In order to improve the accuracy of quantitative precipitation forecasting in Shandong Province, the deep feedforward neural network (DFNN) and the optimal threat score (TS) weight ensemble method for precipitation grading were used to study the multi-model ensemble precipitation forecasting. Four groups of DFNN (ES, EM, SM, ESM) deep learning models were obtained by using the 24-hour cumulative precipitation forecast of Global Numerical Prediction System of the European Centre for Medium-Range Weather Forecasts, the Shanghai Numerical Prediction Model System of the China Meteorological Administration and the Mesoscale Numerical Weather Prediction System of the China Meteorological Administration from April to September 2019 for supervised training, and the Mul-OTS (Multi-mode Optimal Threat Score) integrated model was established by using the optimal TS weight integration method of multi-model precipitation classification. The down-scale grid prediction was made by using the accumulated precipitation of each model for 24 h from April to September 2020, and the comparison test and case analysis of five integrated schemes were carried out. The results show that the average relative error was reduced by the five integrated schemes with different starting time and lead times. The ESM scheme was the best, and the Mul-OTS scheme was the worst. All the four groups of DFNN schemes improved the accuracy of sunny and rainy prediction, the ESM scheme was the best, and the Mul-OTS scheme was lower than the model forecast. The four groups of DFNN schemes all improved the TS and ETS scores of each precipitation grade, and the improvement amplitude of weak precipitation was greater than that of strong precipitation. The Mul-OTS scheme was a negative technique for the correction of small precipitation levels, and the correction effect was better for the correction of large precipitation levels, but it was still inferior to the ESM  scheme. A case study found that the ESM scheme for precipitation intensity and fall area forecast was superior to other integrated schemes. Therefore, the optimal ESM scheme was adopted to establish a quantitative precipitation grid forecasting system, which provides important support for intelligent grid forecasting.

为提高山东定量降水预报准确率，采用深度前馈神经网络（Deep Forword Neural Networks，

DFNN）和降水分级最优TS（Threat Score）权重集成方法对多模式集成降水预报进行研究。对2019年

4—9月欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasting， ECMWF）全球

数值预报系统、中国气象局上海数值预报模式系统（China Meteorological Administration Shanghai

9 km， CMA-SH9）和中国气象局中尺度天气数值预报系统（China Meteorological Administration Meso⁃

scale， CMA-MESO）逐24 h累积降水量预报进行有监督训练，得到4组DFNN（ES、EM、SM、ESM）深度

学习模型，并利用多模式降水分级最优TS 权重集成方法建立Mul-OTS（Multi-mode Optimal Threat

Score）集成模型。用2020年4—9月各模式逐24 h累积降水量进行降尺度格点预报，对5种集成方案

对比检验、个例分析应用。结果表明：不同起报时间、不同预报时效，5组集成方案均降低了平均相对

误差，ESM方案最好，Mul-OTS方案最差；4组DFNN方案均提高了晴雨准确率，ESM方案最好，Mul-

OTS方案低于模式预报；4组DFNN方案均提高了各降水等级TS、ETS评分，对弱降水的提高幅度大于

强降水，Mul-OTS方案对小量级降水等级订正是负技巧，对大量级降水等级的订正效果较好，但仍不

如ESM方案；个例分析发现降水强度和落区预报ESM方案均优于其他集成方案。因此业务上采用最

优的ESM方案建立了定量降水格点预报系统，为智能网格预报提供重要支撑。