【摘要】：Data from the lightning mapping imager on board the Fengyun-4 meteorological satellite(FY-4) were used to study the assimilation of lightning data and its influence on precipitation predictions. We first conducted a quality control check on the events observed by the first Fengyun-4 satellite(FY-4 A) lightning mapping imager, after which the noise points were removed from the lightning distribution. The subsequent distribution was more consistent with the spatial distribution and range of ground-based observations and precipitation. We selected the radar reflectivity, which was closely related to the lightning frequency, as the parameter to assimilate the lightning data and utilized a large sample of lightning frequency and radar reflectivity data from the eastern United States provided by Vaisala. Based on statistical analysis, we found the empirical relationship between the lightning frequency and radar reflectivity and established a look-up table between them. We converted the lightning event data into radar reflectivity data and found that the converted reflectivity and composite reflectivity of ground-based radar observations showed high consistency.We further assimilated the lightning data into the model, adjusted the model cloud analysis process and adjusted the model hydrometeor field by using the lightning data. A rainstorm weather process that occurred on August 8, 2017 in south China was used for the numerical forecast experiment, and three experiments were designed for comparison and analysis: a control experiment, an experiment without the assimilation of FY-4 lightning data(NoLig), and an experiment with the assimilation of FY-4 lightning data(Lig). The results show that after assimilating the FY-4 A lightning data, the accuracies of the intensity, central location and range of the precipitation predicted by the Lig experiment were obviously superior to those predicted by the control and NoLig experiments, and the effect was especially obvious in the short-term(1-2 hour) forecast. The studies in this paper highlight the application value and potential of FY-4 lightning data in precipitation predictions.