Sensitivity of Doppler Radar and AWS Data Assimilation in WRF Microphysics Schemes

Hail or graupel is most frequently observed in April and May in Korea, but 12 December 2013 storm was unusual, in which it occurred in winter when a mini-supercell was identified by an X-band dual-polarized radar. To investigate the predictability of winter hail/graupel event and identify the model's simulation characteristics of such storm, we used the Weather Research and Forecasting (WRF) model with Data Assimilation (DA) system. The model domain is constructed over the Korean Peninsula and nested twice (9-3-1 km) to achieve 1 km resolution from 1 degree NCEP Final Analysis (FNL) data. There are 60 vertical levels, and 400 by 380 grid points in the inner most domain. Sensitivity experiments of assimilating multiple S-band Doppler radar radial velocity, reflectivity, and Automated Weather Station (AWS) data are presented. Several sensitivity studies are performed using five different microphysics parameterization schemes to investigate the effect of Doppler radar and AWS data assimilation in simulating the winter storm. The five schemes tested are WRF Double-Moment 6-class (WDM6), Morrison double-moment, Thompson et al., Milbrandt-Yau double-moment 7-class, and NSSL 2-moment scheme, respectively. The model simulated radar reflectivity shows good agreement with observations and graupel was mainly distributed as hydrometeor types in the vertical profile. However, the model simulated surface temperature was much greater than 0℃ and most of graupel reached ground as rain. A first look at the radar data sensitivity experiments shows increased graupel and precipitation amounts. Detailed analysis of simulated results will be presented at the conference.

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