Improving Radar Echo Lagrangian Extrapolation Nowcasting by Blending Numerical Model Wind Information: Statistical Performance of 16 Typhoon Cases

Abstract

Abstract Severe weather nowcasting is a crucial mission of atmospheric science for the betterment of society to save life, limb, and property. In this study, composite radar data from the Central Weather Bureau of 16 typhoons are collected to examine the statistical performance of the McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation (MAPLE) over Taiwan, an extrapolation algorithm that predicts future precipitation based on current radar echoes. In addition, instead of mixing the precipitation between radar extrapolation and numerical model forecast as in previous studies, a blending system is formed by synthesizing the wind information from model forecast with the echo extrapolation motion field via a variational algorithm to improve the nowcasting system. The statistical results of the radar echo extrapolation for 16 typhoon cases show that while the quantitative precipitation nowcasting skill can persist for up to 2 h, significant distortion for the rotational system is found after 2 h. On the other hand, the blending system helps to capture and maintain the rotation of typhoon rainband structures. The blending system extends the nowcasting skill by 1 h to a total of 3 h. Furthermore, the blending scheme performs especially well after the typhoon makes landfall in Taiwan. For disaster prevention and mitigation, this blending nowcasting technique may provide effective weather information immediately.