| # GraphMet: Learning Spectral Compositional Koopman Operators for Global-to-Regional Weather Forecasting |
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| [](https://neurips.cc/) |
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| **🎉 Accepted at NeurIPS 2026!** |
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| We are excited to announce that our paper, **"GraphMet: Learning Spectral Compositional Koopman Operators for Global-to-Regional Weather Forecasting,"** has been accepted to the **Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2026)**. |
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| GraphMet introduces a spectral Koopman-based framework for global-to-regional weather forecasting, replacing deep message-passing processors with a lightweight, structured linear operator to better preserve fine-scale atmospheric variability over long forecast horizons. |
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| ### Code Release — Coming Soon |
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| **The GraphMet-R implementation will be publicly released in the coming days.** |
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| The release will include: |
| - Pretraining regional fine-tuning pipelines. |
| - Deterministic and probabilistic forecasting workflows. |
| - Evaluation scripts and benchmark configurations. |
| - Documentation and reproducibility instructions. |
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| ### Key Highlights |
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| - **32% reduction** in the 15-day activity deficit compared with GraphCast. |
| - **11–14% improvement** in regional RMSE at 5.5 km resolution. |
| - **~14× lower inference cost** compared with diffusion-based probabilistic forecasting baselines. |
| - **14,336-parameter Koopman processor** replacing a 16-layer message-passing architecture. |
| - **Analytic probabilistic forecasting** without expensive diffusion sampling. |
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| ### Citation |
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| The official BibTeX citation will be added following publication of the NeurIPS 2026 proceedings. |
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| ### License |
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| This code is licensed under **CC BY-NC 4.0**. |
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| license: cc-by-nc-4.0 |
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