| Summary: | Tropical cyclones (TCs) are dangerous because they produce destructive winds, heavy rainfall with flooding, and damaging storm surges. It is valuable to understand tropical cyclogenesis: a meteorological word used to describe TC formation, and its strengthening due to the atmosphere. This study focuses on tropical cyclogenesis frequency and variation and the prediction of the number of tropical cyclones that form in the North Atlantic Basin. Previous studies from physics analysis identified four factors that affect tropical cyclogenesis: potential intensity, vertical shear, relative humidity, and absolute vorticity. In this study, we include other factors in addition to the above four variables. We obtain data for various variables from the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset. Some variables (e.g. potential intensity) are computed from the ECMWF data. From the National Hurricane Center’s track maps, we compile the data on the number of TCs that formed each month from 1979 to 2011 in the North Atlantic Basin. The first part of this study investigates a genesis potential index (GPI) as a function of the four factors. Numerous plots are generated to compare the GPI with the cyclogenesis frequency. The second part of this study employs machine learning methods on the variables that could be linked with cyclogenesis. The methods considered include support vector machine, random forest, naïve Bayes classifier, and nonlinear regression. It is discovered and verified that the GPI matches well with the cyclogenesis frequency for most months. The effectiveness of the machine learning methods is provided.
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