In this study, we introduce the matrix of weights which characterizes classification power of each variable with respect to every class of radar echo. A methodology for optimizing such a matrix is also suggested. Instrumental quality of each radar measurement (such as statistical error and bias) is defined by a confidence factor which is used in combination with the matrix of weights. Such a combination provides much greater flexibility to a classification scheme than the existing methodologies.
The data from several storms observed with the polarimetric prototype of the WSR-88D radar in Oklahoma are used to optimize the matrix of weights in the classification algorithm. This algorithm aims at discriminating between 10 classes of radar echo using 6 radar variables.