Shriver  1    

Assessing the Understanding of Lightning Jumps and their Connection to Thunderstorm Intensity

ALAN D. SHRIVER

Iowa State University, Ames, Iowa

CHRISTOPHER J. SCHULTZ

Department of Atmospheric Sciences, University of Alabama, Huntsville, Alabama

WILLIAM GALLUS

Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa

(Received 24 November 2015, in final form 5 December 2014)

ABSTRACT

This study investigates the relationship between individual mesoscale thunderstorm cells and lightning jumps

by tracking the behavior of three storm metrics, which were picked based on their overall high importance to storm and updraft intensity. The main objective of this research was to determine how the selected storm metrics were individually behaving in response to a lightning jump, effectively testing the theory that lightning jumps signal storms that are likely to grow faster than storms containing no jumps, continue to grow beyond a jump and become stronger than other cells nearby. This study used TITAN (Thunderstorm Identification, Tracking Analysis and Nowcasting) storm-tracking information with a set of files containing the values recorded in 5-minute intervals for the jump ratio developed by University of Alabama researchers. This investigation examined 35 storm cells in the Tennessee valley region in combination with NEXRAD Level II radar data obtained from the National Climactic Data Center. The study tracked storm information and radar data for each cell analyzed, particularly near the time of lightning jumps. A lightning ‘jump’ for the purpose of this study was defined as a rapid increase in flash rate that registered 1.5 or higher on the jump ratio algorithm. The study found that 14 of the 35 cases examined contained at least one lightning jump, with the results also indicating higher values and higher maximum increases in values in the overall averages for reflectivity, estimated hail size and precipitation echo tops for cells that contained jumps.

1. Introduction It has long been known that unusually high lightning activity is frequently synonymous with particularly strong storms. Recent studies, including the extensive work done by a team of researchers at the University of Alabama in Huntsville, have supported the idea that sudden increases in lightning activity can occur just before or during rapid storm growth in a mesoscale storm cell or system. The researchers have been working to develop accurate algorithms to detect lightning jumps and the accuracy rate of what has been termed a “lightning jump” to predict severe weather in storm cells before it is indicated by Doppler Radar systems. Lightning jumps are not simply counted as any marginal increase in lightning FR (flash rate), rather a standard deviation from the average maximum DFR/Dt recorded in non-severe storms is used to determine whether an increase is sharp enough to count as a true jump. If a cell exhibits a rate of change twice the value of the positive standard deviation (2𝜎) for

normal thunderstorm FR increase, this is counted as a lightning jump (Schultz et al. 2009, Schultz et al 2011). Figures 1 and 2 show the evolution of LMA (Lightning Mapping Array) total lightning flash density summaries for 1-hour segments between 20:00 and 22:00 UTC on October 8th, 2014 in northeast Alabama. Two main storm cells in fig. 1 are evident as two bullet maxima of lighting density in the grid surrounded by lower densities in all directions. During the latter half of the two hours, a new cell appeared in the data with a 1-hour density maximum already higher than the scale can categorize. A sheriff’s office observed 2.75” diameter hail at 21:34 UTC, about halfway through the period when this high density of lightning suddenly appeared in the updated 1-hour summary. It is evident that the lightning rate likely became very high fairly quickly in a brand new cell during the second half of the shown timeframe, suggesting that there is a possible connection between the

    Shriver  2    increased lightning rate and the development of very large hail in the storm cell. It is important to note, however, that this particular example does not show what is really occurring on shorter time intervals. Though the lightning increases substantially in the hour summaries, the plots don’t reveal the minute-by-minute changes that occurred

Figure 1. Lightning flash density for a 2x2 km resolution over the 1-hour period from 20 to 21 UTC on October 8th, 2014. Two clusters of high strike density indicate two distinct storm cells southwest of Huntsville. The bars above and to the right display the density of flashes between 0 and 20 km altitude through each north-south and east-west cross-section of the total domain. during each hour. Nevertheless, given that the increase is very large, it is very possible a jump did occur during the segment of time covered by the two summaries. This relationship between rapid increases in lightning activity often with anomalously high lightning rates and the development of severe weather has been supported by multiple recent studies (including Shultz et al. 2009;2011, LaPenta et al. 2005, Chronis et al. 2014, and Ware 2012). This study focused on extending previous research done by the University of Alabama (Schultz et al. 2009; 2011) on the connection between storm intensity and lightning jumps by analyzing two collections of thunderstorm cells in the KHTX Northern Alabama NEXRAD Level II radar range, as in the region shown in fig. 3.

