1. Introduction
The cooling and associated stabilization of the atmospheric boundary layer shortly before and immediately after local sunset, otherwise known as the nocturnal transition, leads to a series of thermodynamic and kinematic changes in the atmosphere. Limited forecasting-based research exists regarding precisely how supercells evolve during the nocturnal transition; there are numerous temporal and spatial changes that occur in the supercell’s local environment, and complex interactions exist between the storm and these variations. A lack of understanding regarding such interactions makes the accurate prediction of the lifetime of a supercell challenging, which therefore makes quality forecasting of supercell track and strength difficult.
The governing dynamics of supercell thunderstorms have been well covered through numerous previous studies (e.g., Lemon and Doswell 1979; Davies-Jones 1984; Rotunno and Klemp 1985; Klemp 1987). However, these processes were revealed through idealized simulations utilizing temporally and spatially fixed environments. The nocturnal transition inherently modifies the environment, including thermodynamic and kinematic parameters that influence dynamical processes within the storm. Thus, as the environment changes, the mechanisms that dictate storm structure and storm behavior also change. For example, after sunset, an isolated supercell may 1) dissipate (e.g., Davenport and Parker 2015a,b); 2) merge with other supercells, a mesoscale convective system (MCS), or other convective cells (e.g., Billings and Parker 2012; French and Parker 2012); 3) grow upscale to a larger form of convection (e.g., Peters and Eure 2016; Peters et al. 2017; Reif and Bluestein 2017); or 4) continue to propagate as maintained convection either through becoming elevated or remaining surface based (e.g., Colman 1990; Nowotarski et al. 2011).
Interactions between stable boundary layers and supercells have been explored in recent research (e.g., Nowotarski et al. 2011; Billings and Parker 2012; Geerts et al. 2017; Macintosh and Parker 2017). These numerical and observational studies suggest that supercells are more resilient to low-level stabilization than previously thought, with supercells persisting as surface-based or elevated convection in moderate to strong stability. The lifting associated with the pressure perturbation resulting from the rotating updraft was hypothesized to lift even stable low-level parcels given sufficient instability aloft (Nowotarski et al. 2011); strong dynamic lifting via the vertical perturbation pressure gradient force (VPPGF) would be further supported by increases in storm-relative helicity (SRH) associated with the nocturnal low-level jet (LLJ; e.g., Kerr and Darkow 1996). Maintenance of strong updrafts via large increases in SRH after sunset also raises the risk of supercellular tornadoes in spite of stronger low-level stability (Davies and Fischer 2009; Fischer and Davies 2009; Mead and Thompson 2011).
The results of simulations from Coffer and Parker (2015) further support the findings of Nowotarski et al. (2011), where simulations of increases in low-level shear over time (mimicking the nocturnal transition and development of a LLJ) resulted in enhanced low- and midlevel vorticity within the simulated supercell. In turn, nonlinear dynamic lifting was strengthened, thus allowing for easier lifting of stable low-level parcels. Davenport and Parker (2015a) also demonstrated that nonlinear dynamic lifting works to continue lifting parcels into the updraft as stability increases over time; however, if the overall buoyancy of these parcels becomes too low, dissipation will instead occur.
These previous research studies yield several questions; for example, how does low-level stabilization and the formation of the LLJ impact supercell evolution (i.e., which of the four possible categories)? Are there systematic differences in the environments of the various storm evolution types? How do these differences influence supercell processes and the associated subsequent evolution? For example, how large of an increase in low-level shear and helicity (enhancing dynamic lifting) is needed to compensate for a certain rate of stabilization, promoting supercell maintenance? Using 157 observed supercells that were originally isolated at sunset, the goals of this research are to 1) identify how the environment evolves in maintained, dissipated, merged, and upscaled supercells during the nocturnal transition and 2) quantify the extent to which the environmental evolution is unique for each category. The intended outcome of such knowledge is to promote enhanced short-term forecasts, as well as a better understanding of how supercells respond to environmental variations.
In the next section we describe the cases collected for this study and the methods used to analyze and compare their environments. We then compare and contrast the supercell evolution categories using a number of different analyses in our results in section 3. A discussion of the implications of our findings is provided in section 4, with overall conclusions given in section 5.
2. Data and methods
Supercell cases utilized for this research were first identified using the Storm Prediction Center’s Severe Thunderstorm Event Archive from 2005 to 2016 for the months of March–June, focusing on storms that occurred in the Great Plains of the United States. Hail, wind, and tornado reports collocated with isolated1 convective cells visible on archived composite radar loops were used as a first guess in identifying supercells; this process produced an initial set of 368 possible supercells. To assess the impact of the nocturnal transition and account for the seasonal change in the onset of diurnal cooling, the local sunset time (SS) was calculated for each potential supercell based on the date of the event and its latitude and longitude at 0000 UTC and then rounded to the nearest hour. This method allowed for a date- and location-relative start time of the nocturnal transition. For example, for a supercell occurring on 10 June 2016 at 34.0°N and 100°W, sunset occurs at 0150 UTC; this sunset time would be rounded to 0200 UTC.
Supercells were confirmed by the presence of a Mesocyclone Detection Algorithm (MDA) flag at the calculated sunset time; a combination of a characteristic hook echo in the reflectivity data and a distinct velocity couplet present in either the 0.5° or 1.5° elevation angles at the nearest National Weather Service radar also sufficed in the absence of an MDA flag (Stumpf et al. 1998; Jones et al. 2004). In questionable cases where the MDA may be unreliable (i.e., the storm was far from the radar), a combination of the storm damage type (e.g., tornado or large hail), storm motion, and the limited radar data was used. Following this interrogation, a total of 157 supercells were confirmed (Table 1). It is acknowledged that this process created a somewhat biased dataset; only examining supercells that have confirmed severe reports is geographically limited (see Gropp 2017) and fails to account for potentially weaker supercells that do not produce severe weather. Indeed, as will be shown in the results, the majority of our cases were moderate or long-lived supercells (Bunkers et al. 2006a,b), indicating stronger storms.
Occurrences of sample supercell cases.
