一种基于神经网络的中国区域夏季降水预测订正算法  

A correction algorithm of summer precipitation prediction based on neural network in China

作　　者：李涛[1] 陈杰 汪方[3] 韩锐[4] LI Tao;CHEN Jie;WANG Fang;HAN Rui(College of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing 210044,China;College of Electronics and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;National Climate Center,Beijing 100081,China;Unit 93117 of PLA,Nanjing 210018,China)

机构地区：[1]南京信息工程大学人工智能学院,江苏南京210044 [2]南京信息工程大学电子与信息工程学院,江苏南京210044 [3]国家气候中心,北京100081 [4]中国人民解放军93117部队,江苏南京210018

出　　处：《干旱气象》2022年第2期308-316,共9页Journal of Arid Meteorology

基　　金：南京信息工程大学无锡校区研究生创新实践项目(WXCX202001)资助。

摘　　要：基于CWRF(climate extension of WRF)区域气候模式的动力降尺度预测技术对夏季降水预测存在一定偏差,难以实现准确预测。本文立足于中国区域夏季降水特点,分析与夏季降水相关的气象要素,采用树突(dendrite,DD)网络与人工神经网络(artificial neural networks,ANN)相结合的方法,针对CWRF模式回报的1996—2019年夏季降水量进行订正,检验其订正效果。结果表明:人工树突神经网络(artificial dendritic neural network,ADNN)算法模型订正的中国夏季降水量整体好于CWRF模式历史回报,距平相关系数和时间相关系数较订正前均提高约0.10,均方误差下降约26%,趋势异常综合检验评分提高6.55,表明ADNN机器学习方法能够对CWRF模式夏季降水预测实现一定程度的订正,从而提高该模式降水预测精度。The prediction based on dynamic downscaling prediction technology of the climate extension of weather research and forecasting(CWRF) model to summer precipitation has a certain deviation,so it is difficult to achieve accurate prediction.This paper analyzed the correlated meteorological elements with summer precipitation based on the climatic characteristics of summer precipitation in the main land of China.And on this basis,the reforecasts of summer precipitation by CWRF model in China during 1996-2019 were corrected by using the combined method of dendritic network (DD) and artificial neural network (ANN).Finally,the correction effect was tested by mean square error (MSE),anomaly correlation coefficient (ACC) and temporal correlation coefficient (TCC),etc.The results show that the correction effect to summer precipitation based on the artificial dendritic neural network(ADNN) algorithm model was better than the historical reforecasts of CWRF model in China.The ACC and TCC both increased by about 0.10,MSE dropped by about 26%,and the overall trend anomaly test scores improved by 6.55,which indicated that the ADNN machine learning method could achieve correction to summer precipitation forecasts of CWRF model to a certain extent,thus it could improve the accuracy of precipitation forecasts of CWRF model.

关 键 词：CWRF模式 夏季降水预测订正 DD与ANN 均方误差 时间相关系数 距平相关系数 

分 类 号：P426.6[天文地球—大气科学及气象学] P456