Comparison of Multi-Model Microphysics Schemes using Radar Observations and Data Assimilation Method

The quantitative precipitation forecast (QPF) capability of the radar variational assimilation method using two numerical weather prediction (NWP) models is investigated over the Korean Peninsula. The two NWP models considered in this study are the UKMO Unified Model (UM) and the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated radar equivalent reflectivity factors using the Korean Meteorological Administration (KMA) Local Data Analysis and Prediction System (LDAPS) based on UM and compared with radar observations in Korea. To compare the cloud microphysics schemes in simulating precipitation, three types of experiments are performed using UM, WRF, and WRF with 3D-Var assimilation method. Comparisons of the 24-hr accumulated rainfall with Automatic Weather Station (AWS) data, contoured frequency by altitude diagram (CFAD), time-height cross sections, and vertical profiles of hydrometeors are performed to evaluate the schemes and understand model predictability. Two heavy rainfall cases during the summer monsoon season of 2016 and one convective case in 2014 are selected for comparison with the 24-hr accumulated precipitation from AWS and CMORPH data. In terms of precipitation, the operational LDAPS, which utilize full data assimilation package, is more accurate than radar assimilated WRF model. However, the difference between bright band and the melting layer height calculated from the two models are noticeable compared to sounding data for stratiform precipitation cases. The results obtained here will be utilitzed to improve the cloud microphysics schemes and NWP model’s QPF and accuracy.

Acknowledgement
This research is supported by the grant (KMIPA-2015-1090) funded by the Korea Meteorological Administration.