NASA/CR-2012- 216312 

Objective Lightning Probability Forecasting 

for Kennedy Space Center and 

Cape Canaveral Air Force Station, Phase IV 

William H. Bauman Ill 

ENSCO, Inc., Cocoa Beach, Florida 

NASA Applied Meteorology Unit, Kennedy Space Center, Florida 

Winifred C. Crawford 

ENSCO, Inc., Cocoa Beach, Florida 

NASA Applied Meteorology Unit, Kennedy Space Center, Florida 

August 2012 
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NASA/CR-2012- 216312 

Objective Lightning Probability Forecasting 

for Kennedy Space Center and 

Cape Canaveral Air Force Station, Phase IV 

William H. Bauman Ill 

ENSCO, Inc., Cocoa Beach, Florida 

NASA Applied Meteorology Unit, Kennedy Space Center, Florida 

Winifred C. Crawford 

ENSCO, Inc., Cocoa Beach, Florida 

NASA Applied Meteorology Unit, Kennedy Space Center, Florida 

National Aeronautics and 

Space Administration 

Kennedy Space Center 

Kennedy Space Center, FL 32899-0001 

August 2012 
Acknowledgements 

The authors wish to thank Mr. William Roeder of the 45th Weather Squadron for his suggestions 

and many consultations throughout the task as well as his efforts to acquire the National 

Lighting Detection Network lightning strike data from the 14th Weather Squadron (14 WS) 

Strategic Climatic Information Service and National Climatic Data Center. Without the individual 

strike data this task would not have been possible. Additionally, the 14 WS delivered the data in 

a format that significantly reduced processing time by the Applied Meteorology Unit. 

Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

This report is also available in electronic form at 

http://science.ksc.nasa.gov/amu/ 
Executive Summary 

The 45th Weather Squadron (45 WS) includes the lightning probability forecast in their daily 

morning briefings. This forecast is most important in the warm season months, May-October, 

when the area is most affected by lightning. The forecasters use this information for planning 

daily ground operations on Kennedy Space Center (KSC) and Cape Canaveral Air Force 

Station (CCAFS) for the current day and the following seven days, and as part of the launch 

forecast process. The daily lightning probability forecast is based on the output from an 

objective lightning forecast tool developed in two phases by the AMU that the forecasters 

supplement with subjective analyses of model and observational data. 

The current operational tool was developed in Phase II and consists of a set of equations, 

one for each month May-September that calculates the probability of lightning occurrence for 

the day more accurately than previous forecast methods (Lambert and Wheeler 2005, Lambert 

2007). The equations are accessed through a graphical user interface (GUI) in the 45 WS 

primary weather analysis and display system, the Meteorological Interactive Data Display 

System (MIDDS). The goal of Phase Ill (Crawford 2010) was to create equations based on the 

progression of the lightning season as seen in the daily climatology instead of an equation for 

each month in order to capture the physical attributes that contribute to thunderstorm formation. 

These Phase Ill equations did not outperform Phase II. Therefore, the Phase II equations are 

still in operational use. 

For this phase, the 45 WS requested the AMU make another attempt to stratify the data by 

lightning sub-season, add three more years of May-September data than used in Phase Ill to 

expand the period of record (POR) to 23 years (1989-2011) and include data from October. The 

AMU did this by using lightning observations across central Florida from the National Lightning 

Detection Network (NLDN) instead of the 45th Space Wing Cloud-to-Ground Lightning 

Surveillance System (CGLSS) data used in Phase Ill. The lightning season could start 

anywhere in central Florida, not just locally at KSC/CCAFS as the CGLSS data would show. In 

the event that lightning sub-seasons cannot be identified, the 45 WS requested that the AMU 

create monthly equations with using the expanded 23-year POR. 

The AMU established a correlation between the NLDN flash count start/ramp-up dates and 

the wet-season start dates used by the National Weather Service in Melbourne after removing 

days with NLDN flashes that were due to large-scale weather patterns. Not all days with NLDN 

flashes could be eliminated based on these patterns, so determining the start/ramp-up of each 

lightning season in the POR still had some level of subjectivity. With the start/ramp-up 

correlation established, the AMU then looked for correlations between the NLDN data and other 

lightning sub-seasons: lightning (plateau), ramp-down, and post. Determining the start of the 

sub-seasons using the NLDN data was also subjective. By examining annual charts of daily 

flash count, the AMU observed there were often multiple sub-seasons of the same type. The 

AMU presented these findings to the 45 WS personnel who would use the tool. This led to a 

consensus that it would be difficult for the forecasters to determine the lightning sub-seasons in 

real-time using NLDN data. 

Therefore, the AMU used monthly stratifications to develop and test new equations. The 

performance of the new equations was compared to that of five other forecast methods 

including the Phase II equations being used in operations. The new equations outperformed 

every method except Phase II. Therefore, the Phase IV equations will not replace the Phase II 

equations in operations. The AMU updated and tested the MIDDS tool by adding the October 

equations and updating the GUI. 

2 
Table of Contents 

Executive Summary .................................................................................................................... 2 

List of Figures ............................................................................................................................. 4 

List of Tables .............................................................................................................................. 5 

1. Introduction ........................................................................................................................... 6 

2. Data ...................................................................................................................................... ? 

