Abstract
Abstract Tropical cyclones (TCs) are destructive weather systems that can trigger flooding, landslides, and other severe hazards. Accurate estimation of key TC attributes is critical for disaster risk management. In particular, the maximum sustained wind speed (MSW) and radius of maximum wind (RMW) are fundamental parameters for operational forecasting and wind‐field modeling. However, existing approaches struggle to capture and fuse the diverse physical information embedded in multi‐modal TC data. We propose Multi‐Modal Hybrid Guided Network (MHG‐Net), a multi‐task deep learning framework that integrates satellite imagery with auxiliary physical factors via soft parameter sharing and correlation graph embedding. This design enables effective inter‐task collaboration while explicitly modeling underlying physical correlations essential for accurate TC estimation. Using Himawari‐8/9 satellite observations and best‐track data from IBTrACS, MHG‐Net achieves low mean absolute errors of 6.49 knots for MSW and 7.08 nmi for RMW. Beyond performance gains, the learned correlation graphs and attribution analyses provide insights into how different physical factors influence TC attributes evolution.
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