Tuesday, 8 January 2019: 9:45 AM
North 129B (Phoenix Convention Center - West and North Buildings)
Recent work has shown that the size and intensity of differential reflectivity (ZDR) arcs in supercell storms are related to environmental storm-relative helicity and low-level shear through these variables’ correlations to the magnitude of size sorting by the storm-relative wind. Thus, a strong or strengthening ZDR arc could be an indication that a storm is entering an environment more favorable for the development of strong low-level rotation and possible tornadogenesis. Although qualitative trends in ZDR arc strength are relatively easy to observe when analyzing dual-polarization radar fields, quantifying these changes by calculating the area, mean ZDR values in the arc, and other arc characteristics is rather time-consuming and makes research into these signatures and their potential operational applications somewhat challenging. To address this problem, an automated Python algorithm was developed to objectively identify and track ZDR arc signatures in Weather Surveillance Radar-1988 Doppler (WSR-88D) radar data and create time series of arc characteristics for each algorithm-identified storm. This presentation will discuss the development of the algorithm and will explore its verification through comparisons with manually-generated time series of ZDR arc characteristics for a large sample of supercells. In addition, the use of machine learning techniques to reduce false ZDR arc detections will be explored.
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