Minor cooling associated with the nocturnal transition occurs shortly before sunset; thus, the start of the nocturnal transition was defined as sunset hour minus one (SS −1; Stull 1988). The confirmed supercells were then classified based on their evolution (dissipating, merging, upscaled, or maintained) from SS −1 to SS +5 (Fig. 1), using the following criteria. A supercell was selected as a dissipation event if the cell remained isolated and ceased displaying supercellular characteristics before SS +5 (e.g., Fig. 1a). A supercell that lost the MDA flag for three consecutive scans and ceased any midlevel rotation, or a supercell that no longer had discernable midlevel rotation or a bounded weak echo region, was considered to have dissipated. If the suspected dissipating supercell was positioned far from a radar, a combination of the above and cessation of hail reports was used to determine if the supercell dissipated. The cessation of hail was used since it would represent a weakening of the updraft; elevated supercells generally cease damaging wind or tornado production (e.g., Colman 1990; Horgan et al. 2007), but continue to produce hail. Maintained cases were chosen if the supercell remained isolated and continued to exhibit supercell characteristics through SS +5 (e.g., Fig. 1b). Upscale cases were selected if the supercell grew into a larger form of convection that was not preexisting (e.g., Fig. 1c). A merger type was selected if the isolated supercell collided with other supercells, or a larger convective feature, such as a squall line (e.g., Fig. 1d). The distinguishing feature between the upscale and merger categories is that merger was selected if the merged cells then dissipated or if the supercell interacted with a preexisting convective feature. A total of 86 dissipation cases, 14 maintained cases, 12 merger, and 45 upscale cases were identified (Table 1).
Examples of supercell evolution categories, including (a) dissipation, (b) maintained, (c) upscale, and (d) merger cases. As needed, the supercell of interest is identified by a circle in the appropriate panels.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
Once each supercell and its evolution type was confirmed, the associated environmental parameters were extracted using the 0-h analyses at each hour between SS −1 and SS +5 of the Rapid Update Cycle (RUC) and the Rapid Refresh (RAP) models (Benjamin et al. 2004; Benjamin et al. 2016). These models were chosen because of both the time and spatial resolution they provide. Note that cases between 2005 and 2008 utilized 20-km grid-spacing RUC data, while cases between January 2009 and April 2012 utilized 13-km grid-spacing RUC data. For cases starting in May 2012, the RAP model was used, similar to the 13-km RUC with the same grid spacing and vertical coordinate system. It is acknowledged that differences in boundary layer schemes between the RUC and RAP provide a source of uncertainty (e.g., Cohen et al. 2015); to assess the representativeness, errors, and biases of the RUC and RAP model profiles, the model soundings were compared to the closest observed profile at 0000 UTC. Comparisons of buoyancy and low-level shear and storm-relative helicity demonstrated errors within similar ranges to those identified by Thompson et al. (2003), Thompson et al. (2007), and Coniglio (2012).
For each hour from SS −1 to SS +5, a storm-relative upwind grid point in the inflow region of the supercell was selected based on the closest latitude and longitude point. Given that the grid spacing of the models ranged from 13 to 20 km, the grid point used varied from 10 to 30 km from the updraft signature (using either the hook echo or velocity couplet of the mesocyclone) of the supercell. The distance of the grid point from the updraft signature varied because of where the supercell was positioned on the grid; no soundings were pulled when the grid point was within 10 km of the updraft signature to avoid contamination from convective adjustment in the model. For upscaled and merged supercells, the grid point chosen was 10–40 km ahead of the direction of propagation of the squall line or MCS, in line with the portion of the system where the supercell interacted. At these selected inflow grid points, a vertical profile was created using the 37 vertical pressure levels; the model-reported surface pressure was used as the base of the profile, with the 25-hPa-spaced levels used thereafter.
Once the model inflow soundings were created, numerous environmental parameters were derived. Standard layers for shear and SRH (using Bunkers-estimated storm motion; Bunkers et al. 2000) were calculated, including effective inflow layers (Thompson et al. 2007); surface-based (SB), lowest 100-hPa mixed-layer (ML), and most unstable (MU) convective available potential energy (CAPE) and convective inhibition (CIN) were calculated along with varying temperature lapse rates and composite parameters, including the supercell composite parameter (SCP) and the significant tornado parameter (STP). Additionally, parameters shown to discriminate between short- and long-lived supercells were also calculated, including 0–8-km bulk vertical shear, 0–8-km storm-relative winds, ML bulk Richardson number (MLBRN), and ML lifting condensation level (Bunkers et al. 2006b). The python library SHARPpy was used for the calculations of all the thermodynamic and kinematic variables (Blumberg et al. 2017).
To quantify similarities and differences among the environments of each evolution category, two statistical tests were utilized, the Student’s t test and the two-way Kolmogorov–Smirnov (KS) test; since the KS test provides more robust statistical significance when the sample sizes are greater than 30, the t test was also used to supplement when sample sizes were small. These tests were performed by comparing distributions of a given parameter between storm evolution types from hours SS −1 to SS +5.
Additional information regarding the environment and history of each supercell at a variety of scales was collected to assess differences between classifications not available through a single proximity sounding. First, model output was utilized to assess the synoptic environment (e.g., 500-hPa geopotential height). Second, the mesoscale environment was evaluated at every hour between SS −1 and SS +5 by examining the spatial distribution of model-derived thermodynamic and kinematic parameters in the region surrounding the supercell. This was achieved using a 208 km × 208 km grid of RAP and RUC soundings in the inflow region of the supercells; the grid contained 16 × 16 interpolated grid points every 13 km. The supercell is located at the third grid point (26 km) south and east of the top-left corner of the grid (e.g., see Fig. 3). The grid is then 182 km south and 182 km east of that point, defining the inflow region. Given the different grid spacings of the RUC and RAP, all data were linearly interpolated onto the same grid, with true grid spacing varying from 20 to 13 km depending on the RUC/RAP version.
One last potentially distinguishing characteristic among the evolution categories that were determined was the total storm lifetime, relative to and after sunset. The storm lifetime is defined as the time from when the supercell first formed (i.e., when it first attained supercellular characteristics) to when the supercell underwent its associated evolution. Note that while maintained supercells sustained their supercellular characteristics past SS +5, for the purposes of comparison to other evolution categories, SS +5 is used as the “end” time.