Figure 2. Lightning flash density from 21 to 22 UTC. A third cell appears to have developed with a large region of flash density exceeding 1000 flashes per 4 square kilometers. About halfway through the hour, 2.75” diameter hail is reported by a sheriff’s office located in Sommerville. (From Northern Alabama LMA, or Lightning Mapping Array) The purpose of this research is to investigate the responses in three selected storm metrics in the time vicinity of lightning jumps. The increased potential for severe weather following a lightning jump has been fairly well established as a true connection between lightning and storm intensity. However, the verification of the lightning jumps in previous studies was largely based on storm reports, which contain many flaws (e.g., timing, size, degree of damage; Witt et al. 1998, Ware et al. 2012, Trapp et al. 2005). Changes in the individual signals of growth in discrete storm cells from different metrics should provide a more reliable method by which one can compare storms to judge storm intensity. This analysis seeks to determine how well increases in base reflectivity, estimated hail size, and precipitation echo tops match up with observed lightning jumps in storm cells. This application would aid forecasters in picking out which storms were the most intense and potentially require a warning to be issued. In addition, the study attempts to test the effectiveness of lightning jumps to detect the strongest thunderstorms in a given cluster of cells by comparing peak values in the metrics to peaks in nearby storm cells. This study hypothesizes that if lightning

    Shriver  3    jumps do tend to occur shortly prior to or during rapid storm growth, then the storm metrics should each show an increase in value in the time vicinity of lightning jumps. The results of this study should help researchers gain a better understanding of the exact connection between lightning and the behavior of thunderstorms.

Figure 3. Region of study, KHTX NEXRAD Radar full range shown. Radar site is located in Hytop, AL near Huntsville. 2. Literature Review Despite the fact that the term “lightning jump” has only been recently used in publications concerning lightning behavior, there is substantial agreement amongst researchers on its basic definition. Lightning jumps are currently defined as a rapid increase in total lightning frequency in both individual convective storm cells and in cells embedded within linear storm segments or in a mesoscale convective system (MCS). Most published work on this phenomenon cites increasing updraft strength as the primary causation of these increases in lightning activity. The implications of lightning jumps concerning thunderstorm intensity are much more important to current and future research. Several research groups, including a team of scientists working closely with lightning jumps at the University of Alabama in Huntsville have conducted empirical investigations on the relationship between lightning jumps and storm strength (e.g, Schultz et al. 2009; 2011, Chronis et al. 2014). The consensus from most of the current research base is that lightning jumps frequently are followed by rapid intensification in overall storm strength, usually before

this intensification appears on any radar scans of the storm. Exactly how frequently lightning jumps successfully predict intensification and the degree to which the jumps can predict a storm reaching severe levels is still slightly unclear. Studies dating back to the 1980s have found very mixed results, with some studies turning up with results less positive than from Schultz et. al (2009). For example, Knapp (1994) found no connection between increases cloud-to-ground lightning strikes and subsequent tornado development. Yet other investigations have found very promising connections between lightning and tornadogenesis. such as LaPenta et al. (2005). This study found large peaks in cloud-to-ground strikes about 25 minutes prior to a tornado touchdown in a severe thunderstorm in Mechanicville, NY. The more recent studies including Lapenta et al. (2005) have generally found more consistent positive correlations between increased lightning and tornado development. The application of lightning jumps in severe weather and storm forecasting is also still, at best, only in its earliest stages of development. a. Scope of Completed Research    The terminology used to describe the pronounced increase in lighting frequency is relatively new; most published articles and reports do not cite the term “lightning jump” earlier than about 2000. However, the theories that increase in lightning activity are related to thunderstorm strength and evolution is not at all new. It has been suggested by a multitude of research papers that there is a distinct connection between lightning behavior and thunderstorm updrafts, some dating back to the 1950s. However, much of this work, particularly less recent writings, emphasizes the same general conclusion: the exact relationship is unclear.

I. Well-Supported Findings  

There is a substantial collection of theories and hypotheses that the overwhelming majority of published work support on lighting jumps. The most important of these is the causation of lightning jumps. It has been demonstrated that the electrical fields that develop within thunderstorms is not fully possible without the presence of solid ice crystals in portions of the cloud. The physical derivation of the electrical fields precisely is greatly complex and is generally implied as a fairly low relevance to the subject of lightning jumps themselves.

    Shriver  4    More crucial to this subject is another theory that follows almost directly form the importance of ice crystals to lightning initiation. Stronger updrafts will lift more mass of water vapor above the freezing level, leading to larger quantities of supercooled droplets and mass of ice crystals within the upper portions of a thunderstorm. Hence strengthening updrafts are frequently cited as causing an increase in lightning activity. Saunders (1993) outlines in great detail the importance of the updraft in a storm carrying negatively charged particles aloft in a thunderstorm. The heavier particles, which obtain an opposite charge after colliding with smaller ice crystals, tend to stay lower in the storm since they have a faster terminal fall speed. One of the leading scientists that contributed to Shultz et al. (2009;2011), Dr. Larry Carey, well states, “With the strong updraft you get hail, small ice and super-cooled water. Those are the three ingredients. Once you have those, you have electrification.” (Livingston, 2012) Another key theory much of the current literature discusses is that lightning jumps cannot discern severe weather types for predicting storms that produce severe weather. Although different lengths of time have been observed on average for different modes of severe weather, there is not enough consistency overall to use this as a diagnostic for expected severe weather type.

