A tropical cyclone (TC) is a rapidly rotating storm system characterized by a low-pressure center, a closed low-level atmospheric circulation, strong winds, and a spiral arrangement of thunderstorms that produce heavy rain or squalls. TC occurs when the sea temperatures are high, and the air above the warm ocean is heated and rises quickly, causing an area of very low pressure, and then drawing more warm moist air, leading to strong winds. The air cools and condenses when rising, so that cumulonimbus clouds formed and produce heavy rainfall. These high winds, excessive rainfall and flooding threaten lives and property.
Our focus of this study is to develop a statistical model to predict TC intensity change in 24 hours, to better identify episodes of TC rapid intensification (RI), which remains the highest operational forecasting priority of the National Hurricane Center (NHC), using advanced machine learning techniques.
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