3. Results
To maintain a persistent rotating updraft (i.e., promote a maintained supercell during the nocturnal transition), it is hypothesized that increases in SRH via the LLJ and the existence of sufficient MUCAPE will provide the necessary support for supercells to be maintained (either as surface based or elevated) through SS +5, independent of surface-based CIN. This hypothesized result would follow suit with the findings of Nowotarski et al. (2011) and Coffer and Parker (2015), who showed that a strong, rotating updraft would still be able to sustain a supercell despite surface-layer stabilization. It is further expected that the major distinguishing characteristic between upscaled and maintained supercells will be a difference in SRH, where upscaled storms will have a statistically lower amount, as upscaled cases are expected to develop more unidirectional shear to promote the linear convective feature. Because of the expected weakening of updraft speed and rotation, the dissipating storms are anticipated to have the smallest amount of MUCAPE and SRH, accelerating their demise once CIN increases, since the VPPGF in the updraft will fail to continue lifting moist parcels to their levels of free convection (LFCs). The extent to which these expectations were fulfilled in our analyses will be described in the following subsections.
a. Synoptic environments
The supercell-relative composite mean 300-, 500-, and 925-hPa geopotential height fields using RUC/RAP data at local sunset time were calculated for each evolution type to assess the extent to which large-scale differences could explain the classification categories; the composite region measured 780 km west, 910 km east, 520 km south, and 742 km north of the supercell location. The 500-hPa mean heights (Fig. 2) for each evolution type displayed similar patterns as at 300 hPa (not shown). Maintained and merger cases display the largest height gradients and the largest southerly component to the flow; upscale and dissipation cases exhibit comparatively lower amplitude and weaker height gradients at 500 hPa. Via the thermal wind, the large height gradients and deeper meridional flow in the maintained and merger cases would infer larger shear and stronger upper-level forcing (e.g., enhanced upper-level divergence, strong vorticity advection). The mean 925-hPa heights and winds (Fig. 2) showed the expected trough and associated southeasterly low-level flow for each evolution type, consistent with the 500-hPa fields; notably, the maintained and merger cases show a deeper trough, larger height gradients, and stronger winds at 925 hPa. Overall, the maintained and merger types were situated in environments with stronger synoptic-scale support, while the upscale and dissipation types tended to occur in the weaker synoptic environments of lower amplitude.
Composite mean local sunset 925-hPa heights (shaded), 925-hPa wind barbs, and 500-hPa (contoured) height fields for each supercell evolution category. Supercell location is indicated by the black dot.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
b. Mesoscale environments
Common forecasting parameters were evaluated in the inflow region, including MUCAPE, MUCIN, and effective SRH; these parameters were selected as being the most likely to illustrate differences among the evolution types based on the findings of previous research (e.g., Nowotarski et al. 2011; Coffer and Parker 2015). Composite grids of these parameters were generated for each respective evolution category.
Substantial horizontal variability in MUCAPE is present across the inflow domain in each evolution category, with the most favorable values present near the supercell (e.g., Parker 2014; Fig. 3); merger cases stand out as the only evolution type with noticeably weaker buoyancy magnitudes by SS +5. Even so, the MUCAPE values shown in Fig. 3 represent relative minimums, with decreases in buoyancy present throughout the nocturnal transition for each evolution type (not shown). These results broadly suggest that MUCAPE may play a lesser role in influencing evolution, since dissipation, upscale, and maintained supercells all illustrate similar magnitudes and trends with time. In contrast, MUCIN displays much greater discrepancies across case types by SS +5 (Fig. 4). Initially, there were small differences between evolution types at SS (domain-wide, weak MUCIN near −50 J kg−1 was evident; not shown), but by SS +5, the inflow significantly stabilized with increasingly negative MUCIN present in dissipation cases, while maintained cases displayed comparatively weaker MUCIN. Upscale cases were similar to maintained cases with more favorable MUCIN present across the upwind portion of the inflow. Merger cases appear to be in a generally less favorable mesoscale environment with large gradients in MUCIN (Fig. 4); however, given the sample size (Table 1), the gradients in merger events may not be a physically consistent signal.
Mean MUCAPE (shaded) and surface winds (barbs) on a 208 km × 208 km inflow grid at SS +5. Note that the grid is normalized to the supercell location, indicated by the black dot.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
Effective SRH (Fig. 5) gradients across the inflow domain vary by evolution type but with local maxima near the supercell in each case; notably, maintained cases have much larger effective SRH values than other evolution types, which could promote enhanced nonlinear dynamic lifting and support maintenance in concert with favorable MUCAPE and MUCIN (Figs. 3 and 4). This alone suggests the potential discriminatory power of effective SRH to at least separate maintained cases from other evolution categories. There also appear to be somewhat different effective SRH values among the remaining categories; the extent to which any differences are statistically significant will be assessed in upcoming comparisons of near-inflow environments.
As in Fig. 3, but for effective SRH.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
c. Storm lifetimes
The total length of time a storm spent with supercellular characteristics, including before the nocturnal transition, was examined to determine its ability to predict the evolution type. Previous research by Bunkers et al. (2006a,b) indicated that long-lived (>4 h) supercells tended to be more isolated and discrete, while short-lived (<2 h) supercells were associated with storm mergers and organizational transitions. Notably, the vast majority of the cases (of all evolution categories) were either moderately (62%) or long-lived (36%) supercells. By definition, maintained cases were the longest lived, with a mean lifetime of 7 h; after that, merger cases lasted on average approximately 5 h, followed by dissipation and upscale cases (both lasting approximately 4 h). Nearly all cases formed around SS −2, with the exception of merger cases, which tended to form only slightly earlier, around SS −3 (Fig. 6). Altogether, this suggests that the initiation time and storm lifetime are not significant factors in determining supercell evolution; rather, the environmental changes occurring through the nocturnal transition are key to producing differing patterns of evolution. This idea will be explored further in the next subsection.
The mean supercellular lifetime of each evolution classification (indicated by the bar), including ±1 standard deviation lines for the start and end times. The bottom of the bar indicates the mean start time when supercellular characteristics were observed, while the top of the bar indicates the mean time of evolution from a supercell to their respective category type. There is no standard deviation bar for the end of maintained cases since they existed at least to SS +5 but the full lifetime was not recorded.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
d. Composite proximity environments
The average proximity sounding environment at SS −1 and SS +5 for each supercell classification type reveals environmental modifications largely focused in the low levels, most notably nocturnal cooling (Fig. 7). The low-level changes produced significantly different environments for each storm type. MUCAPE on average decreased by ~50% over time for each category; in contrast, MUCIN did not change at a universal rate, indicating that MUCIN is potentially a discriminatory parameter. Notably, maintained cases were associated with the smallest increase in stability (i.e., the smallest decrease in MUCIN) relative to the other evolution types (Fig. 7). A slightly warmer elevated mixed layer is seen in dissipation cases; as seen in Fig. 2, the dissipation cases exhibit more westerly winds, consistent with a warmer elevated mixed layer. Thus, dissipation cases are likely to develop CIN the quickest given the same amount of surface cooling across evolution types during the nocturnal transition.
