A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation

Abstract

The purpose of this study is to demonstrate the use of polarimetric observations in a radar-based winter hydrometeor classification algorithm. This is accomplished by deriving bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and platelike snow crystals at X-, C-, and S-band wavelengths based on microphysical theory and previous observational studies. These results are then used to define the expected value ranges, or membership beta functions, of a simple fuzzy-logic hydrometeor classification algorithm. To test the algorithm's validity and robustness, polarimetric radar data and algorithm output from four unique winter storms are investigated alongside surface observations and thermodynamic soundings. This analysis supports that the algorithm is able to realistically discern regions dominated by wet snow, aggregates, plates, dendrites, and other small ice crystals based solely on polarimetric data, with guidance from a melting-level detection algorithm but without external temperature information. Temperature is still used to distinguish rain from freezing rain or sleet below the radar-detected melting level. After appropriate data quality control, little modification of the algorithm was required to produce physically reasonable results on four different radar platforms at X, C, and S bands. However, classification seemed more robust at shorter wavelengths because the specific differential phase is heavily weighted in ice crystal classification decisions. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.

Measurements of Differential Reflectivity in Snowstorms and Warm Season Stratiform Systems

“…It is now widely recognized that ice crystals and aggregates of ice crystals are anisotropic targets for dual-polarization radar. This organized behavior of ice particles in stratiform conditions is not a new observation, and has been previously documented both in winter storms (Sauvageot et al 1986;Moisseev et al 2009;Kennedy and Rutledge 2011;Thompson et al 2014) and in the warm season (Herzegh and Conway 1986;Hall et al 1984;Bader et al 1987;Andric et al 2013). The main purpose here is to document two kinds of radar signature (hereinafter referred to as categories A and B) in as wide a variety of meteorological scenarios as possible.…”

Rain or snow: hydrologic processes, observations, prediction, and research needs

“…Unlike warm-season convective precipitation, the freezing level during a cold-season precipitation event can vary spatially. This phenomenon has prompted the use of polarimetric variables to first detect the melting layer, and then classify hydrometeors (Boodoo et al, 2010;Thompson et al, 2014). Although there has been some success in developing two-stage cold-season hydrometeor classification algorithms, there is little in the published literature that explores the potential contributions of these algorithms for partitioning snow and rain for hydrological modeling.…”

A Balloonborne Particle Size, Imaging, and Velocity Probe for in Situ Microphysical Measurements

“…Thompson et al (2014) expanded on the operational HCA to make it more applicable to winter precipitation events, but still required the use of external temperature information to function. Validation of the HCA classifications is needed, but is difficult to obtain due to the location of the observations within convection.…”

“…Larger particles will tend to survive longer in a warm environment, but aircraft observations and HCA studies tend to indicate that ice particles become exceedingly rare by +5°C (Thompson et al, 2014). Therefore all regular crystals are given a membership weight of unity at 0°C, while the drop off to a weight of zero is To allow some variability in this transition and avoid a sharp cutoff, the confidence of the rain versus ice classification is used.…”