2.1 Lightning Data .............................................................................................................. 7 

2.2 Sounding Data ............................................................................................................. 8 

3. Lightning Season .................................................................................................................. 9 

3.1 Lightning Season Start ................................................................................................. 9 

3.2 Lightning Sub-season Stratifications ........................................................................... 13 

4. Equation Development ........................................................................................................ 15 

4.1 Predictors ................................................................................................................... 15 

4.2 New Equations ........................................................................................................... 15 

4.3 Equation Testing ........................................................................................................ 17 

5. MIDDS GUI ......................................................................................................................... 19 

5.1 Testing ....................................................................................................................... 19 

5.2 GUI Update ................................................................................................................ 20 

6. Summary ............................................................................................................................ 23 

References ............................................................................................................................... 24 

3 
List of Figures 

Figure 1. Map of central Florida showing areal coverage (red square) of the NLDN flash count 

data set. The yellow line is the boundary between east- and west-central Florida ..... 8 

Figure 2. Daily NLDN lightning flash count across central Florida for April-November 1989. The 

steel blue line shows the number of flashes observed each day while the royal blue 

line is the 14-day moving average of the daily flashes. The vertical orange and green 

lines show the 1989 NWS MLB wet season start and end dates for Orlando (MCO) 

and Melbourne (MLB), respectively ........................................................................... 9 

Figure 3. The NLDN flashes across central Florida (see Figure 1) for 5 April 1989. Each yellow 

dot represents one cloud-to-ground lightning flash observed by NLDN. On this day 

there were 1 ,079 flashes across central Florida within the area bounded by the red 

lines ......................................................................................................................... 11 

Figure 4. The NOAA daily weather maps (NOAA 2011) from (a) 5 April 1989 and (b) 6 April 

1989 showing surface synoptic-scale weather systems ........................................... 12 

Figure 5. As in Figure 2, but for the east-central Florida region and with NLDN flashes due to 

synoptic frontal systems removed ............................................................................ 12 

Figure 6. As in Figure 5, but for 1999 and with the lightning sub-seasons highlighted by red 

brackets and text describing each ........................................................................... 13 

Figure 7. As in Figure 6, but for 2004 ..................................................................................... 14 

Figure 8. The total percent reduction in residual deviance from that of the NULL model as each 

predictor was added to the equation using the June development dataset. ............. 16 

Figure 9. The MIDDS Weather Menu showing the 'FCST Tools' button drop-down menu with 

'Lightning Forecast Tool' highlighted ........................................................................ 20 

Figure 10. The predictor dialog box for October. A tab for each month is at the top, followed by 

the date and sounding time, then the predictor values. The 'Dismiss' button closes 

the GUI, the 'Reset Parameters' button resets the sounding parameters to the 

original values, and the 'Calculate Probability' button displays the probability output 

dialog box (Figure 11 ) .............................................................................................. 21 

Figure 11. The output dialog box showing the probability of lightning occurrence for the day as 

calculated by the equation. The 'OK' button closes the box ..................................... 22 

4 
) 

List of Tables 

Table 1. The final predictors for each monthly equation, in rank order of their contribution to 

the reduction in residual deviance. The predictors in red were in every equation, the 

predictors in blue were in five of the six equations, the predictors in green were in 

four of the six equations, the predictors in orange were in three of the six equations 

and the predictors in black were in two or less equations ........................................ 16 

Table 2. The final predictors for each monthly equation from Phase II, in rank order of their 

reduction in residual deviance. The predictors in red were in every equation, the 

predictors in blue were in four of the five equations, the predictors in green were in 

three of the five equations, and the predictors in black were in only one equation ... 17 

Table 3. The percent (%) improvement (degradation) in skill of the new equations over the 

reference forecasts of persistence, daily and monthly climatologies, flow regime 

probabilities, and the operational equations developed in Lambert (2007). These 

scores were calculated using the verification data for each month ........................... 18 

5 
1. Introduction 

The 45th Weather Squadron (45 WS) includes the probability of lightning occurrence in their 

daily morning briefings. This forecast is important in the warm season months, May-October, 

when the area is most affected by lightning. The forecasters use this information for planning 

daily ground operations on Kennedy Space Center (KSC) and Cape Canaveral Air Force 

Station (CCAFS) for the current day and the following seven days, and as part of the launch 

forecast process. 

The daily lightning probability forecast is based in part on the output from an objective 

lightning forecast tool developed by the AMU that the forecasters supplement with subjective 

analyses of model and observational data. The tool developed in Phase II consists of a set of 

equations, one for each warm season month that calculates the probability of lightning 

occurrence for the day more accurately than previous forecast methods (Lambert and Wheeler 

2005, Lambert 2007). The equations are accessed through a graphical user interface (GUI) in 

the 45 WS primary weather analysis and display system, the Meteorological Interactive Data 

Display System (MIDDS). The goal of Phase Ill (Crawford 2010) was to create equations based 

on the progression of the lightning season as seen in the daily climatology instead of an 

equation for each month in order to capture the physical attributes that contribute to 

thunderstorm formation. Five sub-seasons were discerned from the daily climatology, and the 

AMU created and tested an equation for each. The Phase Ill equations did not outperform 

Phase II. Therefore, the Phase II equations are still in operational use. 

For this phase, the 45 WS requested the AMU make another attempt to stratify the data by 

lightning sub-season and include October data in the analysis. The AMU did this by using 

lightning observations across central Florida from the National Lightning Detection Network 

(NLDN) instead of the 45th Space Wing Cloud-to-Ground Lightning Surveillance System 

(CGLSS) data used in Phase Ill. The lightning season could start anywhere in central Florida, 

not just locally at KSC/CCAFS as the CGLSS data would show. By using the lightning season 

across central Florida and relying on the flow regimes to influence the probabilities of lightning in 

the KSC/CCAFS area, a more physically representative model may result. In the event that 

lightning sub-seasons cannot be identified, the 45 WS requested that the AMU create monthly 

equations with six more years of data than used in Phase II. 

