摘要
使用合肥雷达站2007年7月和广州雷达站2008年5—10月的雷达以及雨量计资料提出了使用雷达反射率因子、水平梯度和垂直积分液态水含量测量降水量的方法(简称多因子方法)。此方法在人工神经网络构架之上隐含地实现了在降水类型识别基础上的降水量测量,并与使用单一Z-R关系测量的降水量进行比较。结果表明:多因子方法和使用Z=300R1.4测量的降水量相比,前者的计算结果与雨量计观测值相比具有较高的相关系数和较低的均方根误差,即前者测量降水量的精度高于后者。
A method of rainfall estimation by means of artificial neural network with the reflectivity horizontal gradient and vertical integration of liquid water content is introduced, based on the radar data from the Hefei radar station in July 2007 and the Guangzhou radar station from May to October 2008 corresponding to the rain gauge data. The estimation of rainfall by the method is compared with the result of the Z= 300/R^1.4 relationship. The results show that the rainfall estimation of the multi-factor artificial neural network is better than that of the Z= 300R^1.4 relationship, according to the correlation coefficients and root mean square errors.
出处
《气象科技》
2012年第6期885-889,共5页
Meteorological Science and Technology
关键词
多因子
人工神经网络
Z-R关系
rainfall, artificial neural network, Z-R relationship
