NASA/CR-2013-217921 

Objective Lightning Probability Forecasts 

for East-Central Florida Airports 

Winifred C. Crawford 

ENSCO, Inc., Cocoa Beach, Florida 

Kennedy Space Center, Florida 

May 2013 
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NASA/CR-2013-217921 

Objective Lightning Probability Forecasts 

for East-Central Florida Airports 

Winifred C. Crawford 

ENSCO, Inc., Cocoa Beach, Florida 

Kennedy Space Center, Florida 

National Aeronautics and 

Space Administration 

Kennedy Space Center 

Kennedy Space Center, FL 32899-0001 

May 2013 
Acknowledgments 

The author thanks Mr. Matthew Volkmer and Mr. David Sharp of the National Weather 

Service in Melbourne, FL for their assistance in determining the thresholds for the speedstratified flow regimes, and their input on the design of the graphical user interface used to 

display the lightning occurrence probabilities. The AMU team also played a significant role in the 

success of this task by providing algorithms for and testing the GUI to ensure it contained no 

errors. 

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 forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a 

need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in 

the vicinity of several commercial airports in central Florida at which they are responsible for 

issuing terminal aerodrome forecasts. Such forecasts would provide safer ground operations 

around terminals, and would be of value to Center Weather Service Units serving air traffic 

controllers in Florida. To improve the forecast, the AMU was tasked to develop an objective 

lightning probability forecast tool for the airports. The resulting forecast tool is similar to that 

developed by the AMU to support space launch operations in previous tasks. 

The tool includes a set of equations that forecast the probability of lightning occurrence in 

the warm season months, defined as May-September, within 1 0 NM an airfield's center and 

within four three-hour increments: 1500-1800, 1800-2100, 2100-0000, and 0000-0300 UTC. 

These are the times when lightning is most likely to occur in east-central Florida. The airfields 

are Orlando International Airport, Melbourne International Airport, and Space Coast Regional 

Airport. The 10 NM radius and three-hour time increments reflect the forecast requirements at 

the airfields for which NWS MLB has forecast responsibility. 

The period of record (POR) for this task was the warm season months in the 17 years 1995-

2011. The data sources included the National Lightning Detection Network (NLDN) flash data in 

an area covering central Florida, 1000 UTC CCAFS soundings (XMR}, and 1200 UTC 

Jacksonville (JAX}, Tampa (TBW) and Miami (MFL}, FL, soundings. Data from NLDN were used 

to determine lightning occurrence. The 1200 UTC JAX, TBW, and MFL soundings were used to 

calculate the daily flow regimes, and the 1000 UTC XMR soundings were used to calculate the 

standard stability parameters and to help determine the flow regime on each day. The AMU 

processed the datasets to create the predictand and candidate predictors needed for the 

statistical forecast equation development. The predictand is the element to be predicted from a 

predictor or group of predictors. 

Before developing the equations, the AMU stratified the data into development and 

verification datasets. The AMU then developed a set of four equations, one for each time period, 

for each warm season month and each airfield. The performance of the equations was 

assessed using a technique appropriate for probability forecasts. Although the AMU tested, 

refined, re-created, and further tested the equations, their performance was mixed with a slight 

overall improvement over other forecast methods. The AMU concluded that stratifying the data 

into the shorter time periods reduced the number of lightning occurrences in each time period 

such that robust statistical relationships could not be realized. Nonetheless, NWS MLB 

requested that the equation output be provided through a graphical user interface (GUI) along 

with the other climatological values. They are still interested in seeing the equation output since 

it will be calculated using parameters from the current sounding and may still provide added 

value to the forecast. 

The forecasters interact with the equations through a Microsoft® Excei~C GUI. The GUI 

accesses data in specific worksheets based on user input, then outputs the equation, the daily 

climatology, and flow regime lightning probabilities for the forecasters to compare. NWS MLB 

was involved in the GUI development by providing comments and suggestions on the design to 

ensure that the final product addressed their operational needs. 

Future work should involve collecting more data over a longer POR, developing equations 

for more airfields, and conducting other verification tests of the equations to determine all 

aspects of their performance. This will create equations that provide a good estimate of lightning 

occurrence at the airfields in the time periods of interest and give the forecasters a good first 

guess from which to build a forecast from other data sources. 

1 
Table of Contents 

Executive Summary .. ....................... ...................................... ......................... ......... .................. 1 

Table of Contents .............................................................. ... .................. .. ........... ....................... 2 

List of Figures ........................... ............ ....... .. ........... ................................................................ . 3 

List of T abies ................................................................ ............................................................. 4 

1 Introduction ................................................. ........................................................................ 5 

1.1 Previous Tasks ..... .. .................... ...... ................... ............................................ .. .......... 5 

1.1.1 NWS MLB .............. .. ............................. .................................................... ............ 5 

1.1.2 45WS ........................................................................................... ...... .................. 5 

1.2 Current Work ...................................... .. .. ....... ........................................................... .. . 6 

2 Data ..................................... ............................................. ....... .......................... .. ..... .......... 7 

2.1 NLDN .... ....................... .. .... .... ................ ...... ........................ ............. ..................... ...... 7 

2.2 Florida 1200 UTC Rawinsondes .................................................... .. ....................... ..... 8 

2.3 XMR 1000 UTC Rawinsonde ..................................................... ...... ............................ 8 

3 Creation of Equation Predictand and Predictors .................................................................. 9 

3.1 Binary Predictand ........... ................................................ ......... .............................. ...... 9 

3.2 Candidate Predictors ....... ................ ............................................................................ 9 

3.2.1 NLDN Predictors ....................... .............................. ...... ..... ................................. .. 9 

3.2.2 Flow Regime Probabilities ......... ........ ............................. .... .................................. 11 

3.2.3 Stability Parameters .... .. ..... .. ..... .. ....... ............ ............................. .. ........... .. ...... ... . 14 

3.2.4 Summary of Candidate Predictors ....................................................................... 14 

4 Equation Development and Testing ................................................................................... 14 

4.1 Development and Verification Datasets ... ............................. ............................ ... .. .... . 15 

4.2 Equation Development ........................................................................................... ..... 16 

4.2.1 Development Issues ........................... ............ ... .. .................... ......... ................ ... 16 

4.2.2 Final Equations ............................................. ........... .... ..................................... ... 18 

5 Graphical User Interface ... ........................ ................................... .. .................................... 21 

5.1 Excel Workbook ................................. .. ................................... ........................... ......... 21 

5.2 Initial Input Form ..................... ...... .. .............. .......... .. ..................... .. .. ................. ...... .. 22 

5.3 Predictor Input Forms ............................. ............ ............... ......................................... 24 

5.4 Output Forms ... ... ....................................... .. ....................................... .. ............. ....... .. 28 

6 Summary and Conclusions .................. .............................................................................. 30 

6.1 XMR 1000 UTC Sounding ....................................... ............................................. ....... 30 

6.2 Future Work ............................ ..... .. ......................... .......... .......................................... 30 

References ........................... ... .. ... ................................... ......................... .. ... .. ..................... .... 32 

Acronyms ....................... .. ... .... ...................... .................... .. ...................................................... 34 

2 
Figure 1. 

'\ 

List of Figures . . . : .. i • ~' 1: ,, 

Google Earth image showing the 10 NM radius range rings in yellow for MC01 IVIL ~ .• ,. 

and l iX. The county boundaries and names are in green .......... .... .. : ....... ....... 1 ........ 9 

Figure 2. The NLDN DE corrections for 1994-1998 relative to 1999 values. Th$ grid ll size i$. 

