A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications

AbstractMost of the recent hydrometeor classification schemes are based on fuzzy logic. When the input radar observations are noisy, the output classification could also be noisy, since the process is bin based and the information from neighboring radar cells is not considered. This paper employs cluster analysis, in combination with fuzzy logic, to improve the hydrometeor classification from dual-polarization radars using a multistep approach. The first step involves a radar-based optimization of an input temperature profile from auxiliary data. Then a first-guess fuzzy logic processing produces the classification to initiate a cluster analysis with contiguity and penalty constraints. The result of the cluster analysis is eventually processed to identify the regions populated with adjacent bins assigned to the same hydrometeor class. Finally, the set of connected regions is passed to the fuzzy logic algorithm for the final classification, exploiting the statistical sample composed by the distribution of ...

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