Supercell-External Storms and Boundaries Acting as Catalysts for Tornadogenesis

Jannick Fischer aDepartment of Geosciences, Texas Tech University, Lubbock, Texas

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Johannes M. L. Dahl aDepartment of Geosciences, Texas Tech University, Lubbock, Texas

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Abstract

It has long been observed that interactions of a supercell with other storms or storm-scale boundaries sometimes seem to directly instigate tornadogenesis. First, the authors explore the frequency of such constructive interactions. Radar data from WSR-88D are used to categorize 136 tornadic supercells into isolated supercells and supercells that interacted with external factors within 20 min before tornadogenesis. Most cases (80%) showed some form of external influence prior to tornadogenesis. Common patterns of interactions, the typical supercell quadrant that is affected, and changes in azimuthal shear are also identified. To further study these interactions, two sets of idealized Cloud Model 1 (CM1) simulations are performed. The first set demonstrates that the speed of the near-ground horizontal flow relative to the updraft can control whether a vortex patch develops into a tornado. A weaker updraft-relative flow is favorable because the developing vortex stays in the updraft region longer and becomes less tilted. Building on these results, it is shown that external outflow can lead to tornado formation by a deceleration of the updraft-relative flow. The deceleration is caused by the pressure gradient force associated with the external outflow, which is already noticeable several kilometers ahead of the outflow boundary. This offers one possible mechanism by which external outflow can act as a catalyst for supercell tornadogenesis.

Fischer’s current affiliation: Institute of Meteorology and Climate Research-Department Troposphere Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jannick Fischer, jannick.fischer@kit.edu

Abstract

It has long been observed that interactions of a supercell with other storms or storm-scale boundaries sometimes seem to directly instigate tornadogenesis. First, the authors explore the frequency of such constructive interactions. Radar data from WSR-88D are used to categorize 136 tornadic supercells into isolated supercells and supercells that interacted with external factors within 20 min before tornadogenesis. Most cases (80%) showed some form of external influence prior to tornadogenesis. Common patterns of interactions, the typical supercell quadrant that is affected, and changes in azimuthal shear are also identified. To further study these interactions, two sets of idealized Cloud Model 1 (CM1) simulations are performed. The first set demonstrates that the speed of the near-ground horizontal flow relative to the updraft can control whether a vortex patch develops into a tornado. A weaker updraft-relative flow is favorable because the developing vortex stays in the updraft region longer and becomes less tilted. Building on these results, it is shown that external outflow can lead to tornado formation by a deceleration of the updraft-relative flow. The deceleration is caused by the pressure gradient force associated with the external outflow, which is already noticeable several kilometers ahead of the outflow boundary. This offers one possible mechanism by which external outflow can act as a catalyst for supercell tornadogenesis.

Fischer’s current affiliation: Institute of Meteorology and Climate Research-Department Troposphere Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jannick Fischer, jannick.fischer@kit.edu

1. Introduction

The currently most prevalent conceptual model of supercell tornadogenesis is based on the evolution of a single, discrete supercell (e.g., Lemon 1976; Davies-Jones 2015). However, it is generally acknowledged among researchers and forecasters that storm-external factors can also have an impact on tornado formation. As discussed in this section, these external factors typically come in two forms; (i) supercell interaction with another thunderstorm, broadly referred to as storm-merger events or (ii) supercell interaction with a preexisting external boundary.1

a. Storm mergers

Early studies on severe thunderstorms investigated changes in storm characteristics after merger events [see the review articles by Hamilton (1969) and Westcott (1984)] and found significant impacts of mergers on supercell intensity, supercell structure and tornado potential. The most prominent example in the literature for the impact of storm mergers in tornadogenesis is perhaps the study by Lee et al. (2006), who focused on the 19 April 1996 Illinois tornado outbreak and showed that approximately 54% of tornadoes happened within 15 min of a cell merger. A similar percentage has been reported over multiple independent cases by Rogers and Weiss (2008), although the percentage was lower for significant tornadoes [27% for tornadoes rated EF2 or higher on the enhanced Fujita (EF) scale; Rogers 2012]. This discrepancy seems consistent with the recent study by Flournoy et al. (2022), who analyzed a dataset of merger events, about half of which were associated with tornado reports (M. Flournoy 2022, personal communication). For weak supercells, the merger tended to result in a strengthening of the low-level mesocyclone while stronger supercells more commonly experienced a weakening after the merger. They also found that an increase in azimuthal shear was more likely if multiple mergers were involved and over a longer duration.

Furthermore, a number of case studies have been published in which tornadogenesis seemed to be associated with the interaction and merging of a supercell with another thunderstorm (Dowell and Bluestein 2002a; Wurman et al. 2007; French et al. 2013; Weiss et al. 2015) or a squall line (Goodman and Knupp 1993). In an analysis of 21 squall line–supercell merger events, French and Parker (2012) found a maximum of tornado occurrences in the hour before the merger was complete, leading them to speculate that “in cases where conditions may be favorable for tornado formation, the merger may in some way serve as an instigator.” The term “catalyst” is preferred herein instead of “instigator” or “trigger,” because it emphasizes that the merger is an external influence and more of a help than a necessary criterion for supercell tornadogenesis.2 Throughout the literature, the term “merger” is only loosely defined and can either refer to the collision of the buoyant updrafts or the merging of the precipitation and outflow regions (e.g., Westcott 1984). Consequently, the hypothesized physical mechanisms by which storm mergers can influence tornadogenesis are also rather diverse, as described below.