2°X2° latitude/longitude (image created by Vaisala) .......................................... , ... .. :8 

' . Figure 3. The daily raw and Gaussian-smoothed lightning climatology at MCO .for the f~:>'I,Jr . 

three-hour periods 15-1800, 18-2100, 21-0000, and 00-0300 UTC. The time v.alues > 

in the legend indicate the beginning of the time period ................................. ::.: ....... 10 

' 

Figure 4. Same as Figure 3 but for MLB ......................................................... ...................... :: 10 

Figure 5. Same as Figure 3 but for TIX ................................................................... ................ 11 

Figure 6. The flow regime lightning frequencies for MLB in July. Flow. regimes with an· easterfy 

component (NE, SE-1 and SE-2) are in shades of blue and those with a westerly 

component (NW, SW-1 and SW-2) are in shades of pink. The Other regime is in 

green . ................. .................. ........ .. .. .................................. ..................................... 12 

Figure 7. The frequencies of lightning occurrence for the southeast (SE, shades of blue) and 

southwest (SW, shades of pink) flow regimes for the three-speed ranges of light, 

moderate and strong winds ....................................................................... .. .. .. .. ....... 13 

Figure 8. The initial form showing the default values after starting the GUI (left). The user 

makes choices through the drop-down lists in the boxes with down arrows, manually 

inputs a value for speed. and then clicks the "Continue ... " button to get the predictor 

input form. If a speed value is not input and the user clicks the "Continue .. . " button, 

the form on the right is displayed reminding the user to input a speed value ............ 22 

Figure 9. The GUI initial form after the user has made choices from the drop-down lists (left). 

The drop-down list for the time period choices is displayed as an example The wind 

speed value, covered by the drop-down list, is 10 kt. Two buttons in the initial form 

(red rectangles) provide the user definitions of the flow regimes and speed 

categories. Clicking on each button will display the definitions in separate message 

forms (right) ... .. .. .. ................... ............................................................... .. .. .... ........ .. 23 

Figure 10. The predictor input form for the July MCO 2100-0000 UTC equation (left). The user 

inputs values for the predictors, in this case Yes or No for Pers and the PW value in 

inches, and then clicks the "Display Probabilities" button to get the output form. If 

predictor values are not input and the user clicks the "Display Probabilities" button, 

the form on the right is displayed reminding the user to input a predictor value. The 

"New Input" button closes the form and returns control to the initial input form . ....... 24 

Figure 11 . The GUI input form for the predictor values in the July MCO equations for all four 

time periods. The user makes choices in each category and clicks the "Display 

Probabilities" button to open the output form. The "New Input" button closes the form 

and returns control to the initial input form (Figure 8). The "IMPORTANT Tl and Ll 

INFORMATION" button provides information about Ll calculation differences in 

AWIPS and MIDDS .. .. .. .. .. .. ....................................... ..... ................................ .......... 26 

Figure 12. The message form displayed when the "IMPORTANT Tl and LIINFORMATION" 

button is clicked. It is closed by clicking the "OK" button . ................................ .... ..... 27 

3 
Figure 13. The single time period output form for July 2100-0000 UTC at MCO. The input 

choices from Figure 9 (left) are in the left panel and the climatological and computed 

probabilities are in the right panel. The equation probability was calculated from the 

values chosen in Figure 10 (left) with PW=1 .92. The "Choose New Input" button 

closes the form and returns control to the predictor input form. The "Flow Regime 

Definitions" and "Speed Categories and Ranges" buttons open message forms that 

provide definitions of these parameters (see Figure 9) ................................ .. ........ .. 28 

Figure 14. The output form as in Figure 13 but for all four time periods. The input choices from 

Figure 9 are in the left panel and the climatological and computed probabilities for all 

four time periods are in the right panel. The equation probabilities were calculated 

from the values chosen in Figure 11. The "Choose New Input" button closes the form 

and returns control to the predictor input form for the four time periods (Figure 11). 

The "Flow Regime Definitions" and "Speed Categories" buttons open message forms 

that contain definitions of these parameters as in Figure 9 ......................... ... .......... 29 

Figure 15. Forecast probability distributions for lightning (red) and non-lightning (blue) days in 

· the verification data in each of the four time periods for MCO, all months, and for the 

original POR 1989-1995. The y-axis values are the frequency of occurrence of each 

probability value, and the x-axis values are the forecast probability values output by 

the equations . .... .. ................................................... ... .............................................. 31 

List of Tables 

Table 1. List of the flow regime names and the corresponding sectors showing the average 

1000-700 mb wind directions at each of the stations (Lambert 2007) ................... ... 11 

Table 2. The percentage of lightning days in the MCO development and verification data sets 

used to create and test the equations, respectively. The first column is the month, the 

second identifies the data set and the number of days it contains not including days 

with missing data. The last four columns show the percentages of days with lightning 

in each time period .......................................... ........................................................ 17 

Table 3. The number of times each predictor was used in the 20 MCO and 20 MLB equations, 

and the total number of times they were used in both sets of equations .................. 18 

Table 4. The SS values (positive: black; negative: red) for the MCO and MLB equations for 

the forecast benchmarks of DCiimo, FR, FR2, and FR3. The time periods are in 

UTC. Yellow shading indicates values within 10% of 0 ............................................ 20 

4 
1 Introduction 

The forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a 

need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in 

the vicinity of several commercial airports in central Florida at which they are responsible for 

issuing terminal aerodrome forecasts. Such forecasts would also provide safer ground 

operations around terminals, and would be of value to Center Weather Service Units serving air 

traffic controllers in Florida. To improve the forecast, the AMU was tasked to develop an 

objective lightning probability forecast tool for the airports using data from the National Lightning 

Detection Network (NLDN). The resulting forecast tool is similar to that developed by the AMU 

to support space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air 

Force Station (CCAFS) for use by the 45th Weather Squadron (45 WS) in previous tasks 

(Lambert and Wheeler 2005, Lambert 2007). The lightning probability forecasts are valid for the 

time periods and areas needed by the NWS MLB forecasters in the warm season months, 

defined in this task as May-September. 

1.1 Previous Tasks 

The AMU developed lightning forecast tools in previous tasks for use by NWS MLB and the 

45 WS that are in current operational use. The data and results from those tasks formed the 

basis of the work done in this task. 

