7B.3 A New Surface-based Polarimetric Hydrometeor Classification Algorithm for Transitional Winter Weather to be Used on the WSR-88D Network

Tuesday, 17 September 2013: 11:00 AM
Colorado Ballroom (Peak 5, 3rd Floor) (Beaver Run Resort and Conference Center)
Terry J. Schuur, Univ. of Oklahoma/CIMMS and NOAA/NSSL, Norman, OK; and A. V. Ryzhkov, H. D. Reeves, M. R. Kumjian, K. L. Elmore, and K. L. Ortega
Manuscript (540.7 kB)

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. For example, a shallow 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, and may not be observed at all at distances > 50 km from the radar. Because of this, the fuzzy-logic-based Hydrometeor Classification Algorithm (HCA), which gives classifications on conical surfaces, often provides results in transitional winter weather events that are not at all representative of the precipitation type observed at ground level.

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.

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