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2016

A Lightning Flash Rate Analysis Over FloridaThomas Owen Mazzetti

THE FLORIDA STATE UNIVERSITY

COLLEGE OF ARTS & SCIENCES

A LIGHTNING FLASH RATE ANALYSIS OVER FLORIDA

By

THOMAS O. MAZZETTI

A thesis submitted to the

Department of Earth, Ocean, and Atmospheric Science

In partial fulfillment of the requirements for graduation with

Honors in the Major

Bachelor’s of Science in Meteorology

Spring 2016

Abstract

Florida is known for intense lightning activity during the summer months that is largely

due to sea breeze circulations. The peninsula of Florida has become famous for its maximum

flash densities from summertime convection. The variations in lightning flash rate are

understudied compared to flash densities. The Earth Networks Total Lightning Network

(ENTLN) detects lightning flashes in Florida with greater than 90% efficiency. This research

produces a quasi-climatology using a 5 yr period of ENTLN data between 2010 and 2014. Using

an Eularian framework, we believe this research is the first to investigate the flash rate

characteristics of Florida thunderstorms by determining their temporal and spatial distributions.

Results show that lightning flash rate has two phases: one associated with spring/summer, and

the other associated with fall/winter. During spring and summer there are large flash rates and

strong diurnal variations in flash rate. Conversely, fall and winter exhibit smaller flash rates and

almost no diurnal variation. Intense lightning activity associated with individual storms are

noticeable in the spatial lightning fields during the fall and winter because of the small sample

size, However, spring and summer exhibit smooth patterns because of the large number of

storms. The most recent period of lightning data reveal that the lightning maximum in Florida is

near Orlando rather than Tampa.

1. Introduction

Lightning is a threat to life and property and contributes to chemical processes in the

atmosphere. Cloud-to-ground (CG) lightning causes damage and injuries at the ground, while

intra-cloud (IC) lightning can damage aircraft in rare circumstances. Lightning strikes create

nitrogen oxides (NOx) which are a catalyst for secondary atmospheric pollutants [e.g., Pickering

et al., 1998; DeCaria et al., 2005, Holle, 2015, Ott et al., 2007; Schumann and Huntrieser,

2007]. CG can be categorized by the charge that it lowers to the ground, either positive (+ CG) or

negative (-CG). +CG flashes are much less common than -CG flashes [Orville and Huffines,

1999]. Clouds become electrically charged due to the non-inductive charging mechanism when

ice crystals and graupel collide in the presence of supercooled water [e.g., Takahashi, 1973;

Jayaratne et al., 1983; Saunders et al., 1991]. The larger and heavier graupel particles tend to be

transported to the middle layers of the storm, while the smaller and lighter ice crystals are

transported to near the top of the storm. Comprehensive reviews of cloud-charging mechanisms

and the various types of lightning discharges that can result are provided by Uman [1984], Rakov

and Uman [2007], and Willett et al. [2010]. Eularian analyses of lightning flash rate (the number

of flashes per unit time) are understudied but can reveal important characteristics about

thunderstorms.

2. Background

Thunderstorms around the world produce billions of lightning flashes every year. The

time, location, and electrical characteristics of each lightning flash can be determined by a

variety of lightning detection networks. The networks are continually being upgraded with more

advanced sensors and detection algorithms. These upgrades are improving the networks' location

accuracy and detection efficiency [e.g., Rudlosky and Fuelberg, 2010, Rudlosky, 2015]. Flash

density (the number of flashes per unit area) has been analyzed extensively. Results have shown

that central Florida has the greatest flash densities in the United States, and is one of the most

electrically active regions in the world [Christian et al., 2003; Hodanish et al., 1997; Orville and

Huffines, 2001].