1.1.1 NWS MLB 

In the first lightning forecasting task for NWS MLB, the AMU created 6-, 12- and 24-hour 

gridded lightning density and frequency climatologies (Lambert et al. 2006) based on synopticscale flow regimes over the Florida peninsula (Lericos et al. , 2002) during the warm season 

months. To supplement these gridded climatologies, the AMU created climatological soundings 

of wind speed and direction, temperature and dew point temperature at Jacksonville (JAX), 

Tampa (TBW), Miami (MFL), and CCAFS for each of the flow regimes (Short 2006). NWS MLB 

then requested to have these soundings available for display in the Advanced Weather 

Interactive Processing System (AWIPS) for overlay onto current soundings (Barrett 2008), 

allowing the forecasters to compare the current state of the atmosphere with climatology. This 

helped them adjust the lightning probability forecasts in their county warning area (CWA). 

As an extension of the first task, the AMU created lightning climatologies based on the flow 

regimes for 5-, 10-, 20-, and 30-NM circles and 1-, 3-, and 6-hour increments for several 

airfields in the CWA (Bauman 2007, 2009). In the most recent task, the AMU added more 

airfields, and included a moisture stratification based on precipitable water and a stability 

stratification based on Thompson Index (TI) (Bauman 2011). This created lightning occurrence 

climatologies for environments from moist and unstable to dry and stable, providing valuable 

insight to how the lightning climatologies can change based on these atmospheric parameters. 

1.1.2 45WS 

The AMU developed an objective lightning probability forecast tool for KSC/CCAFS 

consisting of a set of five equations, one for each warm season month, that calculate 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). These equations showed an 

improvement in performance over other standard forecast methods in use and were transitioned 

to operations. 

5 
The goal of two more tasks (Crawford 2010, Bauman 2012) was to create new 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. Neither task resulted in equations that outperformed those created in 

the earlier tasks. Therefore, the MIDDS tool mentioned previously that has an equation for each 

month is still in operational use, with the addition of an equation for October. 

As an extension of the task done for NWS MLB mentioned in Section 1.1.1, the AMU also 

created lightning climatologies based on synoptic-scale flow regimes for 5-, 10-, 20-, and 30-NM 

circles and 1-, 3-, and 6-hour increments for the Shuttle Landing Facility, CCAFS Skid Strip and 

Patrick Air Force Base (Bauman 2009, 2011 ). The procedures followed for this work were the 

same as for the NWS MLB task. 

1.2 Current Work 

This task combines the warm season equation development done for the 45 WS with the 

area around airfields and time period stratifications done for NWS MLB. Specifically, the 

equations forecast the probability of lightning occurrence within 10 NM the airfield center and 

within four three-hour increments in the time period 1500-0300 UTC (11 00-2300 local time): 

1500-1800, 1800-2100, 2100-0000, and 0000-0300 UTC. These are the times when lightning is 

most likely to occur in east-central Florida. The airfields include the Orlando International Airport 

(MCO), Melbourne International Airport (MLB), and Space Coast Regional Airport (TIX). The 10 

NM radius and three-hour time increments reflect the forecast requirements at the airfields for 

which NWS MLB has forecast responsibility. The airfield locations and 10 NM radius circles are 

shown in Figure 1. The forecasters interact with the equations through a Microsoft® Excel© GUI. 

They input values for the predictors and the GUI outputs the probability of lightning for the 

airfield and time period of interest. 

This report will describe the data used in Section 2, the data analysis in Section 3, equation 

development and testing in Section 4, a description of the GUI in Section 5, and conclusions in 

Section 6. 

6 

Figure 1. Google Earth image showing the 10 NM radius 

range rings in yellow for MCO, MLB, and TIX. The county 

boundaries and names are in green. 
2 Data 

The period of record (POR) for this task is the warm season months of May-September in 

the 17 years 1995-2011 . The data sources include the 

• NLDN flash data in an area covering central Florida, 

• 1000 UTC CCAFS soundings (XMR), and 

• 1200 UTC JAX, TBW and MFL soundings. 

Data from NLDN were used to determine lightning occurrence. The 1200 UTC JAX, TBW, 

and MFL soundings were used to calculate the daily flow regimes , and the 1000 UTC XMR 

soundings were used to calculate the standard stability parameters and to help determine the 

flow regime on each day. The following sections describe these data types and how they were 

processed prior to the creation of the predictors and predictand for the forecast equations. All 

data were processed using the TIBCO Spotfire S+® software package (TIBCO 2010). 

2.1 NLDN 

NLDN is a national network of cloud-to-ground lightning sensors owned by Vaisala 

(Cummins and Murphy 2009). The NLDN data were provided to the AMU through Mr. Roeder of 

the 45 WS by the 14th Weather Squadron. The files contain the date, time in UTC, latitude, 

longitude, polarity, and strength of every strike in an area that encompassed central Florida. 

These data were used to determine whether or not lightning occurred on each day in the POR. 

The primary purpose of the NLDN data was to create the binary predictand for the equations. 

The data were also used to create the daily climatological lightning frequency and persistence 

forecasts that would be used as candidate predictors and forecast benchmarks against which to 

test the new equations. 

The NLDN data also caused a change in the POR. The original POR was 1989-2011 . 

During a presentation at Vaisala's International Lightning Meteorology Conference in April 2012 

(http://www. vaisala.com/en/events/ildcilmc/Pages/1 LDC-2012-archive.aspx), the AMU learned 

the NLDN system underwent a major upgrade in 1994 (Cummins et al. 1998), causing 

researchers to not use data from before that year (Hodanish 2012). Dr. Ken Cummins provided 

the AMU with an image of 2°X2° grids containing NLDN detection efficiency (DE) correction 

values over the U.S. during 1994-1998 relative to 1999 DE, shown in Figure 2. The DE 

correction values are proportional to the DE in each grid cell. To normalize the flash counts and 

make them consistent with 1999 performance, the number of strikes detected is divided by the 

DE correction for that year. For a DE correction < 1, this results in a larger number of strikes. 

Since the lightning forecast in this task depends on whether lightning occurred and not the 

number of strikes, the DE corrections values in Figure 2 were not used. However, the low DE of 

0.5-0.6 over Florida in 1994 could indicate strikes were missed by NLDN in the locations and 

times of interest. After consulting with Mr. Matt Volkmer and Mr. Dave Sharp of NWS MLB, the 

AMU deleted warm season data from the six years 1989-1994, years in which the DE correction 

over Florida was < 0.7. This eliminated years with low DE while keeping as many years as 

possible in the POR for equation development and testing, which is the 17 years 1995-2011 . 

7 
Detection Efficiency 

Corrections 

Correction Values 

1 • llO eemcdM llOC sary 

1 o.t•• u.u 

u•u 

OIOJ 

uu 

oc• 

., oc 

u. 

o.•cu 

•01 

Figure 2. The NLDN DE corrections for 1994-1998 relative to 1999 values. The grid 

cell size is 2°X2° latitude/longitude (image created by Vaisala). 

