Christopher J. Schultz

Lightning and Severe Weather: A Comparison between Total and Cloud-to-Ground Lightning Trends

Many studies over the past several decades have attempted to correlate trends in lightning (e.g., rates, polarity) to severe weather occurrence. These studies mainly used cloud-to-ground (CG) lightning information due to the ease of data availability, high detection efficiency, and broad coverage across the United States, with somewhat inconclusiveresults.Conversely, it has been demonstrated that trendsin total lightning aremorerobustlycorrelatedtosevereweatheroccurrence,withrapidincreasesintotallightningobserved10s of minutes prior to the onset of severe weather. Unfortunately, total lightning observations are not as numerous, or available over the same areal coverage domain, as provided by CG networks. Relatively few studieshaveexamined concurrent trendsin bothtotalandCG lightningwithinthesameseverethunderstorm, or even large sets of thunderstorms using an objective lightning jump algorithm. Multiple studies have shown thatthetotalflashraterapidlyincreasespriortotheonsetofsevereweather.Whatisuntestedwithinthesame framework is the use of CG information to perform the same task. Herein, total and CG lightning trends for 711 thunderstorms occurring in four regions of the country were examined to demonstrate the increased utilitythattotallightningprovides overCGlightning,specifically withintheframeworkofdevelopingauseful lightning-based severe weather warning decision support tool. Results indicate that while both lightning datasets demonstrate the presence of increased lightning activity prior to the onset of severe weather, the use oftotal lightningtrendswasmore effectivethanCG trends[probability ofdetection(POD),79% versus66%; false alarm rate (FAR), 36% versus 53%; critical success index (CSI), 55% versus 38%; Heidke skill score (HSS), 0.71 versus 0.55]. Moreover, 40% of false alarms associated with total lightning, and 16% of false alarms with CG lightning trends, occurred when a lightning jump associated with a severe weather ‘‘warning’’ was already in effect. If these false alarms are removed, the FAR drops from 36% to 22% for total lightning and from 53% to 44% for CG lightning. Importantly, average lead times prior to severe weather occurrence werehigherusingtotallightningascomparedwithCGlightning(20.65versus13.54 min).Theultimategoalof this study was to demonstrate the increased utility of total lightning information that the Geostationary Lightning Mapper (GLM) will provide to operational meteorology in anticipation of severe convective weather on a hemispheric scale once Geostationary Operational Environmental Satellite-R (GOES-R) is deployed in the next decade.

Preliminary Development and Evaluation of Lightning Jump Algorithms for the Real-Time Detection of Severe Weather

Abstract Previous studies have demonstrated that rapid increases in total lightning activity (intracloud + cloud-to-ground) are often observed tens of minutes in advance of the occurrence of severe weather at the ground. These rapid increases in lightning activity have been termed “lightning jumps.” Herein, the authors document a positive correlation between lightning jumps and the manifestation of severe weather in thunderstorms occurring across the Tennessee Valley and Washington D.C. A total of 107 thunderstorms from the Tennessee Valley; Washington, D.C.; Dallas, Texas; and Houston, Texas, were examined in this study. Of the 107 thunderstorms, 69 thunderstorms fall into the category of nonsevere and 38 into the category of severe. From the dataset of 69 isolated nonsevere thunderstorms, an average, peak, 1-min flash rate of 10 flashes per minute was determined. A variety of severe thunderstorm types were examined for this study, including a mesoscale convective system, mesoscale convective vortex, tor...

Meteorological Overview of the Devastating 27 April 2011 Tornado Outbreak

By many metrics, the tornado outbreak on 27 April 2011 was the most significant tornado outbreak since 1950, exceeding the super outbreak of 3–4 April 1974. The number of tornadoes over a 24-h period (midnight to midnight) was 199; the tornado fatalities and injuries were 316 and more than 2,700, respectively; and the insurable loss exceeded

Exploring Lightning Jump Characteristics

4 billion (U.S. dollars). In this paper, we provide a meteorological overview of this outbreak and illustrate that the event was composed of three mesoscale events: a large early morning quasi-linear convective system (QLCS), a midday QLCS, and numerous afternoon supercell storms. The main data sources include NWS and research radars, profilers, surface measurements, and photos and videos of the tornadoes. The primary motivation for this preliminary research is to document the diverse characteristics (e.g., tornado characteristics and mesoscale organization of deep convection) of this outbreak and summarize preliminary analyses that are worthy of additional research ...

