Recent studies have shown the utility of polarimetric observables in the discrimination of hydrometeor particle types. Polarimetric radar observables depend on microphysical characteristics of hydrometeors, namely (a) particle size, (b) particle shape, (c) particle orientation relative to the local vertical direction, (d) phase (liquid or ice), and (e) bulk density (wet, dry, aggregation or riming). In addition to traditional reflectivity and Doppler measurements, polarimetric observables include differential reflectivity, linear depolarization ratio, specific differential propagation phase, and correlation coefficient. The values of these observables that delineate different particles overlap and are not crisply defined. Thus, particle discrimination is well suited for a fuzzy logic approach.
In this preliminary study we have developed and tested a fuzzy logic algorithm for hydrometeor particle identification. The method is based on accepted values of radar observables to distinguish the different particles. Currently the algorithm distinguishes between 15 hydrometeor types, including different intensities of rain and hail, different ice particles, super-cooled liquid droplets, mixed phase particles and clear air returns (insects). The technique is simple and efficient enough to run in real time. In this paper we give an overview of the fuzzy logic particle identification method. We also present sensitivity studies designed to reveal the usefulness of each radar observable for particle classification. Finally, we present some preliminary results using data taken with the S-Pol radar during the CASES and PRECIP98 field programs. Although there are no direct measurements available for particle type verification, the initial results are encouraging.
Possible applications of this method include; increasing the accuracy of precipitation estimates, lightning studies, improved model initialization and aviation applications