2.2 Florida 1200 UTC Rawinsondes 

The AMU downloaded the 2010 and 2011 soundings for JAX, TBW and MFL from the 

NOAA Earth System Research Laboratory (ESRL) website (http://www.esrl.noaa.gov/raobsD 

and added them to the existing AMU database of soundings from 1995-2009. These soundings 

were used to calculate the daily flow regimes as described in Lericos et al. (2002). More 

information about this data set can be found in Lambert (2007). 

2.3 XMR 1000 UTC Rawinsonde 

Mr. Madison of Computer Sciences Raytheon provided the 2010 and 2011 XMR soundings. 

The AMU added them to the existing database of soundings from 1995-2009. These data were 

used to supplement the Florida 1200 UTC soundings in determining the flow regime of the day 

and to calculate the sounding parameters normally available to the forecasters through MIDDS. 

The probability of lightning occurrence based on flow regime and the XMR sounding parameters 

were used as candidate predictors in the equation development. 

8 
3 Creation of Equation Predictand and Predictors 

The AMU processed the three datasets described in Section 2 to create the predictand and 

candidate predictors needed for the statistical forecast equation development. The predictand is 

the element to be predicted from a predictor or group of predictors. There was one predictand 

value and one set of candidate predictor values per day in the POR. More details of how the 

data were processed to create these elements are given in Lambert (2007). 

3.1 Binary Predictand 

The predictand is binary, i.e. 1 or 0, and indicates whether lightning occurred within 10 NM 

of the centers of MCO, MLB, and TIX, and during the four three-hour time periods consistent 

with the NWS MLB required forecast products. This determination was straightforward: the 

predictand value was set to 1 if lightning was detected within each defined time period and 

spatial area on each day, otherwise a 0 was assigned. The AMU used a binary predictand 

because the prediction would be for lightning occurrence, not the number of strikes. 

3.2 Candidate Predictors 

The AMU tested the candidate predictors during equation development to determine which 

predictors in what combination would provide the best probability forecast of lightning 

occurrence. The candidate predictor set included one-day persistence and daily climatological 

lightning frequency calculated from the NLDN binary predictand, the flow regimes determined 

from the morning rawinsondes, and 12 stability parameters calculated from the XMR morning 

rawinsonde. 

3.2.1 NLDN Predictors 

The AMU processed the NLDN data to create the one-day persistence and daily climatology 

needed for equation development. One-day persistence was binary like the predictand, and just 

as straightforward to determine. It indicates whether lightning occurred on the previous day for 

each three-hour period. If lightning occurred on one day in a certain time period, the persistence 

value for the next day in the same time period was 1. If lightning did not occur, the persistence 

value was 0. The daily climatology is the percent of days lightning occurred on each date in the 

POR. The binary predictand values were used to create the daily climatology. Details on how 

the values were calculated are in the Phase II final report (Lambert 2007). 

Figures 3-5 show the raw and smoothed daily climatologies for the four three-hour time 

periods at each of the stations. These values were calculated using the same 14-day Gaussian 

smoothing algorithm described in Lambert (2007). For MCO (Figure 3), farther from the coast 

than the other two stations, the values for 1800-21 00 UTC and 21 00-0000 UTC are similar as 

are the values for 1500-1800 UTC and 0000-0300 UTC. For MLB (Figure 4) and TIX (Figure 5), 

the values for 1800-21 00 UTC are highest, and the time periods before ( 1500-1800 UTC) and 

after (2100-0000 UTC) have values that are more similar. This may show a ramp up and down 

of lightning occurrence at these two stations as the sea breeze forms close to the coast in the 

late morning/early afternoon and moves inland in the afternoon, creating higher values for MCO 

during the mid- and late-afternoon periods. 

9 
10 

0.8 

0.7 

110 0.6 

.5 

c 

1:: 0.5 ~ ..... 

-

0 04 ~· 

:ri .a 0.3 

~ 0.2 

0.1 

0 

Warm Season Daily Lightning Climatology 

Within 10 NM of MCO Center 15-03 UTC in 3-Hour Increments 

.------------1_9_9_5_-2_0_1_1 ___ ____ __, ------- Raw lSZ 

- Gaussian lSZ 

Raw 18Z 

+------------+----------1----- ---+------l Gauss an 18Z 

------- Raw 21Z 

!\ - Gaussian 21Z +------------i<l---tt-- 1-------i+--~---++---+i---- ---1 ------- Raw OOZ 

- Gaussian OOZ 

May Jun Jul Aug Sep 

Figure 3. The daily raw and Gaussian-smoothed lightning climatology at MCO for the 

four three-hour periods 1500-1800, 1800-2100, 2100-0000, and 0000-0300 UTC. The 

time values in the legend indicate the beginning of the time period. 

0.8 

0.7 

1111 0.6 c 

c 

1:: 0.5 

:! 

0 >0.4 

~ 

:ri 0.3 " J:l 

e 

0.. 0.2 

0.1 

0 

Warm Season Daily Lightning Climatology 

Within 10 NM of MLB Center 15-03 UTC in 3-Hour Increments 

,...---- -------19_9_5_-_2_011 ________ ---1------- Raw lSZ - Gaussian lSZ 

------- Raw 18Z 

+------------------------------- Gaussian18Z 

------- Raw 21Z 

f1 ! - Gaussian 21Z +------------ -------...,-----t+;:---- ----1 ------- Raw ooz 

. 

" " " " 

May Jun Jul Aug 

--Gaussian ooz 

I 

Sep 

Figure 4. Same as Figure 3 but for MLB. 
Warm Season Daily Lightning Climatology 

Within 10 NM of TIX Center 15-03 UTC in 3-Hour Increments 

1995-2011 

0.8 ·------ Raw lSZ 

- Gaussian lSZ 

------- Raw 18Z 

0.7 - Gaussian 18Z 

------- Raw 21Z 

111 0.6 - Gaussian 21Z 

\;~ r -------Raw OOZ 

I: 

c - Gaussian OOZ 

~ r 0.5 

~ [t1 ... ..... · 0 .,JtJ c •. 

~0.4 

v~' ........ v [! ~ 

. if I"' :a J 0.3 ' I ' 

e ;;r:: ~ i"'o 

0. 0.2 If! ~ ~ 

~ }1-, 1 

j ;\ ~~ ~ \1 i '\ T 0.1 -. 

g '"""' 

\1 v Y! \ Vi v J (Vv 0 Mi ~~ u 

May Jun Jul Aug Sep 

Figure 5. Same as Figure 3 but for TIX. 

