1. Introduction
Societal impacts associated with hazardous weather events often increase as the spatial and temporal dimensions of the phenomenon grow larger. For example, expansive swaths of convective wind gusts are associated with long-lived bow echoes that often yield substantial economic loss, human injuries, and fatalities (Ashley and Mote 2005). Larger hurricanes tend to produce more devastating coastal storm surge (Irish et al. 2008; Needham and Keim 2014), and long-lived supercells (Bunkers et al. 2006a) are more likely to produce strong or violent tornadoes (F/EF2–5, where F and EF indicate the Fujita and enhanced Fujita scales, respectively), significant hail (≥5.08 cm; i.e., 2-in. diameter), and wind (≥33.4 m s−1; i.e., 65-kt thunderstorm gust). Supercell thunderstorms by definition require temporal longevity in the form of a long-lived midlevel mesocyclone (Moller et al. 1994). The longest-lived storms, with lifespans ≥ 4 h, are usually discrete in nature. They occur in environments with strong 0–8-km bulk shear, low 100-hPa mean-layer lifted condensation level (MLLCL) heights, and a storm motion toward a region of increasing buoyancy or along a buoyancy gradient (Bunkers et al. 2006b).
Garner (2007) showed that the environments of long-lived supercells were similar to long-track tornadoes, and Brooks (2004) demonstrated that longer tornado pathlengths increased their chances of encountering structures resulting in higher F/EF-scale ratings. The long-tracked 18 March 1925 “Tri-State Tornado” is the single deadliest tornado (695 fatalities) in U.S. history (Johns et al. 2013). That tornado was originally associated with a damage pathlength of 352 km (219 mi) that occurred over a time span of 3.5 h (Henry 1925). However, Johns et al. (2013) later identified 32 gaps in damage and concluded that there were two separate segments of 280 and 243 km (174 and 151 mi). In addition, an analysis by Maddox et al. (2013) showed that the Tri-State Tornado was produced by a supercell with an estimated life-span of 7 h, which moved downstream along a surface warm front at ~26 m s−1 (50 kt). In contrast to that singular long-track storm, the 3–4 April 1974 “Super Outbreak” (Corfidi et al. 2010) produced 25 F3–F5 tornadoes with pathlengths exceeding 40 km (25 mi) over a geographic area spanning from Michigan south to Mississippi and Alabama. Long-track tornadic supercells during that outbreak emanated from a synoptic-scale extratropical cyclone featuring an eastward-migrating surface low pressure center occurring within the left exit region of a low-amplitude 72 m s−1 (140 kt) 250-hPa jet streak. An expansive area of potential instability formed downstream from the migrating surface cyclone as a large plume of steep midlevel lapse rates spread across a warming and moistening boundary layer. The large number of long pathlength tornadoes produced during the “Super Outbreak” is rare, given that the average pathlength during the period 1973–2011 for significant tornadoes (F/EF-scale ≥ 2) is 15.1 km (9.4 mi) and that for weak (F/EF-scale < 2) tornadoes is 2.9 km (1.8 mi) (Coleman and Dixon 2014).
Documented tornado widths have varied greatly (Fig. 1). And similar to pathlength, increasing tornado diameters generally correspond to increasing tornado F/EF-scale ratings due to larger damage footprints (Brooks 2004). Trapp et al. (2017) demonstrated that wide intense tornadoes should be associated with wide mesocyclones and larger midlevel updraft areas—properties that might be inferred by observing the size of overshooting thunderstorm tops in satellite imagery. To date, the El Reno, Oklahoma, tornado of 31 May 2013 is the widest tornado on record at 4.2 km (4576 yd). That tornadic storm formed in an environment similar to other wide tornadic events, such as the 22 May 2004 Hallam, Nebraska, tornado (4 km; 4374 yd wide), and the 4 May 2007 Greensburg, Kansas, tornadic storm (tornado widths ranged from 1.4 to 3.5 km; 1531–3828 yd). Mean-layer (ML) convective available potential energy (MLCAPE) for all three storms was ≥ 3000 J kg−1, and 0–1-km storm-relative helicity (SRH) ranged from 300 to 400 m2 s−2 (Adams 2005; Bluestein 2009; Mead and Thompson 2011; Bluestein et al. 2015; Kuster et al. 2015). Those values of CAPE and 0–1-km SRH are similar to the distribution of CAPE and 0–2-km SRH associated with violent tornadoes (Johns et al. 1993).
Examples of the range of tornado widths, showing tornadoes that occurred (a) at 2321 UTC 31 May 2013 in central Oklahoma, (b) at ~0004 UTC 10 Apr 2015 in northern Illinois, and (c) at ~2203 UTC 24 May 2016 in southwest Kansas. The photograph in (a) was taken ~5.1 km (3.2 mi) northeast of the tornado, and the width of the tornado at the time of the picture was ~4.2 km (4576 yd). The photograph in (b) was taken ~3.2 km (2 mi) south of the tornado, and the tornado width was ~0.37 km (400 yd). The photograph in (c) was taken ~6.4 km (4 mi) east-southeast of the tornado, and the tornado width was ~0.05 km (50 yd). The image in (a) is provided through the courtesy of S. Brewer and J. Drake. The images in (b) and (c) are provided through the courtesy of W. Ashley.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Observational studies (Rasmussen et al. 1994; Karstens et al. 2010; Lee et al. 2012; Wurman et al. 2012; Wurman and Kosiba 2013) and numerical simulations (Weisman and Klemp 1982, 1984) have documented a range of storm-scale circulations associated with tornadic thunderstorms. Those include deep persistent rotation occurring in the midlevels [~3–9 km above ground level (AGL)] of supercells (Moller et al. 1994), rotation that is typically observed to be broader than a tornado occurring within the lowest kilometer of an updraft (Skinner et al. 2014), intense tornadic-scale circulations (Orf et al. 2017), and subvortices embedded within tornadoes (Wurman et al. 2014). In general, the longevity of a storm-scale circulation decreases as the spatial scale of the circulation decreases (Fujita 1981). For example, a midlevel mesocyclone might display a longevity on the order of hours, while a tornadic subvortex might exist no more than a few seconds. Brooks et al. (1993, 1994a) provide some insight into tornado longevity through numerical modeling experiments. Those studies demonstrated that storm-relative winds that were too strong prevented parcels from residing within the storm-generated forward flank gust front for a sufficient amount of time to acquire baroclinically generated horizontal vorticity. On the other hand, low-level mesocyclogenesis might be rapid for weak storm-relative winds, but excessive amounts of outflow would quickly undercut the low-level circulation. In both instances, tornado development and maintenance are disrupted. Boundary layer humidity and stability can also modulate the amount of convective outflow generated and subsequent longevity of rotation in the low levels of a storm (Markowski et al. 2002; Snook and Xue 2008; Markowski and Richardson 2014; Brown and Nowotarski 2019). Supercells that fail to experience significant tornadogenesis due to excessive outflow often occur in environments with high MLLCL heights (Thompson et al. 2003; Murdzek et al. 2020).
Despite evidence that wider, longer-lived, and longer-tracked tornadoes are responsible for greater societal impacts (Ashley and Strader 2016), few efforts exist in literature to directly relate these attributes to their radar signatures, near-storm environments, and synoptic settings. The similarities noted in the aforementioned tornado events serve as motivation to partially fill that gap by examining environmental similarities and differences across the spectra of tornado longevities, pathlengths, and widths. A description of the data and methods used in this study is presented in section 2. That is followed by results in section 3 and a summary and discussion in section 4.
2. Data and method
a. Tornado environment dataset
Studies of tornadic environments using near-storm proximity sounding launches have been achieved during field experiments such as VORTEX (Rasmussen et al. 1994; Wurman et al. 2012; Coniglio and Parker 2020), but outside of those projects, observed fixed-site proximity sounding studies are limited by small sample sizes when proximity criteria are strict, or by less representative profiles with more relaxed proximity criteria (Darkow 1969; Rasmussen and Blanchard 1998; Brooks et al. 1994b). Those limitations have been reduced through numerical weather prediction models, as demonstrated by Thompson et al. (2003), who utilized 40-km Rapid Update Cycle-2 (RUC; Benjamin et al. 2004) analysis soundings to examine environmental differences between tornadic and nontornadic supercells. More recently, Thompson et al. (2012, hereinafter T12) examined tornado, significant hail, and significant wind reports based on radar-assigned convective modes. Those events were combined with associated environmental information sampled from a RUC and Rapid Refresh (RAP) model analysis grid with 40-km horizontal grid spacing (e.g., Bothwell et al. 2002).
