1. Introduction
The microphysical and kinematic characteristics of lightning’s relationship with convective processes have been characterized extensively, accentuating the key role of the updraft in supplying and transporting the mixed-phase precipitation mass that supports electrification and flash production (Carey and Rutledge 1996, 2000; Lang and Rutledge 2002; Deierling et al. 2008; Deierling and Petersen 2008; Calhoun et al. 2013, 2014). The intrinsic nature of the relationship between lightning production and a storm’s updraft has been leveraged to develop viable operational tools utilizing lightning data to provide objective methods of gauging updraft strength and convective intensification (Schultz et al. 2009; Gatlin and Goodman 2010; Calhoun et al. 2014; Calhoun 2015). The operational lightning jump algorithm serves as a leading example, quantifying rapid increases in a thunderstorm’s combined in-cloud and cloud-to-ground lightning flash rate shown to precede the onset of severe events by as much as 20 min (Williams et al. 1999; Schultz et al. 2009, 2011, 2014, 2016; Darden et al. 2010; Gatlin and Goodman 2010; Goodman et al. 2013; Rudlosky and Fuelberg 2013; Stano et al. 2014; Chronis et al. 2015). Quantification of physical convective processes leading up to and during the time of algorithmic detections of rapid increases in lightning flash rates, or lightning jumps, have furthered the foundational understanding of the strong correlation between flash rates and updraft intensity. Results from detailed case analyses have demonstrated that convective behavior prior to and during lightning jumps is characterized by an updraft-driven increase in graupel mass in the mixed-phase region between −10° and −40°C, increased 10 m s−1 updraft volume, increases in maximum updraft speeds, and reduction in flash size nearest the most intense updrafts during their development (Schultz et al. 2015). Additionally, for increases in lightning flash rate contributing to a lightning jump, the associated growth of the 10 m s−1 updraft volume is nearly 4 times greater in the median, and increases in peak updraft speed are 5 times greater in the median than those of nonjump flash rate increases (Schultz et al. 2017).
While basic physical characteristics of the lightning–updraft relationship such as these have been identified, the extension of this relationship to additional updraft-related thunderstorm processes has not been explored as deeply. Further, conceptual models coupling the information afforded by lightning data with proven tools such as Doppler radar have not been thoroughly developed. To this end, determining how lightning flash rate information relates to radar-inferred metrics of intensity within the context of specific convective modes may enhance the operational utility of existing applications.
The supercell storm mode presents a natural avenue of investigation of this concept because its definition requires the presence of a mesocyclone, defined as a deep, persistent, rotating updraft–downdraft couplet. It is commonly accepted that establishment of a mesocyclone in an ordinary thunderstorm transitioning to a supercell depends on the updraft to first tilt environmental horizontal vorticity into the vertical and then stretch established vertical vorticity to maintain mesocyclonic rotation (Lemon and Doswell 1979; Brandes 1984; Davies-Jones 1984; Moller et al. 1994; Markowski and Richardson 2009). While many supercells are characterized by a single sustained mesocyclone, cyclic mesocyclogenesis in which multiple mesocyclones form sequentially is also occasionally observed (Darkow and Roos 1970; Lemon and Doswell 1979; Beck et al. 2006; French et al. 2008; Kumjian et al. 2010).
The structural organization of a supercell that allows it to form one or more mesocyclones also augments its ability to support hail growth, develop robust downdrafts and strong surface-based straight-line winds, and undergo tornadogenesis (Lemon and Doswell 1979; Markowski and Richardson 2009; Duda and Gallus 2010; Davies-Jones 2015). Though tornadogenesis remains an active area of research, this study specifically addresses the mesocyclone in lieu of tornadic rotation because of the shared dependency of lightning and the mesocyclone on the midlevel updraft. Current tornadogenesis research establishes the role of the mesocyclone as a necessary but insufficient component related to the production of a tornado, whereas tornadic rotation additionally relies upon the complexities of near-surface components currently ill-suited for comparison with predominantly midlevel lightning processes (Markowski and Richardson 2014). While tornadic storms are given specific consideration as a component of this study, analyses herein address lightning’s relationship with the low-level mesocyclone within the context of current tornadogenesis theory rather than explore relationships between lightning and tornadoes or tornadoes and low-level mesocyclones. In this sense, development of a better understanding of the relationship between the mesocyclone and lightning serves as a more tractable precursor to future research addressing the possible indirect links between lightning and tornadoes. Additionally, various large-sample studies have determined that while only approximately 26% of mesocyclones are associated with tornadoes, nearly 90% of mesocyclones are associated with some type of severe weather (Stumpf et al. 1998; Markowski and Straka 2000; Trapp et al. 2005). This relationship with severe weather gives mesocyclones great operational significance. Identification of a mesocyclone is often enough to prompt a convective warning, and as such, several radar-derived tools exist for diagnosing midlevel rotation (Stumpf et al. 1998; Smith et al. 2016).
The fundamental dynamics of supercells and their contributing environmental controls are well recognized as they apply to the production of high-impact phenomena. Combined with these advantages, lightning data may be used in concert with radar-derived mesocyclonic rotation to enhance diagnostic ability and address remaining nowcasting challenges pertaining to supercells. In particular, forecasters anecdotally cite correctly identifying the first ordinary cell that may develop a mesocyclone and identifying which rotating storms may be more likely to persist and pose a higher threat of producing severe weather as pertinent issues (Brotzge and Erickson 2010; Brotzge and Donner 2013). Because of the updraft-dependent nature of both lightning and a supercell’s mesocyclone, lightning utility may be extended beyond signaling updraft intensification to infer the development and strengthening of the midlevel rotation that are beneficial for enhanced situational awareness in supercell-supportive environments. Conceptually, it is suggested that increases in flash rate related to the intensification of the updraft may coincide with or precede the stretching of the midlevel mesocyclonic rotation of the supercell. Direct application of the temporal relationship between lightning flash rate properties and the strength of midlevel mesocyclonic rotation is twofold. Prior to supercell development, lightning flash rate behavior may reinforce the radar-indicated establishment of persistent mesocyclonic rotation in an ordinary cell and, then, may identify periods when the intensification of parent rotation is likely, signaling the intensification of an established supercell.
Williams et al. (1999) and Williams (2001) document the early conceptualization of the connection between vortex stretching and the vertical development of the ice phase connected to thunderstorm electrification. Results from case studies dating back to the late 1980s support the validity of the conceptual model (e.g., MacGorman et al. 1989; MacGorman and Nielsen 1991; Stano et al. 2014). Early studies identified relationships between cyclonic shear in supercells and cloud-to-ground lightning, as well as in-cloud lightning when data were available (MacGorman et al. 1989; MacGorman and Nielsen 1991). A more recent study has specifically addressed the conceptual model with analysis of the temporal relationship between rapid increases in total lightning and the evolution of a mesocyclone in a single robust tornadic supercell (Stano et al. 2014). Additional studies characterizing five or fewer supercell cases have also discussed the development and evolution of lightning behavior alongside details of mesocyclonic rotation (e.g., Seimon 1993; Williams et al. 1999; Carey et al. 2003; Knupp et al. 2003; Goodman et al. 2005; Tessendorf et al. 2005; Steiger et al. 2007; Schultz et al. 2009; Darden et al. 2010; Calhoun et al. 2013; Schultz et al. 2015). The present work addresses the relationship between lightning flash rate characteristics and Doppler-velocity-derived metrics of mesocyclonic rotation throughout the observed life cycles of a larger, diverse sample of 19 supercell cases representing a spectrum of environmental conditions, spatial structure, and convective intensity. Related large-sample studies include the documentation of lightning behavior with respect to violent tornadoes (Perez et al. 1997); a characterization of lightning jumps using radar information, including automated mesocyclone detections, for the purpose of associating lightning jumps with severe event types (Metzger and Nuss 2013); a climatological characterization of mesocyclones in central Europe including diurnal and annual comparisons with lightning activity (Wapler et al. 2016); and separate characterizations of lightning and mesocyclones with respect to hailstorms in central Europe (Wapler 2017). Though these large-sample studies addressed statistical characterizations of tornado or mesocyclone occurrence with respect to lightning, none has similarly investigated the nature of the storm-scale relationships between lightning and the development and evolution of the mesocyclone as set forth in this study.