Mean composite soundings for all case types. Solid temperature and dewpoint lines indicate values at SS −1, while dashed contours indicate values at SS +5. The average MUCAPE and MUCIN values at SS −1 and SS +5 are also annotated. Horizontal lines are the effective inflow layer bounds at SS −1 and SS +5.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
Another trend evident in the average sounding profiles is a decrease in the depth of the effective inflow layer from SS −1 to SS +5 for merger, dissipation, and upscale cases. Maintained cases, however, contained quasi-steady effective inflow layer depths over time, which appears to be tied to the subtle increases in dewpoint during the nocturnal transition. Cooler temperatures coupled with minor increases in dewpoint result in more moist surface parcels, quasi-constant CIN (as noted earlier with regard to average MUCIN in maintained cases), and a constant effective inflow-layer depth over time. Changes in temperature and dewpoint above 800 hPa are smaller in magnitude; the upscale, merger, and maintained cases all see moistening above 600 hPa while dissipation events on average contain subtle increases in moisture.
Composite hodographs were created by averaging the 0–6-km wind profiles for all cases in each storm classification at three times: SS −1, SS +2, and SS +5 (Fig. 8). The strongly curved, veering wind profile, sustained over time in each category, favors right-moving supercells. In all four storm evolution types, low- and midlevel wind speeds increased over time, most significantly between SS +2 and SS +5, likely due to the strengthening of the LLJ. Overall, by SS +5, maintained cases had large increases in 0–3-km SRH, while dissipation and merger cases experienced comparatively smaller increases or decreases, consistent with the patterns previously identified in the mesoscale environments (Fig. 5); the extent to which these differences in SRH are discriminatory and predictive of evolution will be discussed next.
Mean composite hodographs for all case types. Solid curves represent the mean hodograph at SS −1, dashed curves represent SS +2, and dot–dash curves represent SS +5. The average 0–3-km SRH values at SS −1 and SS +5 are also annotated.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
e. Hourly statistical comparisons
Given the notable differences among the storm classifications evident in the composite environments over time, it was of interest to determine whether these differences were statistically significant and, if so, at what point this occurred during the nocturnal transition. Both the Student’s t test and the KS test were used at each hour to compare distributions of a suite of parameters for each possible combination of storm evolution types (e.g., 0–3-km SRH at SS +1 for dissipation vs maintained cases). Figure 9 contains the time series of the mean value at each hour for several statistically and physically significant parameters, while Table 2 summarizes the statistically significant parameters2 at each hour for each combination of case types; we will discuss the key results from each combination in turn.
Time series of mean parameter values (as labeled) at every hour between SS −1 and SS +5 for each evolution category (as labeled).
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
Hourly statistical comparisons where listed parameters are those that showed a statistically significant difference for a given comparison (e.g., comparisons of maintained vs dissipated supercells at SS +1 shows three statistically different parameters).
Overall, Fig. 9 illustrates that by many measures, maintained cases exist in increasingly favorable environments for strong, rotating convection relative to all other evolution categories, especially at later hours. In contrast, however, dissipation cases occur in demonstrably less favorable environments. Specifically, maintained cases exhibit significantly larger effective SRH (0–1- and 0–3-km SRH values are also much larger; not shown) and SCP (a function of effective SRH), as well as weaker MUCIN (Fig. 9; Table 2). Dissipation cases contain generally less SRH and stronger MUCIN values over time (Fig. 9). These differences in effective SRH and MUCIN would importantly influence the ability of the storm to continue to lift parcels to sustain its updraft. In the case of maintained supercells, greater SRH enhances updraft rotation and the VPPGF, thus supporting more dynamic lift that would be able to overcome the comparatively small increases in stability, yielding a long-lived, persistent rotating updraft. On the other hand, increasingly negative MUCIN values present in the dissipation cases would require more dynamic lifting for parcels to reach their LFCs; given that effective SRH gradually decreases over time in these cases (Fig. 9), the VPPGF is not able to provide sufficient lift to compensate for the increasingly stable parcels. Even though comparatively similar MUCAPE values are present in dissipation and maintained cases, the implied difference in dynamic lifting (via changes to effective SRH) means that this buoyancy is not necessarily realized.
When upscale and dissipation environments are compared, effective SRH is again identified as a statistically significant parameter (Table 2), with upscale cases exhibiting generally larger values over time (Fig. 9). In contrast to maintained cases, however, the large SRH is likely signaling enhanced warm-air advection, which has been associated with upscale growth (e.g., Peters and Eure 2016; Reif and Bluestein 2017), as opposed to enhancements in the VPPGF in a rotating updraft. Thermodynamically speaking, only MUCIN is a statistically different parameter at multiple hours between the upscale and dissipation cases, where MUCIN is more favorable for convection in upscale cases, promoting continued convective (though not supercellular) maintenance.
Merger and dissipation events exist in entirely different environmental regimes; differences are seen at all hours through the nocturnal transition in a number of kinematic and thermodynamic parameters (Table 2). Dissipation cases interestingly exhibit a more favorable thermodynamic profile with higher MUCAPE throughout the entire window from SS −1 to SS +5 (Fig. 9). Kinematic variables, however, are more in line with previous comparisons to dissipation events; merger cases contained much larger fixed-layer shear and SRH values (only 0–1-km shear shown in Fig. 9).
It is clear that dissipating supercells exist in a kinematically weaker environment throughout the nocturnal transition than all other evolution categories, as quantified by low-level shear and SRH. The importance of SRH in separating case types where convection is maintained in some way (i.e., as a supercell, upscaled MCS, or merged convective cluster) is further explored in the remaining comparisons.
When comparing nocturnal environments supporting sustained isolated convection (i.e., maintained supercells) versus those associated with upscale growth, Table 2 indicates that SRH (specifically within the fixed 0–3-km layer) again emerges as a key distinguishing factor. The overall trends in 0–3-km SRH are similar over time in both case types (not shown), but the principle difference lies in its magnitude; maintained supercells have environments with significantly larger SRH (e.g., Fig. 8). Given that upscale cases exhibit SRH values that are sufficient to support sustained rotating updrafts (e.g., Fig. 9), yet are not observed to do so, suggests that external factors (such as strong low-level thermal advection, colliding outflow boundaries, etc.) could be forcing the upscale growth. MLBRN differences are not consistently significantly different across the nocturnal transition but there are large differences in means (e.g., Fig. 9) and significant differences at multiple hours (e.g., Table 2); the larger MLBRN values, relative to maintained, for upscale supercells are consistent with the increase in multicellular convection expected with a larger CAPE-to-shear ratio.
