Polarimetric classification of winter weather precipitation type using thermodynamic input from numerical models

The classification of cold-season precipitation type at the surface is complicated by the broad range of precipitation types that might result from processes that occur below the height of the radar's lowest elevation sweep. Furthermore, for any given location at the surface, the radar bright band detection algorithm will typically only provide a single height for a melting level that lies above it when several excursions across the 0ºC isotherm might exist. As an example, a deep layer of subfreezing air near the surface might lead to either a complete refreezing of drops (ice pellets) or refreezing upon contact with the surface (freezing rain). Both of these precipitation types are difficult to determine using radar data alone.

In this study, we present results from the continued evolution of a polarimetric classification algorithm that is designed to take advantage of thermodynamic output from a numerical model in the classification process. The analysis is expanded to include several transitional winter weather events that were observed by either the polarimetric S-band KOUN WSR-88D radar or the polarimetric C-band OU prime radar, both of which are located in central Oklahoma. The algorithm first uses vertical profiles of wet bulb temperature derived from the model output to provide a background precipitation classification type. Polarimetric radar observations are then used to either confirm or reject the background classification. For example, if a radar bright band (suggesting an elevated warm layer) is observed immediately above the area where dry snow is designated by the background classification (suggesting the absence of an elevated warm layer in the model output), the background classification is found to be inconsistent with observations and is modified according to a set of empirical rules. The polarimetric radar data are also used to provide further refinement of precipitation type categories when the observations are found to be consistent with the background classification.

The future expansion of the algorithm to improve the detection of precipitation types aloft, possibly including conditions favorable for aircraft icing, will also be discussed.