Testing a Procedure for Automatic Classification of Hydrometeor Types

Abstract

Examples of automatic interpretation of polarimetric measurements made with an algorithm that classifies precipitation, from an Oklahoma squall line and a Florida airmass storm are presented. Developed in this paper are sensitivity tests of this algorithm to various polarimetric variables. The tests are done subjectively by comparing the fields of hydrometeors obtained using the full set of available polarimetric variables with a diminished set whereby some variables have been left out. An objective way to test the sensitivity of the algorithm to variables and rank their utility is also devised. The test involves definition of a measure, which is the number of data points classified into a category using subsets of available variables. Ratios of various measures (similar to probabilities) define the percentage of occurrence of a class. By comparing these percentages for cases in which some variables are excluded to those whereby all are included, a relative merit can be assigned to the variables. Results of this objective sensitivity study reveal the following: the reflectivity factor and differential reflectivity combined have the strongest discriminating power. Inclusion of the temperature profile helps eliminate a substantial number of spurious errors. Although the absence of temperature information degrades the scheme, it appears that the resultant fields are generally coherent and not far off from the fields obtained by adding temperature to the suite of polarimetric variables.