6 
2. Data 

The AMU collected all the data needed for this task for the period of record (POR) MayOctober 1989-2011. This includes the NLDN flash data in an area covering central Florida, the 

CCAFS 1000 UTC soundings (XMR), and the 1200 UTC soundings from Jacksonville (JAX), 

Tampa (TBW) and Miami (MFL), Fla. The AMU also collected NLDN data for April and 

November in the POR to observe lightning behavior before and after the defined lightning 

season of May-October. The NLDN data were provided by the 14th Weather Squadron (14 WS) 

through Mr. Roeder of the 45 WS and cover the area shown in Figure 1. Mr. Madison of 

Computer Sciences Raytheon (CSR) provided XMR and CGLSS data, and the AMU 

downloaded the National Weather Service (NWS) soundings for JAX, TBW and MFL from the 

National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory 

website (http://www. esrl. noaa.gov/raobs/). 

2.1 Lightning Data 

The AMU wrote scripts in TIBCO Spotfire S+ (TIBCO 201 0) statistical analysis software to 

extract the daily lightning flash counts from the raw NLDN data and export it as Microsoft Excel 

files. In Excel, they used Visual Basic for Applications (VBA) to write scripts to create annual 

charts of the daily NLDN flash count for April-November 1989-2011 . They also developed VBA 

scripts in Excel to export the daily NLDN flash locations to Keyhole Markup Language (KML) for 

display on a map in Google Earth. 

The AMU processed the CGLSS data to create the predictand needed for equation 

development. The predictand is binary and indicates whether lightning occurred within any of 

the 5 NM warning circles on KSC/CCAFS on each day. The AMU also used the CGLSS data to 

create the daily climatology and one-day persistence, two of the candidate predictors for the 

equations. The daily climatology is the percent of days lightning occurred on each date in the 

warm season POR. One-day persistence is binary like the predictand, and indicates whether 

lightning occurred on the previous day. 

7 
Figure 1. Map of central Florida showing areal coverage (red square) of the NLDN flash 

count data set. The yellow line is the boundary between east- and west-central Florida. 

2.2 Sounding Data 

The AMU quality-controlled the XMR sounding data, then determined the flow regimes on 

each day in the 2010 and 2011 warm seasons and October 1989-2009 using the process 

described in Lambert (2007). They also calculated the stability indices from the XMR soundings 

needed as predictors for equation development. The 1989-2009 May-September values were 

created in earlier AMU tasks. 

8 
3. Lightning Season 

The approach the AMU used to determine the start of the lightning season consisted of 

analyzing the NLDN daily flash count charts to determine if these data correlated with known 

NWS Melbourne (NWS MLB) east-central Florida wet season start dates (Lascody 2002). If a 

correlation between the NLDN start/ramp-up of the lightning season could be established with 

the wet-season start dates, the AMU would continue to look for correlations between the NLDN 

data and other lightning sub-seasons: lightning (plateau), ramp-down, and post. The subseasons would be determined by the increase, plateau, and decrease in the number of flashes 

on each day, not just whether or not lightning occurred. They would then develop logistic 

regression equations stratified by lightning sub-season to predict the probability of lightning for 

each day. If a correlation could not be established with the wet season start dates, the 45 WS 

requested that equations be created for each month as in previous work. 

3.1 Lightning Season Start 

The chart in Figure 2 shows the daily NLDN lightning flash count across central Florida for 

April-November 1989. The light blue line shows the number of flashes observed each day while 

the dark blue line is the 14-day moving average of the daily flashes. The vertical orange and 

green lines show the 1989 NWS MLB wet season start and end dates for Orlando (MCO) and 

Melbourne (MLB), respectively. The 1989 wet season start date was 28 May for MCO and 6 

June for MLB. The chart shows an increase in the daily flash count beginning in April and 

peaking in July. It would appear that the start of the lightning season across central Florida 

preceded the start of the wet season at MCO and MLB. This is possible since the AMU was 

Daily Lightning Flash Count- 1989 

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Apr May Jun Jul Aug Sep Oct Nov 

Figure 2. Daily NLDN lightning flash count across central Florida for April-November 1989. 

The light blue line shows the number of flashes observed each day while the dark blue line is 

the 14-day moving average of the daily flashes. The vertical orange and green lines show the 

1989 NWS MLB wet season start and end dates for Orlando (MCO) and Melbourne (MLB), 

respectively. 

9 
attempting to define the lightning season across central Florida (see Figure 1 ), not just at MLB 

and MCO. Most other years in the POR exhibited similar behavior in which the daily NLDN flash 

count start and ramp-up occurred prior to the MLB and MCO wet season start dates. 

After discussing this finding with Mr. Roeder of the 45 WS, the AMU recommended 

considering two additional stratifications to try and account for the difference: (1) limit the areal 

coverage of the NLDN data to east-central Florida and (2) eliminate any lightning days due to 

influences from synoptic weather patterns such as cold fronts that will sometimes penetrate into 

central Florida as late as May. The first option would determine if the technique chose dates 

consistent with the NWS MLB start dates for MLB and MCO. If so, it could be used to cover all 

of central Florida. The second option is consistent with the formation of lightning in the warm 

season being dominated by the interaction of low level boundaries such as the sea breeze 

fronts, river breeze fronts, convective outflows, horizontal convective rolls and others. 

Forecasting lightning early in the season when weak cold fronts play a role is easier for 

forecasters. 

The AMU developed a VBA script to convert Excel NLDN flash data into KML format to 

display each flash on a map of central Florida in Google Earth. They were then able to visually 

inspect the NLDN strike locations occurring on each day from 0000-2359 UTC. The map in 

Figure 3 shows an example of this display for 5 April 1989. By reviewing similar NLDN maps for 

April and May of all years in the POR, the AMU discovered it was not uncommon for lightning to 

occur early in the warm season as in Figure 3, and not be confined to the east or west half of 

the state as is more typical under prevailing westerly or easterly flow warm season regimes. 