3.2.2 Flow Regime Probabilities 

The AMU used the sounding data from MFL, TBW, JAX, and XMR to determine the flow 

regime on each day in the 2010 and 2011 warm seasons using the process described in 

Lambert (2007). The AMU already calculated the flow regimes for the years 1995-2009 for 

previous AMU tasks. Table 1 contains the flow regime definitions. 

Table 1. List of the flow regime names and the corresponding sectors showing the average 

1000-700 mb wind directions at each of the stations (Lambert 2007). 

Rawinsonde Station 

Flow Regime Name and Description MFL TBW JAX 

SW-1 Subtropical ridge south of MFL 180°-270° 180°-270° 180°-270° Southwest flow over KSC/CCAFS 

SW-2 Subtropical ridge north of MFL, south of TBW 90°-180° 180°-270° 180°-270° Southwest flow over KSC/CCAFS 

SE-1 Subtropical ridge north of TBW, south of JAX 90°-180° 90°-180° 180°-270° Southeast flow over KSC/CCAFS 

SE-2 Subtropical ridge north of JAX 90°-180° 90°-180° 90°-180° Southeast flow over KSC/CCAFS 

NW Northwest flow over Florida 270°-360° 270°-360° 270°-360° 

NE Northeast flow over Florida oo-900 oo-900 oo-900 

Other When the layer-averaged wind directions at the 

three stations did not fit in defined flow regime 

Missing One or more soundings missing 

11 
The AMU calculated the frequencies of lightning occurrence for each warm season month 

under each daily flow regime and in each three-hour period within 10 NM of MCO, MLB, and 

TIX. Figure 6 shows the flow regime lightning frequencies for July at MLB. In general, the values 

are low at the beginning of the day, increase at mid- and late-day, and decrease into the 

evening. This was the general trend for all months. The values for the flow regimes with an 

easterly component (NE, SE-1 and SE-2) tend to be highest in the morning and generally 

decrease through the day. This is consistent with morning showers experienced near the east 

coast in these regimes. Conversely, the values with a westerly component (NW, SW-1 and SW2) increase from the morning to mid-day, slightly decrease but remain elevated in the late-day, 

and then decrease significantly after 0000 UTC (2000 EDT). This is consistent with westerly 

flow slowing the progression of the sea breeze inland or pinning it at the coast, providing a lifting 

mechanism for thunderstorm development, and the drop-off in convective activity after sunset. 

0.7 

0.6 

en 

Gl 

·u o.s 

c 

Gl 

::::s 

C" 0.4 

Gl 

... 

LL. 

~0.3 

c 

... fo 0.2 

:::i 

0.1 

0 

Flow Regime Lightning Frequencies for 

MLB in July (1995-2011) 

• NE 

NW 

• Other 

• SE-1 

• SE-2 

• sw-1 

• SW-2 

15-18 18-21 21-00 00-03 

Time Periods (1500-0300 UTC) 

Figure 6. The flow regime lightning frequencies for MLB in July. Flow regimes with an 

easterly component (NE, SE-1 and SE-2) are in shades of blue and those with a westerly 

component (NW, SW-1 and SW-2) are in shades of pink. The Other regime is in green. 

The AMU used the values in Figure 6 as predictors in developing the equations, which is 

described in Section 4. The equations did not perform well, so the AMU met with Mr. Volkmer 

and Mr. Sharp of NWS MLB to determine how to proceed with the task. They decided to 

combine the low-level mean wind speed with the flow regime on each day to create flow 

regime/speed stratifications for these predictors. Speed strength influences how far the sea 

breeze will penetrate inland, which has a direct effect on the location of any storms that form. 

The AMU used the mean speed in the 1000-700 mb layer from the CCAFS 1000 UTC sounding 

to create two- and three-speed ranges for each flow regime. For the two-speed ranges, speeds 

< 10 kt were considered low and speeds ~ 10 kt were considered high. For the three-speed 

ranges , speeds < 6 kt were considered light, speeds~ 6 and :5 14 kt were considered moderate, 

and speeds> 14 kt were considered strong. 

12 
Stratifying the data by flow regime and speed ranges reduced the number of observations in 

each category such that robust and meaningful lightning occurrence frequencies could not be 

calculated. In order to increase the number of observations in each stratification, the AMU 

combined the two southeast (SE-1 and SE-2) and two southwest (SW-1 and SW-2) regimes into 

one regime each (SE and SW) before stratifying by speed ranges. The lightning frequencies for 

these two flow regimes and the three-speed ranges at MLB in July are shown in Figure 7. As an 

example, lightning occurred 62% of the time in the 1800-2100 UTC period when the flow regime 

was SW and the mean 1000-700 mb wind speed was in the strong range, or > 14 kt. The NW, 

NE, and Other regimes were also stratified by speed ranges but are not shown in Figure 7 to 

make the chart less cluttered and easier to interpret. These values were used as candidate 

predictors in addition to the flow regime probabilities not stratified by speed (Figure 6). 

en 

Gl 

0.7 

0.6 

·u o.s 

c 

Gl 

:I 

C" 0.4 

Gl 

a.. 

LL. 

0.3 

·-

c 

... "fo 0.2 

~ 

0.1 

0 

Flow Regime/Speed lightning Frequencies for 

MLB in July {1995-2011) 

• SE- Light -------1 

• SE - Moderate 

-------1 • SE - Strong 

sw- Light 

• SW - Moderate 

• SW- Strong 

15-18 18-21 21-00 00-03 

Time Periods (1500-0300 UTC) 

Figure 7. The frequencies of lightning occurrence for the southeast (SE, shades of blue) 

and southwest (SW, shades of pink) flow regimes for the three-speed ranges of light, 

moderate and strong winds. 

13 
3.2.3 Stability Parameters 

The AMU calculated 12 stability indices and moisture parameters from the 2010 and 2011 

XMR soundings needed as predictors for equation development. The 1995-2009 warm season 

values were created for earlier AMU tasks. They are 

• Total Totals (TT), 

• Cross Totals (CT), 

• Vertical Totals (VT), 

• K-lndex (KI), 

• Lifted Index (LI), 

• Tl (KI - Ll), 

• Severe Weather ThrEAT Index (SWEAT), 

• Showalter Index (SI), 

• Temperature at 500mb (Tsoo), 

• Mean Relative Humidity (RH) in the 825-525 mb layer (RH85) , 

• Mean RH in the 80~00 mb layer (RH86) , and 

• Precipitable Water up to 500mb (PW). 

The formulas in the MIDDS code used for the indices are standard and can be found in several 

sources (e.g. Peppler and Lamb 1989; Ohio State University Severe Weather Products web 

page at http://twister.sbs.ohio-state.edu). The formulations will not be shown here. Only four 

indices in the above list are not readily available to the forecasters: VT, Tl , RH85 and RH86. Tl is 

calculated easily with the equation Tl = Kl- Ll , as is VT with VT = T850 - T500 . RH85 and RH86 

were calculated using a log(p)-weighted average described in detail in Lambert (2007: Section 

3.5 equations 5 and 6). 

