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.
Highlights
Academic Editors: AlexanderThe convective precipitation forecast is one of the most challenging tasks in weather forecasting due to the nonlinear behavior of the atmosphere
We analyzed the difference in the incremental initial fields between the data assimilation experiment and the control run by conducting the weather radar data assimilation
We compared the results of the control run with that of the assimilation run by using the cumulated precipitation information of Automatic Weather Station (AWS) and Global Precipitation Measurement (GPM) (Figures 6 and 7)
Summary
The convective precipitation forecast is one of the most challenging tasks in weather forecasting due to the nonlinear behavior of the atmosphere. During the last few years, for the high-resolution convective forecast, radar observations have been largely used in the data assimilation system in order to improve initial conditions [17]. In a realistic experiment, it was mentioned that the ability to simulate convective clouds by terrain was improved when the radar 3D-Var data assimilation was applied [38]. These studies have shown that the assimilation of radar radial wind and reflectivity data in an NWP system can improve short-range forecast skills.
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