为更好的研究FY4号卫星（FY4A）的LMIE闪电事件资料的应用, 在GRAPES_Meso模式的云分析系统中首次引入了LMIE闪电事件资料, 建立了LMIE闪电频率与雷达反射率的转换关系, 采用松弛逼近（nudging）方式对三维云内信息初始化应用, 通过2019年5月25日一次强降水过程及为期半个月的批量试验, 重点分析了引入LMIE数据对模式计算的雷达反射率、 云微物理变量和降水预报的影响。数值试验结果表明: LMIE计算回波与实况雷达回波具有一致性, LMIE闪电事件资料能够较好的捕捉到强降水信号, 对飑线的预报具有指示作用; LMIE资料能够有效提高初始时刻水成物（云水、 云冰、 云雪等）的总量, 使其水平分布基本与实况雷达回波和降水分布一致, 其垂直分布也与实况降水中心更为吻合; 对比降水结果可以得到在加入LMIE闪电资料后, 能够有效降低漏报率, 并且模式在短时间尺度就可以响应出与实况更为接近的降水预报, 消除或减弱了数值模式的Spin-up现象, 提高短临预报准确率。
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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