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中国东部夏季雨型的人工神经网络集合预测
引用本文:孙照渤,谭桂容,赵振国,卢明.中国东部夏季雨型的人工神经网络集合预测[J].大气科学学报,2013,36(1):1-6.
作者姓名:孙照渤  谭桂容  赵振国  卢明
作者单位:1. 南京信息工程大学气象灾害省部共建教育部重点实验室,江苏南京210044;南京信息工程大学大气科学学院,江苏南京210044
2. 中国气象局国家气候中心,北京,100081
基金项目:公益性行业(气象)科研专项(GYHY(QX)201206016,GYHY,江苏高校优势学科建设工程资助项目
摘    要:在BP(back propagation)人工神经网络方法的基础上,考虑到历史资料的个体差异及其年代际变化会影响到样本均值,由此使得中国东部夏季雨型模拟和预测效果产生差异,故引入交叉检验及集合预报思想,以改进人工神经网络独立预报方法.在利用不同历史样本资料建立人工神经网络模型,并进行交叉检验的同时,对预测年的雨型进行预测,可获得预测年的多次预测结果.该方法在中国东部夏季四类雨型的试验预报中表现出较好效果.

关 键 词:季节预测  集合人工神经网络方法  夏季雨型  中国东部
收稿时间:2012/6/26 0:00:00
修稿时间:2012/10/28 0:00:00

Ensemble prediction of summer rainfall patterns over eastern China based on artificial neural networks
SUN Zhao-bo,TAN Gui-rong,ZHAO Zhen-guo and LU Ming.Ensemble prediction of summer rainfall patterns over eastern China based on artificial neural networks[J].大气科学学报,2013,36(1):1-6.
Authors:SUN Zhao-bo  TAN Gui-rong  ZHAO Zhen-guo and LU Ming
Affiliation:Key Laboratory of Meteorological Disaster of Ministry of Education, NUIST, Nanjing 210044, China;School of Atmospheric Sciences, NUIST, Nanjing 210044, China;Key Laboratory of Meteorological Disaster of Ministry of Education, NUIST, Nanjing 210044, China;School of Atmospheric Sciences, NUIST, Nanjing 210044, China;National Climate Center, China Meteorological Administration, Beijing 100081, China;Key Laboratory of Meteorological Disaster of Ministry of Education, NUIST, Nanjing 210044, China;School of Atmospheric Sciences, NUIST, Nanjing 210044, China
Abstract:On basis of the back propagation artificial neural networks(BP ANN) method,and under consideration of the mean value differences caused by different sample collections for their individual differences and interdecadal variations,which may influence the simulation and prediction of summer rainfall patterns over eastern China,the cross validation and ensemble prediction ideas are adopted to improve the prediction method.An independent prediction is gotten during each cross validation test,and then many times cross validation test by the ANN model established based on different sample collections may give many times prediction results for the predicted year.Its performance seems good in the prediction test of four rainfall patterns over eastern China in summer.
Keywords:seasonal prediciton  ensemble artificial neural networks method  summer rainfall pattern  eastern China
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