Application of a three-dimensional variational method for radar reflectivity data correction in a mudslide-inducing rainstorm simulation

Abstract

Various types of radars with different horizontal and vertical detection ranges are deployed in China, particularly over complex terrain where radar blind zones are common. In this study, a new variational method is developed to correct three-dimensional radar reflectivity data based on hourly ground precipitation observations. The aim of this method is to improve the quality of observations of various types of radar and effectively assimilate operational Doppler radar observations. A mudslide-inducing local rainstorm is simulated by the WRF model with assimilation of radar reflectivity and radial velocity data using LAPS (Local Analysis and Prediction System). Experiments with different radar data assimilated by LAPS are performed. It is found that when radar reflectivity data are corrected using this variational method and assimilated by LAPS, the atmospheric conditions and cloud physics processes are reasonably described. The temporal evolution of radar reflectivity corrected by the variational method corresponds well to observed rainfall. It can better describe the cloud water distribution over the rainfall area and improve the cloud water analysis results over the central rainfall region. The LAPS cloud analysis system can update cloud microphysical variables and represent the hydrometeors associated with strong convective activities over the rainfall area well. Model performance is improved and the simulation of the dynamical processes and moisture transport is more consistent with observation.