A New Melting Layer Detection Algorithm that Combines Polarimetric Radar-based Detection with Thermodynamic Output from Numerical Models

In this paper, we describe work on a new “Hybrid” Melting Layer Detection Algorithm (HMLDA) that uses a combination of polarimetric measurements from the WSR-88D radar network and thermodynamic output from the High Resolution Rapid Refresh (HRRR) model to determine locations where an elevated warm layer is present. There are two primary operational benefits to this new algorithm: 1) to provide a better indication of regions where polarimetric rainfall estimates might be erroneous due to “bright band contamination”, and 2) to provide a better indication of regions where the presence of an elevated warm layer in winter storms might lead to precipitation types such as freezing rain and sleet. The HMLDA is structured to use Gaussian weighting functions to combine HRRR model thermodynamic output of melting layer location with polarimetric radar data from high-elevation scans, which are presently used by the WSR-88D network for melting layer detection, and polarimetric radar data from low-elevation scans, which can provide an indication of melting layer to more distant ranges but are increasingly inaccurate with range due to beam broadening.

Recent work on the algorithm has focused on improving techniques by which “interest fields” of polarimetric variables can be combined to provide optimal melting layer detection at low elevations and on modifying Gaussian weighting function parameters and weights to better account for beam broadening with range and to provide the most physically realistic combination of the high elevation, low elevation, and HRRR model inputs. Examples of the application of the algorithm to several winter storm events are also shown.