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Abstract This study aims to objectively identify storm-scale characteristics associated with tornado-like vortex (TLV) formation in an ensemble of high-resolution supercell simulations. An ensemble of 51 supercells is created using Cloud Model version 1 (CM1). The first member is initialized using a base state populated by the Rapid Update Cycle (RUC) proximity sounding near El Reno, Oklahoma, on 24 May 2011. The other 50 ensemble members are created by randomly perturbing the base state after a supercell has formed. There is considerable spread between ensemble members, with some supercells producing strong, long-lived TLVs, while others do not produce a TLV at all. The ensemble is analyzed using the ensemble sensitivity analysis (ESA) technique, uncovering storm-scale characteristics that are dynamically relevant to TLV formation. In the rear flank, divergence at the surface southeast of the TLV helps converge and contract existing vertical vorticity, but there is no meaningful sensitivity to rear-flank outflow temperature. In the forward flank, warm temperatures within the cold pool are important to TLV production and magnitude. The longitudinal positioning of strong streamwise vorticity is also a clear indicator of TLV formation and strength, especially within 5 min of when the TLV is measured. Significance StatementTornadoes that form in supercell thunderstorms (long-lived storms with a rotating updraft) are heavily influenced by the features created by the storm itself, such as the temperature of a downdraft. In this study, many different iterations of a strong supercell thunderstorm are simulated, in which tornado-like features are formed at different times with widely different strengths. A statistical method is used to identify what the storms had in common when they produced a tornado-like feature, and what they had in common when one failed to form. This study is important because it highlights which storm features are most influential to tornado formation using an objective method, with results that can be used when observing supercells in the field. 

Abstract On 28 May 2019, a tornadic supercell, observed as part of Targeted Observation by UAS and Radars of Supercells (TORUS) produced an EF-2 tornado near Tipton, Kansas. The supercell was observed to interact with multiple preexisting airmass boundaries. These boundaries and attendant air masses were examined using unoccupied aircraft system (UAS), mobile mesonets, radiosondes, and dual-Doppler analyses derived from TORUS mobile radars. The cool-side air mass of one of these boundaries was found to have higher equivalent potential temperature and backed winds relative to the warm-side air mass; features associated with mesoscale air masses with high theta-e (MAHTEs). It is hypothesized that these characteristics may have facilitated tornadogenesis. The two additional boundaries were produced by a nearby supercell and appeared to weaken the tornadic supercell. This work represents the first time that UAS have been used to examine the impact of preexisting airmass boundaries on a supercell, and it provides insights into the influence environmental heterogeneities can have on the evolution of a supercell. 

Abstract The forward-flank convergence boundary (FFCB) in supercells has been well documented in many observational and modeling studies. It is theorized that the FFCB is a focal point fore baroclinic generation of vorticity. This vorticity is generally horizontal and streamwise in nature, which can then be tilted and converted to mid-level (3-6 km AGL) vertical vorticity. Previous modeling studies of supercells often show horizontal streamwise vorticity present behind the FFCB, with higher resolution simulations resolving larger magnitudes of horizontal vorticity. Recently, studies have shown a particularly strong realization of this vorticity called the streamwise vorticity current (SVC). In this study, a tornadic supercell is simulated with the Bryan Cloud Model at 125-m horizontal grid spacing, and a coherent SVC is shown to be present. Simulated range-height indicator (RHI) data show the strongest horizontal vorticity is located on the periphery of a steady-state Kelvin-Helmholtz billow in the FFCB head. Additionally, similar structure is found in two separate observed cases with the Texas Tech University Ka-band (TTUKa) mobile radar RHIs. Analyzing vorticity budgets for parcels in the vicinity of the FFCB head in the simulation, stretching of vorticity is the primary contributor to the strong streamwise vorticity, while baroclinic generation of vorticity plays a smaller role. 

Abstract This study investigates whether the thermodynamics of supercell rear-flank outflow can be inferred from the propagation speed and vertical structure of the rear-flank gust front. To quantify the relationship between outflow thermodynamic deficit and gust front structure, CM1 is applied as a two-dimensional cold pool model to assess the vertical slope of cold pools of varying strength in different configurations of ambient shear. The model was run with both free-slip and semislip lower boundary conditions and the results were compared to observations of severe thunderstorm outflow captured by the Texas Tech University Ka-band mobile radars. Simulated cold pools in the free-slip model achieve the propagation speeds predicted by cold pool theory, while cold pool speeds in the semislip model propagate slower. Density current theory is applied to the observed cold pools and predicts the cold pool speed to within about 2 m s−1. Both the free-slip and semislip model results reveal that, in the same sheared flow, the edge of a strong cold pool is less inclined than that of a weaker cold pool. Also, a cold pool in weak ambient shear has a steeper slope than the same cold pool in stronger ambient shear. Nonlinear regressions performed on data from both models capture the proper dependence of slope on buoyancy and shear, but the free-slip model does not predict observed slopes within acceptable error, and the semislip model overpredicts the cold pool slope for all observed cases, but with uncertainty due to shear estimation.