The Supercell Polarimetric Observation Research Kit (SPORK): An Automated, Python-Based Algorithm for Examining Supercell Dual-Pol Signatures

Recent work has shown that dual-polarization (dual-pol) signatures of supercell storms, including Z_DR arcs and columns, inferred hailfall signatures, and the separation vector between the K_DP foot and Z_DR arc centroids, can be useful in determining what kind of hazards (for example, large hail, accumulating small hail, and / or tornadoes) a supercell may be likely to produce. Although these signatures are often fairly easy to identify manually in radar data, quantifying their characteristics (such as Z_DR arc area, the K_DP-Z_DR separation vector, or Z_DR column depth) by hand can be time-consuming and subjective, especially when examining large numbers of storms for research. This presentation will introduce the Supercell Polarimetric Observation Research Kit (SPORK) as a tool for quickly and objectively quantifying dual-pol characteristics of supercells. Building on a previous Z_DR arc identification and tracking algorithm, SPORK adds the capability to quantify the characteristics of several other supercell dual-pol signatures, including Z_DR column areal extent and depth, areal extent of polarimetrically inferred hailfall, and the K_DP-Z_DR separation vector. Comparisons will be shown between SPORK-calculated supercell dual-pol signatures and manually identified signatures to evaluate the algorithm’s performance, and initial results will be shown from running SPORK on a large sample of tornadic and nontornadic supercells.