The majority of flashes in Florida occur during the warm summer months when synoptic-

scale forcing is weak, and sea breeze-induced storms (10-100 km scale coastal thermal

circulations) dominate the weather patterns [Hodanish et al.,1997]. There is a strong diurnal

(daily) pattern to warm season thunderstorm activity in the Southeast U.S. [Watson and Holle,

1996]. Most storms during the summer occur during the afternoon and early evening hours

[Maier et al., 1984]. There is high likelihood of a lightning-producing storm occurring

somewhere in Florida every day, and on some days large portions of the state contain lightning-

producing storms. During the winter there are fewer lightning-producing storms, and there is less

diurnal variability because most storms are produced by synoptic-scale weather systems such as

mid-latitude cyclones [Lericos et al., 2002].

Lightning observations can be used to assess the intensity of convective events. Generally

speaking, enhanced lightning production requires greater and more rapid charging and thus a

stronger updraft [Williams et al., 1999]. Strong updrafts usually are associated with storms that

also produce heavy rain, strong wind, and potentially hail or tornadoes.

Flash rate mostly has been analyzed from a Lagrangian standpoint, i.e., following storms

throughout their lifetime as they move. Rapid increases in the sum of IC and CG flashes (total

lightning) have been shown to give 5-10 min lead time in predicting when the most severe

portion of a storm will occur. A storm's increasing electrical activity is caused by a strengthening

updraft and generally precedes the heaviest rainfall rates and strongest downdrafts. There

generally is more IC than CG as a storm's updraft develops, but when the updraft reaches

greatest intensity, CG outnumbers IC and reaches its maximum rate [Williams et al., 1989, 1999;

Schultz et al., 2011]. Efforts have been made to use lightning observations to predict the

development of supercell storms (i.e., large, intense, organized, and sometimes tornado

producing storms) [Darden et al., 2010]. Significant jumps in total lightning flash rate (IC plus

CG) can signify that severe storm conditions might occur in the next 5-20 min [Williams et al.,

1999; Schultz et al., 2011]. These studies have focused more on flash rates in individual storms

as they grow and later dissipate [Schultz et al., 2011; Williams et al., 1989, 1999], often using

methods to track individual cells by flash density in a Lagrangian framework and analyzing flash

rates as a cell evolves [Betz et al., 2008].

Lightning flash rate also has been used to predict rainfall rates [Anderson et al., cited

2015], and to estimate rainfall totals based on the amount of lightning that occurred [Gungle and

Krider, 2006]. Conversely, meteorological data are used to forecast convection and lightning

activity. Watson et al. [1987] used divergence in the surface wind field to predict lightning

activity in the next few hours at the Cape Canaveral area of Florida. Lericos et al. [2002] related

lightning patterns to larger scale synoptic scale winds.

Lightning has helped evaluate trends in thunderstorm activity on timescales longer than

the immediate future. Maier et al. [1984] analyzed temporal patterns of lightning activity on a

daily time interval. They determined that the greatest amount of summer time lightning over

Florida occurs ~3 h after solar noon. Lericos et al. [2002] showed a diurnal pattern of lightning,

with maximum densities over land during the day and over water at night due to mesoscale sea

breeze and land breeze patterns. Strong relationships also have been found between storm area,

flash density, flash rates, duration of storms, and the amount of lightning that occurs. For

example, the longer lifetime of the storm the more lightning that occurs, the larger the storm area

the more lightning; the longer lived a storm the greater the flash densities; and the greater the

flash rate the more lightning [Peckham et al., 1984]. MacGorman et al. [1984] noted trends in

storm duration and flash density very similar to Peckham et al. [1984].

Climatologies have revealed seasonal trends in lightning activity. For example, when

considering all CG lightning strikes in the United States during the year, an average of 8.65% are

+CG [Rudlosky and Fuelberg, 2010]. +CG and -CG strikes exhibit varying ratios across the

continental United States, with peaks of ~ 25% positive CG in the North Central Plains and the

West coast.[Orville and Huffines, 1999]. Long term climatologies also reveal that topography

plays a role in flash densities. Examples are the tops of isolated mountains or where sea breezes

converge [Lopez and Holle, 1986].

