Developing a Python-Based Algorithm to Identify and Track ZDR Arcs in Supercells

Recent work has shown that the size and intensity of differential reflectivity (Z_DR) 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 Z_DR 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 Z_DR arc strength are relatively easy to observe when analyzing dual-polarization radar fields, quantifying these changes by calculating the area, mean Z_DR 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 Z_DR 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 Z_DR arc characteristics for a large sample of supercells. In addition, the use of machine learning techniques to reduce false Z_DR arc detections will be explored.