Smith et al. (2012, hereinafter S12) enlarged the T12 dataset by applying a filtering scheme to county segmented tornado reports occurring across a 40-km RUC model analysis grid, such that the largest magnitude report occurring within a gridbox hour was selected. A convective mode was then assigned to the storm responsible for the gridbox report using the closest available radar site located within 230 km. Thereafter, Thompson et al. (2013) accumulated all tornado events within a 120-km neighborhood centered on RUC grid points, which increased the sample size above S12. Supercell and tornado parameters were then analyzed using archived SPC hourly mesoanalysis data (Bothwell et al. 2002; Schneider and Dean 2008). Smith et al. (2015; hereinafter S15) combined the near-storm environmental data discussed above with radar-identified convective modes and peak low-level rotational velocities [Vrot = (|Vin| + |Vout|)/2] to a sample of tornadoes occurring across the CONUS during 2009–13. Only tornado events sampled at or below 3048 m (10 000 ft) height above radar level (ARL), or a range ≤ 165 km, were analyzed in that study in order to avoid sampling limitations related to beam broadening (Wood and Brown 1997). Thompson et al. (2017, hereinafter T17) followed S15 by adding grid-hour tornado events from 2014 to 2015, as well as a robust sample of nontornadic rotating storms that produced either severe hail (≥ 2.54 cm; 1 in diameter) or severe thunderstorm winds (≥26 m s−1; 50 kt). The combined S15 and T17 work resulted in a total sample of 6300 grid-hour tornado events.
b. Additional analysis of tornadic storms
All EF2–EF5 tornadoes and their associated environmental and radar data from T17 were considered for study in the current work. Furthermore, the disproportionately large number of EF0–EF1 tornadoes from T17 were reduced in this study through random selection using a number generator. Additional constraints were then applied to the dataset. First, only cyclonically rotating storms were examined, since very few left-moving anticyclonic rotating storms produce tornadoes (Wakimoto 1983; Brown and Meitin 1994; Bunkers and Stoppkotte 2007). Second, tornadic velocity couplets must have displayed clear and tight circulations (CTC). The CTC criteria maintains consistency with T17, who defined them as an easily identifiable storm-relative (SR) velocity maximum, often associated with a gate-to-gate, or near-gate-to-gate shear signature that is usually collocated with reflectivity signatures such as a hook echo. The CTC criteria also provides an easily trackable feature that can be measured in time and space and is more likely to be associated with a tornado than the weaker and more nebulous areas of azimuthal shear would be (T17). Figure 2 provides an example of reflectivity and SR velocity signatures associated with a CTC.
(a) Radar reflectivity and (b) SR velocity associated with a 0.5° CTC produced by a low-level tornadic mesocyclone located over Tuscaloosa County in Alabama. The tornadic storm was sampled by the Birmingham, Alabama, KBMX radar, which was located approximately 72 km east of the storm at 2215 UTC 27 Apr 2011. Annotated reflectivity features in (a) include a hook echo, an ancillary cell merging into the hook echo region, a weak echo updraft region located to the right of the hook echo, and the forward flank downdraft region of the storm. Annotated SR velocity features in (b) include a gate-to-gate SR velocity signature associated with the CTC and SR inflow located to the right of the CTC. The radar imagery was displayed in the GR2 software.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Tornado pathlength and width estimated directly from storm surveys was acquired from the National Centers for Environmental Information (NCEI) storm data program. In addition, the longevity of each CTC was computed using a corresponding radar identified beginning and end time (Fig. 3). This method may deviate from the true tornado longevity due to 1) CTCs beginning or ending in between radar volume scans, and 2) tornado formation and dissipation differing from the radar identified CTC lifespan. In addition, the authors were uncertain about CTC time and space coordinates as the distance from the radar and associated ARL increased (Speheger and Smith 2006). There also were times when circulations could not be adequately resolved in order to derive the longevity and pathlength of each CTC. Those cases were subsequently discarded. After applying the criteria above, a sample of 1268 tornado events were available for study. Tornado events were then sorted from highest to lowest according to CTC longevity, tornado pathlength, and tornado width. Those categories were further stratified based on three groups defined by quartile rankings (Table 1): 1) 317 events in the upper 25th percentile (UPR), 2) 631 events in the middle 50% (MID), and 3) 320 events in the lowest 25th percentile (LWR). A fourth group consisting of the 50 top CTC longevities, tornado pathlengths, and tornado widths was also created.
Path of the 20 May 2013 Moore, Oklahoma, tornado. EF-scale damage areas are color coded [adapted from Burgess et al. (2014)]. The longevity of the tornado was 37 min, the pathlength of the tornado was 23 km, and the maximum width was 1.7 km. From point A to point B demonstrates the linear pathlength of the CTC. Radar imagery has been appended to the original damage survey figure.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Tornado categories stratified by the top 50 events (TOP 50), as well as UPR, MID, and LWR quartiles.
Geographic spatial analyses of UPR, MID, and LWR events were performed by passing tornado latitude and longitude pairs through a Gaussian kernel density estimate algorithm. Due to the relatively small sample of events, an effective bandwidth of 22 km was used, which minimized noise observed for smaller bandwidths, and increased resolution that was lost at higher bandwidths. In addition, daily composite upper air and surface charts were created for the top 50 CTC longevity, tornado pathlength and tornado widths using the North American Regional Reanalysis (Mesinger et al. 2006). The top 50 width events were further divided into those occurring west of −94°, which is a longitude that roughly delineates the region traditionally designated as “tornado alley,” versus the region to the east, and particularly the southeast, which is now recognized to be an extension of heightened tornado risk (Dixon et al. 2011).
Radar and RUC/RAP environmental grid parameters were analyzed for each category, with all SR calculations derived using the Bunkers et al. (2000) supercell motion estimate. Furthermore, a more detailed analysis was conducted by comparing radar and environmental attributes for the top 50 CTC longevities, tornado pathlengths, and tornado widths. Those features included storm longevity, the length of the storm from its rear-flank reflectivity appendage to the edge of the forward flank downdraft divided by the width of its forward flank downdraft, the diameter of the CTC, the radar-estimated max 0.5° SR inflow, and max 0.5° Vrot. Composite wind profiles were also created for the top 50 events utilizing the closest RUC or RAP proximity analysis sounding located within a region representing the inflow sector of each storm ±1 h from the time of max 0.5° Vrot. Measures of shear and SR flow were calculated for each composite, as well as the average angle between the SR wind vector and shear vector from the surface through 500-m AGL, which is a variation of the “critical angle” defined by Esterheld and Giuliano (2008).
Additional archived NEXRAD Level II radar features were analyzed to augment T17 using Gibson Ridge 2 (GR2) Analyst software.1 The speed of each storm was computed using the begin and end points of the CTC. A proxy for updraft strength was also analyzed by using storm-top divergence, which was calculated by summing the absolute value of the maximum inbound and outbound SR velocities at the storm summit. Storm-top divergence was not calculated when the storm summit could not be viewed due to velocity dropouts in the radar data, and when a storm occurred too close to the radar site. Another indicator of updraft strength was assessed using full volumetric reflectivity data for the presence of a bounded weak echo region (Lemon et al. 1977), which is a vertically oriented weak echo channel bounded by high reflectivity values both laterally and aloft. The occurrence or nonoccurrence of cyclic mesocyclone behavior (Adlerman et al. 1999) was also noted, as well the occurrence of occluding low-level mesocyclones. In addition, ancillary cell mergers with a dominant primary storm were assessed for completeness (Lee et al. 2006).
An analysis of storm type was performed for each tornadic cell using base reflectivity signatures. In particular, the distribution of precipitation with respect to the forward and rear flank of a storm combined with inflow structures associated with an updraft, and cyclonically curved reflectivity patterns associated with the mesocyclone, aided in determining whether a storm was a “classic” or “high precipitation” supercell. A classic supercell was associated with a radar presentation in which the bulk of its low-level hydrometeors was distributed within the forward flank of the storm. On the other hand, a heavy-precipitation (HP) supercell was associated with a substantial amount of low-level rainfall distributed within the rear flank of the storm, which often engulfed the CTC (Beatty et al. 2009). Additional classifications were given to quasi-linear storms, which displayed a length-to-width ratio of at least 3 to 1 (S12), and bowing storm structures (hereinafter “bow” or “bow echo”). A storm was designated as a “hybrid” if a classification could not be determined.