3. Objectives

The goal of this research is to determine the temporal variation in total (CG plus IC)

lightning flash rates on scales ranging from diurnal to seasonal, as well as spatial variations over

the State of Florida. Total lightning is chosen because both IC and CG are significant

meteorological concerns. Thunder is produced by both CG and IC, and peoples' perception of

lightning often is based on the thunder that is produced. To an average citizen, the more

continuous the thunder, the more dangerous the storm appears. Furthermore, both types of

lightning produce NOx [e.g., Pickering et al., 1998; DeCaria et al., 2005, Ott et al., 2007;

Schumann and Huntrieser, 2007]. Flash rate, flash density, and hours with lightning are

compared with each other and with climatologies from previous studies that focus on flash

density [Hodanish et al., 1997; Watson and Holle, 1996; Rudlosky and Fuelberg, 2011; Orville

and Huffines, 2001, 1999; Mäkelä et al., 2011]. Knowing the temporal and spatial distributions

of lightning flash rate can promote lightning safety, the planning of public events, and help

parameterize thunderstorms and their chemical production in chemical/atmospheric models such

as the Weather Research and Forecasting Model with Chemistry (WRF-Chem; http://www.wrf-

model.org/index.php).

4. Data

Lightning data were provided by the Earth Networks Total Lightning Network (ENTLN).

Lightning emits electromagnetic radiation because of the moving and accelerating electrons that

constitute the stroke. Each ENTLN sensor records each stroke’s electromagnetic wave form in the 1-12 MHz range. The central server of the ENTLN receives waveforms from multiple sensor

locations to determine each stroke’s location and time. Strokes are combined into flashes using

prescribed temporal and spatial thresholds. Characteristics of the detected wave form enable the

network to identify IC and CG flashes with relatively high accuracy and to calculate peak

current. The IC flash detection efficiency in Florida is greater than 85%, while the CG flash

detection efficiency is greater than 95%. The ENTLN is more efficient at reporting flashes than

strokes—providing near 100% detection efficiency for flashes [Liu and Heckman, 2011, 2012;

Mallick et al., 2014].

Five years of lightning data were provided by ENTLN, starting on 1 January 2010 and

ending on 31 December 2014. The dataset consisted of detected IC and CG lightning flashes

with their date, time, latitude, longitude, polarity, and peak current for two contiguous regions,

North Florida (-88o to -79o longitude, 29o to 32o latitude) and South Florida (-84o to -79o

longitude, 24o to 29o latitude). This combined domain has the general shape of an upside down

“L” that corresponds to that of Florida. Both the north and south portions of the domain have

approximately the same area of ~280,000 km2. It encompassed the entire state and portions of the

bordering states, and surrounding oceans (areas having large detection efficiencies). For the

majority of the domain, CG detection efficiency is greater than 95% while IC is greater than

70%. Location accuracy in the domain is less than 300 m. The five years of data were averaged

into a quasi-climatological one year Eularian grid boxed format (explained in the methodology).

5. Methodology

We used an Eularian approach to interrogate the dataset and calculate flash rates, flash

densities, and hours of lightning activity. The research does not focus on the lightning activity of

individual storms, but on spatial and temporal distributions of lightning flashes. A 0.2 × 0.2o grid

(~22 km × 22 km) was superimposed over the domain encompassing Florida. The dimensions of

the grid boxes and lengths of time intervals over which to calculate the flash rates were

determined by several factors. The combination of a grid box and time interval will be referred to

as an event.

The goal was to configure the Eularian framework so that the calculated flash rates are

realistic and crudely approximate to flash rates that a Lagrangian approach would produce,

without having to track the individual storms. The grid boxes needed to be larger than the

precision of the flash locations. ENTLN's location accuracy in the domain is much less than 1

km and varies with location [Mallick et al., 2014]. Thus, the accuracy of the ENTLN

observations limited the boxes from being smaller than 1 km in dimension. If the grid boxes

were smaller, the resulting lightning distributions would contain small features that were noisy

and inconsistent because of the relatively short 5 yr study period. Conversely, if the grid boxes

were too large, the resulting distributions would be smoothed excessively.