II. Controversial Findings There are a few conclusions from various research reports on lightning jumps that conflict with conclusions found by other groups. Geophysicist Benjamin Barnum believes that lightning jumps can help predict tornadoes. He asserts that there is behavior in the rate of change of lightning frequency not observed in any other non-tornadic storms. “The rapid increase in lightning rates measured by the single point systems ... were nearly double or triple what we usually measured during other thunderstorms during this spring.” (Livingston, 2012) Other studies, such as Ware et al. (2012) found more mixed cases in the ability of jumps to predict severe weather events in general. Several of the cases they examined had severe hail recorded as much as 19 minutes prior to any lightning jumps, and other cells that successfully predicted severe hail and microbursts by several minutes. Ware et al. (2012) suggests that the accuracy of lightning jumps aiding in the prediction of severe storms is rather limited, and hence would likely be limited as well for predicting tornadoes. It is noteworthy, however, that this study mainly focused on hail alone and did not have a very large sample size, with only 18 total cells analyzed. There is also not necessarily enough evidence to substantially support the notion that lightning jumps have this capability to predict a type of severe

weather with significant weight over another, although there is still growing support that lightning jumps can identify storms that have a higher chance of producing tornadoes and of all other severe weather types. Another area that is subject to dispute is the exact definition of a lighting jump itself. Some articles simply describe a lighting jump as a substantial increase in lightning activity. But substantial and an increase are very vague terminologies. How much of an increase in flash rate is enough to count as a jump? Shultz et al. (2009) has come up with a much more specific, but still not necessarily well agreed upon description of lighting jumps. “All thunderstorms will, by definition, exhibit at least one jump in lightning activity during their lifetime.” (Schultz et al. 2009) The team essentially says that every thunderstorm must have some increase in lightning activity, otherwise it wouldn’t be a thunderstorm; the lightning rate would never go above zero. Importantly, a true lightning jump must be defined as an exceptionally high and sharp increase in overall lightning FR. Rather than simply look for any marginal increase in lightning, specific thresholds are used to identify jumps such that the positive rate of change must surpass a set value to count as a jump that could be large enough to indicate an elevated severe potential. This, Schultz predicts, will help prevent lightning jumps from being improperly utilized by forecasters using smaller increases in lightning activity that are not significant enough to indicate a serious threat. b. Limitations and Need for Further Research In addition to the physical limitations of lighting jumps as a storm predictor, there are many areas of focus where there is little to no completed progress. The most significant area is the actual development and deployment of a system that forecasters nationwide can use to track lightning jumps in real time. The most prominent limiting factor for using lightning in operational severe weather tracking is the lack of a space-based tracking method for in-cloud lightning tracking. The launching of the GOES-R’s GLM in 2016 will give lightning researchers a source of high resolution lightning data that can be integrated with other observational and model data to produce improved real-time warning issuance. Previous projects such as the TRMM satellite program, now terminated, had limitations including maximum range north or south of only 40˚ latitude. There is also a lack of awareness of the importance and potential that lightning jumps contain, or for that matter awareness of lightning jumps at all in much of the meteorological community. The numbers of papers that have received much attention from meteorologists or the general public are few and far between. There is some

    Shriver  5    evidence, however, that lightning jumps are receiving increasing interest within the past couple years. In June of this year, a short video was published by The Weather Channel on their website discussing the potential of lightning jumps to predict thunderstorms (This video can be found at http://wap.weather.com/video/lightning-jumps-49517). This was rather surprising, given that lightning jumps have received virtually no attention in weather media. Another area that needs more investigation is how well lightning jumps can predict the most intense storms in a given cluster. Identifying which cell in a group or line will become the strongest could be crucial to warning issuance and identifying the most dangerous storms early on. Lightning jumps have also heavily been used in correlation to severe weather, but there is not as much attention given to the trends in other parameters that are vital to storm intensity after a lightning jump is noted. c. Concluding Remarks While there are clear limitations to the potential of lightning jumps as a forecasting tool, the research strongly suggests that this potential is real and should not be overlooked. The lack of prominent publications overall suggests that there is much more to be done on this subject, and that there needs to be more awareness in the forecasting community of the lightning jump. Some research reports are promising for the future of lightning jumps, others not as much. Still, nearly all lightning researchers agree that this is an important field of study, and perhaps could be the beginnings of a breakthrough in thunderstorm forecasting. 3. Methods The primary sources of data for this investigation were from a set of files containing storm cells identified and tracked by the TITAN tracking system, listing the precise latitude and longitude of the center of each cell as it moved between each radar scan. In addition, the research utilized two other files containing raw flash data from the Northern Alabama LMA and the DFR/Dt, 2 𝜎 jump algorithm values, and values for the most accurate jump algorithm recently developed for each storm cell analyzed. These files were processed by the same methodology found in Schultz et al. (2009; 2011). The information stored in the files was used in conjunction with retrieved data from the National Climactic Data Center (NCDC) for NEXRAD Level II

