Research on Skeleton-Based Objective Quantization and Identification Algorithm for Quasi-linear Convective Systems

In this paper, we apply the skeleton concept from the field of computer graphics to meteorology and the development of echo image preprocessing, skeleton pruning, and length-width ratio quantization techniques. The quasi-linear convection systems (QLCSs) in radar echo mosaics that conform to meteorological standards can automatically be identified using this method. First, we introduce in detail the identification algorithm based on a double QLCSs process in the Huang–Huai area in 2016. Then, we use this method to objectively identify the QLCSs in the province of Anhui in June 2016 and quantify the moving characteristics of the QLCSs. We then compare weather disaster observations with their subjective identification. The results show that information about the shape of meteorological echoes is well preserved using the skeleton image identification algorithm and effective identification of QLCSs is realized base on the accurate quantification of the long and short axes of the convection system. Quantitative characteristics such as moving vectors can be applied to the classification of QLCS disasters to provide an identification algorithm and quantitative features for long–series statistics as well as an analysis mechanism for weather disasters. In addition, this concept provides a new technique for monitoring and warning by QLCSs.