Calibrating Differential Reflectivity with QZdrCal: An Open System Radar Product Generator (ORPG) Update with an Upgraded Dry Aggregated Snow Method.

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Bibliographic Details
Title:	Calibrating Differential Reflectivity with QZdrCal: An Open System Radar Product Generator (ORPG) Update with an Upgraded Dry Aggregated Snow Method.
Authors:	HU, JIAXI¹ jhu20@albany.edu, ZHANG, PENGFEI^2,3, KRAUSE, JOHN^2,3, RYZHKOV, ALEXANDER^2,3
Source:	Journal of Atmospheric & Oceanic Technology. Jun2026, Vol. 43 Issue 6, p673-686. 14p.
Subjects:	Meteorological precipitation, Snowstorms, Radar signal processing, Coherent radar
Abstract:	Accurate calibration of differential reflectivity (ZDR) on polarimetric weather radars remains a challenge. In addition to the internal system calibration, various techniques based on an analysis of the radar data collected are being used in research and operations. In this study, we focus on the use of dry aggregated snow (DAS) routinely observed just above the melting layer (ML) and below the dendritic growth layer (DGL) in stratiform precipitation. We assume that the intrinsic value of ZDR in DAS varies between 0 and 0.25 dB depending on the intensity of snow and its degree of aggregation. By utilizing range-defined quasi-vertical profiles of polarimetric radar variables, we can provide accurate measurements of ZDR bias above the ML or above the surface during a cold-season scenario. The most critical part of the calibration methodology is to identify DAS as opposed to other snow types with a much wider distribution of ZDR. Several criteria for DAS identification have been developed. These include the value of maximum Z and the vertical gradient of Z above the ML top (or surface for the cold season) up to the DGL bottom, the minimal value of the cross-correlation coefficient within the ML (ignored during the cold season), and the height of the storm above the DGL. This calibration technique is tested for a large number of storms and demonstrates high stability and robustness of the ZDR bias estimation with a standard error lower than 0.1 dB. This algorithm has been adopted into the U.S. Open Radar Product Generator system in FY24. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Accurate calibration of differential reflectivity (ZDR) on polarimetric weather radars remains a challenge. In addition to the internal system calibration, various techniques based on an analysis of the radar data collected are being used in research and operations. In this study, we focus on the use of dry aggregated snow (DAS) routinely observed just above the melting layer (ML) and below the dendritic growth layer (DGL) in stratiform precipitation. We assume that the intrinsic value of ZDR in DAS varies between 0 and 0.25 dB depending on the intensity of snow and its degree of aggregation. By utilizing range-defined quasi-vertical profiles of polarimetric radar variables, we can provide accurate measurements of ZDR bias above the ML or above the surface during a cold-season scenario. The most critical part of the calibration methodology is to identify DAS as opposed to other snow types with a much wider distribution of ZDR. Several criteria for DAS identification have been developed. These include the value of maximum Z and the vertical gradient of Z above the ML top (or surface for the cold season) up to the DGL bottom, the minimal value of the cross-correlation coefficient within the ML (ignored during the cold season), and the height of the storm above the DGL. This calibration technique is tested for a large number of storms and demonstrates high stability and robustness of the ZDR bias estimation with a standard error lower than 0.1 dB. This algorithm has been adopted into the U.S. Open Radar Product Generator system in FY24. [ABSTRACT FROM AUTHOR]
ISSN:	07390572
DOI:	10.1175/JTECH-D-25-0123.1