For one, the coalescence of the buoyant thermals typically results in a single, more buoyant updraft (e.g., Wilkins et al. 1976; Lemon 1976). Furthermore, collision of the outflow of storm mergers frequently causes new updraft development in a “bridge” between the two cells (e.g., Tao and Simpson 1984). Since the outflow characteristics (such as the amount of precipitation and evaporative cooling) often drastically change during a storm merger, Hastings and Richardson (2016) showed that the resulting increase in baroclinity can favor a stronger mesocyclone, which facilitates a stronger low-level updraft through the upward-directed nonlinear-dynamic pressure gradient force. Overall, these storm intensifications increase tornado potential due to the important role of the low-level updraft in intensifying near-ground rotation via vertical vorticity stretching (Markowski and Richardson 2014). Another type of factor is the direct influence of gust fronts associated with the outflow of merging storms. In the cases investigated by Wurman et al. (2007), a supercell repeatedly produced short-lived tornadoes at approximately the same times as the supercell merged with weaker storm cells moving in from the south. They hypothesized that the increase in convergence along the southern storms’ outflows could have contributed to tornadogenesis. Recent numerical simulations by Honda and Kawano (2016) support this idea. A difficulty with predicting the outcome of a merger is that it can also weaken the resulting storm or disrupt ongoing tornadogenesis (e.g., Thompson and Edwards 2000; Bluestein and Weisman 2000; Wurman et al. 2007; French and Parker 2012; Klees et al. 2016), which is perhaps more common than a constructive merger (Flournoy et al. 2022). Furthermore, in hindsight it is often difficult to determine whether the storm merger actually played a role in tornadogenesis, or was just incidental (e.g., Tanamachi et al. 2015). The potential outcome of storm mergers depends on many factors, such as the distance, direction, and relative strength of the two cells [see Lee et al. (2006) and Hastings and Richardson (2016) for a detailed categorization of mergers].

b. Boundary interactions

A second common type of interaction of a supercell is with a local airmass boundary, which can be part of a synoptic front or produced by thunderstorm outflow. The observation that supercells frequently intensify and produce tornadoes when being close to a larger-scale boundary was already made based on early-generation radar and satellite data (e.g., Hamilton 1969; Purdom 1976). Maddox et al. (1980) analyzed multiple tornado outbreaks in the vicinity of boundaries and attributed the enhanced tornado risk to an increase in near-ground moisture, convergence, and vertical vorticity along the boundary. They furthermore concluded that stronger tornadoes were more likely in situations when the supercell traveled along the boundary instead of crossing it. Later studies suggested that the increased horizontal vorticity in vicinity of boundaries is a key factor through intensification of the mesocyclone (Markowski et al. 1998; Atkins et al. 1999; Rasmussen et al. 2000). In the VORTEX-95 cases analyzed by Markowski et al. (1998), around 70% of tornadoes happened near a boundary. In their statistic, they did not differentiate between synoptic-scale fronts and outflow boundaries, which also could have been associated with merger events. Similar to storm mergers, the interaction of a supercell with a boundary can also lead to a decrease in tornado potential (e.g., Blanchard 2008), especially if the storm quickly moves into colder surface air (Maddox et al. 1980; Doswell et al. 2002).

c. Research focus

The studies discussed thus far reveal storm mergers or boundary interactions can promote tornadogenesis and mostly attribute this to an intensification of the low-level updraft [step 3 of tornadogenesis in Davies-Jones (2015)]. Advances in our understanding of tornadoes suggest that a closer look at the vortex-scale dynamics might offer further insights. In observations, the position and movement of near-surface vertical vorticity maxima relative to the storm-scale updraft have been shown to have a large impact on tornado formation and maintenance (Dowell and Bluestein 2002b; Tanamachi et al. 2012; Skinner et al. 2014). In simulations, tornado-like vortices (TLVs) are often preceded by vortex patches or sheets in the supercell outflow (Markowski et al. 2014), which roll-up to symmetric vortices due to self-axisymmetrization (Dahl 2020) and undergo a transition of vorticity dynamics (Fischer and Dahl 2022). Several studies have shown that if the supercell low-level updraft is weak or not collocated with the near-ground circulation (e.g., Skinner et al. 2014; Markowski and Richardson 2014, 2017; Coffer and Parker 2017; Guarriello et al. 2018), or if the vortex patch moves out of the updraft area too quickly (e.g., Markowski and Richardson 2014; Fischer and Dahl 2020), the developing vortex dissipates. In a broad sense, this dissipation happens due to the increasing downward pressure gradient acceleration into the vortex core, which counteracts the upward-directed pressure gradient associated with the mesocyclone and ultimately causes a downdraft within the vortex (e.g., Markowski and Hannon 2006). In this way, which is sometimes described as “choking” or “vortex-valve effect” (Lewellen 1971; Lemon et al. 1975; Davies-Jones 1986, p. 224), vortex genesis often leads to its own demise. It seems that strong vertical pressure gradient accelerations due to the mesocyclone aloft are needed to overcome this effect and to sustain tornadogenesis (e.g., Coffer and Parker 2018). These ideas are consistent with observations of dissipating tornadoes, which are often displaced from the main updraft and vertically tilted (e.g., Golden and Purcell 1977; Bluestein et al. 2019; French and Kingfield 2019; Griffin et al. 2019). Therefore, a critical factor during tornadogenesis seems to be whether the developing vortex can stay in the main updraft region during the intensification period.

The purpose of this study is to apply these vortex-scale concepts in an effort to better understand supercell interactions with external factors. Specifically, this study investigates the question whether external outflow can aid in keeping the near-surface vortex patches within the updraft in situations where they would otherwise have moved away from it. In section 2, the study will be further motivated by quantifying how frequently such interactions could be responsible for tornadogenesis in a relatively large dataset of tornado cases and WSR-88D radar data. Then, sections 3a and 3b will present two sets of idealized simulations to further investigate the possible role of external outflow in supercell tornadogenesis. Sections 4 and 5 offer a discussion and summary of the results, respectively.