Comparison of maintained versus merger environments reveals significant differences in their thermodynamic environments throughout the nocturnal transition; all measures of CAPE were significantly different, with merger events containing much less buoyancy (Table 2; Fig. 9). The MUCAPE values are in line with what has been reported in other merger studies (e.g., French and Parker 2012), possibly as a result of the presence of nearby convection acting to reduce instability. SCP was also identified as a discriminatory parameter (Table 2), consistent with its dependence on MUCAPE. However, the sharp downward trend after SS +1 in mean SCP for merger events closely mirrors the trend in effective SRH (Fig. 9), indicating that SRH is again a relevant discriminatory parameter.
The final comparison is between the upscale and merging categories; as with maintained versus merger cases, a variety of thermodynamic and kinematic parameters were found to be statistically significant (Table 2). Throughout the nocturnal transition, upscale cases exhibited larger MUCAPE yet weaker shear (Fig. 9) and fixed-layer SRH (not shown) relative to merger cases. However, shear and SRH generally increased over time in both evolution types. We suspect that the differing convective evolutions may be related to the balance of buoyancy and shear; MLBRN values are significantly higher in upscale supercell cases (Table 2; Fig. 9) and are supportive of multicellular convection (Weisman and Klemp 1982).
The key result of all of these comparisons is that maintained cases exist in an overall more favorable parameter space for supercells compared to all other evolution types. Specifically, strongly increasing SRH and shear, along with small changes to MUCIN and a nearly constant effective inflow layer depth, are key distinguishing factors; physically, the significant increases in SRH would support enhanced dynamic lifting to sustain rotating convection. In contrast, dissipation cases trend toward weaker SRH and unfavorable thermodynamics (especially stronger MUCIN). Upscale cases show favorable thermodynamics and kinematics for convective maintenance, but shear and SRH increase at a lesser rate with time than maintained cases. Merger cases exist in a notably different type of environment compared to other supercell classifications, with strong increases in shear and fixed-layer SRH, yet increasingly less favorable buoyancy. Next, we address how forecasters can anticipate these varying convective evolutions.
f. Forecasting supercell evolution
To forecast supercell evolution, we first provide estimates of how long a supercell will persist into the nocturnal transition (beyond the average lifetimes; Fig. 6) simply based on parameter values. To accomplish this, supercells were binned every hour from SS to SS +5 based on their hour of dissipation, merger, or upscale growth; maintained cases were assumed to “end” at SS +5. Next, the mean parameter value between SS −1 and the time of evolution (i.e., when supercellular features fail to persist) was then calculated and included in the respective bin. For example, a supercell that dissipated at SS +2 would have its mean MUCIN value between SS −1 and SS +2 placed in the SS +2 bin. This procedure was conducted using effective SRH, MUCIN, 0–1-km shear, and SCP; these parameters were chosen due to their strong statistical significance when comparing the evolution categories (Table 2).
Broadly speaking, there is a strong, statistically significant correlation between effective SRH, 0–1-km shear, SCP values, and length of time as a supercell (Table 3); this relationship also bears out when examining the distributions of parameter values binned by time of evolution (Fig. 10). In other words, forecasters can expect a storm to sustain its supercellular features for longer when there are high values of effective SRH, 0–1-km shear, and SCP, relative to typical climatological values. Notably, MUCIN was not significantly correlated with supercell lifetime, at odds with its significance indicated in Table 2. MUCIN values reach their most unfavorable at SS +3, showing no apparently linear trend (Fig. 10). These results indicate that MUCIN in this context alone cannot predict how long a supercell will be maintained with hourly precision. Yet, the fact that MUCIN is significantly different among the evolution types, especially at later hours (Table 2; Fig. 9), does suggest that the parameter has predictive power in discriminating storm evolution; it is for this reason that MUCIN’s influence will be explored further.
Correlation coefficient and level of significance for the hourly mean trend of effective SRH, MUCIN, SCP, and 0–1-km shear values binned by time of evolution into a given category (SS +5 was used for all maintained cases). The cumulative parameter mean between SS −1 and time of supercell evolution was used in each bin as the y-axis variable and the time from SS −1 to SS +5 as the x-axis variable.
Box-and-whisker plots of effective SRH, MUCIN, SCP, and 0–1-km shear values binned by time of evolution into a given category (SS +5 was used for all maintained cases). The cumulative parameter mean between SS −1 and time of supercell evolution was used in each bin. See the text for more details. The box represents the interquartile range, and the whiskers are the 90th and 10th percentiles. The number of cases in each bin is shown to the right.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
The purpose of the scaling term is to lower the SCP when CIN is present to simulate the negative impact of MUCIN; for example, dissipation cases have significantly less favorable MUCIN than both upscale and maintained events (Fig. 9; Table 2). In our formulation, an SCP of 18 with −100 J kg−1 of MUCIN would scale to a CSCP of 7.2, indicating a reduced possibility of supercells. Note that for observed MUCIN values between 0 and −40 J kg−1, MUCIN is set to −40 J kg−1, resulting in a value of 1 for the CIN term; this accounts for the limited impact that small amounts of CIN will have. The CSCP was tested with a range of values for the MUCIN scaling term (from −10 to −100 J kg−1, every −5 J kg−1); −40 J kg−1 provided the strongest statistical separation for these comparisons. This approach is in line with previous work; notably, CIN scaling was utilized in the significant tornado parameter (Thompson et al. 2012).
In addition to the CSCP being more physically representative than the SCP during the nocturnal transition, it importantly adds further discriminatory power between maintained supercells and all other evolution types; the distribution of CSCP values for maintained cases are more clearly separated from other evolution types, whereas more overlap is present for SCP distributions (Fig. 11). This separation is statistically significant, though the increased skill is mostly confined to maintained versus dissipation events, as the distributions of CSCP in the dissipation, upscale, and merger events still exhibit notable overlap.