3.2.4 Summary of Candidate Predictors 

The full list of the candidate predictors, 17 in all, is given here as a reference. They are 

• One-day persistence for each time 

period (Pers), 

• Daily climatological lightning frequency 

for each time period (DCiimo), 

• Flow regime lightning probability (FR), 

• Two-speed stratified flow regime 

lightning probability (FR2), 

• Three-speed stratified flow regime 

lightning probability (FR3), 

• TI, 

• CT, 

• VT, 

• Kl , 

• Ll , 

• Tl, 

• SWEAT, 

• SSI , 

• T sao, 

• RHas, 

• RHaa, and 

• PW. 

The values for these candidate predictors were used with the binary predictand in the 

development of the logistic regression lightning forecast equations. 

14 
4 Equation Development and Testing 

There were three major steps in this portion of the task: 

• Create the development and verification datasets, 

• Develop the logistic regression equations, and 

• Determine the equation performance. 

Before developing the equations, the AMU stratified the data into development and verification 

datasets. The AMU then developed a set of four equations, one for each time period, for each 

warm season month. The performance of the equations was assessed using the Brier Skill 

Score (SS; Wilks 2006), a verification technique appropriate for probability forecasts. 

4.1 Development and Verification Datasets 

The AMU created the equations using the development dataset, which required enough 

samples so that the resulting equations performance was stable, i.e. the equations would 

maintain consistent forecast accuracy on different datasets. A small dataset is less likely to 

contain a representative set of events. The equations developed from a small set may show 

wide variations in accuracy on different datasets causing forecasters to not have confidence in 

the results. The verification dataset was needed for equation testing in order to have a more 

realistic view of how the equations would perform in operations. It was expected that the 

equations would not perform as well on the verification data as they would on the data from 

which they were developed. However, if performance was a great deal worse with the 

verification data, this would indicate that too many predictors were chosen and the equations 

were fit too strongly to the development data, the development dataset was too small, or the 

equations did not have predictive value. 

The AMU stratified candidate predictors and predictand for each month into development 

and verification datasets. Care was taken to ensure there would be at least 250 events in the 

development dataset (World Meteorological Organization (WMO) 1992), while still having 

enough events in the verification dataset to make reasonable conclusions about equation 

performance. Of the 17 warm seasons in the POR, 14 were used for equation development and 

3 were set aside for equation verification. This ensured that each month in the warm season 

was equally represented in both datasets. 

The stratification did not involve choosing individual warm season years for each dataset, 

but rather individual warm season days. Days for the verification dataset were chosen first. 

Given that there are 153 days in the warm season, the random number generator in Microsoft® 

Excel~ was used to create three sets of 153 numbers representing the years between and 

including 1995 and 2011 . The resulting three sets of years were assigned to each day in the 

warm season. Thus, each day in the warm season was represented by days from three random 

"years". The dates were manually checked to ensure there were no duplicate years for each 

day from the random number generator. For example, the verification dataset contains 1 May 

1995/2005/2007, 2 May 1999/2004/2009, etc. All other dates were made part of the 

development dataset. This random method was chosen to reduce the likelihood that any 

unusual convective seasons would bias the results. The development datasets had well above 

the 250 events defined by the WMO needed to develop reliable equations. 

15 
4.2 Equation Development 

As in previous work, the AMU used logistic regression to create the lightning forecast 

equations. Due to time constraints, equations were developed for MCO and MLB but not TIX. 

Four time periods, five warm season months, and two airfields resulted in 40 equations to be 

developed, 20 for each airfield. The AMU selected predictors using the same method as in 

previous work for each individual equation. Detailed descriptions of logistic regression and the 

predictor selection procedure with supporting figures and equations are found in the Phase II 

final report (Lambert 2007). Those procedures were followed exactly for this task. 

4.2.1 Development Issues 

The AMU used SS values as the first test in determining equation performance. SS shows 

the percent improvement or degradation the equations provide compared to other forecast 

methods, or benchmarks. Positive SS values indicates the equations outperform the forecast 

benchmarks, negative values indicate that the equations were worse than the forecast 

benchmarks. 

Initially, the AMU began equation development for MCO as requested by NWS MLB. The 

MCO equations were tested and refined using SS values to determine their performance 

compared to Pers, DCiimo and FR probability values. All equations outperformed Pers by 

significant amounts, which was also true for the final set of equations. However, results 

compared to DCiimo and FR showed similar performance. More than half of the SS values were 

less than 1 0% indicating that the equations performed similarly to the benchmarks, and close to 

20% of the values were negative. The equations should provide a good first guess when 

forecasters are determining the probability of lightning, but it appeared that this set of equations 

would be ineffective in doing so. 

The final equation performance, shown in Section 4.2.2, was also similar to the other 

forecast benchmarks, with just a slight improvement. The AMU conducted tests to determine the 

cause for this unexceptional performance and consulted with NWS MLB during the process. 

These consultations resulted in creating the speed-stratified flow regimes described in Section 

3.2.2. Several attempts at developing and re-developing the equations did not improve their 

performance. After much examination, the AMU concluded that two major issues caused the 

equations to not outperform DCiimo and FR probability values: the percentage of lightning 

occurrence in the datasets and the operationally defined time and space restrictions for lightning 

occurrence. 

4.2.1.1 Lightning Occurrence Ratios 

The unremarkable equation performance prompted the AMU to compare lightning 

occurrence in the development data set with that in the verification data set. A large percentage 

of lightning in one data set and a small percentage in the other could cause the bad 

performance shown by the SS values. Table 2 shows the percentage of lightning days in the 

MCO development and verification data sets for each month and time period. The equations 

were developed with the development data and tested on the verification data. The values in 

Table 2 were calculated to determine if both data sets contained similar ratios of lightning to 

non-lightning days. That most values are within 10% indicates the data sets were similar with 

respect to lightning occurrence. The largest outlier of 11% occurred for May 1800-21 00 UTC. 

Note the low frequencies in May and September for all four time periods, and during 1500-1800 

and 0000-0300 UTC for the other months. Such low percentages likely made it difficult to 

determine strong relationships between the predictors and lightning occurrence during these 

periods. The larger percentages for 1800-2100 and 2100-0000 UTC in June, July, and August, 

however, appeared to show enough occurrences to develop strong predictand/predictor 

relationships. 

16 
Table 2. The percentage of lightning days in the MCO development 

and verification data sets used to create and test the equations, 

respectively. The first column is the month, the second identifies the 

data set and the number of days it contains not including days with 

missing data. The last four columns show the percentages of days 

with lightning in each time period. 