The interaction of storms such as supercells with preexisting boundaries is a well-documented association leading to both tornadogenesis and augmented tornado intensity (Markowski et al. 1998; Rasmussen et al. 2000). This study has attempted to identify the association between storms and preexisting synoptic and mesoscale boundaries by matching radar identified storm locations in time and space with subjectively analyzed surface and visible satellite observations. Boundaries identified in radar were also observed on occasion but were not recorded due to inconsistencies in detection resulting from variations in ARL from event to event. Admittedly, the authors could not precisely match storm positions with the exact location of boundaries because of insufficient surface observation density. Therefore, the reader is advised to treat the storm–boundary associations described in the paper as inferred interactions.
3. Results
a. Societal impacts
Societal impacts were disproportionately greater for longer-lived, longer-tracked, and larger width tornadoes (Table 2). For instance, over 50% of the top 50 longevity and pathlength events were violent tornadoes (EF4–EF5), while the 50 widest tornadoes were less likely to be violent (32%) and more likely to be strong (66%).2 This may partially be due to the fact that 58% of the 50 widest tornadoes occurred across the Great Plains of the United States, a region in which the density of structures is lower than it is for locations such as the southeastern United States (Ashley 2007).
Distribution of violent (EF4–EF5) and strong (EF2–EF3) tornadoes, average fatalities, the percent of total fatalities for each category, tornadoes that were fatal (percent), and the percent of events occurring nocturnally (0100–1200 UTC) for CTC longevity (LONGEV), tornado pathlength (LENGTH), and tornado width (WIDTH) classified by the top 50 events, UPR, MID, and LWR.
Average fatalities for the top 50 events were 6 for tornado width, 4.3 for CTC longevity, and 3.7 for tornado pathlength. However, the average fatalities for the width category were skewed by the 157 deaths resulting from the 1.5-km-wide (1600-yd-wide) 22 May 2011 Joplin, Missouri tornado. When that tornado was removed, the average number of fatalities decreased to 3.1. Otherwise, the percent of deadly tornadoes (Anderson-Frey and Brooks 2019) in the top-50 category was highest for long-pathlength tornadoes (58%) and lowest for wide tornadoes (34%). In addition, nearly 90% of all fatalities in the dataset occurred in the upper quartile of the longevity, pathlength, and width groups.
Nocturnal tornadoes pose a particularly dangerous risk to society because of lower rates of tornado detection and a greater likelihood that people will not be seeking shelter because they are asleep (Ashley et al. 2008). An assessment of nocturnal tornadoes, defined as those occurring during the period 0100–1200 UTC, showed that 24% of the top 50 widest tornadoes occurred after dark, with 50% of those nocturnal events occurring east of −94° longitude, and the other half occurring west. However, wide deadly tornadoes were strongly favored during daylight, with 324 fatalities occurring between the hours of 1700–0000 UTC, versus 1 nocturnal fatality. Fewer nocturnal events were observed with the top 50 CTC longevities and tornado pathlengths (14% and 6%, respectively), while substantially more nocturnal events ranging from 29% to 36% occurred with the MID and LWR tornado classes.
b. Thermodynamic environment, updraft strength, and low-level rotation
Long-lived CTCs and long-tracked tornadoes were most frequently observed from November through April (65%), and 90% occurred east of −94° longitude (Fig. 4). On the other hand, ~70% of the 50 widest tornadoes were observed during April and May, and 58% occurred west of −94° longitude. Those seasonal and geographic differences influenced measures of buoyancy. For example, top-50-longevity and top-50-pathlength box-and-whisker plots of MLCAPE3 displayed interquartile values ranging from 600 to 2100 J kg−1, while the top 50 width events were associated with an interquartile range from 1200 to 3000 J kg−1 (Fig. 5). In addition, the Mann–Whitney U test (Mann and Whitney 1947)4 for MLCAPE indicated that the difference in medians was statistically significant at the 95% confidence level (α = 0.05) when the top-50 tornado width category was compared with all other event classes. Otherwise, Table 3 shows that CTC longevity, tornado pathlength, and tornado width generally increased as MLCAPE increased. For instance, the UPR group displayed median MLCAPE values ranging from 1195 to 1439 J kg−1 that decreased to 893–957 J kg−1 for LWR events. However, UPR, MID, and LWR events displayed large MLCAPE interquartile overlap, which suggests this measure of buoyancy is best utilized when identifying top-50 tornado potential, particularly the widest tornado width environments.
Geographic spatial analyses from a Gaussian kernel density estimation for (a)–(c) UPR (d)–(f) MID, and (g)–(i) LWR (left) tornado pathlength, (center) CTC longevity, (right) tornado width, valid from 2009 to 2015. The top 50 pathlengths (≥68 km), CTC longevities (≥60 min), and tornado widths (≥1.2 km) are plotted as black points atop the UPR plots.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
MLCAPE (J kg−1) derived from RUC and RAP analysis grids valid from 2009 to 2015. Events are stratified according to CTC longevity (LONGEV), tornado pathlength (LENGTH), and tornado width (WIDTH). Additional stratification is based on the top 50 events, UPR, MID, and LWR. The boxes span the 25th–75th percentiles, and the whiskers extend up to the 90th and down to the 10th percentiles. The median value is the solid horizontal line located within the boxes.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Select median (with standard deviation in parentheses) forecast parameter values for top-50, UPR, MID, and LWR CTC longevities, tornado pathlengths, and tornado widths. Parameters include MLCAPE (J kg−1), MLCIN (J kg−1), MLLCL (m), 0–1-km SRH (m2 s−2), ESRH (m2 s−2), EBWD (m s−1), and EFF-STP (unitless) extracted from RUC and RAP model analysis grids valid from 2009 to 2015. Each parameter/tornado-class grouping has been compared with the full sample to determine statistically significant differences in median values at the 95% confidence level (highlighted boldface type and italicized) using the Mann–Whitney U test.
A direct estimate of updraft strength was derived from radar calculated storm-top divergence. That measure revealed a median value for the top 50 tornado widths of 63 m s−1 (122 kt), which was nearly equivalent to the 75th percentile for top-50-longevity and top-50-pathlength events and exceeded the 75th percentile for all UPR through LWR categories (Fig. 6). In addition, the difference in medians between the 50 widest tornadoes and all other categories was statistically significant at the 95% confidence level. The difference in medians for 0.5° Vrot was also found to be statistically significant when comparing top-50 events with all lower-class events. The three top-50 tornado categories displayed a median Vrot > 36 m s−1 (70 kt), with the largest mean and median values occurring in the width group (see Fig. 7 and Table 4). Median values of Vrot became progressively weaker for lower event classes, averaging around 31 m s−1 (60 kt) for UPR events, 22 m s−1 (43 kt) for MID events, and ~18 m s−1 (35 kt) for LWR events. Correlation coefficients r (Table 5) calculated for a range of parameters showed a moderate linear relationship (r = 0.53) existing between Vrot and storm-top divergence, while a weaker relationship occurred between Vrot and ESRH (r = 0.37) as well as EBWD (r = 0.38). The largest correlation coefficient associated with Vrot was calculated for tornado width (r = 0.63). These results suggest that 1) stretching of vertical vorticity by intense updrafts may be more directly responsible for strong low-level rotation versus environmental wind shear, and 2) the moderately strong correlation between Vrot and tornado width is consistent with the linear relationship existing between near-ground vertical vorticity and updraft size found by Trapp et al. (2017).
As in Fig. 5, but for radar-derived storm-top divergence (kt) valid from 2009 to 2015.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
As in Fig. 5, but for radar-derived 0.5° peak Vrot (kt) valid from 2009 to 2015.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Radar storm characteristics for the 50 longest-lived CTCs (LONGEV50), 50 longest-pathlength tornadoes (LENGTH50), and 50 widest tornadoes (WIDTH50). Mean values include storm longevity (h), the ratio of the length of the forward flank downdraft divided by its width (unitless), the diameter of the radar-identified 0.5° CTC (km), maximum 0.5° SR inflow (m s−1), maximum storm-top divergence (m s−1), and maximum 0.5° Vrot (m s−1).
The correlation coefficient calculated between all LONGEV, LENGTH, and WIDTHs, as well as select variables consisting of storm-top divergence (STDIV), 0.5° Vrot, MLCAPE, storm speed, ESRH, EBWD, and EFF-STP.
