Improving Extreme Wind Prediction with Frequency-Informed Learning

Chenrui Xu, Xi Huang, Ying-Jun Angela Zhang, Jianwei Huang

Keywords: Extreme Weather Forecasting, Meteorological Analysis, AI for Science

Abstract: Accurate prediction of extreme wind velocities has substantial significance in industry, particularly for the operation management of wind power plants. Although the state-of-the-art data-driven models perform well for general meteorological forecasting, they may exhibit large errors for extreme weather—for example, systematically underestimating the magnitudes and short-term variation of extreme winds. To address this issue, we conduct a theoretical analysis of how the data frequency spectrum influences errors in extreme wind prediction. Based on these insights, we propose a novel loss function that incorporates a gradient penalty to mitigate the magnitude shrinkage of extreme weather, and we theoretically justify its effectiveness via a PDE-based energy–enstrophy analysis. To capture more precise short-term wind velocity variations, we design a novel structure of physics-embedded machine learning models with frequency reweighting. Experiments demonstrate that, compared to the baseline models, our approach achieves significant improvements in predicting extreme wind velocities while maintaining robust overall performance.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 25000