The following discussion details use of total lightning data from Lightning Mapping Arrays (LMAs) and Doppler-velocity-derived metrics of mesocyclonic rotation for comparative analysis. While level-III Weather Surveillance Radar-1988 Doppler (WSR-88D) Mesocyclone Detection Algorithm (MDA) data were assessed (Stumpf et al. 1998), the azimuthal shear of Doppler radial velocity was the primary metric of mesocyclonic rotation utilized in this study (Miller et al. 2013; Newman et al. 2013; Smith et al. 2016). An analysis framework similar to that implemented in the lightning jump algorithm was extended to trends in azimuthal shear to facilitate the comparison between lightning flash rates and mesocyclonic rotation for the evaluation of the suggested conceptual model. The temporal relationship between rapid increases in lightning and in azimuthal shear-interpreted rotation is characterized statistically and discussed with respect to the environmental context. In addition to an analysis of the overall dataset, specific relationships between lightning jumps and the low-level mesocyclone are also addressed in tornadic storms. Possible physical explanations and implications of inferred relationships are discussed and extended to operational applications.
2. Data and methods
a. Lightning data and processing
Total lightning data used for this study were obtained from LMAs in north Alabama, Oklahoma, and Washington, D.C., referred to as the NALMA, OKLMA, and DCLMA, respectively (Rison et al. 1999). Each network is composed of a minimum of eight sensors that record the magnitude and timing of the peak emission of very high frequency (VHF) radiation from lightning. Because the technology used in the three networks ranges in age and the locations of each are characterized by different levels of urban electromagnetic noise, variations in their performance have been observed and documented. Of the three networks, the OKLMA is the most sensitive because of the combined effects of less urban noise and a greater number of stations available for source detections and time-of-arrival (TOA) solutions (Fuchs et al. 2015). While the DCLMA is newer than the other LMAs, its poorer relative performance in comparison with the other LMAs used in this study can be partially attributed to its use of fewer sensors. It also operates in the higher VHF region to reduce noise contamination in a more urban area, which is of further detriment to its relative quality. While network discrepancies are acknowledged, the quality of each is considered adequate for the purposes of comparative analysis, particularly with respect to trends in data. More network-specific information may be found in the work of Koshak et al. (2004), MacGorman et al. (2008), and Rudlosky and Fuelberg (2013). Postprocessed, archived NALMA and DCLMA VHF source data are publicly available (NASA 2001, 2006), while postprocessed OKLMA VHF source data were provided by K. Calhoun (2013, 2014, personal communication).
1) Source detection and flash clustering
VHF radiation sources detected by LMAs are located using a TOA technique involving multiple sensors (Thomas et al. 2004). While four stations are required to resolve the four unknown variables of three-dimensional space and time in the TOA equation, six or more stations are used in practice to better differentiate true lightning emission from noise. For this study, NALMA and DCLMA data were processed to use a minimum of six stations. OKLMA data were separately processed to use a minimum of seven stations when eight or more network sensors were in operation to mitigate additional noise detection due to increased network sensitivity. Sources that met the minimum station detection criteria were clustered into flashes using temporal and range-varying spatial criteria according to either the McCaul et al. (2009) algorithm for data from the NALMA or the XLMA algorithm described by Thomas et al. (2004) for data from the DCLMA and OKLMA. Flash rate trends have been shown to be resistant to variation in the constraints used to define source-clustered flashes (Wiens et al. 2005). Further, because the two algorithms return similar flash counts, it is accepted that interchangeable use of the two methods within the dataset does not substantially impact comparisons of flash rate trends or lightning jump analysis (Schultz et al. 2009, 2011).
For case study analysis, proximity of storms to the LMA network centers and related location error and detection efficiency must be taken into consideration to avoid false interpretation of flash information. Within the perimeter of the LMA network of sensors, location errors are typically within 50 m (Koshak et al. 2004; Thomas et al. 2004). Beginning at the periphery of the sensor array, range and altitude location errors increase with the square of the range from the center of the network (Thomas et al. 2004). Determination of source location errors using the NALMA framework with 10-receiver source identification and no noise indicates that horizontal source location errors are generally less than 500 m while vertical errors are less than 1000 m within 100 km of LMA network centers (Koshak et al. 2004). Further, from analysis utilizing the NALMA, spatial errors of the source location were found to conflict with convective scales outside of an approximate range of 160 km (McCaul et al. 2005). Studies have additionally shown that source detection efficiency decreases dramatically with range away from LMA network sensors (e.g., Boccippio et al. 2001; Thomas et al. 2001; Carey et al. 2005), while related impacts to flash detection efficiency (FDE) are more gradual. A reduction in FDE, however, has an increasing impact outside of the 100-km range of the network center. FDE obtained using Monte Carlo simulations with a 12-sensor LMA network was found to be between 96% and 100% within 100 km of the network center, between 89% and 96% from 100 to 150 km from the network center, and between 82% and 89% from 150 to 200 km from the network center (Chmielewski and Bruning 2016). These reported decreases away from the network may occur at shorter ranges when fewer stations are used. In light of these results, storm case analyses were done only for the time that storms were within 150 km of the LMA centers.
2) Association of flashes to supercells
Once flashes were clustered from sources over the LMA domain, they were associated with parent storms over space and time for flash rate analysis and lightning jump determination. Two-dimensional objects in gridded, merged reflectivity were identified for the spatial association of lightning initiation points to supercells of interest using the Warning Decision Support System–Integrated Information (WDSS-II) algorithms (Lakshmanan et al. 2007). Reflectivity gridded at a height of −10°C was utilized for the purpose of identifying storm cell objects physically relevant to the primary zone of charging and flash production in the mixed-phase region (Takahashi 1978).
For each LMA domain, a set of nearby S-band National Weather Service (NWS) WSR-88Ds were identified to contribute to gridded, merged reflectivity fields, listed in Table 1 (Crum and Alberty 1993; Doviak et al. 2000; Lakshmanan et al. 2007). Tracking objects were generated using these and 13-km Rapid Update Cycle (RUC) or Rapid Refresh (RAP) numerical weather model analyses (NOAA NCEI 1991, 2002; Rutledge et al. 2006) according to the methods described in detail by Kingfield et al. (2017). Whereas Kingfield et al. (2017) utilized a reflectivity minimum of 20 dBZ for tracking storm objects at the height of −10°C, reflectivity thresholds that best defined the reflectivity core and isolated individual supercells for analysis were subjectively applied and typically varied between cases in this study. The minimum spatial area required for storm feature identification and tracking was kept consistent at 20 km2 regardless of the minimum reflectivity threshold. This area corresponds to the minimum spatial setting for storm feature identification in the default implementation of the “w2segmotionll” algorithm (Lakshmanan et al. 2009). Tracking parameter details for each case are documented in Table 2. The two-dimensional objects determined in this step were extracted, and their center points and latitude and longitude radii were used to establish tracking boxes for each supercell at each time step. Because the default reflectivity tracking may omit lightning in regions outside of the main updraft and precipitation core that contribute nontrivially to lightning flash counts, flash initiation points and reflectivity fields were visually inspected (Kuhlman et al. 2009). Radii were manually expanded from the default tracking objects by anywhere from 5 to 35 km to better characterize lightning associated with individual supercells. These quantities are reported in Table 2. Without this expansion, 61.5% of lightning flashes would have been falsely omitted from the analysis.