radar data. The radar data was loaded into the Gibson Ridge data viewer to examine the hail and radar data from two selected dates. The dates containing the studied storm cells were August 5th, 2011 and July 4th, 2005. The August 5th set contained both cells that contained lightning jumps and cells where no lightning jump was recorded during the storm cell’s lifespan. The July 4th case contained only cells containing lightning jumps. The use of only lightning jump cells for this set was partly to obtain more equal sample sizes for each cell type. The July 4th case also included cells that had a lack in severe weather reports observed in spite of flash rate exceeding well over 100 F min-1. Thus one of the objectives of this work is to examine trends in radar derived intensity metrics to determine if the lightning jump provides any insight into the future development of a thunderstorm. Lightning jumps were identified using the 2σ technique outlined in Schultz et al. (2009; 2011). For each cell in the set of data the following basic statistics were recorded based on radar data for each cell’s lifespan: the maximum precipitation echo top, base elevation angle (0.5˚) reflectivity maximum, maximum estimated hail size (estimated by Gibson Ridge), and the maximum recorded positive D/Dt of each metric, where D/Dt was measured as the change in value between each set of radar scans. The full scans are completed and plotted every 5 minutes. A number of other statistics involving each of the three storm metrics analyzed (base reflectivity, precipitation echo top and estimated hail size) were taken for cases with at least one lightning jump recorded in the dataset, and a few additional behaviors were also documented in the cells containing no lightning jumps during their active (‘active’ referring to the period where storm cells contained lighting flashes) lifetime. For the additional data recorded for lightning jumps, most involved local behavior of the storm metrics relative to the time of the first lightning jump recorded in the storm. Comparisons were made in the timing of the jump with the timing of maximums in the metric values and maximums in the D/Dt of each metric. For example, if a storm cell had an observed lightning jump at 18:22 UTC, the increase in hail size from the time of the jump to the local maximum hail size estimated in the 20 minutes following the jump was recorded. Information was also gathered on the maximum echo tops and reflectivity values for other cells that fell within 30 nautical miles of the storm center at each time interval. The reason for investigating the maximum values in these other cells was to compare the strength of the studied storm cells to the strength of other cells nearby.

    Shriver  6    Once the entire set of 35 cells (14 with lightning jumps, 21 with no jumps) was fully analyzed with the jump ratio, TITAN storm tracking and NEXRAD Level II radar data, the averages for each statistic were found for the set of cells containing lightning jumps and for the set of cells containing no lightning jumps in the dataset. The total number of cells of each type (Jump and non-jump cells) where the cell contained the highest, second highest and third highest maximum echo tops out of all cells falling within 30 nautical miles of the studied storm’s center were also counted. The number of cells that did not contain a top three maximum echo top was also determined for both cell types. The total statistics were then thoroughly compared and graphed to clearly visualize the data. 4. Results Description The results were mostly consistent with the general expectations, although there were some signals from the metrics that were either more or less definitive than what was anticipated. The total set of 35 cases contained a few

outliers as well, such as a cell that had a maximum estimated hail size of 2.77 inches in diameter, well above the maximum recorded in any other cell in the entire set of cells examined. The majority of cells that were determined to contain at least one lighting jump had overall higher maximum reflectivity values, echo tops, and maximum expected hail sizes. The D/Dt maxes also tended to be higher in cells containing one or more jumps, however the timing of the maximum D/Dt was often roughly 3 minutes prior to the instance of the first lightning jump recorded in the cell’s lifespan. The results from the strength comparison to other local storm cells were fairly mixed, with different metrics indicating different strength behaviors when compared to other cells locally within 30 nautical miles of a studied storm. a. Base Reflectivity  The highest total number of statistics was tracked for reflectivity, given that this metric is most directly tied to the overall storm strength.

Figure 4. Maximum positive rate of change of maximum-recorded reflectivity between each radar scan given in

change per 5-minute interval. The averages over each cell type are plotted to the right, indicating that cells containing lightning jumps averaged a higher maximum increase in reflectivity than ordinary cells containing no jumps, despite the outlier in the non-jump cases appearing on the right side of the plot.

