P14 Improving Lightning and Precipitation Prediction of Severe Convection with Lightning Data Assimilation using NCAR WRF-RTFDDA

Wang, Haoliang, Yubao Liu, Will Y.Y.Cheng, Tianliang Zhao, Mei Xu, Si Shen, Yuewei Liu, Linlin Pan, National Center for Atmospheric Research

In this study, a new lightning data assimilation (DA) scheme based on the NCAR Weather Research and Forecasting – Real-Time Four-Dimensional Data assimilation (WRF-RTFDDA) system was developed. With the lightning DA scheme, graupel mixing ratio (qg) was retrieved using total lightning flash rate. The column-integrated graupel mass was calculated using an observation-based linear equation between graupel mass and total lightning flash rates. Then the graupel mass was distributed vertically according to the empirical qg vertical profiles constructed from model simulations. After the retrieve of graupel fields, the latent heat adjustment approach of RTFDDA was employed to account for the latent heat release associated with the formation of graupel. The time dependent weighting function of RTFDDA was modified for assimilating lightning data. Two severe convection cases were studied to evaluate the lightning DA scheme for improving the accuracy of short-term (0-6-h) lightning and precipitation forecasts. The result demonstrates that the lightning DA can improve the short-term lightning and precipitation forecasts by better representing the graupel field, updrafts, cold pool and frontal position. The improvements were most noticeable in the first two hours, highlights the potential of lightning DA to improve the nowcasting (0-2-h) accuracy for lightning and convective precipitation.