The Chaos of Massive Supercells

A rather unexpected disaster sprang from a ‘slight’ risk the other day in Oklahoma, as several supercells erupted within an energetic warm sector ahead of a slowing cold front. While the northern cells produced hail as they drifted to the northeast then dissipated, the three southernmost cells merged to form a large, rapidly cycling storm (radar loop linked) that would drop 13 tornadoes in 90 minutes, including an EF3 that devastated the town of Cole and an EF2 that wrought extensive damage upon Shawnee. Sometimes called ‘mothership’ supercells for their impressive rotating cloud formations, these storms often form over the high plains where land surface homogeneity favors large near-laminar inflow regions. In contrast to the predictable linear paths of tornadoes within typical supercell or QLCS thunderstorms, these massive supercells produce tornadoes that revolve around a central vortex, fanning out on highly chaotic paths. The tornadogenesis is clearly top-down from the large parent vortex, and the wide inflow largely smooths the effects of any topographical heterogeneities.

10+ tornadoes from a single supercell in central OK, 4/19/2023. NOAA Damage Assessment Toolkit

Rather than belabor my land surface model with a scenario where tornadoes evolved independently of terrain, I want to draw comparisons to tornadic events in recent memory, specifically a pair of events in 2017 when I was just beginning my deep-dive into tornado modeling and prediction. First, on April 14th, a mothership supercell formed in the Texas Panhandle then parked near Dimmitt, cycling in place for three hours and spawning several tornadoes including a mile-wide EF3 wedge outside of town. This spectacular parent supercell made another appearance on this blog for its textbook bubbling outflow as observed by the GOES-16 satellite. Then, just two weeks later, Canton TX was besieged by an analogous storm, a massive rotating supercell that spawned a couple of long-track tornadoes (EF3 and EF4) that ground chaotically northward over the course of more than an hour. While these three examples will be further examined as top-end tornadic events, mega-supercells (is this a good term, or do we need even more prefixes?) that produce several tornadoes are vanishingly rare, responsible for roughly 1% of all recorded tornadoes in the last decade. Worth studying, certainly, but I believe they deserve their own category distinct from conventional supercells because their behavior is so uniquely unpredictable.

Little Rock Tornado 3/31/2023

Perhaps the most predictable tornado in my memory struck Little Rock, Arkansas on Friday afternoon, causing widespread damage and dozens of injuries. The Storm Prediction Center issued a rare “Particularly Dangerous Situation” warning and the convective outlook showed a large swath of Level 5 hazard risk (the highest) from central Arkansas northward into much of the Midwest. Part of a large outbreak of over 100 tornadoes across the warned region, this particular tornado was preceded by radar-confirmed lowerings near Hot Springs about an hour prior to touchdown, prompting a tornado warning with an above-average lead time of 21 minutes. As the storm crossed over some hills into the west Little Rock suburbs, it spawned a high-end EF3 tornado with winds up to 165 mph along a 34-mile path of destruction that crossed the entire metropolitan area.

Zooming in on the beginning of the path, the pre-established rotation intensified after passing over a valley of sheltered air then touched down as a wedge tornado after clearing Ellis Mountain. Presumably, the air in this valley was warmer relative to the surroundings, carrying an additional >100 J/kg of surface-based CAPE (convective available potential energy) when it was forced into the inflow by the topographical obstruction. When cooled, this air mass would contribute to the pressure fall inside the vortex, tighten the rotation and increase the kinetic energy (read: velocity) of the rotating winds. The computational model that I started this blog in part to document specializes in forecasting this exact scenario, quantitatively simulating the gradients in surface temperature and humidity that affect characteristics of the inflow.

Of course, the three-dimensional simulation of a tornado within a parent supercell is computationally expensive, essentially requiring a supercomputer even for a restricted domain. Even generating the differential temperature map can prove challenging, both due to computational demands and because slight deviations in input parameters can yield significantly different results. Perhaps a more practical approach than on-demand simulation would be to aggregate a range of possible pre-storm environments into a base layer that can readily be created/used by forecasters – call it a “tornado intensification risk” map for generalized conditions. While my land surface simulation work garnered only tepid interest from meteorologists and academics, the prospect of this type of hazard map piqued the interest of several representatives for insurance companies. Not that insurance rate adjustment is the only application for this type of information – in the time that this project has sat on the back burner as my life became more hectic since 2020, the idea of an aggregate data layer for tornado risk has grown on me for public access in forecasting and emergency preparedness, sacrificing scientific specificity for ease of use.

An open-access risk map for tornadoes, even as a developmental release, would be highly beneficial, especially for urban areas where tornado strikes pose an elevated threat to life and property. Little Rock was highlighted in my preliminary simulation-based testing as having a tornado risk higher than its surroundings, part of a short list that included Birmingham, Nashville, Louisville, and Tulsa. Smaller cities with elevated risk included Rome (GA), Gadsden and Tuscaloosa (AL), Conway and Fort Smith (AR), Joplin and Cape Girardeau (MO), Quad Cities and Peoria (IL), among a few others. Locations downwind from the highlighted population centers would benefit from this information as well: the town of Wynne, Arkansas was leveled when the same parent supercell regrouped to spawn another long-track EF3 tornado that stayed on the ground for over an hour and carved a track of desolation all the way to Tennessee. For a number of reasons, a comprehensive yet accessible terrain model would be a worthwhile tool for improving tornado forecasts, and while it wouldn’t prevent tragedy outright, it could certainly help to warn people in harm’s way.