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  • Author or Editor: Vivek N. Mahale x
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Vivek N. Mahale
,
Guifu Zhang
, and
Ming Xue

Abstract

The three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor Z H , 2) differential reflectivity Z DR, 3) copolar cross-correlation coefficient ρ hv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(Z H ), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

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Vivek N. Mahale
,
Guifu Zhang
, and
Ming Xue

Abstract

On 14 June 2011, thunderstorms developed along a cold front in central Oklahoma in a thermodynamic environment that was conducive for downbursts. One of the thunderstorms produced a wet downburst in Norman, Oklahoma, that resulted in surface winds in excess of 35 m s−1 (>80 mi h−1) and hailstones in excess of 4 cm in diameter. Unique 1-min observations of the downburst were recorded by an Oklahoma Mesonet station. These observations indicated a 6.6-hPa pressure rise that was coincident with a rain rate of 213 mm h−1 at the center of the downburst. In this event, both the research KOUN (Norman) and operational KTLX (Oklahoma City, Oklahoma) Weather Surveillance Radar-1988 Doppler (WSR-88D) instruments were scanning this downburst and its parent storm at close range (<30 km). KOUN provided polarimetric radar data (PRD) while both radars provided limited dual-Doppler coverage. The evolution of the downburst is analyzed mostly through the use of reconstructed range–height indicators of the PRD. A hydrometeor classification algorithm (HCA) is applied to the PRD to gain further understanding of the microphysical evolution of the downburst. Through the analyses, it is seen that graupel aloft made a transition to a nearly all rain and hail mixture above the 0°C level. This large area of mixed rain and hail eventually descended to the ground, causing the downburst. In this study, the HCA analyses are utilized to develop a conceptual model that characterizes the hydrometeor evolution of the parent downburst storm.

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Reese Mishler
,
Guifu Zhang
, and
Vivek N. Mahale

Abstract

Polarimetric variables such as differential phase ΦDP and its range derivative, specific differential phase K DP, contain useful information for improving quantitative precipitation estimation (QPE) and microphysics retrieval. However, the usefulness of the current operationally utilized estimation method of K DP is limited by measurement error and artifacts resulting from the differential backscattering phase δ. The contribution of δ can significantly influence the ΦDP measurements and therefore negatively affect the K DP estimates. Neglecting the presence of δ within non-Rayleigh scattering regimes has also led to the adoption of incorrect terminology regarding signatures seen within current operational K DP estimates implying associated regions of unrealistic liquid water content. A new processing method is proposed and developed to estimate both K DP and δ using classification and linear programming (LP) to reduce bias in K DP estimates caused by the δ component. It is shown that by applying the LP technique specifically to the rain regions of Rayleigh scattering along a radial profile, accurate estimates of differential propagation phase, specific differential phase, and differential backscattering phase can be retrieved within regions of both Rayleigh and non-Rayleigh scattering. This new estimation method is applied to cases of reported hail and tornado debris, and the LP results are compared to the operationally utilized least squares fit (LSF) estimates. The results show the potential use of the differential backscattering phase signature in the detection of hail and tornado debris.

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