Add English model card

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- WRF
+- Physical Parameterization
+- Radiative Transfer
+- Bidirectional LSTM
+frameworks: PyTorch
+---
+
+<p align="center"><strong><span style="font-size: 30px;">WRF-ML</span></strong></p>
+
+# Model Introduction
+
+WRF-ML bridges WRF v4.3 and Python machine-learning parameterizations. This reproduction focuses on the recommended Model D, using a bidirectional LSTM to emulate RRTMG shortwave and longwave outputs.
+
+Paper: WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer  
+https://doi.org/10.5194/gmd-16-199-2023
+
+# Model Description
+
+The model was proposed by researchers at Alibaba Group's Damo Academy. It was trained with atmospheric-column inputs and RRTMG outputs generated by WRF simulations, with 57 vertical levels per sample. Through layout adaptation and a synchronous request-response protocol, it supports shortwave and longwave flux and heating-rate emulation as an online WRF physical parameterization.
+
+# Use Cases
+
+| Use Case | Description |
+| :---: | :--- |
+| Radiation emulation | Emulate shortwave and longwave fluxes and heating rates. |
+| WRF parameterization | Validate atmospheric-column batching and output layout restoration. |
+| Offline evaluation | Compute RMSE for six radiation outputs. |
+| Coupling protocol | Demonstrate synchronous blocking request, inference, and response. |
+| 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/WRF-ML --local-dir ./WRF-ML
+cd WRF-ML
+```
+
+### 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 `190x170` horizontal domain, 57 vertical levels, and WRF-RRTMG columns sampled every 30 minutes. Synthetic data preserve 57 levels, WRF layout conversion, and six output semantics while reducing column count, hidden width, and epochs; the ten inputs are explicitly an engineering ledger because the paper does not enumerate all input variables. 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 jointly optimizes 57-level radiation profiles and boundary fluxes, and both single-process and two-process DDP training have been verified. It produces one recoverable Model-D-style checkpoint and records normalized mean squared error. Training results are saved to:
+
+```text
+result/checkpoints/wrf_ml.pt
+result/training/metrics.json
+```
+
+## Weights
+
+The paper's code and data are archived at https://doi.org/10.5281/zenodo.7407487. This reproduction does not link a pretrained checkpoint because a separately licensed official weight artifact was not confirmed.
+
+## Inference
+
+```bash
+python scripts/inference.py
+```
+
+Inference restores the checkpoint, runs the synchronous layout-adapted coupler, and produces 57-level profiles and boundary fluxes. Profile and boundary output shapes are `[6,57,4]` and `[6,2]`, respectively, and both have passed finite-value checks. Inference results are saved to:
+
+```text
+result/output/predictions.npz
+```
+
+## Evaluation
+
+```bash
+python scripts/result.py
+```
+
+Evaluation reports RMSE for shortwave and longwave fluxes, heating rates, and boundary shortwave fluxes and creates a vertical-profile comparison. All metrics are finite, and the PNG has passed format and non-empty-pixel checks. 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 WRF-ML specifications, with code licensed under the Apache License 2.0.
+
+The original paper is licensed under CC BY 4.0; the paper, WRF, RRTMG, ONNX Runtime, and related data remain subject to their respective licenses and terms.