Other thermodynamic parameters examined included 0–3-km CAPE and 700–500-hPa lapse rates, both of which displayed minimal ability in discriminating between UPR, MID, and LWR categories of CTC longevity, tornado pathlength, and tornado width. However, minor differences were observed for the top 50 events. For example, the widest tornadoes west of −94° longitude were on average associated with steeper 700–500-hPa lapse rates (7.1°C km−1) when compared with top-50-width events east of −94° longitude (6.7°C km−1), long-lived CTCs (6.5°C km−1), and long-track tornadoes (6.7°C km−1). Steeper midlevel lapse rates typical of Great Plains top-50-width environments reflect upon their close proximity to the elevated mixed-layer source region located over the Intermountain West (Lanicci and Warner 1991). Also, since surface dewpoints averaged ~18.5°C (not shown) for all top-50 categories, the steeper Great Plains lapse rates likely had the greatest direct influence on mean MLCAPE values that were ~1400 J kg−1 larger than eastern U.S. top-50-longevity and top-50-pathlength environments.
Additional parameters, such as the MLLCL (not shown), displayed a modest tendency toward lower interquartile values for top-50 events (540–955 m) relative to LWR events (570–1105 m). However, substantial interquartile overlap was observed across all event classes. Larger CTC longevities, tornado pathlengths, and widths were also found to occur in the presence of slightly lower convective inhibition (not shown), with MLCIN values in the top-50 group averaging around −30 J kg−1, ranging to −39 J kg−1 in the LWR group.
c. Wind profiles and the effective-layer significant tornado parameter
Table 3 presents median values of 0–1-km SRH and effective SRH (ESRH; Thompson et al. 2007) extracted from RUC and RAP analysis grids for top-50, UPR, MID, and LWR events. All categories were associated with SRH values supportive of supercells and tornadoes (Thompson et al. 2003). However, top-50 and UPR events occurred in environments characterized by stronger low-level shear with median 0–1-km SRH and ESRH ≥ 300 m2 s−2, while MID and LWR events were associated with values < 300 m2 s−2. Similarly, median values of effective bulk wind difference (EBWD; Thompson et al. 2007) were larger for top-50 and UPR events (≥29 m s−1; 56 kt) than for MID and LWR event classes (≤26 m s−1; 51 kt).
Compositing measures of shear and thermodynamics into the effective-layer significant tornado parameter (EFF-STP; T12) provides statistically significant discrimination between top-50 environments versus UPR through LWR environments (Fig. 8). For example, top-50 median EFF-STP values averaged around 6.0, which decreased to ~4.0 for UPR events, ~2.0 for MID events, and ~1.5 for LWR classes. Despite the large differences between the four classes of events, interquartile overlap within classes was large, and shows that the EFF-STP by itself cannot be used to determine whether a tornado will be long lived, long tracked, or wide.
As in Fig. 5, but for EFF-STP (dimensionless) valid from 2009 to 2015.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
RUC and RAP proximity composite hodographs were created for the top-50-CTC-longevity and top-50-tornado-pathlength categories (Figs. 9 and 10) as well as top-50 tornado widths occurring west of −94° longitude (Fig. 11). Table 6 shows that all three hodographs yielded 0–8-km BWD favorable for long-lived supercells (Bunkers et al. 2006b). However, the mean u component of the wind at 1 km AGL was 12.6 and 14.2 m s−1, respectively, for the longevity and pathlength hodographs. On the other hand, the low-level flow was out of the south-southeast for the top-50-width hodograph resulting in a mean 1 km AGL u component of 1.0 m s−1. Furthermore, average ground-relative wind speeds at 1 km AGL, 0–1-km BWD, and SR winds in the 0–1-km layer were stronger for the top-50-longevity and top-50-pathlength hodographs (Table 6).
Ground-relative hodographs for (a) the composite of the top 50 long-lived tornadic CTCs from 2009 to 2015 and (b) the 2100 UTC 12 Apr 2020 New Orleans, Louisiana, KLIX radiosonde observation (“raob”), which was released ~125 km south-southwest of a tornadic CTC with a longevity of 80 min. The hodograph in (a) was constructed using model wind data extracted from RUC and RAP proximity analysis soundings. The u and υ wind components for the composite hodograph in (a) are in meters per second, and those for the observed hodograph in (b) are in knots. Height levels for both hodographs are in kilometers AGL. The VRM is the internal dynamics (ID) method for predicting supercell motion (Bunkers et al. 2000) using the average u and υ wind components from the top 50 long-lived CTC events. The hodograph in (b) is provided through the courtesy of the Storm Prediction Center, and the composite hodograph in (a) was created using a modified version of a program found online (https://www.weather.gov/unr/scmr).
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
As in Fig. 9, but for (a) the composite of the top 50 tornado pathlengths and (b) the 0000 UTC 4 Apr 1974 Dayton, Ohio, KDAY raob, which was released ≤ 300 km downstream from long-track tornadoes with measured damage pathlengths ranging from 109 to 175 km (68–109 mi).
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
As in Fig. 9, but for (a) the composite of the subset of the top 50 tornado widths that occurred west of −94° longitude and (b) the 0000 UTC 1 Jun 2013 Norman, Oklahoma, KOUN raob, which was released approximately 62 km southeast from a 4.2-km-wide (4576 yd) tornado that occurred near El Reno, Oklahoma.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Wind parameters derived from composite proximity hodographs constructed using RUC and RAP model analysis soundings for LONGEV50, LENGTH50, and a subset of WIDTH50 occurring west of −94° longitude. Parameters include storm speed (m s−1), the average surface-to-500-m AGL “critical angle,” 0–1-km SRH (m2 s−2), 0–1-km SRW (m s−1), 5-km SRW (m s−1), 7–10-km SRW (m s−1), 0–1-km BWD (m s−1), and 0–8-km BWD (m s−1).
All three composite hodographs were examined for a feature first described by Thompson and Edwards (2000), and later explored more fully by Esterheld and Giuliano (2008) and Coniglio and Parker (2020). They identified a “pronounced kink” in low-level hodograph structure, in which strong speed shear rapidly transitioned to a strongly veering wind profile. Hodographs with a kink favor a more streamwise component of vorticity near the surface because of the tendency for the near-surface storm-relative wind vector to be orthogonal to the near-surface shear vector (the “critical angle”). Kinked hodograph structure was either ambiguous or nonexistent in the composite hodographs presented in Figs. 9–11, possibly due to insufficient vertical resolution in the RUC and RAP wind profiles. However, the average critical angle in the 0–0.5-km AGL layer was larger for the Great Plains width hodograph (78°) when compared with the longevity and pathlength composites (51°, respectively), which implies that horizontal vorticity was more purely streamwise in the Great Plains environment. Lower critical angles with the longevity and pathlength hodographs were likely due to stronger northeasterly storm-motion vectors and larger southwesterly speed shear in the 0–1-km layer.
The composite wind profiles also revealed that the mean 5-km AGL ground-relative wind speed was 3 m s−1 stronger for long pathlength tornadoes relative to long-lived CTCs, and the mean 10-km AGL ground-relative wind speed was 7 m s−1 stronger. Stronger winds in the surface to 10-km AGL layer aided in faster estimated storm speeds (Table 6) for long pathlength tornadoes. On the other hand, upper-level wind profiles were weaker in all respects for the top-50 tornado width composite, and that resulted in slower storm motions and weaker 7–10-km AGL SR flow, which may have contributed toward a greater tendency for HP storm classifications (Fig. 12). Conversely, stronger mid and upper-level SR flow associated with the top 50 long-lived and long-tracked events may have aided in longer forward flank downdrafts relative to their width (Table 4) and a greater frequency for classic supercells (Brooks et al. 1994a; Rasmussen and Straka 1998).
