Tropical Cyclone Size Identification over the Western North Pacific Using Support Vector Machine and General Regression Neural Network.

Knowledge about tropical cyclone (TC) size is essential for disaster prevention and mitigation strategies, but due to the limitations of observations, TC size data from the open ocean are scarce. In this paper, several models are developed to identify TC size parameters, including the radius of maximum wind (RMW) and the radii of 34 (R34), 50 (R50), and 64 (R64) knot winds, using various machine learning algorithms based on infrared channel imagery of geostationary meteorological satellites over the western North Pacific (WNP). Through evaluation and verification, the trained and optimized support vector machine models are proposed for RMW and R34, whereas the general regression neural network models are set up for R50 and R64. According to the independent-sample evaluations against aircraft observations (1981 - 1987)/Joint Typhoon Warning Center best track data (2017 - 2019), the mean absolute errors of R34, R50, R64, and RMW are 54/58, 34/38, N/A/21, and 25/25 km, respectively. The corresponding median errors are 39/46, 34/31, N/A/17, and 17/19 km, respectively. There is an overall slight underestimation of the parameters, which needs to be analyzed and improved in a future study. Despite aircraft observations of TCs in the WNP having ceased in the late 1980s, this new dataset of TC sizes enables a thorough estimation of wind structures covering a period of 40 years [ABSTRACT FROM AUTHOR]