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

🎉 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