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
The increasing maturity of deep learning models for weather forecasting makes the lack of uncertainty quantification a critical, yet addressable, limitation for high-stakes applications.
This development addresses a key weakness in AI-driven critical infrastructure, enabling more reliable decision-making in sectors like disaster management, agriculture, and energy.
AI weather models can now move beyond deterministic forecasts, providing crucial probabilistic outputs essential for operational use and risk assessment.
- · AI model developers
- · Extreme weather preparedness agencies
- · Insurance sector
- · Renewable energy grids
- · Traditional numerical weather prediction models (relative decline)
- · Decision-makers reliant solely on deterministic forecasts
Improved accuracy and reliability of AI-driven extreme weather warnings and forecasts.
Increased adoption of AI in critical infrastructure planning and disaster response due to enhanced trustworthiness.
Potential for integration into autonomous systems that adapt to predicted weather uncertainties, such as smart grids or logistics networks.
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Read at arXiv cs.LG