Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems

Abstract

Radar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated the radar equivalent reflectivity factor using high resolution WRF and compared it with radar observations in South Korea. To compare the precipitation forecast characteristics of the three-dimensional variational (3D-Var) assimilation of radar data, four experiments were performed based on the scales of precipitation systems. Comparison of the 24 h accumulated rainfall with surface observation data, contoured frequency by altitude diagram (CFAD), time–height cross sections (THCS), and vertical hydrometeor profiles was used to evaluate the accuracy of the simulation of precipitation. The model simulations were performed with and without 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The combined effect of the radar and AWS data assimilation experiment improved the location of the precipitation area and rainfall intensity compared to the control run. There is a noticeable scale dependence in the improvement of precipitation systems. Improvements in simulating mesoscale convective systems were larger compared to synoptically driven precipitation systems.