Out of all three storm metrics studied, the lowest elevation base reflectivity showed the strongest correlation between the time the maximum was noted in relation to the a lightning jump and the value of the maximum recorded.

Each of the statistics calculated in tables 1 and 2 were chosen to be included based on their importance to the storm’s intensity both over the entire life of each cell, and for cells with lightning jumps, the behavior of the

    Shriver  7    reflectivity right around the time the first jump in the cell was recorded. Most of the results from testing reflectivity were similar to what was initially anticipated, with a few exceptions. The difference between the highest reflectivity recorded in a tracked cell and the highest recorded in other cells surrounding a tracked cell was oddly lower overall for cells containing no lightning jumps. The expectation was that cells with jumps would tend to get stronger than cells surrounding them, though this does not appear to support this hypothesis. The explanation for the negative lead-time of lightning jumps for the highest rate of change recorded was unclear at first, until further consideration of the requirements for a jump to occur. (Further discussion of this can be found in section 6.)

One of the most remarkable observations is that nearly all cells that contained a jump reached 60 dBZ at some point during their lifetime. In contrast, almost no cells reached this value for the cases containing no jumps. The 60 dBZ threshold was used as a rough estimate for determining whether a storm contains a significant mass of ice and hail, indicating most of the cells containing lightning jumps likely did contain some amount of hail reaching the surface. Overall the results from this portion of the dataset indicate that cells with lightning jumps were generally reaching higher reflectivity values and higher rates of increase than cells that contained no jumps. The reflectivity metric, however, did not appear to support the hypothesis that lightning jumps help identify the strongest storm in a given cluster of cells.

Figure 5. Peak reflectivity recorded during each cell containing lightning jump(s) plotted with the reflectivity value on the y-axis and the time elapsed from the instance of the first lightning jump to the time of peak reflectivity on the x-axis. The trend line indicates a positive correlation between the time relative to the lightning jump and the value of the peak reflectivity recorded.

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Average

+DR/Dt maximum

Percent of cases reaching +DR/Dt of 10 dBZ or higher

Average global maximum reflectivity

Percent of cells reaching 60 dBZ or higher

Percent of cells where maximum ref. was highest local max

Average difference for cell’s max ref. and highest ref in surrounding cells

Lightning Jump Cells

8.86 dBZ/5 minutes

35.71% 61.29 dBZ 78.57% 21.43% -2.00 dBZ

Non-Jump Cells

6.38 dBZ/5 minutes

14.29% 58.52 dBZ 14.29% 28.57% -1.19 dBZ

Table 1. Statistical figures recorded for both cells with no lightning jumps and cells containing at least one lightning jump. For clarification, the last column depicts the average difference between the maximum reflectivity recorded in a studied cell and the maximum reflectivity recorded in any cell within 30 nautical miles from the center of a studied cell. For example, if a storm moved from the east to the west side of Huntsville between two 5 minute scans, the highest reflectivity in any other cell 30 nautical miles from the starting point on the east side would be noted. Then 5 minutes later the highest reflectivity in 30 nautical miles would be noted. The highest value out of these areal searches would then be compared to the highest value recorded in the cell being tracked.

Average time

elapsed prior to DR/Dt max from instance of lightning jump

Average time elapsed from instance of jump to max reflectivity

Average time elapsed from instance of jump to reaching 60 dBZ

Average +20 minute local maximum ref. after instance of jump

Average increase/decrease in ref. from time of jump to 20 minute max

Lightning Jump Cells

-2.85 minutes 8.08 minutes 6.55 minutes* 59.43 dBZ +4.39 dBZ

Table 2. Statistics taken only for cells with 1 or more lightning jump(s) found during their lifespan. Note that the negative value for the first column indicates that the maximum rate of change occurred prior to any lightning jump, on average. *Only includes cells where reflectivity reached at least 60 dBZ in the calculation.

b. Precipitation Echo Tops The total statistical data recorded for Echo tops was slightly smaller in amount9 than for reflectivity, but was

nonetheless an important metric to analyze due to its significance to storm and updraft strength.

Percent of cells

containing top 3 highest local top

Average +DE/Dt maximum

Average global maximum echo top

Lightning Jump Cells 85.71% 8.25K ft. / 5 minutes 50.43K ft.

Cells with No Jumps 57.14% 7.93K ft. / 5 minutes 44.95K ft.

Table 3. Echo top statistics recorded for both cells containing lightning jumps and cells containing no jumps during their lifespan. To clarify, the first column indicates the percentage of cases where the maximum echo top was one of the 3 highest recorded maximum echo tops for all cells lying within 30 nautical miles of a studied cell.

