Abstract:Lightning and conventional ground observations provide complementary atmospheric background information, and the combined assimilation of these two types of data has the potential to further improve the accuracy of forecasting severe convective weather compared to assimilating only one type of data. In this study, a mesoscale convective system occurred in Guangdong Province on June 16, 2017 as an example to analyze the effect of the combined assimilation of the above two kinds of data on the simulation and prediction of mesoscale convective system compared with the single assimilation of one of the two kinds of data. The lightning data is continuously assimilated into the model through the WRF-FDDA system with a lightning accumulation window of 15 minutes, the conventional observations data is assimilated into the model by the WRFDA-3DVAR system at one hour interval. The results show that the introduction of lightning data in the joint assimilation experiment improves the accuracy of updrafts, cold pools, and gust fronts in the background fields, which in turn improves the simulation and forecast accuracy of the convective system, compared with the assimilation of conventional ground observations only. The introduction of conventional ground observations in the joint assimilation experiment reduces the background field errors in temperature, water vapor, and wind fields over a larger area, suppresses the spurious convection in some areas, and overall improves the simulation and forecast accuracy of the convective system. The results of prediction skill score show that the combined assimilation of the two kinds of data also improved the prediction skill score of the analysis period and the forecast period to some extent.