Insight into the Kinematic and Microphysical Processes that Control Lightning Jumps

This studyis concerned with the characteristics of stormsexhibitingan abrupttemporalincrease in the total lightning flash rate [i.e., lightning jump (LJ)]. An automated storm tracking method is used to identify storm ‘‘clusters’’ and total lightning activity from three different lightning detection systems over Oklahoma, northernAlabama,andWashington,D.C.Onaverageandfordifferentemployedthresholds,theclustersthat encompass at least one LJ (LJ1) last longer and relate to higher maximum expected size of hail, vertical integrated liquid, and lightning flash rates (area normalized) than do the clusters without an LJ (LJ0). The respective mean radar-derived and lightning values for LJ1 (LJ0) clusters are 80min (35min), 14mm (8mm), 25kgm 22 (18kgm 22 ), and 0.05flashmin 21 km 22 (0.01flashmin 21 km 22 ). Furthermore, the LJ1 clusters are also characterized by slower-decaying autocorrelation functions, a result that implies a less ‘‘random’’ behavior in the temporal flash rate evolution. In addition, the temporal occurrence of the last LJ provides an estimate of the time remaining to the storm’s dissipation. Depending on the LJ strength (i.e., varying thresholds), these values typically range between 20 and 60min, with stronger jumps indicating more time until storm decay. This study’s results support the hypothesis that the LJ is a proxy for the storm’s kinematic and microphysical state rather than a coincidental value.

Kinematic and Microphysical Significance of Lightning Jumps versus Nonjump Increases in Total Flash Rate

AbstractA detailed case study analysis of four thunderstorms is performed using polarimetric and multi-Doppler capabilities to provide specificity on the physical and dynamical drivers behind lightning jumps. The main differences between small increases in the total flash rate and a lightning jump are the increases in graupel mass and updraft volumes ≥10 m s−1 between the −10° and −40°C isotherms. Updraft volumes ≥10 m s−1 increased in magnitude at least 3–5 min in advance of the increase in both graupel mass and total flash rate. Updraft volumes ≥10 m s−1 are more robustly correlated to total flash rate than maximum updraft speed over a thunderstorm’s entire life cycle. However, peak updraft speeds increase prior to 8 of the 12 lightning jumps examined. Decreases in mean and median flash footprint size during increases in total lightning are observed in all four thunderstorms and are most notable during development stages within the most intense storms. However, this inverse relationship breaks down on l...

Automated storm tracking and the lightning jump algorithm using GOES-R Geostationary Lightning Mapper (GLM) proxy data

Thirty-nine thunderstorms are examined using multiple-Doppler, polarimetric and total lightning observations to understand the role of mixed phase kinematics and microphysics in the development of lightning jumps. This sample size is larger than those of previous studies on this topic. The principal result of this study is that lightning jumps are a result of mixed phase updraft intensification. Larger increases in intense updraft volume (≥ 10 m s-1) and larger changes in peak updraft speed are observed prior to lightning jump occurrence when compared to other non-jump increases in total flash rate. Wilcoxon-Mann-Whitney Rank Sum testing yields p-values ≤0.05, indicating statistical independence between lightning jump and non-jump distributions for these two parameters. Similar changes in mixed phase graupel mass magnitude are observed prior to lightning jumps and non-jump increases in total flash rate. The p-value for graupel mass change is p=0.096, so jump and non-jump distributions for graupel mass change are not found statistically independent using the p=0.05 significance level. Timing of updraft volume, speed and graupel mass increases are found to be 4 to 13 minutes in advance of lightning jump occurrence. Also, severe storms without lightning jumps lack robust mixed phase updrafts, demonstrating that mixed phase updrafts are not always a requirement for severe weather occurrence. Therefore, the results of this study show that lightning jump occurrences are coincident with larger increases in intense mixed phase updraft volume and peak updraft speed than smaller non-jump increases in total flash rate.