2. Observations

This analysis merely serves as an additional evidence for the frequency of external interactions in tornadic supercells, justifying looking at some details in simulations in section 3. Previous studies that offered a statistical analysis of external factors on tornadogenesis either looked at storm mergers (Lee et al. 2006; Rogers and Weiss 2008; Flournoy et al. 2022) or supercell interactions with boundaries (Markowski et al. 1998; Magee and Davenport 2020). However, storm mergers may also imply outflow interactions, even if these local cold pools are often chaotic and not clearly distinguishable on radar. Furthermore, one instigating mechanism for tornadogenesis, which will be proposed in section 3, seems possible with both larger-scale outflow-boundaries and local outflow from storm mergers. For these reasons, the goal of this section is to quantify how many tornadoes occur during or after any interaction with external storms or outflow boundaries. Synoptic boundaries are not included because their role in creating a locally more favorable environment for tornadogenesis (e.g., Maddox et al. 1980; Rasmussen et al. 2000; Magee and Davenport 2020) is not a focus of the simulation part of this study. The presented observations offer an analysis complementary to Flournoy et al. (2022), who filtered their dataset for merger events and then produced relevant statistics. The basis for our analysis is tornado reports and concurrent interactions such as mergers are analyzed.

a. Methods

The Storm Prediction Center tornado reports from 2015 to 2017 and during the Great Plains severe storms season (March–June) were used, accessed using the svrdb Python package (https://github.com/tsupinie/svrdb). Weather Surveillance Radar-1988 Doppler (WSR-88D) data were then manually analyzed for all tornado cases that fulfilled the following filtering criteria. (i) Only cases in the central Great Plains (between 32°–43°N and 90°–103°W) were used to focus on supercell tornadoes and reduce the number of mesoscale convective systems (MCSs), waterspouts, as well as coastal or orographic effects. The remaining nonsupercell tornadoes were later manually removed from the dataset using radar reflectivity and velocity data. (ii) The reported starting location of the tornado was required to be less than 100 km from the nearest radar to have sufficient resolution and quality in the radar data. (iii) Only the first tornado of a supercell was analyzed to avoid the possibility that a previous tornado might have impacted the supercell structure and flow field. This was done by excluding all tornadoes that were preceded by another reported tornado within 90 min and 100 km. This filter was quite strict and likely removed many cases that could have been used, especially during tornado outbreaks. However, there is no obvious reason to assume that the interactions would have a different impact in these different scenarios. Therefore, and since abundant tornado cases were available, these strict criteria were preferred to make sure the analysis contained only the initial tornado that a supercell produced.

The 0.5° elevation angle radar reflectivity and radial velocity data were then analyzed using the Py-ART package (Helmus and Collis 2016). Cases for which radar data were not available or were erroneous, were excluded. All available radar scans (usually around 7–12 depending on the scanning interval, which varied between 3 and 7 min) within 35 min before and 10 min after the reported tornado were considered.

After all of the above filtering, 136 supercell tornado cases remained (20 cases were rated EF2 or higher). The cases were then manually categorized into (i) isolated supercells, and (ii) supercells that interacted with other storms or external boundaries before tornadogenesis. Cases were sorted into the latter category if a region with greater than 30 dBZ (indicating an external storm with significant intensity and outflow) or a fine line in radar reflectivity (indicating a mesoscale boundary) moved within 5 km of the relevant region of the supercell during the 20 min before the tornado report.3 Hence, features included whole-storm mergers, but also radar-detected boundaries and flanking-line cells or other prolonged convection that affected the tornadic supercell. This approach ensured that all possible interactions with radar-detectable storms and outflow were included. Figure 1 provides a detailed example for the analysis, and Figs. 2 and 3 show reflectivity snapshots of example cases of both isolated and nonisolated storms. Animations for all the example cases can be viewed online (https://github.com/Janfisch/FischerDahl2022).

Fig. 1.
Fig. 1.

Example analysis at (a),(b) 2200 and (c),(d) 2225 UTC 25 Mar 2015, the scan immediately after tornadogenesis. The outflow from supercell 2 (indicated in the velocity field) impacted supercell 1 from the southwest. Subsequently, the mesocyclone in cell 1 intensified and a tornado was reported at 2221 UTC. The black triangles show the location of the reported tornado, and the thin black circle indicates a distance of 25 km from this location. The black arrow points to the tornadic supercell from the direction from which the external outflow influenced the supercell, thereby indicating the approximate angle of interaction (see text), which was estimated as 240°. The angle is more readily obtained when comparing all available radar scans (available at https://github.com/Janfisch/FischerDahl2022). The red arrow points in the direction of the radar.

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

Fig. 2.
Fig. 2.

Examples for cases that were categorized as isolated supercells. The scans shown were chosen to best represent the respective case and are at (a) 2108 UTC 18 Apr 2015, (b) 0205 UTC 27 Apr 2015, and (c) 1941 UTC 19 May 2015. The time t relative to the tornado report is indicated near the top of each panel. The calculated path of the supercell (see the text) is shown as a thin dashed line. All other symbols are as in Fig. 1.

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

Additionally, the angle at which a boundary or a merging storm impacted the tornadic supercell was estimated manually by drawing a line from the interacting feature to the supercell mesocylone using the radar scans prior to the interaction. The storm motion of the supercell was taken into account to make the angle storm relative. For instance, the outflow of supercell 2 in Fig. 1 impacted supercell 1 from the southwest because both supercells moved in parallel to each other and the outflow spread out from supercell 2. The goal of this analysis was not to obtain exact angles, which would have been impossible given the complexity and data limits, but to broadly estimate a direction from which most interactions originated.

Last, to compare this dataset with the cases in Flournoy et al. (2022), changes in azimuthal shear over time (in their analysis used as a proxy for supercell intensity) were determined for all cases. Azimuthal shear was calculated from the 0.5° elevation angle radial velocity data in Py-ART. A distance-weighted average interpolation (Barnes 1964) to a Cartesian grid with 0.005° grid spacing was used to smooth the data prior to the analysis. This provided similar azimuthal shear fields as in the MYRORSS dataset used in Flournoy et al. (2022), which was unfortunately not available between 2015 and 2017. The azimuthal shear value range was slightly larger in the present dataset, presumably because of differences in the smoothing. Like in Flournoy et al. (2022), the azimuthal shear analysis was carried out by manually tracking the supercells backward (using radar reflectivity and velocity images to follow the hook echo and mesocyclone region) to the radar scan closest to 30 min before the tornado report. Then, a linear storm motion vector was calculated between this location to the reported tornado. Adding this storm motion in each radar scan resulted in an approximated supercell path (Figs. 2 and 3). The maximum azimuthal shear within a radius of 5 km from this location was then determined for each radar scan, resulting in a time series of maximum azimuthal shear for each supercell.

b. Results

The analysis revealed that 109 of 136 supercell tornadoes (80%) were categorized as nonisolated and 27 (20%) were categorized as isolated. For the tornadoes rated EF2 or higher, the percentage of nonisolated supercells was likewise 80% (16 of 20). Two other scientists that were enlisted by the first author confirmed this assessment.4 Since some of the interactions were likely coincidental, this percentage should be seen as an upper bound for the potential influence of these external factors. Further discussion and comparison with previous literature can be found in section 4.