Primary radar sites used for qualitative assessment and mesocyclone analysis are listed along with secondary radars used in combination with primary radars to obtain merged reflectivity fields for storm cell tracking. Primary sites are denoted by boldface text.
WDSS-II w2segmotionll algorithm tracking parameters used to identify each cell and facilitate lightning associations. Reflectivity values represent the minimum required to identify a storm object of at least 20 km2 in each case. The listed latitude and longitude expansion values were subjectively determined from manual analysis and added to the latitude and longitude radii of the WDSS-II-tracked storm objects at each time step.
3) Lightning jump determination
Once lightning flashes were assigned to storms of interest, flash rates were computed using a 2-min average. The Schultz et al. (2009) 2σ lightning jump algorithm (LJA) was employed to identify rapid increases in lightning flash rate. The LJA, the technical implementation of which is discussed in more detail by Schultz et al. (2017), determines whether a statistical departure in flash rate from the recent trend has occurred. The algorithm returns the number of standard deviations by which the change in flash rate at each 2-min time step is removed from the average change in flash rate over the previous 10-min period. The value of the standard deviation is referred to as the sigma level (Calhoun 2015; Schultz et al. 2015). The sigma level is used as a measure of the departure in lightning behavior from the storm’s recent history. When the sigma level is greater than or equal to 2.0, a lightning jump has occurred. As an example of interpretation, a sigma level of 4.5 at a given time indicates that the current change in flash rate exceeds the recent running average by 4.5 standard deviations, which may be considered more statistically meaningful than a lightning jump with a sigma level of 2.0.
b. Measures of supercell rotation
Supercell rotation was assessed from the mesocyclone strength index (MSI) associated with the level-III WSR-88D MDA product as well as from azimuthal shear, an algorithmic derivative of level-II WSR-88D Doppler velocity data related to the azimuthal shear products found in the operational Multi-Radar Multi-Sensor (MRMS) system (NOAA/NCEI 1991; Stumpf et al. 1998; Miller et al. 2013; Newman et al. 2013; Smith et al. 2016). For both information types, the WSR-88D nearest to a storm at a given time was used in order to obtain the best possible vertical data coverage. Appropriate concerns are raised related to the sampling and algorithmic detection of low-level rotation at ranges greater than 100 km from a radar (Kingfield and LaDue 2015). While storm analysis was not restricted to a given range from a radar, no storm was farther than 120 km from the nearest radar at any time during the analysis. Accounting for the curvature of the earth at standard atmospheric refraction (Doviak and Zrnić 2006) and assuming a minimum radar elevation angle of 0.5°, the minimum altitude of detection at a maximum range of 120 km is 1.9 km above radar level (ARL) versus a minimum altitude of 1.5 km ARL at a range of 100 km. Times during which storms were between 100 and 120 km from the nearest radar and vertical sampling was more restricted are documented in Table 3.
Case characteristics, including LMA domain, radars used, periods of lightning and radar analysis, type of associated severe weather (hail, wind, and/or tornado), and environmental instability. Cases characterized by low instability are listed in the top section (L storms; cases 1–6), cases characterized by medium instability are listed in the middle section (M storms; cases 7–13), and cases characterized by high instability are listed in the lower section (H storms; cases 14–19). Storms were within 100 km of an LMA network center except for parenthetical periods in lightning analysis during which storms were between 100 and 150 km from an LMA network center. Storms were within 100 km of a radar except for parenthetical periods in radar analysis during which storms were between 100 and 120 km from a radar. When “tornado” is listed as a type of severe report for a case, the number of tornadoes that occurred during the analysis period and a range of their maximum EF ratings are provided where applicable.
1) Mesocyclone strength index
WSR-88D level-III MDA detections associated with each storm were tracked manually, and the MSI of each detection was extracted (NOAA/NCEI 1991). Stumpf et al. (1998) provide a comprehensive description of the MDA. Briefly, the MDA is structured such that cyclonic shear measured by the maximum gate-to-gate velocity difference, shear computed along adjacent radar gates, or the velocity difference is taken into account over a series of several steps to determine the horizontally, vertically, and temporally continuous rotation for automatic vortex detection. Though the quality of the shear is computed based on spatial, temporal, and strength metrics at each iteration and put toward building a three-dimensional, temporally continuous mesocyclone feature, the MSI is the final encompassing strength metric assigned to the detection. The MSI takes into account the strength of the rotation of individual shear features composing the mesocyclone detection, preferentially weights low-level shear features, and is normalized by vortex depth. Use of the MDA provides an additional objective means of mesocyclone identification, coupled with the MSI as a metric of mesocyclone strength available in an operational setting.
2) Azimuthal shear
Information from the MDA is only available when a mesocyclone is present. Therefore, it gives no indication of broad or weak rotation during periods leading up to mesocyclogenesis and infrequently provides information during weakening of the mesocyclone. For a more consistent metric of rotation, the maximum azimuthal shear of the radial Doppler velocity found in the operational MRMS (Smith et al. 2016) was calculated in three specific layers for each storm using the WDSS-II “w2circ” algorithm (Miller et al. 2013).
The WDSS-II w2circ algorithm used in the MRMS system employs the local, linear least squares derivatives (LLSD) method of estimating azimuthal shear in place of the traditional “peak to peak” azimuthal shear estimation. The latter estimate is known to be impacted by errors in the Doppler radial velocity field, the range of circulations from the radar, and the location of the radar beam with respect to circulations (Wood and Brown 1997; Smith and Elmore 2004; Newman et al. 2013). Briefly, the LLSD method calculated by WDSS-II and utilized within the MRMS system incorporates multiple velocity values around each calculation point and estimates azimuthal shear by fitting a low-order model (Smith and Elmore 2004; Newman et al. 2013). The LLSD estimate of azimuthal shear is then more resistant to the radar and range impacts that affect the traditional peak-to-peak azimuthal shear estimate.
MRMS-related azimuthal shear data utilized in this study were derived from WSR-88D level-II Doppler velocity data using a series of WDSS-II algorithms. Doppler velocity data were first automatically dealiased based on the nearest NWS upper-air sounding using the WDSS-II “dealias” algorithm (Miller et al. 2013). Dealiased Doppler velocity data were then ingested into the w2circ algorithm, where the maximum azimuthal shear (MAS) in the vertical layers 0–3 km above ground level (AGL), 3–6 km AGL, and 6–9 km AGL were computed in range and azimuth space (Miller et al. 2013). Using the data from the nearest radar during storm analysis, the maximum MAS value in each layer was then manually recorded from the mesocyclone region.
The operational MRMS azimuthal shear products are calculated for the 0–2 and the 3–6 km AGL vertical layers as opposed to the three vertical layers utilized in this study (Smith et al. 2016). These layers are utilized operationally in accordance with guidance from the NWS Warning Decision Training Division (WDTD) suggesting that 0–2 km AGL azimuthal shear is applicable for analysis of increasing rotation at lower levels potentially related to tornadogenesis, while 3–6 km AGL azimuthal shear is thought to be more suitable for the general analysis of the midlevel mesocyclone (NOAA/WDTD 2017). The operational protocol was adapted for this study in order to obtain more complete information in the vertical, including the 2–3 km AGL layer, as well as the 6–9 km AGL region where thunderstorm charging and lightning production are more prevalent.
