-process DDP training have been verified. It produces one recoverable checkpoint and records the CRPSexp training result. Training results are saved to:
+
+```text
+result/checkpoints/rise_unet.pt
+result/training/metrics.json
+```
+
+## Weights
+
+The paper's code is available at https://osf.io/6y4kh/, but an independently licensed official pretrained checkpoint was not confirmed.
+
+## Inference
+
+```bash
+python scripts/inference.py
+```
+
+Inference restores the checkpoint, retains stochastic dropout, and recursively generates weeks 1–5 for 11 members. The output shape is `[11,5,48,96]` and has passed finite-value checks. Inference results are saved to:
+
+```text
+result/output/predictions.npz
+```
+
+## Evaluation
+
+```bash
+python scripts/result.py
+```
+
+Evaluation computes weekly ACC, CRPS, and drought GSS and creates a week-3 spatial error figure. All metrics are finite, and the PNG has passed format and non-empty-pixel checks; synthetic results do not represent paper performance. Evaluation results are saved to:
+
+```text
+result/evaluation/metrics.json
+result/evaluation/comparison.png
+```
+
+# Official OneScience Information
+
+| Platform | OneScience | OneSkills |
+|---|---|---|
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+# Citation and License
+
+This repository is an independent engineering reproduction of the public RISE-UNet specifications, with code licensed under the Apache License 2.0.
+
+The original paper is licensed under CC BY-NC-ND 4.0; the paper and GLEAM, ERA5, GEFSv12, and ECMWF S2S data remain subject to their respective licenses and terms.