Incorporating Hourly Convective Cloud Data Into Tropical Cyclone Rapid Intensification Forecasting With Machine Learning

Abstract In this study, we developed a machine learning (ML) model to predict the rapid intensification (RI) of North Atlantic tropical cyclones (TCs) using 6‐hourly Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors and additional data on very deep convective clouds with an infrared brightness temperature below 208 K. The presence of these clouds is considered a precursor to TC RI. The ML model, which incorporates SHIPS data with hourly cloud coverage, outperformed the ML model with 6‐hourly coverage of very deep convective clouds for TCs in the Atlantic basin from 2018 to 2023, as indicated by improvements in the Brier Skill Score by 5.9%, 9.9%, 1.0%, and 11.3%, for RI thresholds of ≥25, 30, 35, and 40 knots in 24 hr, respectively. These results highlight the potential of hourly cloud data, with pronounced diurnal variations, to enhance TC RI forecasting accuracy.