3) Rapid increase in azimuthal shear
Physically meaningful changes in the mesocyclone related to the updraft and its mutual influence on lightning may be observed by identifying sudden increases in MAS in each layer. The method by which a lightning jump is computed (i.e., Schultz et al. 2017) was adapted to calculate rapid increases in MAS in each 3-km layer for comparison of these properties. MAS observations were made as new radar observations contributed to the solution in each layer. MAS observations were then recorded in approximate 2–5-min intervals dependent upon radar volume scanning times. The change in MAS with time (DMASDT) was evaluated at each 2–5-min time step of recorded data. An initial spinup period of four MAS observations in storm analysis, corresponding to a period of approximately 8–20 min, was applied to parallel the 12-min spinup period in the LJA. Each subsequent DMASDT value was then compared with the standard deviation of the previous three values of DMASDT. A rapid increase in MAS was noted at any time that a value of DMASDT exceeded two standard deviations of the previous data and the corresponding MAS value at that time was at least within 10% of the 1.00 × 10−2 s−1 mesocyclone threshold. This threshold was established based on historical operational considerations (Moller et al. 1994) and related LLSD azimuthal shear work utilizing a Rankine combined vortex model (Smith and Elmore 2004). Similar to the implementation of the LJA, the second of a back-to-back pair of MAS increases was not counted as a separate MAS increase.
c. Environmental data
Data detailing the near-storm environment (NSE) were used for environmental and structural context during case analyses. These gridded NSE data were obtained from the WDSS-II “nse” algorithm by processing 13-km-resolution hourly RUC or RAP analysis data. RUC data are available prior to 1 May 2012, after which the RUC was replaced by the RAP model.
NSE data pertaining to each case were gridded with 1.0 km × 1.0 km horizontal spacing to match the grid spacing of tracked storm objects. Environmental values characterizing a case were obtained by averaging data within a 60.0 km × 60.0 km box around the center location of each tracked object, from the NSE analysis time nearest the tracking time, for each tracked object location over the course of storm analysis. Data within the 60.0 km × 60.0 km box around the initial tracked object location were also included in the average for 2 h prior to the time the storm was first tracked to account for any prestorm environment influence.
While the combination of mixed-layer convective available potential energy (MLCAPE), normalized MLCAPE, bulk shear in the 0–6-km layer, and storm relative helicity (SRH) in the 0–1- and 0–3-km layers were all initially considered, MLCAPE was the primary variable analyzed. It was also used to effectively partition the dataset, as explained further in section 3.
3. Case overview
Case selection was motivated by the intention of constructing a dataset of structurally variant supercell storms characterized by a diversity of regional, seasonal, and environmental controls and characteristics. A total of 19 supercells, each referred to individually as a case, were selected from 13 distinct dates. Table 3 characterizes the 19 cases, including the date, LMA domain, radars utilized, analysis period, nature of any associated severe weather, and average instability. To be considered for analysis, each storm must have (i) passed through one of the three LMA domains, (ii) exhibited a mesocyclone as defined by the MDA or the presence of persistent MAS greater than 1.00 × 10−2 s−1 in any layer, and (iii) remained isolated for the purposes of properly attributing lightning to the storm. More details related to complete case characterization, including full time series of lightning and rotation, are available in Stough (2015).
The 19 supercells were divided into three subsets based on their environmental instability as a method of adequately representing and partitioning the spectrum of supercell intensity and structure. The resulting partitioned set of cases (Table 3) includes six characterized by low environmental instability with MLCAPE values of 800 J kg−1 or less (L storms), seven characterized by medium environmental instability with MLCAPE values of between 1030 and 1330 J kg−1 (M storms), and six characterized by high environmental instability with MLCAPE values of 2490 J kg−1 or greater (H storms). These divisions based on environmental instability capture the susceptibility of the updraft to the weakening effects of precipitation loading, drag from falling hydrometeors, and entrainment of negatively buoyant air at the periphery. In particular, storms in the L subset are suggested to represent “minisupercells” (Markowski and Straka 2000). These atypically small and low-topped, less robust supercells are characterized by the same dynamic processes and are capable of producing similar severe phenomena as the more traditional conceptualization of larger, deeper, more robust supercells that are represented by those in the M and H subsets. However, minisupercells can present a different set of operational challenges in part because of the less unstable, marginally conducive environments in which they develop (Moller et al. 1994; Markowski and Straka 2000; Brotzge and Erickson 2010).
4. Analyses and results
The main objective in characterizing and comparing mesocyclone and lightning behavior in supercells for this study was to specify when and how lightning provides value-added information for nowcasting supercell intensity compared to radar interrogation alone. Generation of the mesocyclone is the primary feature signaling that a storm has transitioned from an ordinary cell to a supercell and has become dynamically more capable of producing impactful weather. Further increases in mesocyclonic rotation often signal intensification of the supercell. The following analyses address these topics.
a. Mesocyclogenesis
Because a lightning jump indicates updraft intensification, the first lightning jump of a nascent supercell may signal the onset of updraft-related dynamic processes that support further development. Mesocyclogenesis was observed within an LMA domain in 14 of the 19 cases. In one of these cases (case 6), no lightning jump occurred. The times of the first lightning jump and mesocyclogenesis in each of the 13 remaining applicable cases were compared to determine the degree to which the lightning jump can be used to qualify and assess the early stages of a supercell.
Mesocyclogenesis may be inferred operationally via direct or algorithmic interpretation of the Doppler velocity or by qualitative assessment of hallmark reflectivity features such as a weak or bounded weak-echo region (WER or BWER, respectively) or hook echo. Unlike the mesocyclone, these features are not unique to supercells. However, development of these reflectivity features in an environment conducive to supercell production signals the presence of strong updrafts and related convective intensification to a trained operational meteorologist (Moller et al. 1994). Mesocyclogenesis was considered in this analysis for each of the 13 applicable cases when each of the following occurred: (i) the first MDA detection was observed from any nearby radar, (ii) MAS in any one layer surpassed and remained above or within 10% of the 1.00 × 10−2 s−1 mesocyclone threshold (Moller et al. 1994; Smith and Elmore 2004) for three continuous volume scans, and (iii) the first WER, BWER, or hook echo was subjectively determined in reflectivity. Times and relevant details corresponding to each of these events are provided in Table 4. Note that for the following discussion, one case (case 1) did not exhibit any MDA detections and another case (case 2) did not exhibit qualifying MAS intensity. However, all 13 cases displayed at least one Doppler-velocity-related indicator in addition to a reflectivity indicator.
Times of initial lightning jump and mesocyclogenesis inferred by radar reflectivity, the MDA, and MAS are listed for 13 applicable cases. The case number, time of first lightning jump, time and type of reflectivity signature used for mesocyclogenesis inference, time of first MDA detection from any nearby radar and radar from which it was observed, and time and layer in which MAS first exceeded the 1.00 × 10−2 s−1 mesocyclone threshold are provided. Values considered to be outliers for a type of indicator are italicized. WER, BWER, and/or hook echo (HE) are the possible listings for the reflectivity types. Cases characterized by low instability are listed in the top section (L storms; cases 1, 2, and 5), cases characterized by medium instability are listed in the middle section (M storms; cases 7, 9, 10, 11, and 13), and cases characterized by high instability are listed in the bottom section (H storms; cases 14, 15, 16, 17, and 19).
