An Initial Assessment of Environmental Influences on QLCS-tornadogenesis from PERiLS Field Campaign Datasets and High-Resolution Simulations

It is well established that tornadic events embedded in quasi-linear convective systems (QLCSs) are difficult to forecast owing to the transient nature of the tornadoes and parent mesovorticies. These difficulties result in shorter warning times compared to tornadoes resulting from, for example, supercell thunderstorms. As a result, there is a growing need to better understand environmental and storm-generated processes that contribute to enhanced or decreased tornado risk in QLCS events and determine if such features are identifiable for warning decision making. Simulations of the processes related to tornado formation suggest that low-level mesovortices are particularly sensitive to low-level shear and buoyancy; for example, heterogeneities in the ambient environment (e.g., due to cold pool generation) can result in shallower and weaker updrafts associated with the mesovortices. On the other hand, there has been limited observation evidence to corroborate these features evident in idealized and high-resolution simulations. Fortunately, a large-scale field project is underway that will help in diagnosing environmental precursors to tornadogenesis. The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) project offers an unprecedented opportunity to sufficiently observe the environmental evolution around QLCS tornadic events to associate environmental influences on vorticity generation and subsequent tornadogenesis.

In order to assess the environmental heterogenieties contributing to enhanced or decreased risk of tornadogenesis, near-surface in-situ observations, vertical profiles of the lower troposphere, and radar observations of mesocyclones along the QLCS leading edge will be examined to characterize the environment leading up to tornadic events. Four intensive observing periods (IOPs) from the 2022 field phase will be explored and the evolution of the low-level kinematic and thermodynamic fields will be presented herein. Specifically, observations taken from sounding teams as well as the Texas Tech University StickNet platforms will be analyzed. The analyses conducted will form the basis for identifying key features in the environment that precluded tornado events. In tandem, high-resolution simulations from a WRF configuration that mimics the operational High-Resolution Rapid Refresh (HRRR) model will be conducted at variable resolutions (e.g., 500 m, 1500 m) to understand the practical limits of predicting the identifying environmental controls and whether such features could be identifiable in a real-time, operational forecasting environment. Together, this work will provide a brief look at how environmental heterogeneities are represented in experimental high-resolution simulations that would conceivably be available to forecasters to diagnose tornado precursors and issue advanced warnings.