Month Data Set(# Days) 15-18 18-21 21-00 00-03 

May Development (390) 5 11 12 6 

Verification (85) 12 22 22 13 

June Development (382) 21 45 42 16 

Verification (86) 22 42 40 24 

July Development (394) 21 50 44 17 

Verification (85) 16 45 44 20 

August Development (387) 24 49 45 15 

Verification (84) 17 45 49 18 

September Development (378) 8 28 29 10 

Verification (79) 11 20 22 10 

4.2.1.2 Time and Space Filters 

The AMU looked at the differences in the area and time filters between this task and the 45 

WS's Objective Lightning Probability tool (Lambert 2007). That tool outperformed all forecast 

benchmarks by significant percentages. The area used for the 45 WS tool was somewhat larger 

than the 10 NM radius in this task but not significantly so. However, the 45 WS time period was 

17 hours (0700 to midnight local time) as opposed to the four three-hour time periods during 

1500 to 0300 UTC, which totals 12 hours (1100 to 2300 local time). If lightning occurred a few 

seconds before or after a time period, it would not be counted as a lightning event for that time 

period. Predictands that might otherwise indicate lightning occurrence would be paired with a 

non-lightning event even if lightning occurred just before or after the time period. The result is 

that a robust relationship between predictors and the predictand would not be fully realized by 

the equations, resulting in poor equation performance. 

The AMU spoke with Mr. Sharp and Mr. Volkmer and suggested increasing the area and/or 

time period to help improve equation performance. There were similar issues with the time filter 

in another AMU task to use Global Positioning System (GPS) PW estimates to forecast lightning 

(Huddleston 2012). Mr. Sharp and Mr. Volkmer said the radius cannot be changed and the time 

periods should remain the same as they reflect their forecast requirements. They also requested 

that the equations be developed for MCO and MLB using the original requested area and time 

period filters, even if they did not perform significantly better than the DCiimo and FR 

probabilities. 

17 
4.2.2 Final Equations 

As described in Lambert (2007), the AMU chose the predictors for each equation in rank 

order of importance. Two stations, five months in the warm season (May-September), and four 

3-hour time periods resulted in 40 equations. The number of predictors in the equations ranges 

from two to five, with an average of three. In lieu of showing the predictors for each equation, it 

is more informative to show which candidate predictors were chosen for what number of 

equations. Table 3 shows all the predictors used in the equations and the number of equations 

in which they appear, both for the 20 MCO and 20 MLB equations separately and for all 40 

equations together. They are listed in order from the greatest number of equations to the least. 

The first and second ranked predictors for both the MCO and MLB equations are FR3 and Tl. 

Thirty-nine equations have one of the flow regime predictors (FR3, FR2, and FR). This shows 

the importance of the flow regime in predicting when and where convection and lightning will 

occur. Representing stability and moisture, 19 equations have Tl, 6 have TT, and 6 have PW. 

The rest of the predictors were not chosen often, but were still important to forecasting lightning 

in their specific airfield 10 NM radius and time period. Note that the only two candidate 

predictors never chosen for the equations were T500 and SWEAT (Section 3.2.4). 

Table 3. The number of times each predictor 

was used in the 20 MCO and 20 MLB 

equations, and the total number of times they 

were used in both sets of equations. 

Predictor MCO MLB TOTAL 

FR3 15 11 26 

Tl 11 9 20 

FR2 3 7 10 

Pers 5 3 8 

DCiimo 4 3 7 

PW 3 3 6 

TT 3 3 6 

Kl 3 2 5 

FR 1 2 3 

Ll 1 2 3 

VT 2 1 3 

RHas 1 2 3 

CT 0 2 2 

RHas 1 0 1 

SSI 0 1 1 

Developing and testing the equations involved creating a base equation, and then adding 

and eliminating predictors from it to find the combination that performed best using the 

verification data. The AMU calculated the SS values showing the percent improvement or 

degradation in skill of the final equations over five forecast benchmarks: Pers, DCiimo, FR, FR2, 

and FR3. Table 4 contains these SS values showing the skill of the MCO and MLB equations 

relative to the other forecast methods using the verification data. The SS values for Pers are not 

shown since all equations outperformed that forecast benchmark by a significant amount. 

18 
The positive SS values in Table 4 indicate the equations had more skill than the 

corresponding forecast method, and negative values indicate less skill. Values with magnitudes 

within 10% of 0, positive or negative, are highlighted in yellow. The results for DC limo and the 

three FR values were mixed. Not considering the values within 10% of 0, the MCO equations 

outperformed all of the benchmarks 81% of the time, and the MLB equations 64% of the time. 

However, when assuming that values within 1 0% of 0 indicate similar performance, the MCO 

and MLB equations outperformed the benchmarks only 26% and 30% of the time, respectively. 

While there were some time periods in which the equations showed a significant 

improvement over the forecast benchmarks, most of the values in Table 4 are > -10% and < 

10%: 73% for MCO and 70% for MLB. Thus, through all the modifications and equation 

development iterations, the final equations still do not outperform the forecast benchmarks by a 

significant amount. 

19 
Table 4. The SS values (positive: black; negative: red) for the MCO and MLB 

equations for the forecast benchmarks of DCiimo, FR, FR2, and FR3. The time 

periods are in UTC. Yellow shading indicates values within 10% of 0. 

Station Forecast Benchmark 15-18 18-21 21-00 00-03 

MCO 

DC limo 1 25 16 2 

May FR (Flow Regime) 6 25 18 -1 

FR2 (FR 2-Speed) 3 23 17 -1 

FR3 (FR 3-Speed 3 22 19 1 

DCiimo 16 -1 20 8 

June FR (Flow Regime) 6 4 7 -2 

FR2 (FR 2-Speed) 4 1 1 -1 

FR3 (FR 3-Speed 7 6 6 3 

DCiimo -4 7 28 4 

July FR (Flow Regime) -6 5 23 -2 

FR2 (FR 2-Speed) 2 5 20 -5 

FR3 (FR 3-Speed -1 4 12 -6 

DC limo -1 11 6 11 

August FR (Flow Regime) 0 8 1 10 

FR2 (FR 2-Speed) -7 9 1 8 

FR3 (FR 3-Speed -10 7 2 -2 

DCiimo 24 8 6 8 

September FR (Flow Regime) 20 4 0 3 

FR2 (FR 2-Speed) 15 4 0 4 

FR3 (FR 3-Speed 13 1 5 3 

MLB 

DCiimo 2 26 16 -5 

May FR (Flow Regime) 3 25 10 -6 

FR2 (FR 2-Speed) 0 24 9 -6 

FR3 (FR 3-Speed -1 22 9 -9 

DC limo 17 21 11 10 

June FR (Flow Regime) 17 8 -7 -1 

FR2 (FR 2-Speed) 15 4 -4 -2 

FR3 (FR 3-Speed 14 7 -6 -5 

DC limo -1 -4 12 3 

July FR (Flow Regime) -2 -4 5 -1 

FR2 (FR 2-Speed) -1 -3 4 0 

FR3 (FR 3-Speed -2 -6 1 -2 

DCiimo 11 13 9 1 

August FR _(_Flow Regime) 9 8 3 -1 

FR2 (FR 2-Speed) 10 6 . 4 -2 

FR3 (FR 3-Speed 7 8 2 -3 

DCiimo 28 29 9 4 

September FR (Flow Regime) 19 14 4 -2 

FR2 (FR 2-Speed) 19 8 1 -5 

FR3 (FR 3-Speed 14 11 3 -4 

20 
5 Graphical User Interface 

Even though the equations did not perform well, NWS MLB requested that their output be 

provided through a GUI along with the other forecast benchmarks DCiimo, FR, FR2, and FR3. 