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Figure 7. Maximum echo top recorded during each cell’s lifespan plotted against the time of time elapsed from the instance of the first lightning jump recorded in each cell and the time of the maximum echo top recorded. The trend line reveals a very weak positive correlation between time of maximum echo top relative to a lightning jump and height of maximum echo recorded during a given cell’s lifetime.

Figure 6. Maximum echo top recorded during each cell’s lifespan. The averages are plotted at right, as is done in fig. 4, indicating cells containing lightning jumps favor slightly higher maximum echo tops than cells containing no jumps. Note one non-jump cell was omitted from this dataset due to the cell’s proximity to the radar site and resulting interference with the echo top. The interference was due to lack of data above a certain elevation in the “cone of silence” where even the maximum elevation angle scans do not reach

    Shriver  10     Average

increase in echo top from instance of jump to +20 minute max

Average time from instance of jump to time of highest +DE/Dt

Average time from instance of jump to time of global max echo top

Average +/-20 minute maximum echo relative to lightning jump

Lightning Jump Cells

+5.71K ft.

-3.37 minutes

8.21 minutes

49.95 ft.

Table 4. Echo top statistics recorded for cells containing lightning jumps only. The echo tops were of particular interest with this investigation, given that the elevation of the top of the storm cloud in a thunderstorm is closely connected with the updraft strength of the storm. The momentum carried in an updraft will determine the ability of the updraft to continue past the equilibrium level at the top of an unstable layer. Hence storms with stronger updrafts will tend to have both higher cloud tops and precipitation echo tops in radar data. Again, much of the results in the echo

top set were similar to the expectations of having higher values for storms containing lightning jumps. There is a rather interesting indication from the comparison of the echo tops in each cell examined to the echo tops of other cells nearby. Most of the cells containing a lightning jump fell inside the top 3 highest local maximum echoes recorded within 30 nautical miles of each studied cell, where as only a little more than half of the non-jump cases were in the top 3. This indicated cells with lightning jumps were more likely to have a highest echo top, and consequently stronger overall peak storm and/or updraft strength. This is on the contrary to what the reflectivity metric indicated about storm strength in comparison to other local cells. (Details on these findings are outlined in section 6.) c. Expected Hail Size The hail size estimated by the Gibson Ridge software was analyzed the least thoroughly, mainly due to its lack of having a consistent tracking indicator in Gibson Ridge and problems with accuracy of expected hail size in comparison to on-the-ground storm reports.

Figure 8. Maximum expected hail size recorded for all cells, with the threshold for severe hail size overlaid with this chart. The averages are plotted as dots to the far left. The cells containing lightning jumps showed a significantly higher incidence of cells with severe hail size estimated, along with a much higher overall average maximum recorded. Note 4 cells that were non-jump cases marked as zero did not reach the minimum tracking threshold in Gibson Ridge and were also not included in the average calculations.

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Figure 9. Maximum hail size recorded plotted against the time the maximum was recorded relative to the time of the first lightning jump in a given cell. Note that the outlier value of 2.77 appears to be heavily impacting the trend line, which shows an overall slightly negative relation between maximum hail size and the time it occurred relative to a lightning jump. Average maximum estimated hail size

(Diameter in inches) Percent of cells reaching severe (>= 1.00” diameter) threshold

Lightning Jump Cells 1.51” 85.71% Cells with No Jumps 1.01”* 42.86%

Table 5. Statistics calculated for both cells with lightning jumps and cells with no jumps for estimated hail sizes. *5 cells with no jump did not reach the tracking threshold of roughly 0.5” diameter, and were not included in the average calculation. Average

increase in hail size from jump to local +20 min. max

Avg. Local +20 min hail size relative to jump

Pcnt. of cells with jump occurring before severe

Avg. time elapsed from instance of jump to severe

Avg. time elapsed from instance of jump to global max

Pcnt. of cells where jump occurred before max

Lightning Jump Cells

+0.51” diameter

1.38” diameter

21.43% -4.67 minutes 2.21 minutes 64.29%

Table 6. Statistics calculated for cells containing lightning jump(s) only. Note that two cells did not reach the severe threshold and are not part of the calculation in column 4. The hail size estimates saw the most varied but also some of the most decisively positive results in the study. Remarkably, storms with lightning jumps on average contained a maximum estimated hail size half an inch

larger in diameter than cells containing no jumps. The percentage of cells containing lightning jumps that had maximum hail sizes estimated above the severe hail threshold of 1 inch in diameter was also roughly double the percentage for non-jump cells. Given that stronger

