Optimization of hydrometeor classification using multivariate statistical techniques
Pamela L. Heinselman, CIMMS/Univ. of Oklahoma, Norman, OK; and K. Elmore
The upcoming polarimetric upgrade of WSR-88D radars provides the opportunity for unprecedented improvement in the classification of hydrometeors. For the aviation industry, this polarimetric upgrade promises to improve classification of the spectrum of hydrometeors that affect operations (e.g., ice, snow, hail, and rain). To identify these hydrometeors, several researchers have implemented fuzzy-logic algorithms. Although the fuzzy-logic approach has shown success in the discrimination of rain and hail, an important consideration is whether a multivariate statistical technique may outperform fuzzy-logic methods. The purpose of this paper is to investigate multivariate statistical relationships among polarimetric variables that may improve the classification of hydrometeors of interest to the aviation industry. The extended abstract will report the findings of this study.
Poster Session 6, Polarimetric Radar Posters
Tuesday, 31 January 2006, 9:45 AM-9:45 AM, Exhibit Hall A2
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