# GraphMet: Learning Spectral Compositional Koopman Operators for Global-to-Regional Weather Forecasting

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**🎉 Accepted at NeurIPS 2026!**

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)**.

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.

### Code Release — Coming Soon

**The GraphMet-R implementation will be publicly released in the coming days.**

The release will include:
- Pretraining regional fine-tuning pipelines.
- Deterministic and probabilistic forecasting workflows.
- Evaluation scripts and benchmark configurations.
- Documentation and reproducibility instructions.

### Key Highlights

- **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.

### Citation

The official BibTeX citation will be added following publication of the NeurIPS 2026 proceedings.

### License

This code is licensed under **CC BY-NC 4.0**.

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license: cc-by-nc-4.0
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