Several patterns of interaction that were commonly observed are briefly described below: (i) a chain of two or more supercells, with the southern cell “pumping” outflow into the northern tornadic cell (Figs. 1a–d and 3a,c), (ii) a quasi-linear convective system (QLCS) or outflow boundary impacting a supercell from the west (Fig. 3d), (iii) a previously discrete storm merging with the supercell either before or during tornadogenesis (in many cases these were relatively small cells merging in the supercell inflow region) (Figs. 3e,f), and (iv) convection along the flanking line of the supercell, with the flanking-line storms producing outflow that influenced the supercell (Figs. 3h,i).

Fig. 3.
Fig. 3.

As in Fig. 2, but for cases that were categorized as nonisolated. The scans shown were chosen to best represent the case and are at (a) 0242 UTC 8 Apr 2015, (b) 2355 UTC 17 Apr 2015, (c) 0002 UTC 27 Apr 2015, (d) 0027 UTC 6 May 2015, (e) 1921 UTC 6 May 2015, (f) 2116 UTC 6 May 2015, (g) 0012 UTC 7 May 2015, (h) 0226 UTC 26 May 2015, and (i) 2008 UTC 28 May 2015. The arrows indicate the approximate direction from which the external storm or boundary influenced the supercell. Cases in which no angle could be determined because the interactions were too complex are shown in (b) and (g).

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

A distribution of the approximate angles at which external cells or boundaries impacted the supercells is shown in Fig. 4a. Almost all interaction prior to tornadoes happened from the southwestern quadrant. This result is consistent with Rogers and Weiss (2008) and the rear-flank mergers investigated by Hastings and Richardson (2016).

Fig. 4.
Fig. 4.

(a) For the 109 supercells with external influence, the angle from which the external boundary or cell moved into the region of tornadogenesis is shown, binned in 20° increments (from initial 5° estimates). The angles start at 0° north and are storm relative, so, e.g., an influence directly from the south would correspond to an angle of 180°. (b) Scatterplot of all 136 cases, showing the difference in azimuthal shear between the radar scan immediately before the tornado report (using the radar scan after the tornado report instead did not meaningfully change the results) and the scan closest to 30 min before the report (y axis) as a function of the latter value (x axis). Circles show the nonisolated cases, and crosses show the isolated ones. Red coloring indicates cases with tornadoes rated EF2 or higher.

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

Figure 4b, which is comparable to Fig. 11 by Flournoy et al. (2022), shows that the vast majority of cases featured an increase in azimuthal shear over the 30 min before tornadogenesis. This is perhaps not surprising considering that all cases became tornadic. In other words, only constructive interactions were included here, in contrast to both constructive and destructive merger-events included in Flournoy et al. (2022). Similar to their results, a decrease in azimuthal shear (in their analysis interpreted as a destructive merger) was more frequent if the mesocyclone was already strong before the interaction. However, this trend is also found in the isolated cases here, which suggests that it might be more of a data artifact than a physical relationship. It is acknowledged, however, that the isolated supercell dataset is small and hence might not be as statistically robust and that the nonisolated category is not identical to the merger cases in Flournoy et al. (2022). Last, no obvious relationship between tornado intensity and azimuthal shear change was found (Fig. 4b). Overall, the observations emphasize that interactions are common prior to tornadogenesis. One way such interactions might facilitate tornadogenesis will now be studied with idealized simulations.

3. Idealized simulations

In this section, two different idealized simulation approaches are used to further investigate the possibility that supercell-external factors sometimes serve as catalysts for tornado formation. First, it is shown why tornadogenesis might fail in a highly idealized scenario. These results are then used to understand the impact of external outflow on tornadogenesis.

a. The impact of varying near-ground updraft-relative flow

1) Model setup

The goal of this section is to shed more light on the near-ground vortex dynamics during tornadogenesis in different updraft-relative flow situations. A highly idealized simulation approach was chosen, using Cloud Model 1 (CM1; Bryan and Fritsch 2002), version 19.5. The horizontal grid spacing was 100 m in the central domain with a horizontal dimension of 15 km × 15 km and then stretched to 500 m near the outer domain boundary with a total size of 60 km × 60 km. The vertical grid spacing was 20 m in the lowest 600 m and then stretched to 180 m between 600 and 5600 m (model top). The Coriolis parameter was set to zero. Since the focus was on the tornadogenesis period and not on a realistic representation of the developed TLV structure, the free-slip bottom boundary condition was used (see e.g., Fischer and Dahl 2022).5 The model configuration and base-state potential temperature profile (constant ∂θ/∂z) were based on the idealized simulations in Fischer and Dahl (2022). The setup is shown in Fig. 5a. A constant heat source was used to generate a strong updraft in the lowest few kilometers [Sw0 = 0.08 K s−1, xw = −1000 m, yw = 0 m, zw = 700 m, horizontal radius Rw = 2000 m, and vertical radius Zw = 500 m; see Markowski and Richardson (2014) and Fischer and Dahl (2020) for details]. The simulations were dry and no heat sink was employed, hence no cold outflow was present. After 620 s, when the updraft was fully developed, an elliptical vortex patch was artificially added in the lowest 400 m, identical to the one used in Fischer and Dahl (2022) (x = 300 m, y = 100 m, Dmajor = 4 km, Dminor = 1 km, and ζmax = 0.03 s−1). This vortex patch then rolled up to a mostly symmetric vortex while intensifying. The simulations were run for 1800 s.

Fig. 5.
Fig. 5.

Idealized model setups for (a) the evolution of an elliptical vortex patch below an updraft in section 3a and (b) simulations with external outflow as opposed to without one in section 3b. Heat sources are illustrated as red cylinders, heat sinks are shown as blue cylinders, and the artificial vortex patch is a black ellipsoid.

