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Timothy B. Higgins

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#artificial intelligence Preprint Oct 2026

Mechanistic Interpretability of Atmospheric Rivers in GraphCast

While AI weather models now rival operational forecasts, how they represent the atmosphere internally remains an open question: feature attribution reveals which input patterns matter, not what the model computes or how it combines information internally. We train sparse autoencoders (SAEs) on GraphCast to uncover its...

Madelyn Mathai, Timothy B. Higgins, K. Grise et al. · 1 citation
Jul 2026

Generating a 1000-member Ensemble of Integrated Water Vapor Transport Forecasts with Diffusion

The diffusion ensemble, a form of generative artificial intelligence, is used to make ensemble forecasts consisting of 1000 members from single-member deterministic dynamical West-WRF forecasts over the North Pacific, which demonstrated utility in representing real outcomes of extreme destructive events with realistic...

Timothy B. Higgins, William E. Chapman, A. Subramanian et al. · 0 citations

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