Development of a neural network based algorithm for radar snowfall estimation

Using radar to measure snowfall accumulation has been a research topic in radar meteorology for decades. Traditionally, a parametric reflectivity-snowfall (Z-S) relationship is used to estimate ground snowfall amounts based on radar observations. However, the accuracy and reliability of Z-S relationship are limited by the wide variability of the Z-S relationship with snowfall type. In this paper, the authors introduce a neural network based approach to address the problem of snowfall estimation from radar by taking into account the vertical structure of precipitation. The motivation for using a multilayer feedforward neural network (MFNN), such as the radial-basis function (RBF) network, is the good universal function approximation capability of the network. The network is trained using vertical reflectivity profiles averaged over a 9-km/sup 2/ area as the input and ground snowfall amounts as the target output. Separate data, which are not part of the training data, are used to test the generalization performance of the RBF network after the training is done. Radar reflectivity data collected by the CSU-CHILL multiparameter radar and ground snowfall measurements recorded by snowgages located at the Stapleton International Airport (SIA), Stapleton, CO, and the Denver International Airport (DIA), Denver, CO, during the Winter and Icing and Storms Projects (WISP94) were used for this study. The snowfall estimates from the RBF network are shown to be better than those obtained from conventional Z-S algorithms. The neural network based approach provides an alternate method to the snowfall estimation problem.