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

An additional layer of complexity was added to this idealized tornadogenesis model by conducting four CM1 runs with different near-ground wind speeds relative to the stationary heat source. In the control simulation (SR3), a uniform easterly wind of u0 = −3 m s−1 was used as by Fischer and Dahl (2022). In the three additional runs, the magnitude of the easterly storm-relative wind was increased linearly toward the surface in the lowest 400 m, (see insets in Fig. 6), so that u0(z = 0) = −6 m s−1 (SR6), −9 m s−1 (SR9) and −12 m s−1 (SR12) at the surface, respectively.6 Hence, the four simulations had varying degrees of near-ground updraft-relative easterly winds between 3 and 12 m s−1, representing different flow situations of supercell outflow relative to the updraft. This led to different advection speeds for the vortex patch during its roll-up to a TLV.

Fig. 6.
Fig. 6.

(a) SR3, (b) SR6, (c) SR9, and (d) SR12 simulations at 900 s, showing vertical velocity (10 m s−1 contour in gray), vertical vorticity (0.1 s−1contour in red), and velocity vectors (black). Only data below 2.5 km AGL are shown. The vector positions follow a uniform spatial distribution. The small insets in each corner show the wind profile of each simulation (m s−1). Note that the y axes (m) in the insets are not uniformly scaled and that in the 3D visualizations the scale vector and distance markers may slightly vary in accuracy because of the visual distortion.

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

2) Results

In all four cases, after initialization of the vortex patch, the vertical-vorticity-rich air was quickly ingested into the updraft and the vortex patch rolled up and was advected vertically over a depth of several km, similar to Fischer and Dahl (2022). As discussed in their study, this implies vertical vorticity stretching and vortex symmetrization. Figure 6 shows the four cases at 900 s [an animation of the full evolution is available online (https://github.com/Janfisch/FischerDahl2022)]. In the cases with larger updraft-relative flow speed, the near-ground vortex was advected away from the updraft relatively quickly, while the upper part of the vortex stayed rooted in the updraft. The TLVs in SR3 and SR6 were the strongest and most vertically coherent (Figs. 6a,b). In the SR9 run, the near-ground circulation was displaced around 600 m farther west than aloft. Therefore, the vortex was slightly tilted between around 0 and 1.5 km AGL (Fig. 6c). At these lower levels, the vortex was also much weaker and the largest vertical vorticity was found farther aloft. These differences were even more pronounced in the SR12 run, in which the vertical vorticity mostly stayed below 0.1 s−1 in the lowest km (Figs. 6d and 7a). Figure 7 shows that this weaker rotation is likely attributable to the transport of the low-level vortex out of the main area of convergence (the largest surface convergence is near the vortex itself and disturbances in the flow in the lee of the updraft while broad and deep convergence is only found near the heat source).

Fig. 7.
Fig. 7.

SR12 simulation at (a) 900 and (b) 1200 s. Horizontal divergence at 10 m AGL is shaded, and that at 590 m is contoured black (−0.01 s−1). Vertical vorticity is contoured at 0.05 and 0.1 s−1 at three different levels: 10 (red), 590 (teal), and 1200 (orange) m. Horizontal wind vectors at 10 m AGL were also added.

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

These characteristics seem consistent with some funnel clouds in observations of tornadogenesis failure (e.g., Tanamachi et al. 2013). Here the vortex visibly rotates near the cloud base and is often vertically tilted (Fig. 8). The simulations suggest that a closed circulation through all levels is likely present in these cases. However, the circulation can have vertically varying intensity. The rotation aloft (e.g., in area of the funnel) can be much stronger than near the ground (Fig. 7a, also see Figs. 6d and 8a). Alternatively, the vortex can be relatively strong aloft and at the ground, but weaker in between (Fig. 7b, also see Figs. 6c and 8b), likely because the convergence field close to the ground is at times locally (e.g., close to the vortices; see Fig. 7) decoupled from the convergence farther aloft associated with the main updraft. Consistent with observations (e.g., Tanamachi et al. 2013), this suggests that damaging winds are possible near the ground, even if no funnel is observed and no rotation is detected at the lowest radar scan.

Fig. 8.
Fig. 8.

(a) Short-lived funnel cloud (black arrow) with no noticeable ground circulation (provided by author J. Fischer). (b) Vertically tilted funnel cloud; weak ground circulation can be observed toward the bottom left (black arrow) (provided by A. Schueth).

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

Throughout the rest of the simulation, the TLV dissipated first in the SR12 run and then in the SR9 run, due to an increasing displacement of the near-ground vortex from the main updraft. In the SR6 run, the vortex persisted throughout the whole simulation but also slowly moved west at all levels, away from the stronger updraft, resulting in a gradual decrease in TLV intensity. These later stages of the simulations might be slightly influenced by the different shapes of the updraft with the different wind profiles (see e.g., Fig. 6) but these vertical velocity differences tended to be small and the vortices themselves also had some influence on the updraft structure.

Overall, these idealized simulations demonstrate the importance of the storm-relative advection of the developing vortex, which has previously been indicated in observations (e.g., Dowell and Bluestein 2002b; Tanamachi et al. 2012; Skinner et al. 2014) and simulations (e.g., Markowski and Richardson 2014; Guarriello et al. 2018; Murdzek et al. 2020; Gray and Frame 2021). The results are important in light of the main research focus of this study because any external factors that could influence the near-ground flow could thereby also influence tornadogenesis. In the following, this possibility will be explored with a second set of simulations.

b. External outflow acting as catalyst for tornadogenesis

The previous section supports the idea that the duration that intensifying vortices spend below a sufficiently strong updraft region is critical for tornadogenesis. The updraft-relative flow appears to be an important factor in this regard. The equivalent in real supercells is the outflow air, in which the developing tornado would be embedded (e.g., Markowski and Richardson 2014). This section explores how this near-ground flow in the updraft region could be altered by external outflow, thereby influencing tornadogenesis.

