In order to better study the application of LMIE lightning event data from the FY4 satellite (FY4A), we first introduced it into the GRAPES_Meso model cloud analysis system. We established the conversion relationship between LMIE lightning frequency and radar reflectivity, and used the nudging method to initialize the information in 3D cloud. The heavy rainfall process on 25 May 2019 is selected for a case test and half-month time serial of experiments are designed. The influence of LMIE data on radar reflectivity, cloud microphysical variables and precipitation forecast calculated by model was mainly analyzed. The results indicate that: The radar reflectivity calculated by LMIE is consistent with the observed radar reflectivity; The LMIE lightning event data can capture the heavy precipitation signal well, and can indicate the prediction of the squall line; The LMIE data can effectively increase the total amount of precipitation (cloud water, cloud ice, cloud snow, etc.) at the initial moment, of which the horizontal distribution is basically consistent with the real-time radar reflectivity and precipitation distribution, and its vertical distribution is more consistent with the observed precipitation center. By comparing the results of precipitation, it can be concluded that after adding LMIE lightning data, the missing report rate can be effectively reduced, and the model can respond to the precipitation forecast that is more closely to the observation in a short time scale, which eliminates or reduces the Spin-up phenomenon of the numerical model. Finally, the accuracy of short-term prediction is improved.
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