Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels

arXiv:2606.02886v1 Announce Type: new Abstract: Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estimates, a critical gap for high-stakes decisions during extreme weather events. This paper proposes Neural Tangent Kernel-based uncertainty quantification (NTK-UQ) using last-layer empirical features. Theoretical analysis predicts that UQ quality is architecture-dependent through two mechanisms. First, a variance collapse mechanism explains when UQ fails: when the eigenva

Source: arXiv cs.LG — read the full report at the original publisher.

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