BP神经网络在多普勒雷达降水量的估测中的应用

Application of Back-Propagation Neural Network in Precipitation Estimation with Doppler Radar

利用2005年4次降雨过程的多普勒雷达体扫的回波强度资料及相应的雨量计观测资料,通过BP神经网络方法来估测临沂地区的降雨量,同时以改进的最佳窗概率配对法建立的Z-R关系估测的降雨量为对照,进一步验证BP神经网络方法的优越性。根据各个站点的平均相对误差、均方根差、相关系数和相关曲线斜率4个指标的比较,小时雨量和累计降雨量估测结果表明:BP神经网络估测精度要明显优于Z-R关系式,训练样本的精度高于检验样本的精度,BP神经网络估测的降雨量与站点实测雨量吻合性较好,能够较真实地反映地面降雨情况;Z-R关系式估测的降雨量随着雨强的不同表现为不同程度的低估现象。

By means of the Doppler radar measurements and automatic precipitation station data collected in the Linyi region during four precipitation processes of 2005. The Back-Propagation Neural Network （BPNN） was used to estimate the rainfall. In order to contrast with neural network, the improved window probability matching method（WPMM）was employed to determine the relationship between radar echo intensity （Z） and precipitation intensity （R）, and the Z-R relation was further used to estimated the rainfall. Based on analysis index study such as mean relative error, root mean square error, correlation coefficient, correlation curve slope, the results suggested that the hourly rainfall and accumulation rainfall - estimation of the precision from BPNN is higher than from Z-R relation, and the precision from calibration samples is higher than evaluation samples. Rainfall estimation of BPNN was in good consistence with those observation by rain-gauge and can truly reflect the precipitation status over the ground surface. Rainfall estimation of Z-R relation would yield underestimation of different degree with the change of rainfall intensity.