1) Model setup

It is reasonable to assume that an instigating mechanism for tornadogenesis would only work if the parent supercell already had the general potential to produce a tornado (e.g., Goodman and Knupp 1993; French and Parker 2012). Therefore, using supercell simulations to analyze a potential instigating effect of storm mergers is difficult, because it is somewhat subjective whether the modeled supercell has tornado potential or not at a given time. For this reason, this section used the idealized setup of Fischer and Dahl (2020), in which the tornadic and nontornadic cases were clearly separated and depended on the prescribed updraft and cold pool strengths (their Fig. 15).

The CM1 configuration is similar to the one in section 3a, except that near-ground vertical vorticity was generated in outflow air by employing an array of heat sinks northeast of the heat source [Fig. 5b; see Fischer and Dahl (2020) for more details]. This setup resulted in a complex outflow structure and the repeated generation of vortex patches [following the downdraft mechanism based on reorientation of baroclinically generated horizontal vorticity in descending parcels (Davies-Jones and Brooks 1993; Dahl et al. 2014; Dahl 2015; Parker and Dahl 2015; Fischer and Dahl 2022)]. A simulation from Fischer and Dahl (2020) with a heat source strength of Sw0 = 0.08 K s−1 and heat sink strength of Sc0 = −0.0105 K s−1 was chosen as control run here, serving as a representative example of a barely nontornadic simulation.

In a restart run of this control simulation, a large and constant heat sink (x = −3 km, y = −5 km, z = 0 km, Rw = 2 km, Zw = 3 km, and Sc0 = −0.01 K s−1) was started at 1-h simulation time in the area southwest of the updraft (Fig. 5b). This heat sink generated a growing cold pool, thereby mimicking the impact of an external outflow boundary or the outflow of a storm merger on the supercell-like pseudo-storm from the southwest. Therefore, this second simulation will be referred to as the external outflow (EO) run.

2) Results

The control simulation remained nontornadic over the whole 2-h simulation time (<0.2 s−1). As discussed by Fischer and Dahl (2020) and shown in Figs. 9a,c,e,g, TLV genesis failed because the vortex patches did not fully intensify to TLV strength before being advected out of the updraft area (note the similarity of tornadogenesis failure to e.g., the SR3 run in section 3a). However, the pseudo-storm could be seen as being on the verge of becoming tornadic, because simulations with only slightly stronger heat source and heat sink strength produced a TLV [see Fig. 15 in Fischer and Dahl (2020)]. Therefore, this control run served as an ideal comparison with the EO run to test the possible impacts of external outflow.

Fig. 9.
Fig. 9.

Comparison of the (left) control run and (right) the external outflow run at (a),(b) 4200; (c),(d) 4500; (e),(f) 4800; and (g),(h) 5100 s. Vertical vorticity is shaded at 30 m AGL and contoured at 590 m (teal) and 1.2 km (orange). The 30-m (AGL) −1-K potential temperature contour is indicated in dashed blue, and horizontal wind vectors are shown. Vertical velocity at 1.2 km is contoured in black (+3 m s−1 is solid line, and −3 m s−1 is a dotted line). The area with external outflow is labeled with “EO.”

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

In the EO run, the additional heat sink generated a strong cold pool (minimum potential temperature perturbation around −5 K) that spread northeast against the surrounding flow into the updraft area of the pseudo-supercell. In Fig. 9b, the still weak external outflow can be seen in the southwestern corner 600 s after the restart. The vorticity and horizontal velocity fields in the updraft area were still very similar between the two simulations at this time. Another 300 s later at 4500 s (Fig. 9d), more differences appeared in the EO run, with the wind field in the region southwest of the vortex patch attaining a more westerly component and the vortex patch staying a few hundred meters farther northeast, being less tilted with height, and slightly stronger. These differences became more pronounced over the next 300 s (Fig. 9f). Furthermore, the vortex patch developed into a symmetric TLV around this time, with a rapid pressure decrease in the core (Fig. 10b). At 5100 s (Fig. 9h), the TLV weakened before it recycled into an even stronger TLV a few minutes later, which persisted throughout the rest of the EO run. In contrast, the vortex patch in the control run was already advected a few km farther downstream and rather asymmetric (Fig. 9g).

Fig. 10.
Fig. 10.

Comparison of (a) the control run and the (b) external outflow run at 4680 s when the TLV was rapidly intensifying and just reached <1 hPa p′ (red contour). Divergence at 30 m AGL is shaded. Vertical velocity at 1.2 km AGL is contoured in black (+3 m s−1 is a solid line, and −3 m s−1 is a dotted line). The TLV and the boundary between the two outflow areas are labeled.

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

The change of tornado potential between the control and EO run can only be a result of the additional heat sink and resulting external outflow, because all other parameters were identical. This is perhaps surprising because the external outflow boundary stayed several kilometers southwest of the updraft throughout the period discussed. Therefore, enhanced convergence, as suggested by Wurman et al. (2007) and Honda and Kawano (2016), cannot be the reason for why a TLV formed here. This is supported by Fig. 10, which shows that the near-ground convergence is similar in the two simulations in the vicinity of the TLV and convergence along the external outflow boundary is only enhanced around 2 km southwest of the developing TLV. It appears instead that the added heat sink caused changes in the flow ahead of the actual boundary, which resulted in the vortex patch remaining in the updraft region for a longer time. To explain this rather passive influence, the perturbation pressure field is analyzed next.

Figure 11a shows that the external outflow was associated with an average high pressure perturbation near the ground, consistent with what would be expected from the hydrostatic pressure increase due to larger density in the cooled region. However, the positive pressure perturbation extended beyond the external outflow and hence several kilometers ahead of the gust front (Fig. 11b). This pattern is consistent with 2D simulations of density currents (e.g., Markowski and Richardson 2010, their Fig. 2.6) and likely dominated by the dynamic (nonhydrostatic) forcing by the interaction of the outflow boundary with the opposing flow (Droegemeier and Wilhelmson 1987). This conclusion is supported by Fig. 12, which shows that, in the relevant region in which the vortex patch was advected (central region of the blue box), the hydrostatic gradient (green vectors in Fig. 12b) was mostly weak or even opposing the total Δp′ gradient (Fig. 11b).

Fig. 11.
Fig. 11.

