New Polarimetric Radar Algorithm for Melting-Layer Detection and Determination of Its Height

Alexander Ryzhkov aCooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
bNOAA/National Severe Storms Laboratory, Norman, Oklahoma

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John Krause aCooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
bNOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Abstract

A novel polarimetric radar algorithm for melting-layer (ML) detection and determination of its height has been developed and tested for a large number of cold-season weather events. The algorithm uses radial profiles of the cross-correlation coefficient (ρhv or CC) at the lowest elevation angles (<5°–6°). The effects of beam broadening on the spatial distribution of CC have been taken into account via theoretical simulations of the radial profiles of CC assuming intrinsic vertical profiles of polarimetric radar variables within the ML with varying heights and depths of the ML. The model radial profiles of CC and their key parameters are stored in lookup tables and compared with the measured CC profiles. The matching of the model and measured CC radial profiles allows the algorithm to determine the “true” heights of the top and bottom of the ML, Ht and Hb, at distances up to 150 km from the radar. Integrating the CC information from all available antenna elevations makes it possible to produce accurate maps of Ht and Hb over large areas of radar coverage as opposed to the previous ML detection methods including the existing algorithm implemented on the U.S. network of the WSR-88Ds. The initial version of the algorithm has been implemented in C++ and tested for a multitude of cold-season weather events characterized by a low ML with different degrees of spatial nonuniformity including cases with sharp frontal boundaries and rain–snow transitions. The new ML detection algorithm (MLDA) exhibits robust performance, demonstrating spatial and temporal continuity, and showing general consistency of the ML designations matching those obtained from the regional model and the quasi-vertical profiles (QVP) methodology output.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Alexander Ryzhkov, alexander.ryzhkov@noaa.gov

Abstract

A novel polarimetric radar algorithm for melting-layer (ML) detection and determination of its height has been developed and tested for a large number of cold-season weather events. The algorithm uses radial profiles of the cross-correlation coefficient (ρhv or CC) at the lowest elevation angles (<5°–6°). The effects of beam broadening on the spatial distribution of CC have been taken into account via theoretical simulations of the radial profiles of CC assuming intrinsic vertical profiles of polarimetric radar variables within the ML with varying heights and depths of the ML. The model radial profiles of CC and their key parameters are stored in lookup tables and compared with the measured CC profiles. The matching of the model and measured CC radial profiles allows the algorithm to determine the “true” heights of the top and bottom of the ML, Ht and Hb, at distances up to 150 km from the radar. Integrating the CC information from all available antenna elevations makes it possible to produce accurate maps of Ht and Hb over large areas of radar coverage as opposed to the previous ML detection methods including the existing algorithm implemented on the U.S. network of the WSR-88Ds. The initial version of the algorithm has been implemented in C++ and tested for a multitude of cold-season weather events characterized by a low ML with different degrees of spatial nonuniformity including cases with sharp frontal boundaries and rain–snow transitions. The new ML detection algorithm (MLDA) exhibits robust performance, demonstrating spatial and temporal continuity, and showing general consistency of the ML designations matching those obtained from the regional model and the quasi-vertical profiles (QVP) methodology output.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Alexander Ryzhkov, alexander.ryzhkov@noaa.gov
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