Examples of the four primary storm types analyzed and their frequency of occurrence for UPR, MID, and LWR classifications: (a) classic supercell sampled by the KBMX radar valid at 2129 UTC 27 Apr 2011, (b) HP supercell sampled by the Springfield, Missouri, KSGF radar valid at 2248 UTC 22 May 2011, (c) quasi-linear storm sampled by the Chicago (Romeoville), Illinois, KLOT radar valid at 0248 UTC 1 Jul 2014, and (d) bow echo sampled by the KSGF radar valid at 1239 UTC 8 May 2009. The radar imagery was displayed in the GR2 software.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Box-and-whisker plots of storm speed calculated linearly from sequential 0.5° radar scans are presented in Fig. 13. Speeds gradually decrease moving from UPR events rightward across LWR events. Statistically significant differences are observed in the top-50 tornado pathlength boxplot in which the 25th percentile of 22 m s−1 (43 kt) nearly exceeds the 75th percentile for all UPR, MID, and LWR events. It is also evident that storms producing top-50 tornado widths are much slower than top-50-longevity and top-50-pathlength storms. Slower storm motions for top-50-width tornadoes would be more typical of their seasonally favored time of the year (April and May) as compared with stronger flow during the November–April time period in which long-lived and long-tracked events occurred. Those differences are further evident in Fig. 14a. The top-50-longevity and top-50-pathlength storms cluster in a region where storm speeds are ≥ 15 m s−1 (29 kt) and MLCAPE values are ≤ 2000 J kg−1, whereas a large number of top-50-width storms occurred with storm speeds generally below 15 m s−1 (29 kt) and MLCAPE values ≥ 2000 J kg−1.
As in Fig. 5, but for radar-derived storm speed (kt).
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Scatterplot of (a) storm speed (kt) vs MLCAPE (J kg−1), (b) 0–1-km SRH (m2 s−2) vs MLCAPE (J kg−1), (c) MLCAPE (J kg−1) vs Vrot (kt), and (d) storm-top divergence (kt) vs Vrot (kt) for the top-50 CTC longevities (LONGEV50; filled circles), tornado pathlengths (LENGTH50; black crosses), and tornado widths (WIDTH50; open circles). Storm speed, Vrot, and storm-top divergence were calculated from observed radar data, and MLCAPE and 0–1-km SRH were derived from RUC and RAP analysis grids valid from 2009 to 2015.
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
d. Synoptic and mesoscale analysis
Daily composite charts of 250- and 850-hPa vector winds, as well as mean sea level pressure, revealed varying phases of synoptic pattern evolution for the top 50 events, with the widest tornadoes occurring west of −94° longitude (“Great Plains”) associated with leeside cyclogenesis, and top-50-longevity and top-50-pathlength tornadoes occurring within eastward-migrating extratropical cyclones. Average jet stream winds at 250-hPa featured a ~45 m s−1 (87 kt) speed maximum that was centered over north Texas in the longevity composite, and northeast Texas to central Illinois for the pathlength composite (Figs. 15a–c). On the other hand, the top-50 Great Plains width composite displayed a 250-hPa jet streak that was ~10 m s−1 (19 kt) weaker and positioned over southeast Arizona and southwest New Mexico. In addition, a zone of strong anticyclonic flow (~30–40 m s−1; 58–78 kt) was located in the top-50 longevity and pathlength composite from the Great Lakes region east across southeastern Canada and New England.
Daily composite means for (a)–(c) 250-hPa vector wind (m s−1), (d)–(f) 850-hPa vector wind (m s−1), and (g)–(i) mean sea level pressure (Pa). The composites were calculated for the (left) top 50 CTC longevities, (center) tornado pathlengths, and (right) tornado widths. The tornado width composite is valid for events occurring west of −94° longitude. In addition, the surface low pressure minimum in (g)–(i) has been annotated with a red “L.” The data source is the NCEP North American Regional Reanalysis provided online by NOAA/OAR/ESRL/Physical Sciences Laboratory, (https://psl.noaa.gov/).
Citation: Weather and Forecasting 36, 4; 10.1175/WAF-D-20-0230.1
Variations in the position of the composite southerly 850-hPa low-level jet (LLJ) were consistent with the position of the 250-hPa jet. For instance, the top-50-longevity LLJ was centered over western Tennessee, the top-50-pathlength LLJ was positioned from southeastern Louisiana north-northeast across Mississippi into western Tennessee, and the Great Plains LLJ was centered over eastern and central Oklahoma (Figs. 15d–f). In addition, the strongest composite LLJ was associated with CTC longevity (~18 m s−1). Otherwise, surface low pressure positions (Figs. 15g–i) ranged from southeast Nebraska/northeast Kansas for top-50 CTC longevity, and the Kansas City, Missouri, metropolitan area for top-50 tornado pathlength. On the other hand, the Great Plains tornado width low pressure center was positioned over southeastern Colorado, which is a climatologically favored region for surface cyclogenesis (Bentley et al. 2019).
An analysis of variance in the synoptic fields presented above indicates that the pattern of cyclogenesis in the lee of the Rocky Mountains may have contributed toward a more consistent pattern evolution for top-50 tornado width events occurring in the Great Plains. For instance, variance in the average surface low pressure center was 505 km and 598 km for the longevity and length composites, respectively, as compared with 344 km for the width composite. Similarly, the variance in mean sea level pressure was 2.5 Pa lower for the width composite than for the longevity and length composites. Variability in the magnitude and position of jet stream features at 250 and 850 hPa was also larger for top-50 longevity and length composites than for the top-50-width plots.
An analysis of inferred storm–boundary interactions revealed that roughly one-half of the UPR events occurred near a boundary versus ~80% for LWR events. Cold fronts were the most frequently observed storm–boundary association (Table 7), with around 60% occurring during the cooler months of October through March. Cold fronts were followed by a slightly lower frequency of occurrence associated with warm fronts, stationary fronts, preexisting outflow boundaries, and triple points. Boundaries in which very few inferred interactions occurred included the dryline, preexisting surface wind shifts, coastal boundaries, and terrain.
Storm–boundary associations classified according to boundary type for UPR, MID, and LWR CTC LONGEV, LENGTH, and WIDTHs.
Typically, the longest-lived and longest-tracked tornadoes formed in the vicinity of a cold front or dryline and moved into an increasingly buoyant warm sector downstream. On the other hand, short track tornadoes were more common with storm–boundary interactions, which could reflect the tendency for boundaries to provide more favorable mesoscale corridors on otherwise marginal tornado days. It was also found that the average top-50 CTC longevity was 87 min when occurring across the warm sector, versus 79 min when associated with a boundary. On the other hand, top-50 tornado pathlengths were roughly the same for boundary and warm-sector events (122 km vs 127 km, respectively), while the mean top-50 tornado width was 0.18 km (200 yd) wider for boundary storms.
4. Summary and discussion
The following conclusions were derived from a detailed examination of the longevity of low-level tornadic circulations, tornado pathlengths, and tornado widths:
Approximately 90% of the top 50 long-lived and long-tracked tornadoes occurred east of −94° longitude, and 65% formed between the months of November and April.
Roughly 70% of the top 50 tornado widths occurred during April and May, and ~60% were located west of −94° longitude.
The top 50 CTC longevities and tornado pathlengths frequently occurred with an eastward-migrating extratropical cyclone, while the widest tornadoes occurring west of −94° longitude were associated with surface cyclogenesis occurring over eastern Colorado.
Mean and median EFF-STP values were near 6.0 for the top 50 CTC longevities, tornado pathlengths, and tornado widths. Thus, EFF-STP provides a valuable signal alerting forecasters to the potential for a high-end event, but does not allow forecasters to determine whether the event will be a long-lived CTC, long-tracked tornado, or wide tornado.
The widest tornadoes occurred in large MLCAPE environments with 0–1-km AGL wind profiles displaying large hodograph curvature, as well as near-surface horizontal vorticity that was more purely streamwise. In addition, top-50 tornado widths were produced by storms that on average displayed the largest values of radar-derived storm-top divergence and Vrot.
MLCAPE and storm-top divergence were lower for the 50 longest-lived CTCs and longest-pathlength tornadoes. In addition, 0–1-km AGL wind profiles were more likely to be veered to southwesterly, and ground-relative winds, SR winds, ESRH, EBWD, and storm speed were all stronger than top-50 tornado width environments while 0.5° Vrot was only slightly lower.
In general, thermodynamic parameters were similar among all event classes, with the exception of top-50 tornado widths, which were associated with much larger MLCAPE values. On the other hand, ESRH and EBWD were stronger for long-lived and long-tracked events. However, environmental compensation occurred for the relative weakness in buoyancy for long-lived/long-track events, and shear for width events, such that mean EFF-STP values were similar within the categorical top-50 groups. This demonstrates that EFF-STP provides a useful signal for potentially higher- or lower-end events but does not allow forecasters to determine whether a tornado will be long-lived, long-tracked, or exceptionally wide. Forecasters must consider other factors in order to discriminate.
