Estimation of tropical cyclone’s radius of maximum wind using ensemble machine learning approach

This study examines the complex behaviour of tropical cyclones, specifically focusing on accurately estimating the Radius of Maximum Wind (RMW) and its implications for climate and oceanic processes. We use RMW data provided by the India Meteorological Department (IMD) to evaluate three existing methods for estimating RMW and introduce a new method based on machine learning techniques. The findings underscore the critical role of the RMW in shaping the intensity and impact of tropical cyclones, emphasizing the need for refined estimation techniques to bolster forecasting and preparedness measures. We use four cases to test our method. The results of our method show the root square mean error is 10.63 nautical miles and the error percentage of 17.00 \(\%\) $\%$ , which are lower than other methods. Therefore, the proposed ensemble machine learning model, designed to estimate RMW, showcases considerable promise in attaining precise RMW estimation.