Impact of Assimilating Additional Thermodynamic Variables with Radar Observations in the EnKF System at Convective Scale

The quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction between 0-6 hr, and severe weather and extreme rainfall in Taiwan usually cause disaster in a short period of time. Assimilating radar observations over Taiwan (both radial velocity and reflectivity) have showed the significant impact of improving the short-term forecast. However, with the observations from radar network, it still requires several data assimilation cycles to improve QPF. This study investigates the impacts of assimilating additional state variables such as temperature and humidity in 3D at convective scale. By using the WRF-LETKF Radar Assimilation System (WLRAS), observation system simulation experiments (OSSE) are conducted. The performance of analysis and short-term forecast are both examined. Preliminary Results show that one is able to reduce cycling procedure and obtain better analysis if thermodynamic variable is assimilated.