| --- |
| 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. |
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