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

Xiaoqin Lu, Wai Kin Wong, Hui Yu, Xiaoming Yang

Journal of the Meteorological Society of Japan Ser II · 01 January 2022 · China Meteorological Administration (CMA)

In one sentence. SVM and general regression neural network models estimate western North Pacific tropical cyclone size parameters (RMW, R34, R50, R64) from infrared satellite imagery.

Abstract

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, while the general regression neural network models are set up for R50 and R64.

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