However, as the warm season progressed, there were clear divisions between east and 

west coast daily NLDN events dependent on the flow regime. Therefore, the AMU reduced the 

NLDN data set to flashes only occurring in east-central Florida, which covered the eastern half 

of the red square in Figure 3. However, further analysis revealed that reducing the NLDN events 

to east-central Florida did not explain the non-correlation of the beginning of lightning flash 

count ramp-up with the NWS MLB wet season start dates. 

10 
Figure 3. The NLDN flashes across central Florida for 0000-2359 UTC 5 April 1989. Each 

yellow dot represents one cloud-to-ground lightning flash observed by NLDN. On this day there 

were 1 ,079 flashes across central Florida within the area bounded by the red lines. 

To account for synoptic weather pattern influences other than the warm season peninsular 

flow regimes, such as low pressure systems and fronts, the AMU reviewed all April and May 

daily weather maps (NOAA 2011) for days with NLDN flashes in central Florida to find these 

events and eliminate them from the data set. As an example of a synoptic weather system 

influence, the daily weather maps for 7:00 AM EST (1200 UTC) on 5 and 6 April 1989 are 

shown in Figure 4. On 5 April, a cold front was draped across the southeast United States and a 

pre-frontal squall line was located in north Florida. By 7:00 AM EST on 6 April, the cold front 

was located just south of central Florida. It is highly likely that this cold front and/or its 

associated pre-frontal squall line was responsible for the NLDN flashes shown in Figure 3. 

Therefore, the NLDN flashes on 5 April were eliminated from the data set. This methodology 

eliminated most NLDN flash days in April and many in May throughout the POR resulting in a 

better correlation between the NLDN-based lightning season and NWS MLB wet season start 

dates. 

11 
Figure 4. The NOAA daily weather maps (NOAA 2011) from (a) 5 April 1989 and (b) 6 April 

1989 showing surface synoptic-scale weather systems. 

The daily weather maps did not always show lows or frontal systems in the area on days 

with lightning in April and May, so the AMU could not conclude that these systems were the 

cause of the lightning. They did not eliminate these days from the data set. However, some of 

these fronts may have been weak and not be fully depicted in the NOAA daily weather maps. 

Since not all days with NLDN flashes could be eliminated based on synoptic patterns, 

determining the start/ramp-up of each lightning season in the POR still had some level of 

subjectivity. Figure 5 shows the same time period as Figure 2 except it includes only NLDN 

flashes from east-central Florida with the synoptic weather system influenced flashes removed. 

Hence, the 1989 east-central Florida lightning season started within a week of the NWS MLB 

wet season start at MCO. 

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Daily Lightning Flash Count- 1989 

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Apr May Jun Jul Aug Sep Oct Nov 

Figure 5. As in Figure 2, but for the east-central Florida region and with NLDN flashes due to 

synoptic frontal systems removed. 

12 
Over the entire POR, the median NLDN flash count ramp-up started six days prior to the 

mean MCO/MLB wet season start dates. The earliest NLDN start/ramp-up was 21 days prior to 

wet season start (1999) and the latest was 3 days after (1996 and 2007). In 65 percent of the 

years (15 of 23) the start dates were within 7 days of each other. Lascody (2002) notes that in 

determining the start of the wet season, "It must be stated that a purely objective analysis is not 

possible since the exact onset of the Wet Season is difficult to determine in some years." Given 

the subjectivity in determining both the wet season start and NLDN start/ramp-up and the data 

presented, it appears there is a correlation between the wet season method and the NLDN 

method. Also, as noted previously, the start date for central Florida could be earlier than for 

MLB and MCO. 

3.2 Lightning Sub-season Stratifications 

With a correlation established between the NLDN flash count start/ramp-up dates and the 

NWS MLB wet-season start dates, the AMU looked for correlations between the NLDN data and 

other proposed lightning sub-seasons: lightning (plateau), ramp-down , and post. Just as 

determining the start of the lightning season using the NLDN data was subjective, so was 

determining sub-seasons. In examination of the annual charts of daily flash count, the AMU 

observed there were often multiple sub-seasons of the same type. For example, in Figure 6, it 

appears there were two ramp-up sub-seasons in 1999 followed by a relatively consistent flash 

count (plateau) from mid-June through early September, a ramp-down sub-season from midSeptember to mid-October, and then a post lightning sub-season. In a second example from 

2004 (Figure 7), after the ramp-up there were two distinct consistently high lightning flash count 

periods (plateaus) with a lull in lightning flash count from mid-July through early August followed 

by a ramp-down sub-season and post lightning sub-season. 

Daily Lightning Flash Count- 1999 

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Figure 6. As in Figure 5, but for 1999 and with the lightning sub-seasons highlighted by red 

brackets and text describing each. 

13 
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Figure 7. 

Daily Lightning Flash Count- 2004 

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As in Figure 6, but for 2004. 

A question that surfaced while assessing the sub-season stratifications was how the 

operational forecasters would be able to declare the lightning season start and the sub-seasons 

in real-time in order to know which equation to use in the tool. Since it was subjective and 

somewhat difficult to make those determinations using climatological data, the AMU consulted 

with Mr. Roeder on how to address this issue. They decided to present the findings to the 

45 WS personnel who would use the tool. The discussion led to a consensus that it would be 

difficult for the forecasters to determine the lightning sub-seasons in real-time using NLDN data. 

