[2603.22314] Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction

Computer Science > Machine Learning arXiv:2603.22314 (cs) [Submitted on 20 Mar 2026] Title:Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction Authors:Peisong Niu, Haifan Zhang, Yang Zhao, Tian Zhou, Ziqing Ma, Wenqiang Shen, Junping Zhao, Huiling Yuan, Liang Sun View a PDF of the paper titled Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction, by Peisong Niu and 8 other authors View PDF HTML (experimental) Abstract:Tropical cyclones (TCs) pose severe threats to life, infrastructure, and economies in tropical and subtropical regions, underscoring the critical need for accurate and timely forecasts of both track and intensity. Recent advances in AI-based weather forecasting have shown promise in improving TC track forecasts. However, these systems are typically trained on coarse-resolution reanalysis data (e.g., ERA5 at 0.25 degree), which constrains predicted TC positions to a fixed grid and introduces significant discretization errors. Moreover, intensity forecasting remains limited especially for strong TCs by the smoothing effect of coarse meteorological fields and the use of regression losses that bias predictions toward conditional means. To address these limitations, we propose BaguanCyclone, a novel, unified framework that integrates two key innovations: (1) a probabilistic center refinement module that models the continuous spatial distribution of TC centers, enabling fin...