Characteristics of Lightning Within Electrified Snowfall Events Using Lightning Mapping Arrays

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA), which are important elements in the transition of the LJA concept from a research to an operational based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid, VIL), and lightning information (flash rate density). Evaluations showed that the spatial scale of tracked features or storm clusters had a large impact on the lightning jump system performance, where increasing spatial scale size resulted in decreased dynamic range of the systems performance. This framework will also serve as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the systems performance is evaluated with adjustments to parameter sensitivity. The systems performance is evaluated using the probability of detection (POD) and false alarm ratio (FAR) statistics. Of the algorithm parameters tested, sigma-level (metric of lightning jump strength) and flash rate threshold influenced the systems performance the most. Finally, verification methodologies are investigated. It is discovered that minor changes in verification methodology can dramatically impact the evaluation of the lightning jump system.

Microphysical and Kinematic Processes Associated With Anomalous Charge Structures in Isolated Convection

This study examined 34 lightning flashes within four separate thundersnow events derived from lightning mapping arrays (LMAs) in northern Alabama, central Oklahoma, and Washington DC. The goals were to characterize the in-cloud component of each lightning flash, as well as the correspondence between the LMA observations and lightning data taken from national lightning networks like the National Lightning Detection Network (NLDN). Individual flashes were examined in detail to highlight several observations within the dataset. The study results demonstrated that the structures of these flashes were primarily normal polarity. The mean area encompassed by this set of flashes is 375 km2, with a maximum flash extent of 2300 km2, a minimum of 3 km2, and a median of 128 km2. An average of 2.29 NLDN flashes were recorded per LMA-derived lightning flash. A maximum of 11 NLDN flashes were recorded in association with a single LMA-derived flash on 10 January 2011. Additionally, seven of the 34 flashes in the study contain zero NLDN identified flashes. Eleven of the 34 flashes initiated from tall human-made objects (e.g., communication towers). In at least six lightning flashes, the NLDN detected a return stroke from the cloud back to the tower and not the initial upward leader. This study also discusses lightnings interaction with the human built environment and provides an example of lightning within heavy snowfall observed by GOES-16s Geostationary Lightning Mapper.

Investigating the Relationship between Lightning and Mesocyclonic Rotation in Supercell Thunderstorms

Microphysical and kinematic characteristics of two storm populations, based on their macroscale charge structures, are investigated in an effort to increase our understanding of the processes that lead to anomalous (or inverted charge) structures. Nine normal polarity cases (mid-level negative charge) with dual-Doppler and polarimetric coverage that occurred in northern Alabama, and six anomalous polarity cases (mid-level positive charge) that occurred in northeastern Colorado are included in this study. The results show that even though anomalous polarity storms formed in environments with similar instability, they had significantly larger and stronger updrafts. Moreover, the anomalous polarity storms evidently have more robust mixed-phase microphysics, based on a variety of metrics. Anomalous polarity storms in Colorado have much higher cloud base heights and shallower warm cloud depths in this study, leading us to hypothesize that anomalous polarity storms have lower amounts of dilution and entrainment. We infer positively charged graupel, and therefore high supercooled water contents, in the mid-levels of the anomalous storms based on the relationship between colocations of graupel and inferred positive charge from Lightning Mapping Array data. Using representative updraft speeds and warm cloud depths, the time required for a parcel to traverse from cloud base to the freezing level was estimated for each storm observation. We suggest this metric is the key discriminator between the two storm populations and leads us to hypothesize that it strongly influences the amount of supercooled water and the probability of positive charge in the midlevels, leading to an anomalous charge structure.