Difference in the 30-m (AGL) perturbation pressure field between the external outflow run and the control run (shaded), averaged between 3900 and 4500 s (the period 10 min before tornadogenesis). (a) The 30-m (AGL) horizontal convergence (0.01 s−1; dashed green) and the 500-m (AGL) vertical velocity (3 m s−1; black) at 4500 s in the EO run were added as visual reference. (b) A smaller region around the vortex patch. The green vectors at every second grid point represent the gradient of the shaded pressure difference field, i.e., the difference in the averaged pressure gradient between the simulations. The black vectors represent the vector difference of the horizontal wind between the two runs averaged over the same time. The white dashed line indicates the approximate location of the gust front at 4500 s.

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

Fig. 12.
Fig. 12.

As in Figs. 11a,b, but for the difference in the surface hydrostatic pressure ( ph=30mtopρgdz+ptop, with ptop being the pressure at the top of the domain) between the external outflow (EO) run and the control run, averaged between 3900 and 4500 s (shaded). The black dashed line indicates the approximate location of the gust front at 4500 s. The total p′ difference in Fig. 11a is a result of this hydrostatic component and nonhydrostatic effects (e.g., Markowski and Richardson 2010).

Citation: Monthly Weather Review 151, 1; 10.1175/MWR-D-22-0026.1

The pressure difference field in Fig. 11b also shows a dipole in the area of the vortex patch itself (x = −1.2 km and y = −0.7 km), indicating that the vortex patch in the EO simulations remained farther northeast than in the control run, as already qualitatively observed in Fig. 9. Except for in this dipole region, the average pressure gradient force was directed to the northeast in this area (green vectors in Fig. 11b). Likely attributable to this pressure gradient, the flow in which the vortex patch was embedded, had a smaller component away from the updraft (black vectors in Fig. 11b). This is supported by a simple evaluation of the pressure gradient acceleration term dυ dt−1 = −ρ−1p. Assuming ρ = 1 kg m−3, an average p gradient from −5 to −10 Pa km−1 (Fig. 11a) over 10 min would result in a velocity change of 3–6 m s−1.

The idealized simulations in section 3a have shown that such a decrease in the motion speed of the vortex patch on the order of several meters per second can lead to tornadogenesis success. Therefore, it seems that the influence of the pressure field ahead of the external outflow boundary led to changes in the flow, ultimately leading to the formation of a TLV.

A relevant analogy to the observed flow changes might be a stagnation point flow (e.g., Batchelor 2000, chapter 5.5), in which the horizontal flow encounters a rigid wall and is deflected perpendicular to the initial flow direction, which requires a high pressure perturbation. Here, the nonrigid external outflow boundary acted similarly to such a wall, albeit three-dimensionally and on a larger scale, generating pressure perturbations that decelerated and deflected the opposing flow several kilometers ahead. The results do not mean, however, that a well-defined boundary is always necessary for the proposed mechanism, since a pressure gradient can already result from hydrostatic pressure increase in the external outflow alone (Fig. 12). The contribution of hydrostatic effects on tornadogenesis was relatively small in the simulations but it might be more important in cases where no well-defined outflow boundary is impacting the supercell (e.g., Figs. 3a–c,e–i).

4. Discussion

a. Robustness of the idealized simulations

The idealized simulations in section 3a indicate that whether a near-ground vortex patch can intensify into a TLV can depend on the advection speed relative to the updraft, which is consistent with observations (e.g., Dowell and Bluestein 2002b; Tanamachi et al. 2012; Skinner et al. 2014) and previous simulations (e.g., Markowski and Richardson 2014; Guarriello et al. 2018; Murdzek et al. 2020; Gray and Frame 2021). Building on this argument, section 3b showed that the residence time of vortex patches in the updraft area could be increased by external outflow. Due to the locally fixed nature of the heat sinks and because the outflow from the pseudo-storm blocked farther northward advancement of the external boundary, the external outflow never reached the TLV throughout the 2-h simulation time. Therefore, the simulation here can be seen as an ideal scenario, because negative effects of external outflow were mostly excluded. In many real cases, outflow of e.g., merging storms does ultimately seem to have a detrimental effect on the tornado, leading to subsequent tornado dissipation and storm weakening (e.g., Markowski et al. 1998; Wurman et al. 2007; Flournoy et al. 2022). The influence of external pressure perturbations on the flow found in this study could also play a role in this destructive scenario, for instance if developing vortices were forced out of the updraft area.

The robustness of the results was tested with additional simulations in the parameter space of Fischer and Dahl (2020). Variations of the EO run with other combinations of heat source and sink strengths, slightly different positions of the additional heat sink, or a breaking dam style heat sink that produced a straighter oriented density current, were performed. These cases were mostly consistent with the findings here. The only simulation that evolved differently was one with a very weak heat sink array (Sc0 = −0.006 K s−1). The initially much weaker vortex patches (than in the case presented here) did not form a TLV until the external outflow boundary reached the updraft area. At that point, it seems that vertical vorticity in the much stronger external outflow could be increased to TLV strength by the strong updraft. This could mean that in some cases vertical vorticity generation on the cool side of the external boundary could also be important.

b. Comparison of the different external factors for tornadogenesis

In this study, the percentage of supercells that were influenced by external storms or boundaries prior to tornadogenesis is even higher (around 80%) than in previous studies [around 50% in Lee et al. (2006) and Rogers and Weiss (2008), and 70% in Markowski et al. (1998)]. Reasons for this increase could be that only initial supercell tornadoes were included here, and that not only one type of interaction was considered. It is important to realize that the significance of this and the above cited studies is weakened by the fact that the frequency of interactions in nontornadic storms is unknown and could be similarly high over the same period (here 20 min). Since the focus of the present study was on the modeling side and the observations served mainly as a motivation, this problem is left for future studies. Nevertheless, the large percentage found herein is at least consistent with the different hypothesized mechanisms (as reviewed in section 1). In the present study, an additional mechanism is proposed, which relies on changes of the vortex-scale flow fields.