Determining lightning sub-seasons to stratify the data more on the basis of physical processes 

than calendar dates is desirable, but the AMU showed how difficult it can be. Therefore, with 

agreement from Mr. Roeder, the AMU proceeded with this task using monthly stratifications to 

develop the new equations. 

14 
4. Equation Development 

The AMU developed and tested six new lightning probability forecast equations, one for 

each month May-October, using a development dataset consisting of 19 warm seasons (83% of 

total seasons available) and a verification dataset consisting of 4 warm seasons ( 17% of total). 

The verification and development datasets did not consist of individual warm season years, but 

rather individual warm season days chosen through a random process described in Lambert 

and Wheeler (2005). They also followed the same iterative procedure for choosing the 

predictors in the development dataset as outlined in Lambert and Wheeler (2005). The 

verification data set was not used in developing the equations so it would provide an 

independent assessment of the expected performance when the equations are used with new 

data in operational forecasting. 

4.1 Predictors 

The AMU used 12 stability and moisture parameters from the 1000 UTC XMR soundings as 

candidate predictors in the equation development: 

• Total Totals, 

• Cross Totals, 

• Vertical Totals (VT), 

• K-lndex (KI), 

• Lifted Index (LI), 

• Thompson Index (TI; Kl- Ll), 

• Severe Weather Threat Index, 

• Showalter Index, 

• Temperature at 500 mb, 

• Mean Relative Humidity in the 825-525 mb layer, 

• Mean Relative Humidity in the 825-600 mb layer,and 

• Precipitable Water up to 500 mb (PW). 

These were added to the three predictors shown below, described in the AMU Quarterly Report 

Q4 FY06 and updated using warm season data in the years 1989-2011, for a total of 15 

candidate predictors: 

• Daily climatological lightning frequency (Clime), 

• 1-day persistence (Pers), and 

• Flow regime lightning probability (FRProb). 

4.2 New Equations 

The procedure to develop the new equations involved adding one predictor at a time and 

checking the associated reduction in residual deviance. A large reduction in residual deviance 

meant that a predictor accounted for a large percentage of the variance in the predictand. 

Therefore, the AMU chose the predictors that effected the largest reduction. They stopped 

adding predictors as soon as a candidate predictor accounted for < 0.5% of the reduction in 

residual deviance. This ensured predictor variables were selected in the optimal order and 

avoided statistical over-fitting of the prediction equations. 

Figure 8 shows the percent reduction in residual deviance from the NULL model as each 

predictor was added for the May equation. Tl reduced the residual deviance the most (14.31 %) 

15 
and was, therefore, the first predictor in the May equation. The second predictor was Pers, 

which accounted for an additional 6.14% reduction. FRProb was the third predictor, reducing the 

residual deviance by 4.42%. For May, Clime reduced the residual deviance by 0.26%, therefore, 

Cl ime was not chosen. The May equation consists of the predictors Tl , Pers and FRProb. 

cu 

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Reduction in Residual Deviance by Predictor 

May 1989-2011 

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10% 

15% 

20% 

25% 

30% 

Null 

24.87% 25.13% 

Tl Pers FRProb Climo 

Predictor 

Figure 8. The total percent reduction in residual deviance from that of the NULL model as 

each predictor was added to the equation using the May development dataset. 

Table 1 shows the final predictors for each of the monthly equations in rank order of their 

reduction in residual deviance. FRProb was the only predictor in all six months. The two 

strongest predictors in June, July, August and September were Tl and FRProb, varying between 

first and second. FRProb and Tl were also strong predictors in May but Pers was more 

important than FRProb. October is the outlier with Lifted Index and Mean Relative Humidity in 

the 825-600 mb layer as the top two predictors. 

Table 1. The final predictors for each monthly equation, in rank order of their contribution to the 

reduction in residual deviance. The predictors in red were in every equation, the predictors in 

blue were in five of the six equations, the predictors in green were in four of the six equations, 

the predictors in orange were in three of the six equations and the predictors in black were in 

two or less equations. 

May June July August September October 

Thompson Thompson Flow Regime Flow Regime Thompson Lifted Index 

Persistence Flow Regime Thompson Thompson Flow Regime 825-600 RH 

Flow Regime 825-525 RH Total Totals 825-525 RH Persistence Flow Regime 

Persistence Persistence Total Totals Total Totals 

Daily Clime Daily Clime 

16 
For comparison with the results in this task, Table 2 shows the final predictors for each of 

the Phase II monthly equations currently used in operations in rank order of their reduction in 

residual deviance. Predictors for October were not assessed in Phase II. 

Table 2. The final predictors for each monthly equation from Phase II , in rank order of their 

reduction in residual deviance. The predictors in red were in every equation, the predictors in 

blue were in four of the five equations, the predictors in green were in three of the five 

equations, and the predictors in black were in only one equation. 

May June July August September October 

K-lndex Thompson Thompson Thompson 825-525 RH N/A 

Flow Regime Flow Regime Flow Regime Flow Regime Flow Regime 

Vertical Totals Persistence Total Totals Daily Clime Persistence 

Daily Clime Vertical Totals Persistence 825-525 RH Vertical Tot 

Persistence 825-525 RH Vertical Totals Daily Clime 

4.3 Equation Testing 

The AMU tested the performance of the equations using the verification dataset, which 

consisted of four warm seasons. None of the days in the verification set were contained in the 

development set to allow for an independent evaluation of performance. The first step was to 

determine if the new equations showed improvement in skill over five simple forecast 

benchmarks. Four of the benchmarks were the same as those in the Phase I task: Pers, Clime, 

FRProb, and monthly climatology, each evaluated as a separate single predictor. The fifth was 

the forecasts from the equations developed in Phase II of this work (Lambert 2007) and 

currently used in operations, hereafter designated as the operational equations. 