    Shriver  12    updrafts are needed to keep larger hail sizes airborne, these results strongly support the hypothesis that cells with lightning jumps tend to become stronger cells, although it is important to remember that these are only estimated hail sizes, which may differ from confirmed spotter or public reports that would confirm or deny the existence of the indicated hail sizes. The results from the hail analysis also have some marginally negative results, as is depicted in figure 9. The plot showing timing of a jump in comparison to the timing of maximum hail size shows that 5 of 14 cases had their maximum hail size recorded prior to any jump occurring. The trend between hail size and time elapsed from a lightning jump also appears to be slightly negative, although the outlier of 2.77” diameter hail estimated in one of the cases may be greatly altering this trend line, particularly given that there are only 14 cases plotted to begin with. 6. Results Interpretation The majority of the results found were consistent with what was initially anticipated, although some of the findings were somewhat surprising at first. In particular, the original expectation for the maximum positive rate of change for each metric studied was that the maximum would generally occur shortly after the observation of a lightning jump. However, the actual findings indicated that each metric tended to have a maximum rate of change roughly 3 minutes prior to the instance of any lightning jumps being observed in each cell. This is best explained by considering the conditions required for a lightning jump’s existence. In order for a rapid increase in lightning activity to occur, a substantial mass of ice must be present in the storm due to the importance of ice crystals in the ionization of particles within the cloud mass. Typically a considerably strong updraft is necessary in order for vast quantities of ice to develop from liquid water droplets or supercooled droplets becoming lifted far enough above the freezing level to become ice crystals. During the development of an updraft, an exchange of mass occurs between small ice crystals and larger graupel, giving both particles a net charge. The smaller and lighter negatively charged ice crystals are pushed to the top of the storm by the updraft while the positively charged graupel stays lower down due to having a higher terminal fall speed. Eventually the difference in electric potential between the two masses of charged particles in enough to initiate a lightning strike between the electric fields. This helps to understand the behavior of the storm shortly before a

lightning jump; strengthening of the updraft in the core of the storm must occur prior to a jump to allow a stronger charged fields of particles to develop and for more frequent flash rates to occur. This would also help explain the existence of severe hail estimates before lightning jumps. These larger Gibson Ridge hail size estimates were likely coming from high reflectivity values and vertically integrated liquid values at the uppermost portions of the storm, where a high mass of ice would already be present with hailstones possibly forming aloft. Overall the results show that strengthening is occurring both before and after a lightning jump, and that the metrics tend to reach their peak values after the first recorded jump in a given cell. However, there are conflictions in the data for indicating whether a given cell with an observed lightning jump is more likely to be the strongest storm within a cluster of cells. Comparison of echo tops to surrounding cells yielded a distribution in the graphic below indicated that storms containing jumps were indeed more likely to be one of the strongest echo tops within 30 nautical miles of the cell’s center. The figure shows both actual cell counts and a corrected version of the distribution comparison to account for the difference in the number of cells containing jumps and cells not containing jumps. Note that even the unadjusted values for lightning jump cases exceed the number of non-jump cases in column one. The number of cells that contained the second highest local maximum echo top is more equal in comparison for both the adjusted and raw figures. The number of cells for both cell types having the third highest top is also substantially lower than in either of the first two columns. This could be partly because not all storms analyzed even had more than one or two other storm cells present in their surroundings, meaning some cells would be guaranteed to have an echo top in the top three highest storm tops. However, the number of cells that did not contain one of the highest three storm tops shows the clearest indication that cells containing lightning jumps were much more likely to have higher storm tops. Very few cells with jumps did not contain a top three echo, yet more than a third of the total non-jump cases did not contain one of the highest echo tops. This strongly suggests that the updrafts needed to propel hydrometeors to their maximum height were generally stronger in storm cells containing lightning jumps. This is also consistent with the requirement for a higher ice mass in storms with higher amounts of lightning activity, with stronger updrafts yielding higher cloud tops with a greater depth of clouds composed of ice crystals.

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Figure 10. Distribution for cells containing the first, second and third highest maximum echo top out of all cells within 30 nautical miles of the studied cell. The last column shows the number of cells for each type that did not contain a maximum echo top in the three highest locally recorded echoes. The dashed line for the lightning jump-containing cells is a corrected distribution for the unequal number of cells that contained jumps and cells that did not. 14 cells contained jumps and 21 did not, hence the corrected distribution effectively adds about 7 more cases by multiplying each jump column by 1.5 to estimate a fair comparison. The results from comparing the highest reflectivity recorded in a studied cell to the highest reflectivity values in surrounding cells seemingly did not agree with the indications from the echo top analysis. The average storm cell, whether it contained a lightning jump or not, tended to have another cell nearby that at some point reached a slightly higher maximum base reflectivity. However, as the following figure shows, the cells containing lightning jumps were slightly more likely to have an even lower maximum reflectivity than the maximum in surrounding storm cells than for cells that showed no jumps. The tendency for cells containing jumps to have lower reflectivity values than surrounding cells by a larger amount than for cells that did not contain jumps appears to be a negative result at face value. However, there is also a large outlier in the lightning jump case of -12.5 dB, which is largely weighing down the average. Removing this case from the average gives a result that is more comparable to the non-jump storms. It is also important to note that this study only looked at the very strongest reflectivity in the local area of a studied cell. An average of the maximum reflectivity values of all identifiable storm cells nearby may have given a more representative strength of the typical storm cell in a given cluster of storms. It is also quite possible that a few other storms