Figure 1 shows that mesocyclogenesis was inferred after the first lightning jump took place within a storm in the majority of the 13 cases. Considering traditional reflectivity signatures first, the interquartile range (IQR) of values was roughly centered around the time of the first jump, from 11 min before to 13 min after. The median time that the first reflectivity indicator was observed was 5 min after the time of the first lightning jump, whereas on average, the first reflectivity indicator was consistent with the time of the jump (0 min). For comparison, Metzger and Nuss (2013) observed the first BWER in a supercell within 15 min of a lightning jump in 15 cases. In 12 of their 15 cases, the BWER was reported to have occurred before the jump (Metzger and Nuss 2013). The IQRs of Doppler-velocity-based metrics were shifted later in time than the reflectivity indicators. The IQR of the first MDA detection was from 2 min before to 24 min after the time of the first lightning jump, with a median value of occurrence 9 min after the time of the first lightning jump. On average, the first MDA detection was 15 min after the time of the first lightning jump for this dataset. Similarly, Metzger and Nuss (2013) observed the initial MDA detection in 12 supercells within 15 min of a lightning jump, with an average of 9.5 min between the associated jump and the first detection. The IQR of MAS-inferred mesocyclogenesis with respect to the time of the first lightning jump was from 11 min before to 18 min after, with a median value of observation 5 min after the time of the first lightning jump. MAS-inferred mesocyclogenesis occurred on average at the time of the first lightning jump.
Distribution of the times of mesocyclogenesis observations relative to the first lightning jump in 13 storms, where time observations (min) are given as time of mesocyclogenesis − time of first lightning jump. The boxes represent the quartiles and median while the whiskers provide the range of data within 1.5 times the nearest quartile. Data that are either less than the first quartile by more than 1.5 times the IQR or greater than the third quartile by more than 1.5 times the IQR are considered to be outliers and are marked as dots.
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
b. Mesocyclone intensification
Temporal evolution of the relationship between lightning and an established supercell’s mesocyclone is best interpreted through a comparative analysis centered on updraft pulses. As lightning jumps are thought to relate to updraft pulses through the mixed-phase region, rapid increases in MAS in each layer are suggested to represent updraft enhancements as they relate to the mesocyclone. For most cases, a number of MAS increases were observed in the three layers. MAS increase and lightning jump counts are provided in Table 5. The relative number of lightning jumps was similar per case, while the total number of MAS increases was greater for the M and H subsets than the L subset, particularly in the 3–6 and 6–9 km AGL regions. These characteristics demonstrate that mesocyclone intensification inferred from rapid increases in MAS is predominant in more traditional, deeper supercells that form in more unstable environments. The spatial distributions of MAS increases within each subset were relatively similar, with only between 16% and 27% of increases occurring in the 6–9 km AGL layer for each. Rapid mesocyclone increases were more prevalent in the 0–3 km AGL layer of each storm type, and particularly in the L subset characterized by more shallow storms in the least unstable environments. The relative spatial distributions of MAS increases were similar between the 0–3 and 3–6 km AGL layers for the M and H subsets at 34%–39%, while over half of the L subset’s associations at 52% occurred in the 0–3 km AGL layer.
Results of analyses of lightning jumps and MAS increases are provided for the 19 cases, given in total and partitioned by case subset type. In each of the five row sections, the count indicates the number of observed lightning jumps or MAS increases per subset, while the average (avg) indicates the average number of observed lightning jumps or MAS increases per storm in the subset. The number of MAS increases that occurred within ±10 min of at least one lightning jump (and vice versa) are listed in the “No. associated” category, along with the percentage of the total count of observed jumps or MAS increases that were included in an association in parentheses. Because individual jumps and MAS increases could contribute to multiple associations, the total number of associations involving a lightning jump or specified MAS increase is provided in the “Total No. of associations” row in each row section. In the three row sections corresponding to an individual MAS layer, the percentage of increases attributed to the layer for each subset is denoted in parentheses.
c. Temporal association of MAS increases and lightning jumps
Comparison of MAS increases to lightning jumps provides further information concerning how the updraft interacts with lightning and mesocyclone strengthening in space, time, and with respect to storm structure. However, such a comparison is more useful when confined to a temporal window centered around each observation to assess relationships based on isolated updraft processes. Association windows smaller than ±5 min with respect to the time of a lightning jump would not be adequate to assess changes in radar-based MAS increases because of temporal radar sampling limitations. Conversely, temporal windows larger than ±20 min would exceed the time expected for a parcel to traverse the depth of a supercell and heighten the risk of associating lightning jumps with MAS increases through unrelated updraft processes.
A sensitivity test was done using temporal durations for association ranging from ±5 to ±20 min to ensure that the selection of the temporal window did not alter the nature of any inferred relationship. Because the data are insufficient for differentiating the updraft processes responsible for lightning jumps and MAS increases, each MAS increase was allowed to be temporally associated with more than one lightning jump and vice versa. The resulting distributions of temporal relationships between associated MAS increases and lightning jumps were statistically compared to determine whether any imposed temporal constraint had a quantifiable impact. Figure 2 shows the distributions of time of MAS increases with respect to the time of a jump using ±5-, ±10-, ±15-, and ±20-min association windows. Assessing the distributions of the remaining three temporal windows, median values were similar at 2 min each, while the values of the ranges increased understandably with increasing time periods. Qualitative assessment of the distributions supports that there was no appreciable difference based on the allowable period for association between events. A Kolmogorov–Smirnov (K–S) test (Wilks 2011, 151–154) between each of the normalized distributions returned that the null hypothesis that any two distributions are from the same parent could not be rejected with more than 44% confidence.
Temporal distribution of MAS increases relative to lightning jumps within each of the tested association windows, where time observations (min) are given as time of MAS increase − time of associated lightning jump. The number of associations determined using each definition of a temporal association window is provided along the vertical axis. Design of box plots is as described in Fig. 1.
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
The ±10-min association window was selected to allow adequate time for an updraft pulse to influence increases in rotation through the column and lightning activity farther aloft while reducing the risk of falsely associating MAS increases and lightning jumps related to separate updraft processes. Observational evidence supports this selection as well. As an example, although updraft speeds have been shown to correlate poorly with flash rate, case studies including time series of updraft speeds retrieved from multi-Doppler analysis compared with the lightning flash rate show an approximate 10-min delay between large updraft increases and similar increases in flash rate (Schultz et al. 2015). Additional analyses from future kinematic observations or modeling studies would contribute to refining or validating this choice as well.
Using the ±10-min window, 115 MAS increases over the 19 storms were determined to occur within ±10 min of at least one lightning jump, the pairing of which is termed an association. Table 5 documents the associations between lightning jumps and MAS increases attributed to each case subset. Of the identified MAS increases in L cases (less instability), 23% occurred within ±10 min of a lightning jump, with double and triple that at 52% and 77% occurring in M and H storms (more instability), respectively. This trend was similar when lightning jumps were considered, where 33%, 77%, and 85% of lightning jumps identified in the L, M, and H cases, respectively, occurred within ±10 min of an MAS increase.