The skill test began by first calculating the mean squared error (MSE) between the forecasts 

and observations for all forecast methods. The MSE was calculated using the equation 

J n 

MSE =-I (P; - o;) 2 (Wilks 2006), n i = l 

where n is the number of forecast/observation pairs, Pi is the probability associated with the 

forecast method, and oi is the corresponding binary lightning observation (Wilks 2006). Then the 

skill of the new equations was calculated over the five forecast benchmarks using the equation 

for the Brier Skill Score (SS): 

SS = eqn ref * 100 (Wilks 2006), ( 

MSE -MSE l 

MSEperfect - MSEref 

where MSEeqn was the MSE of the new equations, MSEret was the forecast benchmark against 

which the new equations were tested, and MSEpertect was the MSE of a perfect forecast, which is 

always 0. The SS represents a percent improvement or degradation in skill of the equation over 

the reference forecast when it is positive or negative, respectively. 

The SS values for each of the monthly equations are shown in Table 3. The predictors in the 

equations used to calculate the skill scores in Table 3 produced the best results with the 

verification dataset and were chosen using the method described in the previous section. The 

new equations show a double-digit improvement in skill for the first four benchmarks in the table 

except for October daily climatology. The results for the operational equations also showed 

double-digit improvement over these four benchmarks (Lambert 2007). The new equations 

show degradation in skill compared to the operational equations for May, July, August and 

September. The values of -12% for May and -19% for September are a significant reduction in 

17 
performance. The values of +3% for June, -2% for July and -1% for August are almost 

negligible, show similar skill between the new and operational equations for these months, and 

are likely not statistically significantly different. There is no value for October because there is 

not a current operational equation for October. The AMU created and tested equations with 

varying sets of predictors for the four months with a degradation in skill in an attempt to improve 

the skill of the new equations, but none was realized. The AMU discussed the results with Mr. 

Roeder of the 45 WS and all agreed not to transition the new May-September equations into 

operations, but keep the current operational equations in place and add the equation for 

October. 

Table 3. The percent(%) improvement (degradation) in skill of the new equations over the 

reference forecasts of persistence, daily and monthly climatologies, flow regime probabilities, 

and the operational equations developed in Lambert (2007). These scores were calculated 

using the verification data for each month. 

Forecast Method May June July August September October 

Persistence 52 48 48 59 37 37 

Daily Climatology 31 16 10 17 14 7 

Monthly Climatology 36 37 13 35 24 16 

Flow Regime 34 35 12 34 12 16 

Operational Equations ( -12) 3 ( -2) ( -1) ( -19) N/A 

18 
5. MIDDS GUI 

The AMU updated the MIDDS GUI developed in Phase II (Lambert 2007) with the October 

equation and delivered it to the 45 WS after testing to ensure proper performance. They 

modified the existing GUI using the Tool Command Language (Tci)/Toolkit (Tk) capability in 

MIDDS. 

5.1 Testing 

The AMU ran the Objective Lightning tool for two weeks prior to the warm season start to 

make sure the parameters derived from the morning sounding were correctly displayed in the 

October tab of the GUI. Then at the beginning of the 2012 warm season, the AMU staff began 

running the Objective Lightning tool and the AMU-developed Severe Weather Forecast tools 

daily and discovered several errors in the Tel/ Tk code for the GUI in both tools. 

5.1.1 Vertical Totals 

The normal range of the VT stability parameter is 20-35 but the Objective Lightning tool was 

intermittently displaying a value of 0. The AMU and Mr. Madison of CSR began troubleshooting 

the Objective Lightning tool Tcl/tk GUI code and tracked down the error to a file that was 

common between this and the Severe Weather Forecast tool. Both tools were creating a file 

with the same name to read in the date and time. However, the output format of the file being 

created was not the same. Once the file was created on any given day, it would not be 

overwritten. Therefore, if the Severe Weather Forecast tool was run first, the Objective Lightning 

Tool would input the date and time incorrectly causing the VT to be 0. To solve this dilemma, 

Mr. Madison updated the code in both tools such that each one created a different filename for 

the date and time. He then moved the updated Tci/Tk GUI code from the AMU MIDDS to the 

operational MIDDS. 

5.1.2 Lifted Index 

The only month that directly outputs Ll in the Objective Lightning tool is October, although 

other months use Ll to calculate Tl. Tl is calculated by subtracting Ll from Kl. While running the 

Objective Lightning tool, the AMU noticed the Tl value was always 0 while the Tl value in the 

sounding was not 0. The error was in the Objective Lightning tool Tci/Tk code that called the Kl 

instead of Ll to compute Tl. Therefore, Tl was being calculated by the difference of Kl and Kl, 

resulting in a value of 0. The AMU updated the code by changing Kl to Ll, which solved the 

problem. Mr. Madison again moved the updated Objective Lightning tool code to the operational 

MIDDS. 

5.1.3 Relative Humidity 

The AMU noticed that the relative humidity (RH) value was the same in the Objective 

Lightning tool and the Severe Weather Forecast tool. This is unlikely because the Objective 

Lightning tool calculates the layer-averaged RH in the vertical layer from 825-525 mb and the 

Severe Weather Forecast tool calculates layer-averaged RH in the vertical layer surface-700 

mb. Upon review of the Tci/Tk code, they found that both tools retrieved their respective layeraveraged RH values from the sounding and then wrote out a file containing the RH to be used 

later in each program. The error in the code was that both tools called the same file containing 

the layer-averaged RH but the file was only generated once on any given day. Therefore, the 

tool that was run first created the file that both tools then used for their RH values. They 

changed the name of the output file in the Objective Lightning tool code, which solved the 

19 
problem. After further testing, Mr. Madison moved the updated Objective Lightning tool code to 

the operational MIDDS. 

