Add English model card

README.md

@@ -0,0 +1,57 @@
+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- Climate Downscaling
+- Diffusion
+- Probabilistic Forecasting
+- Coherence
+frameworks: PyTorch
+---
+<p align="center"><strong><span style="font-size: 30px;">Climate2Weather</span></strong></p>
+# Model Introduction
+Climate2Weather uses conditional score diffusion to transform coarse climate simulations into probabilistic high-resolution weather trajectories.
+
+Paper: A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation  
+https://doi.org/10.1038/s41612-025-01157-y
+# Model Description
+The method was proposed by the University of Tübingen and Tübingen AI Center. It was trained with 2006–2013 COSMO-REA6 reanalysis and conditions on climate-model fields only during inference. Score-based data assimilation jointly downscales four variables from `8x8` to `128x128` and from six-hourly to hourly resolution.
+# Use Cases
+| Use Case | Description |
+|---|---|
+| Probabilistic downscaling | Generate high-resolution ensemble trajectories. |
+| Coherent generation | Jointly model variables and time. |
+| Multivariate generation | Downscale wind, temperature, and sea-level pressure jointly. |
+| Climate impacts | Generate fine-scale drivers for regional impact studies. |
+| ModelScope/OneCode execution | Validate data, training, inference, metrics, and visualization. |
+| Multi-GPU training | Start multi-process training through `torchrun`. |
+# Usage Instructions
+Use a GPU or DCU when available; CPU supports the default smoke configuration.
+```bash
+hf download OneScience-Group/Climate2Weather --local-dir ./Climate2Weather
+cd Climate2Weather
+python scripts/fake_data.py
+```
+For single-process training, use:
+```bash
+python scripts/train.py
+```
+For multi-process training, use:
+```bash
+torchrun --standalone --nproc_per_node=2 scripts/train.py
+```
+Run inference and evaluation with:
+```bash
+python scripts/inference.py
+python scripts/result.py
+```
+Training minimizes denoising score matching. Inference produces a finite `[8,3,4,128,128]` ensemble with positive spread; evaluation reports RMSE, spread, PIT, and temporal differences.
+## Trained Weights
+No weights are bundled under `weight/`. The authors provide trained diffusion-model weights and experiment code at https://github.com/schmidtjonathan/Climate2Weather; this compact implementation does not claim compatibility.
+# Citation and License
+This repository is an independent engineering reproduction of the public Climate2Weather specifications.
+
+The original paper is licensed under CC BY 4.0; the original paper, official code, model weights, and related data remain subject to their respective licenses and terms.