An Assessment of Mesovortices in Quasi-Linear Convective Systems From 2013-2023 Using GridRad-Severe

Abstract

Tornadoes produced by quasi-linear convective systems (QLCSs) often develop abruptly and are generally transient in nature, presenting forecasting challenges for operational meteorologists and posing a significant risk to the public. When nowcasting QLCS tornadogenesis, radar observations provide one of the primary sources of information for warning decision-making. Prior studies and operational techniques, such as the Three Ingredients Method (Schaumann and Przybylinski, 2012), have identified radar patterns that have predictive value and can support warning decision-making for QLCS mesovortex (MV) production and tornadogenesis. However, research supporting many of these signals is limited by small sample sizes, inconsistent storm mode classifications, manual radar interrogation practices, and a lack of dual-polarization variables. The exclusion of polarimetric signatures is particularly notable given recent findings that suggest signatures such as specific differential phase (KDP) drops and midlevel KDP cores may aid in predicting low-level rotation or MV development and intensification. Here, we investigate the use of radar signatures as a predictive tool for QLCS MV tornadogenesis through two major efforts: (1) Development a radar-based dataset of QLCS MVs with automatically-classified storm segments and an extensive spatiotemporal range, and (2) comparison the radar characteristics of tornadic and nontornadic MVs to better understand the possible patterns that can indicate a MV’s tornadic potential. The dataset is constructed using the GridRad-Severe archive, a high-resolution, multi-radar dataset with over ten years of single- and dual-polarization radar volumes. To properly isolate QLCS storm objects from the radar data, a seven-mode classification scheme is applied that differentiates convective objects based on composite reflectivity and mid-level rotation (Potvin et al., 2022). Thousands of QLCS objects are objectively identified from 2013 to 2023 across the CONUS. The spatial and temporal distribution of the QLCS objects and the MVs embedded within them are analyzed and compared with previous climatologies, validating the identification and classification process. The storm objects are also tracked through time using an echo-top height tracking algorithm and linked to storm reports, yielding a comparative analysis of the radar signatures between tornadic and nontornadic MVs. Assessment of the dataset distribution shows that the spatiotemporal behavior of the MVs follows the previously understood behaviors of QLCS objects very closely. Some of these behaviors include being most densely concentrated over the Deep South, occurring most frequently at night, and being most common during the spring and cool season. The radar patterns of tornadic and nontornadic MVs were examined through probability-matched composite means. Analyzing the radar patterns of both tornadic and nontornadic MVs, the discriminatory value of radar variables is mixed. For single-polarization variables, there appears to be significant discriminative utility in the strength of azimuthal shear throughout the vertical and low-level radial divergence. For polarimetric variables, the value is less clear; the distribution of differential reflectivity does not appear significantly different between tornadic and nontornadic populations, but low-level KDP does show different structure between the two populations. Overall, these findings provide further insight into the radar characteristics of tornadic QLCS MVs and suggest that certain radar signatures may provide useful supplemental guidance for the warning decision process.