5.2 GUI Update 

The user accesses the GUI through the MIDDS Weather menu by clicking on the 'FCST 

Tools' button and choosing 'Lightning Forecast Tool ' from the drop-down list (Figure 9). This 

activates the GUI Tci!Tk code to determine the date and gather the appropriate data for the 

equation from MIDDS. The code checks the time and date of the most recent CCAFS sounding 

(XMR). If it does not match the current day and is not within the time period 0900-1159 UTC, an 

error message dialog box is displayed. This ensures that data from the previous day and data 

from sounding times other than 1000 UTC are not used in the equations. The 0900-1159 UTC 

period allows for the fact that not all 1000 UTC soundings are released precisely at 1000 UTC. 

fCST Tools 

__ __. B WJ Fog 

Ref"resh 1 ~ "" Drawing Tool 

- Labeling Tool 

Figure 9. The MIDDS Weather Menu showing the 'FCST Tools' button drop-down menu with 

'Lightning Forecast Tool ' highlighted. 

Whether or not the 1000 UTC XMR sounding for the current date is available, the equation 

predictor dialog box is displayed (Figure 1 0). This will allow the forecasters to use the GUI to 

create their seven-day forecasts even if data for the current day are not available. The dialog 

box has six tabs, one for each month. The tab of the current month is displayed initially if the 

GUI is run between May and October, otherwise 1 May is displayed. The current month, day 

and sounding time are printed along the top of the dialog box. If the current day's sounding is 

not available, 'No Current Sounding' will be displayed in place of the date and time in the upper 

right. The day value can be changed by the up/down arrows or by entering a value manually in 

the text box. This allows forecasters flexibility when making the seven-day Weekly Planning 

Forecast. The sounding date and time is formatted by year, day of year, and UTC time. 

Forecasters begin by choosing a flow regime. They do not have to enter the sounding 

parameters as those values are already input by the GUI code and are displayed in their 

associated text boxes. If there is not a current sounding, the text boxes will be populated with 

the values from the most recent sounding available. The 'No Current Sounding' message in the 

top right corner will inform the forecaster that this is the case. If the routines cannot find a 

sounding file of any kind, the text boxes will be populated with the extreme low value in the 

range of available values for each sounding parameter. 

The final step is to click on the 'Calculate Probability' button in the lower right corner of the 

dialog box. The 'Dismiss' button in the lower left closes the GUI. If the forecaster does not 

choose a flow regime, an error message dialog box is displayed telling the forecaster to make a 

choice. 

20 
MAY 1 JUNE 1 JULY 1 AUGUST 1 SEPTEMBER 1 OCTOBER 

Current Day: Oct I 1 ~ Sounding: 2012073 10:15 

11 Row Reglme----------------------r 

i, SW: Low-leVel (1000-700 mb) ridge south of XMR 

(SWl and swz regimes combined) 

v SE-1: Low-level ridge between TOW and JAX 

v SE-Z: Low-level ridge Nor1h of JAX 

v Uniform NW flow across the peninsula 

v Uniform NE flow across the peninsula 

v Other: None of the Above 

[

Total Totals (TT) 

1

37 ~ Enter the Total Totals from this morning's 10 UTC 

~ ><tv1R SOll1ding 

[

Ufted Index (ll) 

1

14 6 ~ Enter the Lifted Index from this morning's 10 UTC 

• ~ ><tv1R SOll1ding 

Enter the average 825 - 525 mb layer relative hLrnidity from 

[

Average 6Z5- 5Z5 mb RH 

139 ~ th.is mo~ning's 1 0 UTC »AR soll1ding yol.l'lded integer value 

w1thoLt %; e.g. enter 65.~/o as 65, 65.7% as 66) 

Dismiss I I Reset 

Pwameters I I C*ulate 

Probability 

-

I 

Figure 10. The predictor dialog box for October. A tab for each month is 

at the top, followed by the date and sounding time, then the predictor 

values. The 'Dismiss' button closes the GUI, the 'Reset Parameters' 

button resets the sounding parameters to the original values, and the 

'Calculate Probability' button displays the probability output dialog box 

(Figure 11). 

When the user clicks the 'Calculate Probability' button in the equation predictor dialog box, 

the probability of lightning occurrence for the day is displayed in a dialog box (Figure 11 ). The 

GUI code also outputs a file that contains all of the parameter values input by the user to 

calculate the probability. This file is currently named LtgProb.txt and resides in the MIDDS data 

directory. 

21 
The probability of lightning being 

observed in at least one of the 

KSC/CCAFS advisory circles on 

Oct 1, from 0700 - 2400 EDT is: 

5 o/o 

Figure 11 . The output 

~ dialog box showing the 

probability of lightning occurrence for the day as 

calculated by the equation. The 'OK' button closes 

the box. 

22 
6. Summary 

The AMU created new logistic regression equations in an effort to increase the skill of the 

Objective Lightning Forecast Tool developed in Phase II (Lambert 2007). The 45 WS requested 

the AMU make another attempt to stratify the data by lightning sub-season, add three warm 

seasons to expand the (POR) to 23 years (1989-2011) and include data from October. The 

AMU did this by using lightning observations across central Florida from the NLDN instead of 

the CGLSS data used in Phase Ill. In the event that lightning sub-seasons could not be 

identified, the 45 WS requested that the AMU create monthly equations with six more years of 

data than used in Phase II. 

