摘要
利用1999—2009年安徽省淮河以南地区60个县市站夏季逐日降水资料和安庆市探空站逐日资料,研究了中低层不同风向配置下局地降水与大尺度降水场之间的关系,以3种不同预报对象及相应的预报因子分别采用神经网络和线性回归方法设计6种预报模型对观测资料进行逼近和优化,从而实现空间降尺度.分析对比6种预报模型46站逐日降水量的拟合和预报效果,结果表明:采取相同的预报对象及预报因子的BP神经网络模型在拟合和预报效果上均好于线性回归模型,可见夏季降水场之间以非线性相关为主;神经网络模型预报结果同常用的Cressman插值预报相比,能很好地反映出降水的基本分布及局地特征;预报对象为单站降水序列的神经网络模型在以平原、河流为主要地形的区域预报效果较好,预报对象为REOF主成分的神经网络模型则在山地和丘陵地形区域预报效果较好.
Based on the summer daily precipitation data of 60 meteorological stations in Anhui province from 1999 to 2009 and observation data of Anqing sounding station,the relationship between local precipitation and large-scale precipitation circulation in different mid-low wind directions are studied in this paper.The neural network and linear regression method,combined with 3 forecasting objects and corresponding predictor variables are employed to design 6 downscaling function models to approximate and optimize the precipitation data.The 6 models are used to simulate and forecast the daily precipitation data of 46 meteorological stations in Anhui province,and the results show that BP neural network models generally outperform the linear regression models in simulation and forecasting accuracy, indicating the nonlinear correlation between different scales of summer rainfall. Compared with the commonly used Cressman interpolation methods,the neural network models can reflect the basic distribution and local characteristics of summer precipitation in forecasting results.The BP neural network model with single station precipitation series as prediction object has good forecasting results in areas of plains or rivers,while the BP neural network model with the REOF principal components as predicting object is good in mountainous area.
出处
《南京信息工程大学学报（自然科学版）》
CAS
2014年第5期449-458,共10页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
公益性行业(气象)科研专项(GYHY201006017)
关键词
统计降尺度
日降水
BP神经网络
旋转主成分分析(REOF)
statistical downscaling
daily precipitation
BP neural network model
Rotated Empirical OrthogonalFunction （REOF）