The box-and-whisker distributions from SS +3 to SS +5 of (left) CSCP and (right) SCP with corresponding t-test p values comparing the distributions. The box represents the interquartile range, and the whiskers are the 90th and 10th percentiles.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
The distinguishing capability of CSCP is further evident when plotting its distribution for each evolution type in a phase space with 0–1-km shear. Comparing 0–1-km shear with CSCP was chosen as another statistically significant parameter (Table 2) not already incorporated into CSCP. The combination of 0–1-km shear and CSCP covers five different environmental measurements: stability (MUCIN), buoyancy (MUCAPE), low-level shear/low-level turning (0–1-km shear and effective SRH), and deep-layer shear (effective bulk shear). This phase space qualitatively illustrates the separate environments of the storm evolution categories at later hours (SS +3 and onward); maintained cases exist in the highest 0–1-km shear and CSCP space, followed by upscale cases (Fig. 12). Merger cases, as expected, exhibit large 0–1-km shear relative to dissipation but similar CSCP. Upscale cases tend to lie in the moderate shear and moderate CSCP between maintained and dissipation. While the CSCP shows promise in predicting supercell evolution during the nocturnal evolution, its true predictive power has yet to be determined; a much more rigorous assessment of the parameter’s utility using a large, independent sample will need to be undertaken, which is beyond the scope of the present study.
Phase space of CSCP vs 0–1-km shear for each supercell evolution category. The center of each box represents the median value from SS −1 to SS +5, while the horizontal and vertical extents of each box are ±1 median absolute deviation.
Citation: Weather and Forecasting 33, 4; 10.1175/WAF-D-17-0150.1
4. Discussion
Statistical comparisons among the four different evolution categories yielded a fairly distinctive set of distinguishing environmental factors; the set of factors for each comparison has a physical implication and thus forecasting use. Dissipation versus maintained cases are by far the most statistically and physically distinct storm classifications. MUCIN and effective SRH are the primary distinguishers with the lowest and physically significant p values, along with the composite parameters SCP and CSCP (Table 2; Fig. 11). With weaker MUCIN values in maintained cases, parcels are more easily lifted to their LFCs, thus utilizing available MUCAPE to increase the velocity of the updraft, allowing for further dynamical stretching and lifting. These results agree with previous studies contrasting supercell behaviors (i.e., Mead and Thompson 2011) based on CIN. Additionally, the strong increases in SRH directly promote enhanced updraft rotation and vertical velocities via the VPPGF, leading to a sustained supercell. Dissipation cases, however, contain stronger MUCIN, shallower effective inflow layers, and lower effective SRH; as a result, weaker updraft rotation leads to reduced dynamical forcing and an inability to overcome nocturnal stabilization, thus dissipating.
When comparing supercell dissipation to other evolution categories where convection was sustained as either upscale or merged, SRH again emerged as a key distinguishing parameter (Table 2). However, the role of larger SRH in supporting either merging or upscale growth appears to be different than in the maintained cases, given that sustained, isolated rotating updrafts were not observed. It is possible that increases in SRH instead imply strong low-level warm-air advection, supporting more widespread convection (e.g., Peters and Eure 2016; Peters et al. 2017); indeed, inflow grids indicate that merger and upscale cases contain broad warm-air advection, a signal not present in dissipation cases (not shown). Differences in the thermodynamic environments also separated sustained (merged or upscale) and dissipating supercells. In the case of upscale versus dissipation cases, MUCIN was significantly weaker in magnitude for supercells that grew upscale, which would generally support more widespread convection. Interestingly, merging supercells contained significantly less buoyancy than dissipating supercells (Table 2; Fig. 9); the potential physical relevance of this signal in isolation is unclear. However, note the influence of external factors not captured in proximity soundings (such as mesoscale boundaries or gravity waves) in these types of events; such features likely generate or further develop sustained convection. A more detailed examination of the contribution of these factors is left for future work.
By definition, convection is sustained in both the maintained and upscale evolution categories. Thus, it is perhaps not surprising that no thermodynamic parameters discriminated between these events (Table 2), with fairly similar trends in MUCAPE and MUCIN (Fig. 9). The key difference lies in the magnitude of SRH; while both generally increased over time, maintained supercells exhibited much larger SRH values. This enhanced SRH indicates greater streamwise vorticity ingestion and thus a greater propensity for a rotating updraft, supporting sustained isolated convection. The upscale cases contained more moderate SRH; coupled with higher MLBRN values, more linear convection would be favored, likely further aided by mesoscale boundaries and other forcings not captured in a proximity sounding.
Maintained and merger cases both exist in strongly sheared environments, consistent with the presence of more sustained isolated convection. As with the upscale versus merger comparison, thermodynamic indices discriminated best between maintained and merger events (Table 2), with maintained cases containing higher CAPE and lower CIN, especially at later hours (Fig. 9). However, as indicated by the statistical significance of SCP and CSCP (Figs. 11 and 12), SRH also discriminates in this case. We suspect that the physical significance of these parameters may lie in their associated influences on storm motion; propagation of a supercell depends on the intensity and orientation of updraft pressure perturbations, which are functions of the mean wind, shear vector, CAPE, and SRH. Decreases in effective SRH after SS +1 (Fig. 9) would lead to weaker updraft rotation, reducing the deviant storm motion to the right of the mean wind; in the presence of a synoptically forced squall line or other convection, this could then promote a merger. A deeper investigation of this possibility is beyond the scope of this study. However, it is clear that forecasters must assess the possibility of other convective development in the vicinity of a supercell; the orientation of the individual storm motion vectors with respect to one another should be determined.
Despite both evolutions having linear convective features, merger versus upscale supercells showed stark differences; merger cases tend to be observed in a higher-shear, lower-CAPE environment relative to upscale events (Fig. 9). Accordingly, MLBRN was discriminatory at many hours during the transition (Table 2); the higher-shear and lower-CAPE merger environments were associated with lower BRN values while the comparatively higher-CAPE and lower-shear upscale environments were associated with larger MLBRN values (Fig. 9), consistent with the assigned classification categories of more isolated versus multicellular convection. In other words, forecasters are more likely to observe merger events in environments with high low-level shear relative to the buoyancy; strong synoptic-scale forcing (Fig. 2) will also generate other convection with which to merge. Upscale events are similarly dependent on external factors such as frontal boundaries, but occur in moderate-to-high shear, moderate-to-high CAPE environments.
A common thread among all of the comparisons investigated herein is the extent to which increases in shear and SRH typically found during the nocturnal transition are sufficient to support sustained, rotating updrafts in the face of decreasing buoyancy. This is encapsulated in the CIN-scaled SCP parameter, a newly developed, statistically and physically discriminatory parameter. Its utility in forecasting is highlighted in the phase-space diagrams of CSCP and 0–1-km shear; these two parameters were chosen for the phase space since they encapsulate all major categories of parameters in this study: CAPE, CIN, SRH, deep-layer shear, and low-level shear. While CSCP has limited discriminatory power early on, from SS +3 to SS +5, differences between evolution types become much more apparent (Fig. 12). This is largely due to the increased effective SRH in the maintained cases and stronger MUCIN for the merger, upscale, and dissipation cases. A forecaster could leverage the phase-space diagram in combination with a RAP forecast for SS +3, SS +4, and SS +5 to predict whether supercells will be maintained. For example, if the RAP forecast indicates high CSCP (~7) and large low-level shear (~35 kt, 1 kt = 0.51 m s−1) maintained supercells are likely. Conversely, low CSCP and 0–1-km shear would suggest a forecaster can lower the risks associated with nocturnal supercells. However, if the RAP forecast predicts values that fall within overlaid areas in the phase-space diagram, further assessment of factors beyond a proximity sounding is necessary (e.g., synoptic forcings, surface fronts, outflow boundaries, etc.).