5. Discussion
a. Utility of the lightning jump in diagnosing mesocyclogenesis
Considering the initial lightning jump as a forecast for mesocyclogenesis in supercells, the POD was greatest for MDA and reflectivity indicators (PODs of 0.6) and was slightly lower for MAS indicators (POD of 0.5). However, when particular levels of MAS were considered independently, the greatest POD of 0.7 was associated with mesocyclogenesis when it could be inferred in the 6–9 km AGL layer (not applicable in case 5; not shown), suggesting that the lightning jump is most capable of forecasting deep mesocyclonic rotation in supercell environments. Results from mesocyclogenesis analysis indicate that in environments conducive to supercell development, an organizing storm’s first lightning jump typically occurs in close temporal proximity with the earliest indications of mesocyclogenesis. Broad patterns inferred from various POD statistics and portrayed by the temporal distributions in Fig. 1 suggest that for the knowledgeable operational meteorologist who may be using a combination of experience in assessing a particular environment, radar reflectivity, Doppler velocity, and lightning data, the presence of a jump may best serve as a source of confirmation or decision support instead of as a prognostic indicator of supercell development.
The first lightning jump preceded all three methods of inferring mesocyclogenesis and could be considered a successful forecast in 5 of the 13 cases (Table 4). As these cases were split between the M and H subsets, this behavior may be more frequently observed in environments more conducive to supercell development. The tornadic supercell in north Alabama on 2 March 2012 (case 7) serves as an example of storm development in similar instances (Fig. 3). The supercell displayed its first lightning jump at 1446 UTC, 6 min after flash rates of 10.0 flashes per minute (fpm) were observed (Fig. 3). At 1451 UTC, it began to display evidence of a WER (not shown), and an MDA detection and 0–3 km AGL MAS above the mesocyclone threshold were first observed at 1456 and 1501 UTC, respectively.
Time series of lightning and rotation trends along with available storm reports for a tornadic supercell in AL on 2 Mar 2012 (case 7). (top) The total lightning flash rate (black), LJA sigma level (orange), the sigma level jump threshold (dashed horizontal orange line), and lightning jumps with time stamps (vertical red lines). Note that the 1-min flash rate (fpm) was binned in 2-min intervals. Where the top panel background is gray, the storm was between 100 and 150 km from the LMA network center. Otherwise, the storm was within 100 km of the LMA network center. (middle) MDA detections plotted by MSI (teal), the 0–3 km AGL MAS (solid purple lines), the 3–6 km AGL MAS (dashed purple lines), the 6–9 km AGL MAS (dotted purple lines), and the mesocyclone strength threshold of 1.00 × 10−2 s−1 (horizontal dashed purple line). Where the middle panel background is gray, the storm was between 100 and 120 km from the nearest radar. Otherwise, it was within 100 km of the nearest radar. Arrow annotations marking the times that mesocyclogenesis was inferred from reflectivity (black), the MDA (teal), and MAS (purple) are also included in the middle panel. (bottom) Available severe storm reports. Note that the ordinate axis carries no quantitative or qualitative meaning. Severe storm reports noted in this panel may include hail reports [green triangles labeled with maximum report size (in.)], straight-line winds (blue squares; not applicable to case 7), and reported tornado duration (red horizontal lines with rating label).
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
Data suggest that in some instances, the jump may be useful in confirming intensification interpreted by reflectivity features ahead of the Doppler-velocity-indicated development of rotation. Specifically, reflectivity-inferred mesocyclogenesis occurred prior to the first velocity indicators in two of the three L cases by 32 and 34 min (cases 5 and 2). As marginal environments present unique operational challenges, further informative analysis would consider the frequency with which storms in low-instability environments developed similar reflectivity signatures and exhibited a lightning jump without a Doppler-velocity-based indication of mesocyclogenesis. In the other low-instability case (case 1), MAS-inferred mesocyclogenesis was technically observed 61 min prior to the first lightning jump and 50 min prior to the first reflectivity mesocyclogenesis indicator. Although mesocyclonic rotation in the 3–6 km AGL MAS layer was sustained for longer than three volume scans, it subsided below the mesocyclone threshold for a period of 29 min from 1926 to 1955 UTC (Fig. 4). At 1955 UTC, 3–6 km AGL MAS was the first to again surpass and remain above the mesocyclonic threshold, 9 min after the first reflectivity indicator of mesocyclogenesis was observed at 1946 UTC and 2 min prior to the first lightning jump at 1957 UTC. First reports of severe weather produced by the supercell occurred 28 min after the initial jump at 2025 UTC, indicating that the second period of sustained rotation nearly coincident with the jump was associated with more robust development of the storm. Combined, the reflectivity, lightning, and rotation relationships observed in L storms suggest that compelling reflectivity signatures and increasing rotation coupled with a lightning jump provide value-added information.
As in Fig. 3, but for the nontornadic supercell in the Washington, D.C., area on 4 Jul 2007 (case 1). There were no MDA detections during the case 1 analysis period. An annotation depicting the start of a second distinct period of mesocyclonic rotation in 3–6 km AGL MAS is provided (pink) in addition to annotations representing reflectivity-inferred and MAS-inferred mesocyclogenesis, as described in Fig. 3.
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
Alternatively, increased rotation in the absence of reflectivity characteristics and enhanced lightning activity may signify that a storm has not yet undergone adequate dynamic development to sustain a mesocyclone. However, when considering the lightning jump as a diagnostic indicator of imminent supercell development in marginally conducive environments, it is cautioned that complete lightning flash information should be used alongside the jump algorithm. For instance, the low-topped supercell that occurred on 2 March 2012 (case 6) in southern Tennessee produced maximum flash rates of 4.0 fpm that were consistently too low to trigger activation of the LJA although MAS increases in each layer were observed (not shown). In similar storms that display low-topped structure in more stable environments, shallow mixed-phased region depths have been observed to inhibit lightning production and may limit the utility of lightning data (e.g., Schultz et al. 2017).
Total lightning data with respect to LMA range should also be considered during storm interrogation in any environment. For example, although it was classified as an H storm, the tornadic Oklahoma supercell on 24 May 2011 (case 17) was the only case to exhibit all three indicators of mesocyclogenesis on the order of 20 min prior to the time of its first lightning jump. Figure 5 shows that its first MDA detection and persistent MAS above 1.00 × 10−2 s−1 occurred 25 min and 23 min prior to the first lightning jump at 2122 UTC, respectively. In this instance, though the storm initiated and developed into a supercell within the analysis domain, it did so at the edge of the 150-km range of the OKLMA. Unlike the 2 March 2012 supercell (case 6), maximum 18.5-dBZ echo-top heights greater than 12.0 km and reflectivity values exceeding 50 dBZ in the mixed-phase region were evident from 2054 UTC, well in advance of the first lightning jump at 2121 UTC. However, flash rates remained low and uniform between 7.0 and 13.0 fpm until and somewhat after the first lightning jump. The gradual increase in low flash rates from 0.0 to 13.0 fpm between 2052 and 2122 UTC (Fig. 5) despite radar indication of growth in the storm’s mixed-phase region (not shown) suggests possible LMA range-related impacts.
As in Fig. 3, but for the tornadic supercell in OK on 24 May 2011 (case 17).
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
b. MAS increases associated with lightning jumps
Temporal analysis revealed that a number of increases in MAS following initial development of the mesocyclone occurred in close temporal proximity with lightning jumps. Not only were observed MAS increases more numerous per M case and per H case, more lightning jumps and more MAS increases in these subsets were included in associations. This suggests that in more robust storms with stronger updrafts, it is more likely for lightning jumps and increases in rotation to occur in temporal proximity. Interestingly, in only two of the six H cases (cases 16 and 19) were there any lightning jumps not associated with at least one MAS increase. These unassociated jumps typically occurred near the time of mesocyclogenesis and prior to any observed MAS increases.
Binomial probability assessment of observing n of four jumps associated with at least one MAS increase for the L, M, and H case subsets.
