In this paper, we describe a new, surface-based polarimetric HCA that uses thermodynamic output from the High Resolution Rapid Refresh (HRRR) model. In its current form, the algorithm allows fuzzy-logic-based classifications from the lowest elevation sweep to be projected to the surface as snow or ice crystals for cold season events where the entire atmospheric column above a location has T < -5ºC and as rain, big drops, or hail for warm season events where the surface temperature at a location has a T > 5ºC. For intermediate conditions typical of transitional winter weather events, the algorithm uses vertical profiles of wet bulb temperature profiles derived from the HRRR to provide a background precipitation classification type. Polarimetric radar observations, when available, are then used to either confirm or reject the background classification. In short, the introduction of thermodynamic output from the HRRR provides an opportunity to not only enhance classification in regions where radar data are available, but also to extend classification capabilities to more distant ranges where low-level radar data are not available.
The algorithm is tested on selected winter weather events and compared against a validation data set obtained from the Precipitation Identification Near the Ground (PING) project.