The key findings of this study allow for general assumptions to be made about the lifetime of a supercell in the nocturnal transition; the differences found between maintained and dissipated supercells are the most robust and exhibit the highest confidence.
5. Summary and future work
To assess how the environmental changes associated with the nocturnal transition impact supercell thunderstorm evolution and lifetime, inflow proximity soundings from RUC/RAP were collected from sunset-relative hours −1 to +5 for 157 Great Plains supercells occurring over a span of 11 years. Each supercell was classified based on its evolution from SS −1 to SS +5 as either dissipating, merging with another convective feature, growing upscale, or maintained (Fig. 1).
While there were some notable differences in the synoptic and mesoscale patterns among the evolution categories (Figs. 2–5), the most significant variations were found in comparing near-inflow environments. Overall, MUCIN, SRH, and low-level shear were the most common discriminatory parameters (Table 2), with the clearest separation between maintained and dissipating supercells. A new composite parameter, CSCP, encompasses these discriminatory parameters. When used in combination with 0–1-km shear, there is significant forecasting skill (Fig. 12).
Our findings are based on a relatively small sample of observed nocturnal supercells. Collection and analysis of additional cases is necessary to determine the robustness of our statistical conclusions. Numerical modeling studies should also be conducted; the base-state substitution (Letkewicz et al. 2013) approach would be an ideal way to vary the environment over time to replicate the nocturnal transition and assess the influence of individual parameters identified in this study on storm dynamics and lifetime. Additional case studies of individual supercells could also help verify these results.
Acknowledgments
The authors thank Dr. Matthew Eastin and Terry Shirley for providing valuable feedback for this study. The authors also thank the Severe Storms Research Group at UNC Charlotte for input during this research. Comments from three anonymous reviewers and the journal’s editor greatly improved this manuscript.
REFERENCES
Benjamin, S., T. Smirnova, J. Brown, G. Grell, and R. Bleck, 2004: Mesoscale weather prediction with the RUC hybrid isentropic–terrain-following coordinate model. Mon. Wea. Rev., 132, 473–494, https://doi.org/10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2.
Benjamin, S., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1.
Billings, J., and M. D. Parker, 2012: Evolution and maintenance of the 22–23 June 2003 nocturnal convection during BAMEX. Wea. Forecasting, 27, 279–300, https://doi.org/10.1175/WAF-D-11-00056.1.
Blumberg, W. G., K. T. Halbert, T. A. Supinie, P. T. Marsh, R. L. Thompson, and J. A. Hart, 2017: SHARPpy: An open-source sounding analysis toolkit for the atmospheric sciences. Bull. Amer. Meteor. Soc., 98, 1625–1636, https://doi.org/10.1175/BAMS-D-15-00309.1.
Bunkers, M. J., B. A. Klimowski, J. W. Zeitler, R. L. Thompson, and M. L. Weisman, 2000: Predicting supercell motion using a new hodograph technique. Wea. Forecasting, 15, 61–79, https://doi.org/10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2.
Bunkers, M. J., M. Hjelmfelt, and P. Smith, 2006a: An observational examination of long-lived supercells. Part I: Characteristics, evolution, and demise. Wea. Forecasting, 21, 673–688, https://doi.org/10.1175/WAF949.1.
Bunkers, M. J., B. Klimowski, J. Grzywacz, L. Czepyha, J. Johnson, and M. Hjelmfelt, 2006b: An observational examination of long-lived supercells. Part II: Environmental conditions and forecasting. Wea. Forecasting, 21, 689–714, https://doi.org/10.1175/WAF952.1.
Coffer, B., and M. D. Parker, 2015: Impacts of increasing low-level shear on supercells during the early evening transition. Mon. Wea. Rev., 143, 1945–1969, https://doi.org/10.1175/MWR-D-14-00328.1.
Cohen, A., M. Coniglio, S. Cavallo, and H. Brooks, 2015: A review of planetary boundary layer parameterization schemes and their sensitivity in simulating southeastern U.S. cold season severe weather environments. Wea. Forecasting, 30, 591–612, https://doi.org/10.1175/WAF-D-14-00105.1.
Colman, B., 1990: Thunderstorms above frontal surfaces in environments with positive CAPE. Part I: A climatology. Mon. Wea. Rev., 118, 1103–1121, https://doi.org/10.1175/1520-0493(1990)118<1103:TAFSIE>2.0.CO;2.
Coniglio, M. C., 2012: Verification of RUC 0–1-h forecasts and SPC mesoscale analyses using VORTEX2 soundings. Wea. Forecasting, 27, 667–683, https://doi.org/10.1175/WAF-D-11-00096.1.
Davenport, C. E., and M. D. Parker, 2015a: Impact of environmental heterogeneity on the dynamics of a dissipating supercell thunderstorm. Mon. Wea. Rev., 143, 4244–4277, https://doi.org/10.1175/MWR-D-15-0072.1.
Davenport, C. E., and M. D. Parker, 2015b: Observations of the 9 June 2009 dissipating supercell from VORTEX2. Wea. Forecasting, 30, 368–388, https://doi.org/10.1175/WAF-D-14-00087.1.
Davies, J. M., and A. Fischer, 2009: Environmental characteristics associated with nighttime tornadoes. Electron. J. Oper. Meteor., 10 (3), http://nwafiles.nwas.org/ej/pdf/2009-EJ3.pdf.
Davies-Jones, R., 1984: Streamwise vorticity: The origin of updraft rotation in supercell storms. J. Atmos. Sci., 41, 2991–3006, https://doi.org/10.1175/1520-0469(1984)041<2991:SVTOOU>2.0.CO;2.
Fierro, A. O., M. S. Gilmore, E. R. Mansell, L. J. Wicker, and J. M. Straka, 2006: Electrification and lightning in an idealized boundary-crossing supercell simulation of 2 June 1995. Mon. Wea. Rev., 134, 3149–3172, https://doi.org/10.1175/MWR3231.1.
