MultiCyclone: Multi-Modal Learning for Tropical Cyclone Intensity Prediction

Abstract

Accurate prediction of tropical cyclone (TC) intensity is crucial for early warning systems and disaster preparedness but remains challenging due to the complex interactions influencing cyclone development. Traditional machine learning models often rely on single-modal data sources—such as satellite imagery or atmospheric measurements—which may not capture the full spectrum of factors affecting TCs. In response to recent events like the rapid intensification of Hurricane Milton in Florida, we present MultiCyclone, a multi-modal machine learning approach that integrates tabular atmospheric data from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset, satellite images processed from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and textual weather reports from the National Hurricane Center (NHC) Tropical Weather Discussion (TWDAT) archives. By leveraging the strengths of each data modality, our model enhances predictive accuracy, discovers novel cyclone intensity predictors, and remains computationally efficient for real-time forecasting applications. Furthermore, our multi-modal learning framework is generalizable to other atmospheric phenomena and complex weather events. This project exemplifies the application of advanced ML techniques to a critical real-world problem, offering practical tools for disaster preparedness and mitigation effort.