A necessary condition for long-track tornadoes is a long-lived tornado that moves fast, which was the essential finding of Garner (2007). Although not examined in this study, another possible requirement for the occurrence of those events is a wide zone of moisture and buoyancy sufficient to sustain a surface-based CTC over a long period of time. Perhaps long-lived and long-tracked tornadoes are less likely over the Great Plains because the corridor for surface-based thunderstorm development is small because of smaller zones of surface-based destabilization, greater CIN due to a stronger EML, and rapid boundary layer stabilization after sunset. On the other hand, long-lived and long-tracked tornadoes are more likely over the southeastern United States because of close proximity to the Gulf of Mexico (Molina and Allen 2019), which allows 1) a greater spatial window for tornadic activity with typically broader warm sectors and 2) a greater temporal window for tornadic activity due to continuous northward moisture flux and smaller diurnal boundary layer variations, which both suppress the stabilizing effects of the evening transition such as is found in the Great Plains. Future research on warm-sector geometry and its spatiotemporal variations with respect to storm motion could provide beneficial insights into the relationship between long-lived CTCs, long-tracked tornadoes, and their synoptic environments.
The widest tornadoes in this study, particularly the ones occurring over the Great Plains, occurred in environments characterized by large MLCAPE, larger hodograph curvature in the 0–1-km AGL layer, and near-surface horizontal vorticity that was more purely streamwise. Furthermore, radar observed storm-top divergence and Vrot were large for wide tornado producing storms. Unlike other studies, such as Coffer and Parker (2017), the current study identifies an important forecast utility for MLCAPE due to its relationship with wide tornadoes. McCaul and Weisman (2001) showed that storms occurring in large CAPE environments can yield intense updrafts that favor stronger tilting and vertical stretching of horizontal vorticity resulting in stronger updraft rotation. On the other hand, Trapp et al. (2017) found that CAPE was not a good discriminator of tornado EF scale, but current results show that CAPE was generally largest for the widest tornadoes, and those tornadoes were associated with the largest Vrot values in the dataset. By extension, increasing values of Vrot are more likely to be associated with higher EF-scale tornado ratings (T17). That paradox is reconciled by noting that many of the widest tornadoes occurred across the Great Plains of the United States, a region where EF-scale ratings may be lower (relative to the southeastern United States) as a result of lower building density—a feature of the Great Plains that seemed relevant during the 31 May 2013 El Reno, Oklahoma tornado (Snyder and Bluestein 2014; Bluestein et al. 2015). The events with the top 50 tornado widths also moved more slowly than long-lived and long-tracked events, which would further reduce their destructive footprint.
As highlighted throughout this paper, variations in hodograph shape and the magnitude of MLCAPE appear to have some control over predicting the type of tornado that a storm might produce. High MLCAPE and large low-level hodograph curvature were observed with the widest tornadoes, while lower values of MLCAPE and minimal low-level hodograph curvature were associated with long-lived and long-tracked events. Furthermore, large 0.5° Vrot was observed for all three classes of tornadoes, but on average, values were greater for the top-50-width storms, which may be due to stronger stretching of vertical vorticity resulting from the effects of larger buoyancy and hodograph curvature (Brooks and Wilhelmson 1993; McCaul and Weisman 2001). Reproducing tornado-like vortices in idealized numerical simulations that resemble wide, long-lived, and long-tracked events might aid in identifying the physical processes leading to strong low-level rotation with environments characterized by high CAPE and large low-level hodograph curvature versus environments in which low-level speed shear is strong and CAPE is low to moderate in magnitude. Those insights would in turn help forecasters to anticipate an important class of atmospheric vortices that have been shown to produce a disproportionate impact upon society.
Acknowledgments
The authors thank Dr. Walker Ashley, Simon Brewer, and Juston Drake for granting permission to use their photography in Fig. 1, and we also thank Donald Burgess for granting permission to use and annotate Fig. 3. The authors thank Drs. Robert Maddox, Matthew Gilmore, and their coauthors for providing an estimated hodograph associated with the Tri-State Tornado. Mel Nordquist, Dr. Mark Conder, Chris Burling, Dr. Patrick Marsh, Chris Broyles, and National Weather Service Western and Southern Region Headquarters are acknowledged for their suggestions and support for this work. The authors also thank Dr. Rebecca Adams-Selin for providing an archived preprint utilized in the introduction, and we thank three anonymous reviewers and our editor who contributed valuable suggestions that greatly improved this paper.
Data availability statement
The database of tornadoes utilized in this study, their radar characteristics, and associated environments is maintained by the Storm Prediction Center, with supplemental information added by the first two authors. Archived Level II WSR-88D data are made freely available by the National Centers for Environmental Information (http://ncdc.noaa.gov/nexradinv/). Tornado storm survey information is also available at the NCEI website (https://www.ncdc.noaa.gov/stormevents/). RUC and RAP analysis sounding data are archived and made freely available by Iowa State University (http://mesonet.agron.iastate.edu/archive/data/). The geographic spatial analyses were created using Python and associated freely available libraries. The program used to create the composite hodographs can be found at https://www.weather.gov/unr/scmr. The Gibson Ridge 2 (GR2) Analyst software is available for a fee (http://www.grlevelx.com/). Plots generated using the North American Regional Reanalysis dataset were obtained online from NOAA/OAR/ESRL/PSL (https://psl.noaa.gov/).
REFERENCES
Adams, R., 2005: An examination of the 22 May 2004 Hallam, Nebraska tornado. Fourth AMS Student Conf., San Diego, CA, Amer. Meteor. Soc., P1.37, https://ams.confex.com/ams/Annual2005/techprogram/paper_88523.htm.
Adlerman, E. J., K. K. Droegemeier, and R. Davies-Jones, 1999: A numerical simulation of cyclic mesocyclogenesis. J. Atmos. Sci., 56, 2045–2069, https://doi.org/10.1175/1520-0469(1999)056<2045:ANSOCM>2.0.CO;2.
Anderson-Frey, A. K., and H. Brooks, 2019: Tornado fatalities: An environmental perspective. Wea. Forecasting, 34, 1999–2015, https://doi.org/10.1175/WAF-D-19-0119.1.
Ashley, W. S., 2007: Spatial and temporal analysis of tornado fatalities in the United States: 1880–2005. Wea. Forecasting, 22, 1214–1228, https://doi.org/10.1175/2007WAF2007004.1.
Ashley, W. S., and T. L. Mote, 2005: Derecho hazards in the United States. Bull. Amer. Meteor. Soc., 86, 1577–1592, https://doi.org/10.1175/BAMS-86-11-1577.
Ashley, W. S., and S. M. Strader, 2016: Recipe for disaster: How the dynamic ingredients of risk and exposure are changing the tornado disaster landscape. Bull. Amer. Meteor. Soc., 97, 767–786, https://doi.org/10.1175/BAMS-D-15-00150.1.
Ashley, W. S., A. J. Krmenec, and R. Schwantes, 2008: Vulnerability due to nocturnal tornadoes. Wea. Forecasting, 23, 795–807, https://doi.org/10.1175/2008WAF2222132.1.
Beatty, K., E. N. Rasmussen, J. M. Straka, and L. R. Lemon, 2009: The supercell spectrum. Part II: A semi-objective method for radar classification of supercell type. Electron. J. Severe Storms Meteor., 4 (1), https://ejssm.org/ojs/index.php/ejssm/article/viewArticle/45.
Benjamin, S. G., and Coauthors, 2004: An hourly assimilation forecast cycle: The RUC. Mon. Wea. Rev., 132, 495–518, https://doi.org/10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2.
Bentley, A. M., L. F. Bosart, and D. Keyser, 2019: A climatology of extratropical cyclones leading to extreme weather events over central and eastern North America. Mon. Wea. Rev., 147, 1471–1490, https://doi.org/10.1175/MWR-D-18-0453.1.
Bluestein, H. B., 2009: The formation and early evolution of the Greensburg, Kansas, tornadic supercell on 4 May 2007. Wea. Forecasting, 24, 899–920, https://doi.org/10.1175/2009WAF2222206.1.
Bluestein, H. B., J. C. Snyder, and J. B. Houser, 2015: A multiscale overview of the El Reno, Oklahoma, tornadic supercell of 31 May 2013. Wea. Forecasting, 30, 525–552, https://doi.org/10.1175/WAF-D-14-00152.1.
Bothwell, P. D., J. A. Hart, and R. L. Thompson, 2002: An integrated three-dimensional objective analysis scheme in use at the Storm Prediction Center. 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., JP3.1, https://ams.confex.com/ams/pdfpapers/47482.pdf.
