Short-Term Forecasting

Is There a “Tipping Point” between Simulated Nontornadic and Tornadic Supercells in VORTEX2 Environments?

Brice E. Coffer and Matthew D. Parker


Previous work has suggested that the lower-tropospheric wind profile may partly determine whether supercells become tornadic. If tornadogenesis within the VORTEX2 composite environments is more sensitive to the lower-tropospheric winds than to either the upper-tropospheric winds or the thermodynamic profile, then systematically varying the lower-tropospheric wind profile might reveal a “tipping point” between nontornadic and tornadic supercells. As a test, simulated supercells are initiated in environments that have been gradually interpolated between the low-level wind profiles of the nontornadic and tornadic VORTEX2 supercell composites while also interchanging the upper-tropospheric winds and thermodynamic profile. Simulated supercells become tornadic when the low-level wind profile incorporates at least 40% of the structure from the tornadic VORTEX2 composite environment. Both the nontornadic and tornadic storms have similar outflow temperatures and availability of surface vertical vorticity near their updrafts. Most distinctly, a robust low-level mesocyclone and updraft immediately overlie the intensifying near-surface circulation in each of the tornadic supercells. The nontornadic supercells have low-level updrafts that are disorganized, with pockets of descent throughout the region where surface vertical vorticity resides. The lower-tropospheric wind profile drives these distinct configurations of the low-level mesocyclone and updraft, regardless of the VORTEX2 composite upper-tropospheric wind profile or thermodynamic profile. This study therefore supports a potentially useful, robust link between the probability of supercell tornadogenesis and the lower-tropospheric wind profile, with tornadogenesis more (less) likely when the orientation of horizontal vorticity in the lowest few hundred meters is streamwise (crosswise).

Volatility of Tornadogenesis: An Ensemble of Simulated Nontornadic and Tornadic Supercells in VORTEX2 Environments

Brice E. Coffer, Matthew D. Parker, Johannes M. L. Dahl, Louis J. Wicker, Adam J. Clark


Despite an increased understanding of the environments that favor tornado formation, a high false-alarm rate for tornado warnings still exists, suggesting that tornado formation could be a volatile process that is largely internal to each storm. To assess this, an ensemble of 30 supercell simulations was constructed based on small variations to the nontornadic and tornadic environmental profiles composited from the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). All simulations produce distinct supercells despite occurring in similar environments. Both the tornadic and nontornadic ensemble members possess ample subtornadic surface vertical vorticity; the determinative factor is whether this vorticity can be converged and stretched by the low-level updraft. Each of the 15 members in the tornadic VORTEX2 ensemble produces a long-track, intense tornado. Although there are notable differences in the precipitation and near-surface buoyancy fields, each storm features strong dynamic lifting of surface air with vertical vorticity. This lifting is due to a steady low-level mesocyclone, which is linked to the ingestion of predominately streamwise environmental vorticity. In contrast, each nontornadic VORTEX2 simulation features a supercell with a disorganized low-level mesocyclone, due to crosswise vorticity in the lowest few hundred meters in the nontornadic environment. This generally leads to insufficient dynamic lifting and stretching to accomplish tornadogenesis. Even so, 40% of the nontornadic VORTEX2 ensemble members become weakly tornadic. This implies that chaotic within-storm details can still play a role and, occasionally, lead to marginally tornadic vortices in suboptimal storms.

Forecaster perceptions and climatological analysis of the influence of convective mode on tornado climatology and warning success.

Kelsey Ellis, Daniel Burrow, Kelly Gassert, Lisa Reyes Mason, Megan Porter


Tornadogenesis occurs in a variety of storm types, or convective modes, each having a unique climatology and challenges in their detection and warning. Some warnings result in false alarms, meaning that no tornado occurred within the warning polygon. We used a mixed-methods approach to assess how convective mode –– discrete supercell, cell in cluster, cell in line, or quasi-linear convective system (QLCS) –– affects the tornado climatology and National Weather Service (NWS) procedures within three County Warning Areas (CWAs): Memphis (MEG), Nashville (OHX), and Morristown (MRX), Tennessee. We used three data sets: tornadoes (2003–2014) categorized by convective mode, false alarms (2012–2016) categorized by convective mode, and eleven interviews of NWS forecasters. The CWAs had no significant difference in mode frequency when removing replication from multiple-tornado events. However, when outbreaks were included, discrete supercell and QLCS signals were identified in MEG and OHX, respectively. Convective mode, season, and time of day were strongly associated. Tornadic discrete supercells followed a traditional severe weather pattern of spring and daytime occurrences and caused fewer false alarms. More QLCS tornadoes happened at night and in winter. Cells in lines and clusters accounted for larger proportions of events in the false-alarm data set than the tornado data set. Forecasters noted challenges in detecting tornadoes in convective modes other than discrete supercells, including short-lived QLCS tornadoes. Key forecaster concerns other than convective mode included storm speed, outbreaks, and lack of ground-truthing at night. Forecasters differed in their motivation to either warn on every tornado or avoid false alarms.