The relative importance of these different mechanisms is difficult to quantify. In many observed cases herein, tornadogenesis happened during a period when the high reflectivity region of an external storm continuously influenced the supercell, without overtaking it (e.g., Fig. 1). In others, the precipitation regions were fully merged before the circulation intensified and the tornado was reported (e.g., Figs. 1e,f). Such a variety in timing or relative location was also found in Markowski et al. (1998) and Lee et al. (2006), which might suggest different mechanisms. For instance, the pressure field influence proposed in this study is more likely to be relevant in cases where tornadogenesis happened relatively early during the interaction (e.g., Figs. 3a,c,d).

Three final aspects are noteworthy. (i) A common perception is that isolated supercells tend to have the highest tornado potential, which at first seems to be contradicting the findings here. However, most interactions in this dataset impacted the supercells from the southwestern quadrant (Figs. 4a and 3c,d,h), which means that the supercell inflow was usually undisturbed. In other cases, the merging cells were often small or weak (e.g., Figs. 3b,e,f). Hence, it seems that a supercell needs to be “isolated enough” but some interactions of the right type could actually be beneficial. (ii) Local descending reflectivity cores (DRCs) or downbursts in the rear-flank outflow could have a similar effect as the external boundary in section 3b and change the flow structure favorably for tornadogenesis (e.g., Rasmussen et al. 2006; Byko et al. 2009). In fact, Markowski et al. (2012) observed a similar pressure gradient southwest of the developing tornado as shown here, but associated with a DRC. As suggested by Markowski et al. (2018), such an impact of storm-internal dynamics and microphysics would contribute to the complexity and weak internal predictability of tornadogenesis (see also Coffer et al. 2017; Flournoy et al. 2020; Markowski 2020). This, as well as external influences such as the ones discussed in this study (see also Dowell and Bluestein 2002b; Klees et al. 2016), are likely reasons for the variety of tornado outcomes that are possible in a given environment. (iii) Much of our current knowledge about tornadoes relies on computer simulations of supercells in horizontally homogeneous environments. Storm interactions are actively avoided in most of these studies by using a single, localized initiation (e.g., a warm bubble). It seems that many (first) tornadoes could depend on such interactions. Therefore, more research on nonisolated storms is warranted.

5. Summary

To complement the extant literature on supercell interactions with external boundaries and mergers, 136 supercell tornado cases were analyzed using radar data from WSR-88D. A broad spectrum of interactions (storm-merger events, as well as preexisting storm-scale boundaries and outflow from neighboring storms) were included. In this dataset, 80% of supercell tornadoes happened during or after a period of such a storm-storm or storm-boundary interaction. Almost all interactions were estimated to occur within the southwestern quadrant of the supercell. Several common patterns of interactions were also identified, such as a chain of supercells, flanking-line convection, a QLCS or outflow boundary catching up to a supercell, and small inflow mergers. Although it remains unclear how many of these interactions were merely coincidental, these results support the idea that external factors can sometimes serve as catalysts for tornadogenesis.

To shed more light on the possible dynamics of these interactions, two sets of idealized simulations were performed in CM1, focusing on the vortex dynamics during tornadogenesis. The first experiment modeled the evolution of a pre-tornadic vortex patch into a tornado. It was shown that the near-ground updraft-relative flow has a direct effect on tornado potential. If the near-ground updraft-relative flow is relatively fast, the developing vortex remains in the region with largest vertical velocity gradient for a shorter time, limiting the stretching of vertical vorticity. Hence, the vortex tends to be weaker and more vertically tilted, both reducing tornado potential. Furthermore, these simulations may offer some insights into the characteristics of funnel clouds, showing that the associated vortex column can be strongly tilted and sometimes vary in intensity with height.

A second set of idealized simulations was performed to relate these results to the importance of supercell interactions. An external outflow was added to the southwest of a previously nontornadic pseudo-supercell. It was found that this resulted in a high pressure perturbation within the external outflow and several km downstream of the boundary, the latter being dominated by the dynamic pressure contribution. Since the outflow impacted the pseudo-supercell from the southwest, this led to a pressure gradient acceleration opposing the horizontal near-surface flow in the updraft region. Hence, the vertical vorticity rich supercell outflow air was able to remain beneath the main updraft in a region of horizontal convergence, which facilitated tornadogenesis in an otherwise nontornadic setup.

This mechanism can be seen as a possible catalyst for tornadogenesis, in addition to the ones previously suggested (e.g., enhanced convergence along outflow boundaries, updraft intensification via coalescence of two buoyant thermals, or more favorable mesoscale environments close to airmass boundaries). Which one of these factors is most relevant, or how common externally instigated tornadogenesis really is, remain open questions.

Acknowledgments.

This research was supported by NSF Grant AGS-1651786; Dr. George Bryan is acknowledged for providing CM1. Alex Schueth and Billy Faletti were of essential help for the analysis and categorization of tornado cases. Thanks are also extended to Dr. Matt Flournoy as well as the two anonymous reviewers. Discussions with Dr. Chris Weiss, Dr. Matt Parker, Dr. Eric Bruning, and Cameron Nixon contributed to early stages of the study.

Data availability statement.

The animations referred to in the text, the CM1 source code, namelist files, and a list of the tornado cases can be found online (https://github.com/Janfisch/FischerDahl2022) or upon request to the first author.

Footnotes

1

Changes in the environment over time (e.g., Coffer and Parker 2015; Klees et al. 2016) and supercell interaction with convective boundary layer features (e.g., Nowotarski et al. 2015) have also been shown to influence supercell structure. However, since these could be seen as properties of the respective environments, they are not specifically discussed here.

2

The analogy to a chemical catalyst is imperfect because the merging storm is usually not unchanged after the interaction.

3

This range in space and time was chosen to be consistent with the simulations in section 3. The “relevant” region where the tornado formed was typically the hook echo (in reflectivity) and mesocyclone region (couplet in radial velocity).

4

The percentages of nonisolated supercells were 86% and 82% in these independent assessments.

5

When using semislip, the results did not meaningfully change.

6 This was realized by having a domain motion of u = 3 m s−1 and adding a layer of linearly increasing winds with height in the lowest 400 m. The different wind profiles led to small changes in the shape of the updrafts (Fig. 6), but the influence on the updraft strength was small in the area of the vortex.

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