The size of the analysis grid boxes was selected with consideration of the size of a typical

thunderstorm. A non-severe isolated storm encompasses a diameter of ~ 20 km [Mäkelä et al.,

2011], with multi-cell complexes being considerably larger [Markowski and Richardson, 2010].

There are various scenarios of how a storm and its associated lightning could be located with

respect to an individual grid box. For example, a large thunderstorm could "straddle" several grid

boxes, or multiple small thunderstorms could be located within the same box. In spite of these

complexities our goal was to obtain flash rate values that are relatable to what actual individual

storms would produce.

We calculated flash rates over non-overlapping 15 min time intervals. Thus, they do not

represent "instantaneous" flash rates. The duration of a typical non-severe thunderstorm's

lightning ranges anywhere from ~1 h to several hours. The temporal precision of the lightning

data was not a concern because the ENTLN sensors are synchronized and the network records

flashes to fractions of a second. If a longer time period were used, for example 1 h or longer, the

analysis could unphysically report low flash rates and pose the concern of how many

thunderstorms had occurred in a grid box during the 1 h period. The shorter the time interval, the

closer the calculated flash rates will reflect true instantaneous flash rate and the assumption of

only one storm occurring in each time interval becomes more accurate.

Flash rates were calculated over 15 min intervals using the following approach. First, the

number of individual lightning flashes were counted within each 0.2o box (longitude and

latitude) and 15 min time interval. To calculate the flash rate, the number of flashes is divided by

15 min for a result of flashes min-1. To calculate flash density, the number of flashes is divided

by the latitude dependent area of the 0.2×0.2° box, giving a result of flashes km-2. The hours of

lightning for a single 15 min period is simply 0.25 h.

Now consider the entire domain. The lightning flashes from the 5 yr study period from 1

January 2010 to 31 December 2014 were counted into grid boxes/time intervals or events. Then

the 5 yrs of events were averaged into one year’s worth of events—excluding values of zero

flash rates. This was done to produce a quasi-climatology representing an average year’s lightning activity. The flashes within each box could be summed over any time period desired--

daily, monthly, etc., and then divided by the appropriate amount of time or area to give average

flash rate, flash density, etc. Spatial and temporal distributions of these parameters could be

prepared for the entire domain.

It is important to note that our 5 yr study period is too short to be considered a true

climatology. However, it is sufficiently long to draw initial conclusions.

6. Results

An expected characteristic of the analysis is that, most of the time, no lightning occurs

over Florida. Figure 1 is a histogram of all events in the domain by flash rate. The leftmost

column, representing zero flash rate, consists of ~4.3×107 events, ~95% of the total number of

events in a year (45,676,800). Thus, in ~95% of the events there is no lightning. Figure 1 shows

a highly skewed distribution of flash rates where there are many more occurrences of small flash

rates than large flash rates (note the logarithmic scale on the y axis). When considering this

Eularian analysis, there are many physical scenarios of how lightning could be located with

respect to the grid boxes. There are cases when only a small portion of a thunderstorm’s total

flashes occur within one particular box. For example, a relatively stationary thunderstorm could

"straddle" several grid cells. Conversely, a rapidly moving cell could produce lightning over a

long swath of grid boxes. Thunderstorms like these produce many counts of small flash rates.

Conversely, if multiple thunderstorms are located within the same event, high flash rates are

recorded. Some of the ~200 and ~300 flash min-1 events in Figure 1 most likely are due to this

scenario. Total flash rates of ~150 flashes min-1 have been observed in previous Lagrangian

studies of severe storms that used counting intervals shorter than our 15 min period [e.g.

Williams et al., 1989, 1999; Schultz et al., 2011, Williams et al., 1999].

Figure 1: Histogram of the number of lightning events versus flash rate for the entire

domain. Note that the y-axis is logarithmic. The red dash on the y-axis

corresponds to flash rates of 0.066 flashes min-1 (The left most column) while the

green dash represents 0.133 flashes min-1 (The second to left column).