Although the AMU was able to establish a correlation between the NLDN flash count 

start/ramp-up dates and the wet-season start dates used by the NWS MLB by removing days 

with NLDN flashes that were due to large-scale weather patterns, not all days with NLDN 

flashes could be eliminated based on these patterns. Determining the start/ramp-up of each 

lightning season in the POR still had some level of subjectivity. With the start/ramp-up 

correlation established, the AMU then looked for correlations between the NLDN data and other 

proposed lightning sub-seasons: lightning (plateau), ramp-down, and post. Just as determining 

the start of the lightning season using the NLDN data was subjective, so was determining subseasons. After the AMU presented these findings to the 45 WS personnel who would use the 

tool, all agreed it would be difficult for the forecasters to determine the lightning sub-seasons in 

real-time using NLDN data. Therefore, the AMU completed with this task using monthly 

stratifications to develop the new equations. 

The procedures used to create the predictors and develop the equations were identical to 

those in Phase II. The equations were made up of one to five predictors. Flow regime probability 

was the only predictor in all six months. The two strongest predictors in June, July, August and 

September were Tl and flow regime probability, varying between first and second most 

important. Flow regime probability and Tl were also strong predictors in May but persistence 

was more important than flow regime probability. The performance of the new equations was 

compared to that of five other forecast methods including the Phase II equations being used in 

operations. The new equations outperformed four other forecast methods by 7-59% using the 

verification dataset, but the new equations were outperformed by the Phase II equations in May, 

July, August and September. The new equations outperformed the Phase II equations in June 

by only 3%. Since there were no previous equations for October, no comparison could be made. 

Based on these results, the Phase IV equations did not replace the Phase II equations in 

operations. 

The AMU updated the MIDDS tool by adding the October equations and updating the GUI. 

Through extensive testing, the AMU discovered three errors caused by interactions between the 

Objective Lightning Probability tool and Severe Weather Forecast tool. The errors were fixed 

and the updated tool was implemented on the operations MIDDS. 

The AMU did not conduct formal training since the only change to the GUI was the addition 

of October equations. The discovery of the errors and subsequent fixes were communicated to 

the 45 WS by the AMU. 

23 
References 

Crawford, W. , 2010: Objective Lightning Probability Forecasting for Kennedy Space Center and 

Cape Canaveral Air Force Station, Phase Ill. NASA Contractor Report CR-201 0-216292, 

Kennedy Space Center, FL, 34 pp. [Available from ENSCO, Inc., 1980 N. Atlantic Ave. , 

Suite 830, Cocoa Beach, FL, 32931 , and http://science.ksc.nasa.gov/amu/finalreports/objective-ltg-fcst -phase3. pdf.] 

Lambert, W. and M. Wheeler, 2005: Objective lightning probability forecasting for Kennedy 

Space Center and Cape Canaveral Air Force Station. NASA Contractor Report CR2005-212564, Kennedy Space Center, FL, 54 pp. [Available from ENSCO, Inc. , 1980 N. 

Atlantic Ave., Suite 830, Cocoa Beach, FL, 32931 , and 

http://science. ksc. nasa.gov/amu/final-stfi. html] 

Lambert, W., 2007: Objective lightning probability forecasting for Kennedy Space Center and 

Cape Canaveral Air Force Station, Phase II. NASA Contractor Report CR-2007-214732, 

Kennedy Space Center, FL, 59 pp. [Available from ENSCO, Inc., 1980 N. Atlantic Ave., 

Suite 830, Cocoa Beach, FL, 32931 , and http://science.ksc.nasa.gov/amu/final-stfi.html] 

Lascody, R. , 2002: The Onset of the Wet and Dry Seasons in East Central Florida - A 

Subtropical Wet-Dry Climate?, http://www.srh.noaa.gov/mlb/?n=wetdryseason. 

NOAA, 2011 : NOAA Central Library U.S. Daily Weather Maps Project, 

http://docs.lib. noaa.gov/rescue/dwm/data rescue daily weather maps. html. 

TIBCO, 2010: TIBCO Spotfire S+® 8.2 Programmer's Guide, TIBCO Software Inc., Seattle, WA, 

532 pp. 

Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2d ed. Academic Press, 

Inc., San Diego, CA, 467 pp. 

24 
List of Acronyms 

14WS 14th Weather Squadron MLB Melbourne, Fla. 3-letter 

identifier 

45WS 45th Weather Squadron MSE Mean Squared Error 

AMU Applied Meteorology Unit 

CCAFS Cape Canaveral Air Force NLDN National Lightning Detection 

Station Network 

CGLSS Cloud-to-Ground Lightning NOAA National Oceanic and 

Surveillance System Atmospheric Administration 

CSR Computer Sciences Raytheon NWS MLB National Weather Service 

Melbourne, Fla. 

GUI Graphical User Interface POR Period of Record 

JAX Jacksonville, FL 3-letter 

identifier PW Precipitable Water 

Kl K-lndex RH Relative Humidity 

KML Keyhole Markup Language ss Skill Score 

KSC Kennedy Space Center TBW Tampa, FL 3-letter identifier 

LCC Launch Commit Criteria Tci!Tk Tool Command 

Language!T oolkit 

Ll Lifted Index Tl Thompson Index 

MCO Orlando International Airport, 

Fla. 3-letter identifier VBA Visual Basic for Applications 

MFL Miami, Fla. 3-letter identifier VT Vertical Totals 

MIDDS Meteorological Interactive Data XMR CCAFS rawinsonde 3-letter 

Display System identifier 

25 
.. 

NOTICE 

Mention of a copyrighted, trademarked or proprietary product, service, or document does not 

constitute endorsement thereof by the author, ENSCO Inc., the AMU, the National Aeronautics 

and Space Administration, or the United States Government. Any such mention is solely for the 

purpose of fully informing the reader of the resources used to conduct the work reported herein. 

26