UniExtreme: A Universal Foundation Model for Extreme Weather Forecasting

TL;DR

UniExtreme is a universal foundation model designed to improve extreme weather forecasting by capturing spectral disparities and hierarchical event diversity, outperforming existing models in various extreme weather scenarios.

Contribution

It introduces a novel framework combining spectral analysis and event priors, addressing the limitations of previous models in predicting diverse extreme weather events.

Findings

Outperforms state-of-the-art baselines in extreme weather forecasting

Demonstrates superior adaptability across diverse extreme scenarios

Effective in both extreme and general weather prediction

Abstract

Recent advancements in deep learning have led to the development of Foundation Models (FMs) for weather forecasting, yet their ability to predict extreme weather events remains limited. Existing approaches either focus on general weather conditions or specialize in specific-type extremes, neglecting the real-world atmospheric patterns of diversified extreme events. In this work, we identify two key characteristics of extreme events: (1) the spectral disparity against normal weather regimes, and (2) the hierarchical drivers and geographic blending of diverse extremes. Along this line, we propose UniExtreme, a universal extreme weather forecasting foundation model that integrates (1) an Adaptive Frequency Modulation (AFM) module that captures region-wise spectral differences between normal and extreme weather, through learnable Beta-distribution filters and multi-granularity spectral aggregation, and (2) an Event Prior Augmentation (EPA) module which incorporates region-specific extreme event priors to resolve hierarchical extreme diversity and composite extreme schema, via a dual-level memory fusion network. Extensive experiments demonstrate that UniExtreme outperforms state-of-the-art baselines in both extreme and general weather forecasting, showcasing superior adaptability across diverse extreme scenarios.