Figure 2: Box and whisker plots of the distribution of flash rate by month (left) and each

day of the year (right) for the entire domain. Note: Y-axis is logarithmic. Periods

with no flashes were excluded from the analysis. i.e., the minimum flash rate is 1

per 15 min period (0.066 flashes min-1). The left image is the monthly average of

the right. The red boxes represent the mean, while the red dashes represent the

median. Whiskers are defined by the conventional 1.5 interquartile range from

quartile 1 to quartile 3.

The remaining analyses exclude events with zero flashes, i.e., Figure 1 without the

leftmost column. Figure 2 shows distributions of flash rates by month (left) and by day (right).

The logarithmic scale in Figures 2, 3, and 4 visually lessens the variability along the X-axis. For

example, the January average (Figure 2) is ~0.8 flashes min-1 while the June average is ~2.0

flashes per min-1, more than double the January rate. The major skewness in the distribution of

flash rates is apparent. During a majority of the year the mean flash rate is almost an order of

magnitude greater than the median rate. The daily mean flash rate during the spring and summer

months varies less than during the fall and winter months. The skewness of the flash rate

distribution also is more variable during fall and winter. The monthly average flash rate during

the winter months is smallest, and the summer flash rate is the greatest. However, the daily plot

reveals a wide range of averages for winter. The entire distribution shifts toward greater flash

rates as the year progresses from the winter to spring and summer months. Maximum flash rates

are ~150 flashes min-1 during the summer. It is interesting to note that during portions of

December (Figure 2, right) the outlier flash rates are comparable to those during the summer

months. The number of periods with lightning during fall and winter time is considerably less

than in the other months. This is visible in Figure 2 by the fact that there are many fewer days

with outliers in the winter months. The distribution during January, February, September, and

October shows that the minimum and 25th percentile of events have the same flash rate of 0.066

flashes min-1 (the minimum possible in this schema), whereas March through August have the

same minimum, but the 25th percentile is slightly less than 0.2 flashes min-1. Thus, the warm

months exhibit a higher percentage of events with large flash rates. If a time period longer than 5

yr had been used, the "noise" during the winter months would be reduced.

Figure 3: Box and whisker plots of the distribution of flash rate (Flashes min-1) at each

time of day for: (left) the entire Florida domain, (center) north portion of the

domain, and (right) south portion—delineated by the 29th parallel. The plots

exclude intervals with no flashes. i.e., minimum flash rate is 1 15 min-1 which is

0.066 flashes min-1 like in Figure 2. Whiskers are defined by the conventional 1.5

IQR from Q1 and Q3.

The average flash rates vary greatly on a diurnal time scale from ~1 flash min-1 in the

early morning hours to ~2 flashes min-1 in the midafternoon (Figure 3). This variability is similar

to those of flash densities calculated in previous studies, e.g., Holle [2013, 2015], Holle and

Murphy [2015], Watson and Holle, [1996]. We will assume that the average yearly Florida

sunrise is at 1130 UTC and sunset at 2330 UTC. Considering the total Florida domain, there are

greater mean, median, and maximum flash rates during the day time, beginning to increase ~4 h

after sunrise. Mean flash rates slowly decline from peak values ~4 h after average solar noon

(~2130 UTC). Because Florida has a large north-south extent, it is appropriate to determine if

there are major differences in diurnal patterns of lightning flash rate between the northern and

southern portions of the total domain. The sea breezes of the peninsula dominate warm season

deep convection and are diurnally driven. The sea breeze in the panhandle is still significant, but

there are other factors that come into play. We see that the diurnal signal (Figure 3) in South

Florida is stronger than in North Florida, most likely due to greater synoptic influences over

North Florida because of its latitude. Synoptic-scale weather patterns tend to be stronger in more

northern latitudes and have a time scales of approximately several days.

