基于BP神经网络的浙北夏季降尺度降水预报方法的应用

Application of downscaling forecast for the North of Zhejiang precipitation in summer based on the BP neural network model

利用NCEP提供的全球空间分辨率为2.5°×2.5°、2007—2012年6—8月日平均500 h Pa高度场再分析格点资料和浙北地区158个站点观测资料,研究了不同大气环流型下局地降水与大尺度降水场之间的关系,以4种不同环流型下的预报对象和预报因子分别采用BP神经网络方法对观测资料进行逼近,得到4种空间降尺度的预报模型,分析对比4种预报模型158站逐日的降水量的预报。结果表明:神经网络模型的隐层节点数为2时,对降水的拟合效果最好;对降水的极值拟合效果中,环流分型中NW型和C型的效果优于SW型和SE型;从4种分型下的误差空间分布来看,浙北地区沿海的宁波、舟山一带的误差小于浙北其他区域;把雨量分等级后进行预测,发现模型对暴雨的预测能力最好。

Based on the daily 500 hPa geopotential height data between June and August,2007-2012,the historical reanalysis grid data of NCEP global 2.5°×2.5°and the daily precipitation data of 158 meteorological stations in north of Zhejiang province,the relationships between local precipitation and large-scale precipitation in different atmospheric circulations are studied in this paper.The BP neural network combined with 4 forecasting objects and corresponding predictor variables in different circulations are employed to design 4 downscaling function models to approximate the precipitation data.The 4 models are used to simulate and forecast the daily precipitation data of 158 meteorological stations in north of Zhejiang province,and the results show that the BP neural network model with 2 hidden layers has good simulation accuracy.Through Jenkinson atmospheric circulation to classify the pre cipitation into SE（SE type）,NW （NW type）,C （C type） and SW （SW type）,NW type and C type generally outperform the SW type and SE type in simulation of the extreme precipitation.Compared with the area of Ningbo and Zhoushan,other areas of north Zhejiang reflect the greater error value from 4 atmospheric circulations.The prediction accuracy of the downscaling model is the best of three types of rainstorm forecast after categorizing rainfall into different levels.