A dual-polarization radar hydrometeor classification algorithm for winter precipitation

The purpose of this paper is to demonstrate the extent to which polarimetric radar observations can be used to operate a winter hydrometeor classification algorithm. This is accomplished by deriving the bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and plate-like 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 verifies that the algorithm is able to successfully discern dominant winter hydrometeor types (except sleet and freezing rain) based solely on polarimetric data, with guidance from a melting level detection algorithm but without external temperature information. Little modification of the algorithm was required to produce equally positive results across four different radar platforms at X-, C-, and S-band. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.