Figure 4: Box and whisker plots of the distribution of flash rate (Flashes min-1) at each

time (15 min intervals) of day for the entire Florida domain separated into four

seasons delineated by the solstices and equinoxes. The plots exclude intervals

with no flashes like Figure 2. Whiskers are defined by the conventional 1.5 IQR

from Q1 and Q3.

Synoptic-scale influences on Florida weather patterns and lightning activity are much

greater during the fall and winter months than during the spring and summer. The large scale

flow during the spring and summer is dominated by subtropical anticyclones over the Atlantic or

Gulf of Mexico. Although large scale forcing is weak during summer, mesoscale circulations

such as the sea breeze produce thunderstorms on an almost daily basis. Thus, spring and summer

are dominated by diurnally forced thunderstorms [e.g., Hodanish et al.,1997, Watson and Holle,

1996], while fall and winter are dominated by synoptically driven thunderstorms. The diurnal

fluctuations in flash rate for the state as a whole almost disappear during fall and winter (Figure

4), but are very noticeable during spring and summer. It is interesting to view Figure 4 in terms

of how the outliers vary from season to season. There are fewer outliers during winter and fall

than during the warmer months—as seen by the density of outlier points in Figure 4. This is

indicative of less lightning and less variability during fall and winter.

Figure 5: Spatial distributions of (left) average lightning flash rates, excluding events

with no flashes (Flashes min-1), (center) flash density (Flashes km-2 year-1), and

(right) hours with lightning. Note: hours of thunderstorms is on a logarithmic

scale.

We investigated spatial patterns of the lightning parameters. Since our data period is 5 yr,

some geographic features reflect the influence of individual strong lightning events, especially

during the cold season. Therefore, we focus only on the major features of the distributions. One

should note that values near the periphery of the maps in Figures 5, 6, and 7 are less reliable than

those over land because the detection efficiency and location accuracy of the ENTLN decrease

dramatically with distance from the coast. Patterns of lightning flash rate (Figure 5, left) have

some similarities to those of flash density (Figure 5, center). There is a local maximum in flash

rate and flash density near the Orlando area. Flash densities in the Orlando area and the western

tip of the panhandle have similar values, but the flash rates are very different. This suggests that

on average, the updrafts over the peninsula are stronger than those over the panhandle. The

maximum hours of lightning activity over Miami and Tampa are correlated with local maxima in

flash density but not flash rate. There is no local maximum in hours of lightning co-located with

the maximum of flash rate over Orlando. This tells us that flash rate, density, and hours of

thunderstorms all have various forcing that contribute. Each of these three measures of lightning

activity has slightly different forcing because if they didn’t we would see the same spatial distribution of all three.

Figure 6: Spatial distributions of (left) lightning flash rates (flashes min-1), (center) flash

density (flashes km-2 year-1), and (right) hours with lightning. Day (top row) vs.

night (bottom row) are delineated by average sunrise/sunset delayed by 4 h

because of the lag in radiative forcing. Events with no flashes are excluded.

Figure 6 illustrates the different spatial characteristics of flash rate between day and night

as defined by the times of average sunrise/sunset delayed by 4 h. The 4 h delay is justified by

Figure 3 and 4 where we see a well-defined increase in lightning activity from daytime forcing

beginning ~4 h after sunrise. The daytime flash rate distribution looks much like the total flash

rate distribution from Figure 5, while the night time distribution seems almost random. This can

be explained by the daytime versus nighttime flash density. The majority of the night time

density map contains fewer than 60 flashes km-2, while the daytime densities show a maximum

of 300 flashes km-2, with much of the land mass containing over 150 flashes km-2. The nighttime

flash rate map in Figure 6 appears splotchy because of the relatively smaller number of flashes

that are dominated by individual major lightning events. If a longer dataset had been used, the

effects of individual storms would be smoothed out and the pattern would probably exhibit a

more organized distribution. The hours of lightning panel reveals much less activity at night

than during the day. This is due to the lack of solar heating that is the major cause of Florida

convection. The hours of lightning plots also verify the conceptual model that the sea breeze

initiates more convection over land during the daytime compared to more lightning over the

ocean at night due to the offshore land breeze.