Fischer, A., and J. M. Davies, 2009: Significant nighttime tornadoes in the plains associated with relatively stable low-level conditions. Electron. J. Oper. Meteor., 10 (4), http://nwafiles.nwas.org/ej/pdf/2009-EJ4.pdf.
French, A. J., and M. D. Parker, 2012: Observations of mergers between squall lines and isolated supercell thunderstorms. Wea. Forecasting, 27, 255–278, https://doi.org/10.1175/WAF-D-11-00058.1.
Geerts, B., and Coauthors, 2017: The 2015 Plains Elevated Convection at Night Field Project. Bull. Amer. Meteor. Soc., 98, 767–786, https://doi.org/10.1175/BAMS-D-15-00257.1.
Gropp, M. E., 2017: Assessing the impact of the nocturnal transition on the lifetime and evolution of supercell thunderstorms in Great Plains. M.S. thesis, Dept. of Geography and Earth Sciences, University of North Carolina at Charlotte, 155 pp.
Horgan, K., S. Corfidi, J. Hales, D. Schultz, and R. Johns, 2007: A five-year climatology of elevated severe convective storms in the United States east of the Rocky Mountains. Wea. Forecasting, 22, 1031–1044, https://doi.org/10.1175/WAF1032.1.
Jones, T., K. McGrath, and J. Snow, 2004: Association between NSSL Mesocyclone Detection Algorithm-detected vortices and tornadoes. Wea. Forecasting, 19, 872–890, https://doi.org/10.1175/1520-0434(2004)019<0872:ABNMDA>2.0.CO;2.
Kerr, B., and G. Darkow, 1996: Storm-relative winds and helicity in the tornadic thunderstorm environment. Wea. Forecasting, 11, 489–505, https://doi.org/10.1175/1520-0434(1996)011<0489:SRWAHI>2.0.CO;2.
Klemp, J., 1987: Dynamics of tornadic thunderstorms. Annu. Rev. Fluid Mech., 19, 369–402, https://doi.org/10.1146/annurev.fl.19.010187.002101.
Lemon, L., and C. Doswell III, 1979: Severe thunderstorm evolution and mesocyclone structure as related to tornadogenesis. Mon. Wea. Rev., 107, 1184–1197, https://doi.org/10.1175/1520-0493(1979)107<1184:STEAMS>2.0.CO;2.
Letkewicz, C. E., A. J. French, and M. D. Parker, 2013: Base-state substitution: An idealized modeling technique for approximating environmental variability. Mon. Wea. Rev., 141, 3062–3086, https://doi.org/10.1175/MWR-D-12-00200.1.
Macintosh, C. W., and M. D. Parker, 2017: The 6 May 2010 elevated supercell during VORTEX2. Mon. Wea. Rev., 145, 2635–2657, https://doi.org/10.1175/MWR-D-16-0329.1.
Markowski, P. M., E. N. Rasmussen, and J. M. Straka, 1998: The occurrence of tornadoes in supercells interacting with boundaries during VORTEX-95. Wea. Forecasting, 13, 852–859, https://doi.org/10.1175/1520-0434(1998)013<0852:TOOTIS>2.0.CO;2.
Mead, C. M., and R. L. Thompson, 2011: Environmental characteristics associated with nocturnal significant-tornado events in the central and southern Great Plains. Electron. J. Severe Storms Meteor., 6 (6), https://www.spc.noaa.gov/publications/mead/noctors.pdf.
Nowotarski, C., P. Markowski, and Y. Richardson, 2011: The characteristics of numerically simulated supercell storms situated over statically stable boundary layers. Mon. Wea. Rev., 139, 3139–3162, https://doi.org/10.1175/MWR-D-10-05087.1.
Parker, M. D., 2014: Composite VORTEX2 supercell environments from near-inflow storm soundings. Mon. Wea. Rev., 142, 508–529, https://doi.org/10.1175/MWR-D-13-00167.1.
Peters, J. M., and K. C. Eure, 2016: Factors that influence the growth of tornadic supercells into MCSs after sunset. 28th Conf. on Severe Local Storms, Portland, OR, Amer. Meteor. Soc., P21, https://ams.confex.com/ams/28SLS/webprogram/Paper301972.html.
Peters, J. M., K. C. Eure, and R. S. Schumacher, 2017: Factors that drive MCS growth from supercells. 17th Conf. on Mesoscale Processes, San Diego, CA, Amer. Meteor. Soc., 9.6, https://ams.confex.com/ams/17MESO/webprogram/Paper320248.html.
Reif, D. W., and H. B. Bluestein, 2017: A 20-year climatology of nocturnal convection initiation over the central and southern Great Plains during the warm season. Mon. Wea. Rev., 145, 1615–1639, https://doi.org/10.1175/MWR-D-16-0340.1.
Rotunno, R., and J. Klemp, 1985: On the rotation and propagation of simulated supercell thunderstorms. J. Atmos. Sci., 42, 271–292, https://doi.org/10.1175/1520-0469(1985)042<0271:OTRAPO>2.0.CO;2.
Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic, 666 pp.
Stumpf, G., K. Thomas, M. Eilts, J. Johnson, P. Spencer, E. Mitchell, A. Witt, and D. Burgess, 1998: The National Severe Storms Laboratory Mesocyclone Detection Algorithm for the WSR-88D. Wea. Forecasting, 13, 304–326, https://doi.org/10.1175/1520-0434(1998)013<0304:TNSSLM>2.0.CO;2.
Thompson, R. L., R. Edwards, J. A. Hart, K. L. Elmore, and P. M. Markowski, 2003: Close proximity soundings within supercell environments obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 1243–1261, https://doi.org/10.1175/1520-0434(2003)018<1243:CPSWSE>2.0.CO;2.
Thompson, R. L., C. Mead, and R. Edwards, 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102–115, https://doi.org/10.1175/WAF969.1.
Thompson, R. L., B. T. Smith, J. S. Grams, A. R. Dean, and C. Broyles, 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part II: Supercell and QLCS tornado environments. Wea. Forecasting, 27, 1136–1154, https://doi.org/10.1175/WAF-D-11-00116.1.
Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504–520, https://doi.org/10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2.
We required a supercell to be isolated at local sunset time, located away from frontal boundaries, and/or in the warm sector to limit the effects of external influences on supercell strength and lifetime (e.g., Markowski et al. 1998; Bunkers et al. 2006a,b; Fierro et al. 2006).
