A dual-branch typhoon-induced wave height forecasting network with tail-aware extreme value optimization

Abstract

Accurate forecasting of typhoon-induced wave height (WH), which supports timely evacuation and informed emergency responses, is essential for the effectiveness of early warning systems. Despite recent advances in deep learning for WH forecasting, a critical gap persists: current models often fail to reliably predict rare but catastrophic extreme WH under typhoon conditions due to data scarcity. To address this challenge, we propose a physics-guided multi-scale attention framework, named the typhoon-induced wave height network (TWHN), which adopts a dual-branch architecture that separately captures wind sea and swell features. Unlike architectures that rely on initial WH inputs, TWHN forecasts WH directly from historical wind fields, thereby reducing error accumulation and supporting predictions at future time steps. To enhance the representation of extreme WH events, we introduce a tail-aware extreme value optimization (TEVO) strategy, which integrates a progressive training scheme to shift model focus from global patterns to tail data and a quantile-aware hybrid loss to penalize underestimation of high-magnitude waves. Additionally, a feature distribution smoothing mechanism is employed to stabilize training in data-sparse regimes by mitigating feature dominance from frequent samples. The model is trained, validated, and tested on WH records from 1982 to 2022, using a reanalysis dataset that includes 1 060 typhoons in the Northwest Pacific. Evaluation based on regional fields and nearshore station comparisons suggests that TWHN maintains strong potential for forecasting high-impact typhoon wave events. This work may provide implications for the advancement of operational wave forecasting and the support of risk decision-making in response to typhoon-induced marine hazards.