A Hydrometeor Classification Method for X-Band Polarimetric Radar: Construction and Validation Focusing on Solid Hydrometeors under Moist Environments

Abstract

fuzzy-logic-based hydrometeor classification (HC) method for X-band polarimetric radar (X-pol), which is suitable for observation of solid hydrometeors under moist environments producing little or no hail, is constructed and validated. This HC method identifies the most likely hydrometeor at each radar sampling volume from eight categories: 1) drizzle, 2) rain, 3) wet snow aggregates, 4) dry snow aggregates, 5) ice crystals, 6) dry graupel, 7) wet graupel, and 8) rain?hail mixture. Membership functions are defined on the basis of previous studies. The HC method uses radar reflectivity Zh, differential reflectivity Zdr, specific differential phase Kdp, and correlation coefficient ?hv as its main inputs, and temperature with some consideration of relative humidity as supplemental information. The method is validated against ground and in situ observations of solid hydrometeors (dry graupel, dry snow aggregates, and ice crystals) under a moist environment. Observational data from a ground-based imaging system are used to validate the HC method for dry graupel and dry snow aggregates. For dry snow aggregates and ice crystals, the HC method is validated using simultaneous observations from a balloonborne instrument [hydrometeor videosonde (HYVIS)] and an X-pol range?height indicator directed toward the HYVIS. The HC method distinguishes effectively between dry graupel, dry snow aggregates, and ice crystals, and is therefore valid for HC under moist environments.