Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning

Ryan Eusebi, Hui Su, Longtao Wu, Pingping Rong, Karthik Balaguru, Lai-Yung Ruby Leung, Yong‐Sang Choi, Pak Wai Chan, Jianping Gan A, Mark DeMaria, Galina Chirokova

Environmental Research Letters · 21 January 2025 · California Institute of Technology (Caltech), Colorado State University (incl. CIRA), NASA

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In one sentence. Calibrated machine learning with satellite pre-storm sea surface salinity improves tropical cyclone rapid intensification probability forecasts in several ocean basins.

Abstract

Abstract Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is salinity, which is important for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the impact and efficacy of using salinity information in state-of-the-art forecasting, we use a statistical model consisting of a variety of machine learning (ML) methods. For salinity data, we use satellite measurements of pre-storm sea surface salinity (SSS) as a proxy for the salinity stratification. We train and test the model on various ocean basins, including the Atlantic, eastern North Pacific and western North Pacific. A calibrator is trained on top of the ML models to correct and enhance probability forecasts. The calibrator significantly improves probability forecasts relative to recent works. The ML model performance is improved with the addition of SSS in the Eastern North Pacific, western North Pacific, and the Caribbean subregion of the North Atlantic, and the overall model performance is better than previous studies. SSS decreases model skill for a model trained on the full Atlantic basin. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

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