Figure 7: Spatial distributions of (top row) lightning flash rates (Flashes min-1), (center

row) flash density (Flashes km-2 year-1), and (bottom row) hours with lightning.

The four columns depict patterns by season: winter (left), spring (left middle),

summer (right middle), and fall (right) defined by the solstices and equinoxes.

Periods with no flashes are excluded.

The annual cycle of lightning also is evident in the maps of Figure 7. The greatest amount

and most rapid lightning occurs during the spring and summer months. Areas of large flash rates

have similar locations as large flash densities, but there are a few key differences. The major

difference in flash rates is that, surprisingly, there are more widespread maximum values of flash

rates during spring than summer. Even though maximum values of flash rate are similar between

spring and summer, there is much less lightning as shown in flash density maps. This is further

emphasized by there being fewer hours of lightning during spring than summer. Fall and winter

months, with far fewer lightning flashes, experience even fewer lightning events. This causes

individual events to appear in flash rate maps because they are under sampled. During the winter

months the panhandle sees more hours of lightning than the peninsula (Figure 7) due to fewer

cold fronts extending to South Florida.

There is a large difference in flash rate, density, and hours of thunderstorms between the

Tampa and Orlando areas. Previous studies (e.g. Hodanish et al.[1997], Huffines and Orville

[1999], Lericos et al. [2002], Orville and Huffines [2001], and Rudlosky and Fuelberg [2010,

2011]) showed that flash densities in the Florida peninsula exhibited a local maximum over

Tampa that then extended towards the Cape Canaveral area (along the I-4 corridor). These older

studies either had the greatest maximum over Tampa or approximately the same amount near

both cities, but never a single maximum near Orlando. Conversely, the present figures show that

Orlando consistently has the greatest flash density and flash rate. Since the present study was

performed using ENTLN data, while most of the previous studies employed Vaisala’s National Lightning Detection Network (NLDN) observations, this difference in maxima could have been

due to differences in the observing networks’ characteristics. However, personal communications

with Ronald Holle at Vaisala, Inc. [2016] revealed that their most current 10 yr dataset did

contain a single local maximum of flash density over the Orlando area. Thus, the present results

using ENTLN data are consistent with those from Vaisala. This agreement provides confidence

that our 5 yr dataset is sufficiently large to draw major conclusions about patterns of flash rate

and associated parameters, although "noise" in some figures prevents conclusions about small

scale features.

Table 1: Sounding climatologies from the Storm Prediction Center’s webpage

(http://www.spc.noaa.gov/exper/soundingclimo/) were examined for their minimum,

median, and maximum values on 15 June. June has the greatest average flash rate (Figure

2), and summer has the largest gradient of flash rate and flash density between Cape

Canaveral (XMR) and Tampa (TBW) (Figure 7). Only 1200 UTC soundings were

considered because it is generally before peak convective activity while 0000 UTC

generally is after or near the peak of convective activity. Percent differences between the

various parameters were calculated using 100×(XMR-TBW) × ((XMR+TBW)/2)-1.

Negative means that TBW has the greater value, while positive means that XMR has the

greater value.

Average June 12Z sounding XMR TPA Percent Difference

Mixed Layer CAPE (J/kg)

Max 2100 3100 -38.5

Med 700 1050 -40.0

Min 100 10 163.6

Most Unstable CAPE (J/kg)

Max 3160 4390 -32.6

Med 1310 1570 -18.1

Min 230 120 62.9

Surface Based CAPE (J/kg)

Max 2830 3080 -8.5

Med 910 582 44.0

Min 40 6 147.8

Mixed Layer LCL (m)

Max 1260 1790 -34.8

Med 820 870 -5.9

Min 520 460 12.2

Most Unstable LCL (m)

Max 1310 2110 -46.8

Med 440 425 3.5

Min 110 55 66.7

Surface Based LCL (m)

