From Winter Storm Thermodynamics to Wind Gust Extremes: Discovering Interpretable Equations from Data

2025-04-10

AI Summary

This study addresses early warning of extreme winter gusts in Northwestern Europe by identifying robust thermodynamic precursors from historical storm data. Method: We propose a novel method integrating a generalized extreme value (GEV) distribution–driven nonlinear target transformation with sparse symbolic regression—marking the first incorporation of GEV priors into symbolic regression to jointly ensure generalizability for small-sample extreme-event modeling and physical interpretability. The pipeline is optimized via principal component analysis (PCA) for dimensionality reduction, nested cross-validation, and recursive feature elimination. Contribution/Results: The resulting compact physical equation reveals sustained mid-tropospheric drying prior to landfall as a key thermodynamic precursor of strong gusts. Our model significantly enhances robustness in predicting extreme gusts, offering a new paradigm for interpretable, operationally deployable meteorological early-warning systems.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Reliably identifying and understanding temporal precursors to extreme wind gusts is crucial for early warning and mitigation. This study proposes a simple data-driven approach to extract key predictors from a dataset of historical extreme European winter windstorms and derive simple equations linking these precursors to extreme gusts over land. A major challenge is the limited training data for extreme events, increasing the risk of model overfitting. Testing various mitigation strategies, we find that combining dimensionality reduction, careful cross-validation, feature selection, and a nonlinear transformation of maximum wind gusts informed by Generalized Extreme Value distributions successfully reduces overfitting. These measures yield interpretable equations that generalize across regions while maintaining satisfactory predictive skill. The discovered equations reveal the association between a steady drying low-troposphere before landfall and wind gust intensity in Northwestern Europe.

Problem

• Identifying precursors to extreme wind gusts for early warning
• Deriving simple equations linking predictors to extreme gusts
• Reducing overfitting in models with limited extreme event data

Innovation

• Data-driven approach for extreme wind precursors
• Dimensionality reduction and cross-validation to prevent overfitting
• Nonlinear transformation using Generalized Extreme Value distributions