Improve the Performance of the Wrf-Radar Local Ensemble Kalman Filter on Short-Term Heavy Rainfall Nowcasting

The WRF-Local Ensemble Kalman Filter with Radar data Assimilation System (WLRAS) has been established for improving the very short-term heavy rainfall prediction in Taiwan. Although WLRAS can provide significant improvement on heavy rainfall prediction, there are still many challenging issues due to the complex terrain, complicated mechanisms for strong convections and heavy rainfall at different time scales and unknown errors in model physics. This proposal aims to improve the rainfall prediction by proving a better model moisture field before conducting WLRAS. This will be achieved by assimilating moisture-related observations, such as the satellite-derived temperature and moisture profiles and GPS-ZTD data, to improve the model condition in the parent domain. Also, we would like to improve the representation of model errors by using different types of perturbation generators, including the stochastic multi-scale perturbation, stochastic physical parameterization tendency and Meiyu front-related environmental perturbations in order to increase the ensemble spread for radar data assimilation. The optimal goal is to improve very short-term rainfall prediction and provide a reliable ensemble production, such as the PQPF. Preliminary results from the first year of this project show that applying SPPT or the additive Meiyu-front related environmental perturbations are useful for improving the performance of WLRAS and has a great impact on the 6th to 12th rainfall forecast. The improvements include the location and intensity of the heavy rainfall.