Brooks, H. E., 2004: On the relationship of tornado path length and width to intensity. Wea. Forecasting, 19, 310–319, https://doi.org/10.1175/1520-0434(2004)019<0310:OTROTP>2.0.CO;2.
Brooks, H. E., and R. B. Wilhelmson, 1993: Hodograph curvature and updraft intensity in numerically modeled supercells. J. Atmos. Sci., 50, 1824–1833, https://doi.org/10.1175/1520-0469(1993)050<1824:HCAUII>2.0.CO;2.
Brooks, H. E., C. A. Doswell III, and R. Davies-Jones, 1993: Environmental helicity and the maintenance and evolution of low-level mesocyclones. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 97–104.
Brooks, H. E., R. B. Wilhelmson, and R. B. Wilhelmson, 1994a: The role of midtropospheric winds in the evolution and maintenance of low-level mesocyclones. Mon. Wea. Rev., 122, 126–136, https://doi.org/10.1175/1520-0493(1994)122<0126:TROMWI>2.0.CO;2.
Brooks, H. E., R. B. Wilhelmson, and J. Cooper, 1994b: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606–618, https://doi.org/10.1175/1520-0434(1994)009<0606:OTEOTA>2.0.CO;2.
Brown, M., and C. J. Nowotarski, 2019: The influence of lifting condensation level on low-level outflow and rotation in simulated supercell thunderstorms. J. Atmos. Sci., 76, 1349–1372, https://doi.org/10.1175/JAS-D-18-0216.1.
Brown, R. A., and R. J. Meitin, 1994: Evolution and morphology of two splitting thunderstorms with dominant left-moving members. Mon. Wea. Rev., 122, 2052–2067, https://doi.org/10.1175/1520-0493(1994)122<2052:EAMOTS>2.0.CO;2.
Bunkers, M. W., and J. W. Stoppkotte, 2007: Documentation of a rare tornadic left-moving supercell. Electron. J. Severe Storms Meteor., 2 (2), https://ejssm.org/ojs/index.php/ejssm/article/viewArticle/14/17.
Bunkers, M. W., 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. W., M. R. Hjelmfelt, and P. L. 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. W., J. S. Johnson, L. J. Czepyha, J. M. Grzywacz, B. A. Klimowski, and M. R. 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.
Burgess, D., and Coauthors, 2014: 20 May 2013 Moore, Oklahoma, Tornado: Damage survey and analysis. Wea. Forecasting, 29, 1229–1237, https://doi.org/10.1175/WAF-D-14-00039.1.
Coffer, B. E., and M. D. Parker, 2017: Simulated supercells in nontornadic and tornadic VORTEX2 environments. Mon. Wea. Rev., 145, 149–180, https://doi.org/10.1175/MWR-D-16-0226.1.
Coleman, T. A., and P. G. Dixon, 2014: An objective analysis of tornado risk in the United States. Wea. Forecasting, 29, 366–376, https://doi.org/10.1175/WAF-D-13-00057.1.
Coniglio, M. C., and M. D. Parker, 2020: Insights into supercells and their environments from three decades of targeted radiosonde observations. Mon. Wea. Rev., 148, 4893–4915, https://doi.org/10.1175/MWR-D-20-0105.1.
Corfidi, S. F., S. J. Weiss, J. S. Kain, S. J. Corfidi, R. M. Rabin, and J. J. Levit, 2010: Revisiting the 3–4 April 1974 super outbreak of tornadoes. Wea. Forecasting, 25, 465–510, https://doi.org/10.1175/2009WAF2222297.1.
Darkow, G. L., 1969: An analysis of over sixty tornado proximity soundings. Preprints, Sixth Conf. on Severe Local Storms, Chicago, IL, Amer. Meteor. Soc., 218–221.
Dixon, P. G., A. E. Mercer, J. Choi, and J. S. Allen, 2011: Tornado risk analysis: Is Dixie Alley an extension of Tornado Alley? Bull. Amer. Meteor. Soc., 92, 433–441, https://doi.org/10.1175/2010BAMS3102.1.
Esterheld, J. M., and D. J. Giuliano, 2008: Discriminating between tornadic and non-tornadic supercells: A new hodograph technique. Electron. J. Severe Storms Meteor., 3 (2), https://ejssm.org/ojs/index.php/ejssm/article/viewArticle/33.
Fujita, T. T., 1981: Tornadoes and downbursts in the context of generalized planetary scales. J. Atmos. Sci., 38, 1511–1534, https://doi.org/10.1175/1520-0469(1981)038<1511:TADITC>2.0.CO;2.
Garner, J. M., 2007: A preliminary study on environmental parameters related to tornado path length. Electron. J. Oper. Meteor., 2007-EJ5, http://nwafiles.nwas.org/ej/pdf/2007-EJ5.pdf.
Henry, A. J., 1925: The tornadoes of March 18, 1925. Mon. Wea. Rev., 53, 141–145, https://doi.org/10.1175/1520-0493(1925)53<141:TTOM>2.0.CO;2.
Irish, J. L., D. T. Resio, and J. J. Ratcliff, 2008: The influence of storm size on hurricane surge. J. Phys. Oceanogr., 38, 2003–2013, https://doi.org/10.1175/2008JPO3727.1.
Johns, R. H., J. M. Davies, and P. W. Leftwich, 1993: Some wind and instability parameters associated with strong and violent tornadoes. Part II: Variations in the combinations of wind and instability parameters. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 583–590.
Johns, R. H., D. W. Burgess, C. A. Doswell III, M. S. Gilmore, J. A. Hart, and S. F. Piltz, 2013: The 1925 Tri-State tornado damage path and associated storm system. Electron. J. Severe Storms Meteor., 8 (2), https://ejssm.org/ojs/index.php/ejssm/article/viewArticle/109–.
Karstens, C. D., T. M. Samaras, B. D. Lee, W. A. Gallus Jr., and C. A. Finley, 2010: Near-ground pressure and wind measurements in tornadoes. Mon. Wea. Rev., 138, 2570–2588, https://doi.org/10.1175/2010MWR3201.1.
Kuster, C. M., P. L. Heinselman, and M. Austin, 2015: 31 May 2013 El Reno tornadoes: Advantages of rapid-scan phased-array radar data from a warning forecaster’s perspective. Wea. Forecasting, 30, 933–956, https://doi.org/10.1175/WAF-D-14-00142.1.
Lanicci, J. M., and T. T. Warner, 1991: A synoptic climatology of the elevated mixed-layer inversion over the southern Great Plains in spring: Part I: Structure, dynamics, and seasonal evolution. Wea. Forecasting, 6, 181–197, https://doi.org/10.1175/1520-0434(1991)006<0181:ASCOTE>2.0.CO;2.
Lee, B. D., B. F. Jewett, and R. B. Wilhelmson, 2006: The 19 April 1996 Illinois tornado outbreak: Cell mergers and associated tornado incidence. Wea. Forecasting, 21, 449–464, https://doi.org/10.1175/WAF943.1.
Lee, B. D., C. A. Finley, and C. D. Karstens, 2012: The Bowdle, South Dakota, cyclic tornadic supercell of 22 May 2010: Surface analysis of rear-flank downdraft evolution and multiple internal surges. Mon. Wea. Rev., 140, 3419–3441, https://doi.org/10.1175/MWR-D-11-00351.1.
Lemon, L. R., R. J. Donaldson Jr., D. W. Burgess, and R. A. Brown, 1977: Doppler radar application to severe thunderstorm study and potential real-time warning. Bull. Amer. Meteor. Soc., 58, 1187–1193, https://doi.org/10.1175/1520-0477(1977)058<1187:DRATST>2.0.CO;2.
Maddox, R. A., M. S. Gilmore, C. A. Doswell III, R. H. Johns, C. A. Crisp, D. W. Burgess, J. A. Hart, and S. F. Piltz, 2013: Meteorological analyses of the Tri-State tornado event of March 1925. Electron. J. Severe Storms Meteor., 8 (1), https://ejssm.org/ojs/index.php/ejssm/article/viewFile/114/88.
Mann, H. B., and D. R. Whitney, 1947: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat., 18, 50–60, https://doi.org/10.1214/aoms/1177730491.
Markowski, P. M., and Y. P. Richardson, 2014: The influence of environmental low-level shear and cold pools on tornadogenesis: Insights from idealized simulations. J. Atmos. Sci., 71, 243–275, https://doi.org/10.1175/JAS-D-13-0159.1.