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
Poisson probabilities of obtaining n MAS increases within 10 min of a lightning jump are plotted as continuous lines. Probabilities corresponding to each case subset type are separated and annotated along with the λ value used.
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
In addition to environmental tendencies, spatial tendencies in associations were also observed. Figure 8 shows that the lower quartile of 6–9 km AGL associations was shifted later in time following a lightning jump than distributions in the 3–6 km AGL layer. The similarity between the 0–3 and 3–6 km AGL IQRs could be partially attributed to reduced sampling of the 0–2-km region of storms at distances of between 100 and 120 km from the radar (Table 3). When storms are at greater distances from the nearest radar, observations in the 0–3 km AGL layer may be more influenced by behavior closer to 3 km and thus more similar to what is observed in the 3–6 km AGL region. It is also possible that mesocyclone behavior near the 3-km region dominates the 0–3 and 3–6 km AGL MAS regardless of the vertical sampling resolution. Over half of the distribution of 0–3 km AGL MAS increases occurs after the lightning jump. It has been suggested that the fallout of lofted mixed-phase precipitation, likely having contributed to lightning production aloft, may subsequently enhance downdrafts that influence an increase in low-level rotation (Williams et al. 1999). Progressive increases in rotation with altitude reflected by the portion of the 0–3 km AGL MAS distribution that occurs prior to the jump may be reflective of in-storm parcel trajectory. Without the benefit of vertical velocity data, it is speculated that rising motion contributing to updraft enhancement may act progressively through the column to first affect the low- to midlevel (near 3 km AGL) mesocyclone, followed by lightning production with height, and then influence stretching to increase rotation in the 6–9 km AGL layer. Though the tornadic vortex is not a focus of this study, it is worth noting similarities observed in tornadic vortex signatures (TVSs) in Doppler velocity in which a TVS is first observed near the ground prior to developing aloft (e.g., Trapp et al. 1999; French et al. 2013). Generally, this sequence agrees with current tornadogenesis theory, discussed further in the next section.
Distributions of the timing of MAS increases relative to lightning jumps for each of the three MAS layers, where time observations (min) are given as time of MAS increase − time of associated lightning jump. The number of associations determined in each layer is provided along the vertical axis. The design of box plots is as described in Fig. 1.
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
c. Low-level mesocyclone in tornadic supercells
While the definition of a supercell thunderstorm requires the presence of a mesocyclone, a mesocyclone is a necessary but insufficient condition for tornadogenesis (Trapp et al. 2005; Markowski and Richardson 2014). However, a stronger low-level mesocyclone is thought to play a supporting role in the complex process of tornadogenesis by supplying a source of strong dynamic lifting aloft resulting from enhanced negative pressure perturbations (Markowski and Richardson 2010, 2014). Specifically, current theory suggests that baroclinically generated circulation transported to the surface by the downdraft relies upon the relative strength of the cold pool to align with a source of adequate dynamic lifting for the formation of a tornadic vortex (Markowski and Richardson 2014). The data utilized in this study are insufficient to diagnose the complex processes of tornadogenesis, particularly where microphysical processes relevant to lightning production, downdraft generation, and subsequent thermodynamic and kinematic processes near the surface are concerned. However, coarse relationships between lightning and the low-level mesocyclone may be examined with respect to ideas that relate downdrafts to increased low-level rotation (Williams et al. 1999). Observations of lightning jumps, MAS increases in the 0–3 km AGL layer, and tornadoes were specifically considered and suggest that specific temporal associations between the three are more prevalent prior to the strong-to-violent tornadoes in this study. Of the 19 analyzed supercells, 12 produced a total of 29 reported tornadoes varying between EF0 and EF5 ratings (Table 3). Three of these tornadoes were not considered because two were in progress prior to the time their parent supercells entered the analysis domain (cases 8 and 12) and one occurred during a storm merger when lightning could not be appropriately isolated to the storm of interest for analysis (case 15). Of the remaining 26 tornadoes, seven were strong-to-violent tornadoes with ratings of EF3–EF5 (cases 7, 8, 12, 14, 15, 17, and 19).
When the pretornadic and early tornadic periods of the 26 tornadoes were examined, a pattern of a lightning jump prior to an increase in 0–3 km AGL MAS before or during the early stages of strong tornadoes was noted that was not evident near similar times for EF0–EF2 tornadoes. Time series of lightning flash rate, lightning jumps, 0–3 km AGL MAS, and MAS increases are shown in Fig. 9 with respect to the time of four of the seven strong-to-violent tornadoes. In the Oklahoma supercell on 19 May 2013 (case 19), an EF3 tornado occurred at 2141 UTC. A lightning jump had occurred 9 min prior at 2132 UTC, followed within 6 min by an increase in 0–3 km AGL MAS at 2138 UTC (Fig. 9a). Similarly, a lightning jump at 2217 UTC and a 0–3 km AGL MAS increase 3 min later at 2220 UTC occurred 15 and 12 min prior to the 2232 UTC start of an EF4 tornado in a 10 May 2010 supercell (case 15; Fig. 9b). Also in case 15, a subsequent lightning jump at 2233 UTC, followed by an increase in 0–3 km AGL MAS at 2235 UTC, were noted prior to a reported damage increase (NOAA/NWS Norman 2017).
Periods of lightning and low-level rotation relative to strong tornado events. Each time series considers a period of ±20 min with respect to the start time (T = 0) of a strong-to-violent tornado (EF3–EF5) in cases (a) 19, (b) 15, (c) 8, and (d) 12. Time series of flash rate (black) and LJA sigma level (red) are plotted according to the left vertical axes while 0–3 km AGL MAS (purple) and MAS increase sigma level (gold) are plotted according to the right vertical axes. Lightning jumps (vertical red lines), 0–3 km AGL MAS increases (vertical gold lines), and the LJA and MAS increase sigma threshold (horizontal dashed line) are also plotted. Annotation arrows are plotted to highlight key associations between lightning jumps and increases in 0–3 km AGL MAS.
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
When a lightning jump and subsequent 0–3 km AGL MAS increase were not noted prior to the tornado in these seven storms, they occurred within minutes of the start of strong tornadoes, shown in Figs. 9c and 9d, and prior to tornado intensification, as inferred from storm survey reports. In the case of the 29 April 2014 EF3 tornado (case 8; Fig. 9c), the 0–3 km AGL MAS increase occurred at 0116 UTC, 7 min after the initial report of the tornado and near the approximate time that the beginning of EF3-related damage was estimated (NOAA/NWS Huntsville 2017a). In the case of the 25 April 2010 EF3 tornado (case 12; Fig. 9d), a lightning jump occurred at 0309 UTC and a 0–3 km AGL MAS increase occurred at 0314 UTC, 5 and 9 min following the initial report of the tornado where the most substantial damage was not estimated until 0327 UTC (NOAA/NWS Huntsville 2017b).
For the remaining three of the seven strong tornadoes, the pattern between a lightning jump, 0–3 km AGL MAS increase, and pre- or early tornadic period did not strictly occur. Rather, in each of these cases, either a rapid increase in lightning flash rate or a rapid increase in 0–3 km AGL MAS occurred that failed to meet the threshold sigma value to determine a lightning jump or MAS increase by no more than 0.2 sigma. Time series similar to those in Fig. 9 are shown for these cases in Fig. 10 with the addition of a marker at the time of each “near-lightning jump” or “near-MAS increase” of note.
