Objective characterization of intense vortices in very high-resolution supercell simulations

Vortex detection algorithms are required for both research and operational applications in which data volume precludes timely subjective examination of model or analysis fields. Unfortunately, objective detection of convective vortices is often hindered by the strength and complexity of the surrounding flow. To address this problem, a variational vortex-fitting algorithm previously developed to detect and characterize vortices observed by Doppler radar was modified to operate on gridded horizontal wind data. The latter are fit to a simple analytical model of a vortex and its proximate environment, allowing the retrieval of important vortex characteristics. This permits the development of detection criteria tied directly to vortex properties (e.g., maximum tangential wind), rather than to more general kinematical properties that may poorly represent the vortex itself (e.g., vertical vorticity) when the background flow is strongly sheared. Thus, the vortex characteristic estimates provided by the technique should permit more effective detection criteria while providing useful information about vortex size, intensity, and trends therein. Potential applications of the technique include investigating relationships between tornado characteristics and mesocyclone attributes, environmental parameters or model settings in simulations, and detecting tornadoes, mesocyclones and mesovortices in real-time ensemble analyses and forecasts.

In previous work, the technique was successfully applied to 1000 m and 333 m supercell simulations. The method has since been tested with supercell simulations having grid spacings as fine as 50 m. The method proficiently detects and characterizes tornado- and mesocyclone-like vortices in the simulations, even in the presence of highly complex flow (e.g., vortices embedded within larger vortices). Representative vortex retrievals will be shown.