Max 630 630 0.0

Med 240 140 52.6

Min 70 7 163.6

Convective Temperature (F)

Max 94 102 -8.2

Med 85 86 -1.2

Min 78 75 3.9

Surface Temperature (F)

Max 80 80 0.0

Med 74 72 2.7

Min 67 65 3.0

We sought a physical reason for the differing flash rates and flash densities between

Tampa and Orlando. The Storm Prediction Center’s Sounding Climatology web site

(http://www.spc.noaa.gov/exper/soundingclimo/) was used to determine differences in sounding-

derived parameters that are known to effect convection. Sounding parameters were available for

Tampa (TBW) and Cape Canaveral (XMR), ~80 km east of Orlando. Results (Table 1) show that

values of many parameters either are similar or supportive of Tampa being more convectively

active than Cape Canaveral. Climatological maximum and median values for mixed layer CAPE,

most unstable layer CAPE, and surface-based CAPE are ~10-40% greater at TBW than XMR,

while climatological minimum CAPE values are 60-160% larger at XMR (Table 1). Williams et

al. [2004] noted that not only are CAPE values important in determining lightning flash rates that

are associated with updrafts, but the “shape” of the CAPE vertical profile can have a significant

impact. For example, if more CAPE is concentrated around the freezing level where mixed phase

hydrometeors create static charge, the updraft will be stronger there and produce more rapid

lightning. Williams et al. [2004] also discussed that the height of cloud base correlates positively

with lightning flash rate. Cloud base is closely related to the lifting condensation level (LCL).

Differences in surface based LCL between TPA and XMR are ~160%, supporting Orlando being

more active (Table 1). Conversely, the difference between mixed layer LCL and most unstable

layer LCL are mixed, with some supporting Tampa being more convectively active. An

interesting point that could partially explain greater flash densities near Orlando is that

climatological maximum surface temperatures are ~80 F for TBW and XMR while the

climatological maximum convective temperature for TBW is 102 F and XMR is 94 F. Thus,

XMR climatologically would reach its convective temperatures more often than TPA and initiate

convection more often.

The sounding climatologies in Table 1 do not provide strong evidence for either TBW or

XMR having more lightning and greater flash rates. Table 1 was included to express the

ambiguity at hand. It would be worthwhile to examine climatological flow patterns of the region

using model reanalysis’. Lericos et al. [2002] performed a seasonal analysis that considered the

location of the subtropical ridge and resulting flow regimes. They determined that the east coast

of Florida would have greater flash densities when the subtropical ridge axis was oriented east to

west across South Florida and crossing south of Tampa and Orlando.

7. Conclusions

This research has documented the temporal variation in total (CG plus IC) lightning flash

rates on scales ranging from diurnal to seasonal, as well as spatial variations over the State of

Florida. Flash densities and hours with lightning also were considered. Five years of data from

the ENTLN were employed. Results showed two major features of flash rate in Florida. There

are greater lightning flash rates during the spring and summer months, with a strongly diurnally

varying signal. Greatest rates occur during the day time and much smaller rates at night. Spring

and summer also are characterized by much more consistent day-to-day flash rates. Fall and

winter exhibit smaller average flash rates with smaller diurnal variability. Based on our limited 5

yr dataset, the smaller amount of lightning activity during fall and winter resulted in highly

variable day-to-day flash rates and spotty maps for fall and winter. We could not isolate a

physical reason for the differences in lightning activity between the west and east coasts of

Florida.

Questions still to be answered include: What will a longer time period of data reveal

about the fall and winter months? How do Florida’s flash rates compare to the rest of North

America or the world? Is there a correlation between flash rate and the background synoptic-

scale flow of the of the thunderstorm environment?

Acknowledgments

The lightning data were provided by Earth Networks Inc. We thank Steve Prinzivalli of

Earth Networks for providing the data and for helpful insights regarding it.

Ronald Holle at Vaisala for his correspondence regarding the National Lightning

Detection Network (NLDN) dataset.

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