As in Fig. 9, but for cases (a) 7, (b) 17, and (c) 14. In these cases, rapid increases in lightning flash rate and 0–3 km AGL MAS that failed to meet the sigma level thresholds to be classified as lightning jumps and 0–3 km AGL MAS increases are noted with respect to tornado start times. These “near” lightning jumps and 0–3 km AGL MAS increases are plotted as dashed, bold vertical red and gold lines, respectively.
Citation: Weather and Forecasting 32, 6; 10.1175/WAF-D-17-0025.1
While these patterns existed for strong tornadoes and were absent prior to and during the initial stages of weaker tornadoes in this study, it is imperative to note that 0–3 km AGL MAS increases following lightning jumps also occurred without subsequent tornadogenesis in supercells. Neither lightning jumps nor these observed patterns should be used alone to infer tornadogenesis or tornado intensification. The results presented here are preliminary and require further investigation with the availability of more detailed observations, particularly with respect to the relationship between lightning and downdrafts and within the context of current tornadogenesis theory. However, these observations offer another example of how the combined use of comprehensive lightning and radar information may inform on dynamic processes. That is, in addition to the MAS increases observed, low-level mesocyclonic rotation was noticeably intense with 0–3 km AGL MAS values in excess of 2.00 × 10−1 s−1 for much of the analyses periods in cases 8, 14, 15, 17, and 19 (Figs. 9 and 10). Further, along with the observed lightning jumps, flash rates were at or above 100.0 fpm in cases 8, 14, 15, 17, and 19 (Figs. 9 and 10).
6. Summary and conclusions
This study addressed the nature of the relationship between lightning and mesocyclonic rotation. Analyses were conducted in an effort to broaden understanding of how lightning relates to the thunderstorm’s updraft and can be further employed in an operational setting when coupled with radar metrics of storm intensity. The basis for analysis was the suggested conceptual model that the establishment and strengthening of a supercell’s mesocyclone may be related to increases in lightning because of similar responses to updraft intensification under the dynamic processes present in a supercell thunderstorm. A set of 19 supercells offering a broad spectrum of diversity in intensity and characteristics were selected from three LMA domains for study. Of these, lightning flash rates, qualitative radar reflectivity features, the WSR-88D level-III MDA product, and operational MRMS-related azimuthal shear were considered. Through comparative analysis of lightning jumps and mesocyclogenesis and subsequent mesocyclone intensification, the following observations were made:
The first lightning jump during supercell development occurred near the time of mesocyclogenesis with a median value of at least 5 min prior to Doppler-velocity-based methods of inference.
Seventy percent of lightning jumps observed in supercells occurred within 10 min of increases in mesocyclonic rotation interpreted through azimuthal shear.
The most robust relationships were observed between lightning jumps and MAS increases in deeper supercells that formed in the most conducive environments. In storms in high instability environments (H cases), 85% of lightning jumps were temporally associated with at least one MAS increase and 77% of MAS increases were temporally associated with at least one lightning jump. Of the three subsets, the greatest percentages of MAS increases in each of the three analysis layers were associated with a lightning jump in H cases.
It was first shown that when mesocyclogenesis was observed and the storm was capable of producing flash rates ≥ 10.0 fpm, the first lightning jump of the developing supercell occurred near the time of the first Doppler-velocity-based indication of sustained mesocyclonic rotation. In low-instability environments more conducive to the development of atypically smaller supercells, reflectivity identification of supercell structure was often apparent first as BWERs, WERs, or hook echoes. In similar environments, the evolution of lightning characteristics may provide more forecaster confidence in the radar-based assessment of supercell transition. Conversely, in more unstable and traditionally supercell-supportive environments, the lightning jump may confirm that the updraft is intensifying and may soon facilitate dynamic processes for supercell development. This may be of particular utility when identifying the first cell of an event that may transition to a supercell and require more intensive monitoring (Moller et al. 1994).
The continuous relationship between MAS as a proxy for mesocyclone strength and lightning jumps was assessed to determine commonalities in their updraft-driven evolution. Rapid increases in MAS were first determined using an algorithm similar to the one used for the computation of lightning jumps. There were more MAS increases observed in deeper, more traditional supercells. However, these deeper supercells that developed in more unstable environments were not characterized by a higher percentage of MAS increases in the 6–9 km AGL layer in comparison with the smaller supercells that formed in less conducive environments. This observation is indicative of expected dynamic similarity between storms, despite discrepancies in size and intensity, reinforcing the physical relevance of comparison between lightning jumps and MAS in discrete layers for all cases.
When these MAS increases were considered alongside lightning jumps within a time frame of ±10 min, it was observed that not only do more robust supercells produce more MAS increases, but that a higher percentage of MAS increases occur in temporal proximity to lightning jumps. Statistically, these data indicate that it is most likely to observe fewer associations in smaller storms in weaker environments and more associations in more traditional supercells that develop in more conducive environments. This suggests that the updraft-centric relationship between increases in lightning flash rate and mesocyclonic rotation is more robust in more traditional supercell thunderstorms that form in the most unstable environments. These relationships may be used as a basic assessment of supercell organization and as a way to infer periods of strengthening in robust supercells more meaningfully than through radar interpretation alone. Also, though analyses in this study did not specifically address cyclic mesocyclogenesis, these relationships may similarly be applied to assess patterns of intensification in cyclic mesocyclones.
Finally, this study initiated further investigation into how lightning may relate to processes that contribute to tornadogenesis. No immediate temporal relationship was observed between a lightning jump, enhancement of the low-level mesocyclone, and weaker tornadoes in these data. However, a consistent pattern of lightning jumps prior to intensification of the low-level mesocyclone either before tornadogenesis or during early intensification of strong-to-violent tornadoes was observed. Despite the pattern that emerged in these few cases, results of this analysis should not be used to suggest that the lightning jump may discriminate between weak and strong tornadoes or even tornadic and nontornadic supercells. These results should instead prompt further study of how lightning may relate to the dynamic processes that contribute to tornadogenesis. Although the measurements available for this work were inadequate to assess kinematics that may relate the relevant processes, it is suggested that the increased precipitation mass that participates in a lightning jump may contribute to downdraft-driven vorticity generation and subsequent enhancement of the low-level mesocyclone or near-surface circulation that leads to a tornado under the right conditions. Further high-resolution, multi–Doppler radar observations and complementary modeling work are necessary to adequately address this hypothesis.
In summary, it has been documented that in the early stages of supercell development, lightning may corroborate traditional radar-based methods of inferring the transition of an ordinary thunderstorm to a supercell and the increased ability of the storm to produce severe weather. During the overall lifetime of supercells, lightning enhancement is more closely associated with the strengthening of the supercell’s mesocyclone. This is particularly evident in more robust, traditional supercells. Future work will aim to define the kinematic basis and spatial context of associations between lightning and the mesocyclone while refining our understanding of the specific updraft properties that drive the relationship.
Acknowledgments
We would like to acknowledge NOAA GOES-R Risk Reduction Research funding, which was received under contracts from the National Aeronautics and Space Administration Marshall Space Flight Center (NASA MSFC; NNM11AA01A) and the University of Maryland (Z7813005), and Dr. Steven J. Goodman for supporting this research. This work was additionally supported by NASA Severe Storms Research funding (NNH14ZDA001N), also provided to UAH authors under contract from the NASA MSFC. We sincerely thank Drs. Kristin Calhoun and Geoffrey Stano for providing LMA data for this study and Jeff Bailey and Blair Breitreiter for their efforts in maintaining the NALMA. We also gratefully acknowledge Drs. Earle Williams and Kristin Calhoun and an anonymous reviewer for their thorough reviews and thoughtful comments that helped to improve the manuscript.
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