A cluster-based method for hydrometeor classification using polarimetric variables. Part II: Classification

Each prototype is defined as a probability distribution of the polarimetric variables and ambient temperature corresponding to a hydrometeor type. The first algorithm is a maximum prototype likelihood classifier that uses all prototypes attributed to the different hydrometeor types in Part I. The hydrometeor type is assigned as the prototype with the highest likelihood when comparing the polarimetric variables and temperature with each prototype.

The second algorithm is a Bayesian classifier that uses the probability density functions (PDFs) as derived from the prototype set associated with the identical hydrometeor type. The posteriori probability in the Bayesian method is calculated from a combination of the PDFs and the prior probability, the maximum of which corresponds to the most likely hydrometeor type. The respective merits of the two techniques are discussed.