Characteristics of Tornado Events and Warnings in the Southeastern United States

Alexandra Anderson-Frey, Yvette Richardson, Andrew Dean, Richard Thompson, Brian Smith


The southeastern United States has become a prime area of focus in tornado-related literature due, in part, to the abundance of tornadoes occurring in high-shear low-CAPE (HSLC) environments. Through this analysis of 4133 tornado events and 16 429 tornado warnings in the southeastern United States, we find that tornadoes in the Southeast do indeed have, on average, higher shear and lower CAPE than tornadoes elsewhere in the contiguous United States (CONUS). We also examine tornado warning skill in the form of probability of detection (POD; percent of tornadoes receiving warning prior to tornado occurrence) and false alarm ratio (FAR; percent of tornado warnings for which no corresponding tornado is detected), and find that, on average, POD is better and FAR is worse for tornadoes in the Southeast than for the CONUS as a whole. These measures of warning skill remain consistent even when we consider only HSLC tornadoes. The Southeast also has nearly double the CONUS percentage of deadly tornadoes, with the highest percentage of these deadly tornadoes occurring during the spring, the winter, and around local sunset. On average, however, the tornadoes with the lowest POD also tend to be those that are weakest and least likely to be deadly; for the most part, the most dangerous storms are indeed being successfully warned.

Using Overshooting Top Area to Discriminate Potential for Large, Intense Tornadoes

G. R. Marion, R. J. Trapp, S. W. Nesbitt


Recent work established strong links between storm updraft width and the tornado intensity, suggesting that updraft width could be used to gauge potential tornado intensity. It was also posited that overshooting top area (OTA) could be used as an analog for updraft width and, thus, as a means to assess potential tornado intensity in observed storms. The implementation of new high‐resolution GOES‐R series satellites presents a unique opportunity to investigate these findings in severe weather observations. Herein, a method using GOES‐16 longwave infrared satellite data to quantify OTA of tornadic storms is explored. A comparison between observed tornado strength and OTA yields a strong correlation (2 = 0.54). These results show the potential of these quantifications to be used with real‐time observations of tornadic storms, irrespective of storm mode, seasonality, or geographic location, allowing forecasters to determine which storms pose the highest risk to life and property.

Observed Relationship between Tornado Intensity and Pretornadic Mesocyclone Characteristics

Michael Sessa, Robert Trapp


In a previous study, idealized model simulations of supercell thunderstorms were used to demonstrate support of the hypothesis that wide, intense tornadoes should form more readily out of wide, rotating updrafts. Observational data were used herein to test the generality of this hypothesis, especially to tornado-bearing convective morphologies such as quasi-linear convective systems (QLCSs), and within environments such as those found in the southeastern United States during boreal spring and autumn. A new radar dataset was assembled that focuses explicitly on the pretornadic characteristics of the mesocyclone, such as width and differential velocity: the pretornadic focus allows us to eliminate the effects of the tornado itself on the mesocyclone characteristics. GR2Analyst was used to manually analyze 102 tornadic events during the period 27 April 2011–1 May 2019. The corresponding tornadoes had damage (EF) ratings ranging from EF0 to EF5, and all were within 100 km of a WSR-88D. A key finding is that the linear regression between the mean, pretornadic mesocyclone width and the EF rating of the corresponding tornado yields a coefficient of determination (R2) value of 0.75. This linear relationship is higher for discrete (supercell) cases (R2 = 0.82), and lower for QLCS cases (R2 = 0.37). Overall, we have found that pretornadic mesocyclone width tends to be a persistent, relatively time-invariant characteristic that is a good predictor of potential tornado intensity. In contrast, the pretornadic mesocyclone intensity (differential velocity) tends to exhibit considerable time variability, and thus would offer less reliability in anticipating tornado intensity.