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.
Markowski, P. M., J. M. Straka, and E. N. Rasmussen, 2002: Direct surface thermodynamic observations within the rear-flank downdrafts of nontornadic and tornadic supercells. Mon. Wea. Rev., 130, 1692–1721, https://doi.org/10.1175/1520-0493(2002)130<1692:DSTOWT>2.0.CO;2.
McCaul, E. W., and M. L. Weisman, 2001: The sensitivity of simulated supercell structure and intensity to variations in the shapes of environmental buoyancy and shear profiles. Mon. Wea. Rev., 129, 664–687, https://doi.org/10.1175/1520-0493(2001)129<0664:TSOSSS>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.ejssm.org/ojs/index.php/ejssm/issue/view/31.
Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, https://doi.org/10.1175/BAMS-87-3-343.
Molina, M. J., and J. T. Allen, 2019: On the moisture origins of tornadic thunderstorms. J. Climate, 32, 4321–4346, https://doi.org/10.1175/JCLI-D-18-0784.1.
Moller, A. R., C. A. Doswell III, M. P. Foster, and G. R. Woodall, 1994: The operational recognition of supercell thunderstorm environments and storm structures. Wea. Forecasting, 9, 327–347, https://doi.org/10.1175/1520-0434(1994)009<0327:TOROST>2.0.CO;2.
Murdzek, S. S., P. M. Markowski, Y. P. Richardson, and R. L. Tanamachi, 2020: Processes preventing the development of a significant tornado in a Colorado supercell on 26 May 2010. Mon. Wea. Rev., 148, 1753–1778, https://doi.org/10.1175/MWR-D-19-0288.1.
Needham, H. F., and B. D. Keim, 2014: Correlating storm surge heights with tropical cyclone winds at and before landfall. Earth Interact., 18, https://doi.org/10.1175/2013EI000527.1.
Orf, L. R., R. Wilhelmson, B. Lee, C. Finley, and A. Houston, 2017: Evolution of a long-track violent tornado within a simulated supercell. Bull. Amer. Meteor. Soc., 98, 45–68, https://doi.org/10.1175/BAMS-D-15-00073.1.
Rasmussen, E. N., and D. O. Blanchard, 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 1148–1164, https://doi.org/10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.
Rasmussen, E. N., and J. M. Straka, 1998: Variations in supercell morphology. Part I: Observations of the role of upper-level storm-relative flow. Mon. Wea. Rev., 126, 2406–2421, https://doi.org/10.1175/1520-0493(1998)126<2406:VISMPI>2.0.CO;2.
Rasmussen, E. N., J. M. Straka, R. Davies-Jones, C. A. Doswell III, F. H. Carr, M. D. Eilts, and D. R. MacGorman, 1994: Verification of the Origins of Rotation in Tornadoes Experiment: VORTEX. Bull. Amer. Meteor. Soc., 75, 995–1006, https://doi.org/10.1175/1520-0477(1994)075<0995:VOTOOR>2.0.CO;2.
Rasmussen, E. N., S. Richardson, J. M. Straka, P. M. Markowski, and D. O. Blanchard, 2000: The association of significant tornadoes with a baroclinic boundary on 2 June 1995. Mon. Wea. Rev., 128, 174–191, https://doi.org/10.1175/1520-0493(2000)128<0174:TAOSTW>2.0.CO;2.
Schneider, R. S., and A. R. Dean, 2008: A comprehensive 5-year severe storm environment climatology for the continental United States. 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 16A.4, https://ams.confex.com/ams/pdfpapers/141748.pdf.
Skinner, P. S., C. C. Weiss, M. M. French, H. B. Bluestein, P. M. Markowski, and Y. P. Richardson, 2014: VORTEX2 observations of a low-level mesocyclone with multiple internal rear-flank downdraft momentum surges in the 18 May 2010 Dumas, Texas, supercell. Mon. Wea. Rev., 142, 2935–2960, https://doi.org/10.1175/MWR-D-13-00240.1.
Smith, B. T., R. L. Thompson, J. S. Grams, C. Broyles, and H. E. Brooks, 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part I: Storm classification and climatology. Wea. Forecasting, 27, 1114–1135, https://doi.org/10.1175/WAF-D-11-00115.1.
Smith, B. T., R. L. Thompson, A. R. Dean, and P. T. Marsh, 2015: Diagnosing the conditional probability of tornado damage rating using environmental and radar attributes. Wea. Forecasting, 30, 914–932, https://doi.org/10.1175/WAF-D-14-00122.1.
Snook, N., and M. Xue, 2008: Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophys. Res. Lett., 35, L24803, https://doi.org/10.1029/2008GL035866.
Snyder, J. C., and H. B. Bluestein, 2014: Some considerations for the use of high-resolution mobile radar data in tornado intensity determination. Wea. Forecasting, 29, 799–827, https://doi.org/10.1175/WAF-D-14-00026.1.
Speheger, D. A., and R. D. Smith, 2006: On the imprecision of radar signature locations and storm path forecasts. Natl. Wea. Dig., 30, 3–10, http://nwafiles.nwas.org/digest/papers/2006/Vol30No1/Pg3-Speheger.pdf.
Thompson, R. L., and R. Edwards, 2000: An overview of environmental conditions and forecast implications of the 3 May 1999 tornado outbreak. Wea. Forecasting, 15, 682–699, https://doi.org/10.1175/1520-0434(2000)015<0682:AOOECA>2.0.CO;2.
Thompson, R. L., R. Edwards, J. A. Hart, K. L. Elmore, and P. 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. M. 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.
Thompson, R. L., B. T. Smith, A. R. Dean, and P. T. Marsh, 2013: Spatial distributions of tornadic near-storm environments by convective mode. Electron. J. Severe Storms Meteor., 8 (5), http:www.ejssm.org/ojs/index.php/ejssm/issue/view/47.
Thompson, R. L., and Coauthors, 2017: Tornado damage rating probabilities derived from WSR-88D. Wea. Forecasting, 32, 1509–1528, https://doi.org/10.1175/WAF-D-17-0004.1.
Trapp, R. J., G. R. Marion, and S. W. Nesbitt, 2017: The regulation of tornado intensity by updraft width. J. Atmos. Sci., 74, 4199–4211, https://doi.org/10.1175/JAS-D-16-0331.1.
Wakimoto, R. M., 1983: The West Bend, Wisconsin storm of 4 April 1981: A problem in operational meteorology. J. Climate Appl. Meteor., 22, 181–189, https://doi.org/10.1175/1520-0450(1983)022<0181:TWBWSO>2.0.CO;2.
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.
Weisman, M. L., and J. B. Klemp, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 2479–2498, https://doi.org/10.1175/1520-0493(1984)112<2479:TSACON>2.0.CO;2.
Wood, V. T., and R. A. Brown, 1997: Effects of radar sampling on single-Doppler velocity signatures of mesocyclones and tornadoes. Wea. Forecasting, 12, 928–938, https://doi.org/10.1175/1520-0434(1997)012<0928:EORSOS>2.0.CO;2.
Wurman, J., and K. Kosiba, 2013: Finescale radar observations of tornado and mesocyclone structures. Wea. Forecasting, 28, 1157–1174, https://doi.org/10.1175/WAF-D-12-00127.1.
Wurman, J., D. Dowell, Y. Richardson, P. Markowski, E. Rasmussen, D. Burgess, L. Wicker, and H. B. Bluestein, 2012: The second verification of the origins of rotation in tornadoes experiment: VORTEX2. Bull. Amer. Meteor. Soc., 93, 1147–1170, https://doi.org/10.1175/BAMS-D-11-00010.1.
Wurman, J., K. Kosiba, P. Robinson, and T. Marshall, 2014: The role of multiple-vortex tornado structure in causing storm researcher fatalities. Bull. Amer. Meteor. Soc., 95, 31–45, https://doi.org/10.1175/BAMS-D-13-00221.1.
More information on GR2 Analyst software is available online (http://www.grlevelx.com/about.htm).
A strong tornado is defined as EF2–EF3.
All ML parameters are calculated using the average temperature and mixing ratio over the lowest 100 hPa of the RUC/RAP thermodynamic profile.
Quotient–quotient (Q–Q) plots were created for the various parameter categories. The Q–Q plots revealed that portions of the dataset displayed a nonnormal distribution. Therefore, the Mann–Whitney U test was utilized to analyze statistical significance because it does not rely on the assumption that the data are normally distributed.