nearby to the studied cells also contained lightning jumps. Given that lightning jumps can indeed identify the strongest storms in a cluster, it would be quite plausible that these cells would frequently contain higher reflectivity values. Hence the results from this portion of the analysis are somewhat inconclusive given these complications. It also might have been quite helpful to omit reflectivity values recorded in local storms that also contained jumps with a similar comparison without data from other jumps. The vast majority of the other results not yet discussed from the statistical comparisons show an overall positive result in that they indicate storms containing lightning jumps were more likely to see more rapid strengthening, strengthen to a maximum after a lightning jump and were possibly more likely to become stronger than most nearby storm cells. In general, strengthening was occurring prior to a jump occurring, but as outlined earlier, this is an expectable observation given the requirements for lightning initiation to occur. Further testing is still needed to better define the exact correlation between lightning jumps and storm strength, which could include using a similar approach to this study with a much larger sample size.

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Figure 11. Difference between the highest reflectivity value recorded in each studied cell and the highest reflectivity recorded in any cell 30 nautical miles from a studied cell’s center. The center point of the circular area searched for adjacent storm cells moved with each updated position of the storm on radar. The highest value of any of the areas searched was then compared to the maximum reflectivity recorded in the studied cell. The zero values indicate cases where the maximum reflectivity was tied with at least one other local cell’s maximum reflectivity. The averages are plotted to the right side, both below zero. The below zero values represent cases where the studied cell did not contain the strongest reflectivity in the area, and the positive values indicate cells containing the highest reflectivity out of all local storm cells.

7. Conclusions In summary, lightning jumps have the potential to predict both the development of severe weather and are likely an indicator of unusually rapid storm growth. The three selected metrics for the study all showed support for the hypothesis that lightning jumps can predict both storm strengthening, and to a degree, stronger storms in a given storm cluster. Despite some slightly mixed indications from the data, the consensus was that storm cells containing jumps were more likely to grow faster and reach higher values for each of the three metrics examined. The results generally support the expectations underlying the theory that lightning jumps can be used to identify rapidly growing storms with higher severe potential. This study indicates, in addition to the work completed by Schultz et .al (2009; 2011) and other studies, the true usefulness of lightning jumps as a forecasting tool. If an effective method of quickly and accurately identifying jumps in real time is developed, it

could aid in the decision-making stages of issuing severe weather warnings and alerting forecasters of storms that are growing very quickly and could soon become strong or severe. It is important to note that this study is should be a key addition in the current database of research for truly understanding the relationship between lightning jumps and thunderstorm behavior. The need for expanding or elaborating on this and other studies is still evident, particularly given some of the conflicts in the data where the sources of the conflicts are sometimes not readily identifiable. Further research may take a similar approach to that of this investigation, although it would likely consist of a more in-depth analysis with a larger sample size and more storm metrics to be examined. The more completely and accurately lightning jumps can be understood, the more useful they will be to forecasters. The usefulness of lightning jumps in warning issuance has already been demonstrated in case examples such as the February 6th, 2008 tornado outbreak. The rapid increasing

    Shriver  15    trends in lightning density from the LMA in Alabama helped aid in several forecasters’ confidence in issuing early warnings on one of the many storm cells that developed on this date. A lightning jump occurred in the cell 17 minutes before a tornado touched down, quickly growing to EF-4 strength (Darden et al. 2012). If this type of successful application of lightning jumps can be utilized so well once, it can likely be used again. The potential for lightning jumps to become a vital tool in forecasting in the very near future appears promising. 8. Acknowledgments This paper acknowledges Chris Shultz as a major contributor to this project by allowing access to his pre-processed datasets and for helping the author to define the best approach to testing the relation between lightning jumps and thunderstorms. This study was also completed under the advising and guidance of Dr. T.C. Chen and William Gallus, who helped the project stay on a working schedule and provide suggestions for improvement for the final copy of this report. The study also would like to thank the NCDC, LMA and NLDN for providing data necessary for the completion of the project and for flash rate data used in the jump ratio algorithm. 9. References Cited Arthur Witt, Michael D. Eilts, Gregory J. Stumpf, E. De Wayne

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