The forecasters are still interested in seeing the equation output since it is calculated using 

parameters from the current sounding and may still provide added value to the forecast. NWS 

MLB was involved in the GUI development by providing comments and suggestions on the 

design to ensure the final product addressed their operational needs. 

The AMU built the GUI within an Excel workbook using Microsoft Excel Visual Basic for 

Applications (VBA). It accesses data in specific worksheets based on user input. The GUI itself 

has three basic dialog boxes. The first asks for the general information such as the airfield and 

time period of interest, the second asks for equation predictor values, and the third displays the 

equation and climatological probability values. 

5.1 Excel Workbook 

The Excel workbook in which the GUI resides contains 14 worksheets. The first worksheet 

contains brief instructions on how to start and use the GUI. The AMU recommends first-time 

users read these instructions in their entirety before using the GUI. The next three worksheets 

contain Excel PivotTables of the DCiimo, FR, FR2, and FR3 values. The other 10 worksheets 

contain information for each station/month combination: 

• Predictor names for each time period and their coefficients in the equations, and 

• Minimum, maximum, median, mean, standard deviation, and first and third quartile 

values of the observed sounding stability indices for the month. 

The first worksheet, named Introduction, is displayed automatically upon opening the Excel 

file. The user opens the GUI by clicking on the yellow button on the Introduction sheet. When 

the GUI is initiated, the first form requesting station, month, time period, and other input is 

displayed. This will define which equation or equations the user needs to access for the 

lightning probabilities. After choosing all of the parameters in this form and continuing, another 

form is displayed that allows the user to input the predictor values for the equation. Once all of 

these values are chosen, the final form displays the requested output. When the user is finished 

and exits out of all the dialog boxes, the Introduction sheet will be displayed again before 

closing the file. 

21 
5.2 Initial Input Form 

Figure 8 shows the first form in the GUI to input initial values needed to output the lightning 

probability values. The month, day, station and time are needed for the daily climatology. These 

values plus the flow regime are needed for the flow regime probabilities, and all values including 

the mean speed in the flow regime layer are needed for the speed-stratified flow regime 

probabilities. The user must input the speed value manually, which is the average speed in the 

surface to 700 mb layer as observed in the XMR 1000 UTC sounding. The initial speed value is 

-999. If this value is not changed and the user clicks the "Continue .. . " button, the error message 

form on the right side of Figure 8 is displayed. 

22 

UGHTNING CUMATOLOGY 1995-2011 

Input for a.atology 

Choose Date 

01oose the Month before the Day value 

Month J M.ay 3 Day [ 1 B Us.tforml ..... 

Enter an integer value for r Site and 1We Period the mean speed in the 

Station I M.CO B ~"q l ALL a surface-700 mb layer 

from today's 1000 UTC 

'"" 11100-700 .. Whls :-li ~ sounding 

~ SW-!3 ~ -m ~ ~ 

II I OK 

I~ Flow Regime I Speed categortes I Deftntions and Ranges 

r em I K .. _ Qlnti~~·· ·. __ J 

Figure 8. The initial input form showing the default values after starting the GUI (left). 

The user makes choices through the drop-down lists in the boxes with down arrows, 

manually inputs a value for speed and then clicks the "Continue ... " button to get the 

predictor input form. If a speed value is not input and the user clicks the "Continue ... " 

button, the form on the right is displayed reminding the user to input a speed value. 
Figure 9 shows the initial input form after the user has made choices and with the time 

period drop-down list displayed. There are two other buttons on the initial input form that provide 

the user with information. The "Flow Regime Definitions" and the "Speed Categories and 

Ranges" buttons are surrounded by red rectangles in Figure 9. Arrows point to their respective 

message forms that provide definitions for all the flow regimes and speed categories. They can 

only be displayed one at a time and are closed by clicking their "Dismiss" buttons. 

UGHTtiNG CLIMATOLOGY 1995-20U 

Input for alnlatology '=""'~===~1 

--ChoMa Date.....,.,.,.,...,.,.,...,.,.,.,..,.,.,.,...,.,.,..,.==:::::J 

Choose the Month before the Day value 

Month I, lui !:] J Day 1 [..:JI 

Site and n.e Period ~~ 

Time / 

Statton L MCO .1:1 (UTC) AU.J A 

.,. 10110-700.., Winds At: 

Flow 1 sw-t l•JI sA oo-o3 

Regime ' 

Flow Regime 

Definitions 

I Continue... I 

SW-1: Subtropical ridge south d Mfl. 

Southwest flow over central Aorlda 

SW-2: Subtropical ridge north of MR/south of TBW, 

Southwest flow over central Aorlda 

SE-1: Subtropical ridge north ofTBW/south of JAX, 

Southeast flow over central Aortdil 

SE-2: Subtropical ridge north of JAX, 

Southeast flow over central Aorlda 

NW: Northwest flow over Aorlda 

NE: Northeast flow over Aorlda 

OTHER: Row cannot be determined by 

MA../TBW!JAX/CCAFS soundings 

ll Dismiss I 

ROW R£<lM SPUD CATEGOIIIES ... 

Low: Mean Speed < 10 kt Dismiss r"' lWo-Speed 

High: Mean Speed >• 10 kt 

~ 

~Speed ~=-~=-~~~~~~· 

Ught: Mean Speed < 6 kt 

Moderate: Mean Speed >• 6 kt and <• 14 kt 

Strong: Mean Speed > 14 kt 

Figure 9. The GUt initial input form after the user has made choices from the drop-down lists 

(left). The drop-down list for the time period choices is displayed as an example. The wind 

speed value, covered by the drop-down list, is 10 kt. Two buttons in the initial input form (red 

rectangles) provide the user definitions of the flow regimes and speed categories. Clicking on 

each button will display the definitions in separate message forms (right). 

23 
5.3 Predictor Input Forms 

If the user makes all the choices shown in the initial form in Figure 9, including "21-00" for 

the time period, and then clicks the "Continue ... " button, the predictor input form for the 2100-

0000 UTC equation in July at MCO is displayed (Figure 1 0). The list of predictors in each 

equation is at the top of the predictor input form. In this example, there are four predictors: FR3, 

PW, Pers, and DCiimo. The FR3 and DCiimo probabilities are retrieved from a table by the GUI 

using the information provided in the initial input form (Figure 9, left). The user inputs the PW 

from the 1000 UTC CCAFS sounding and chooses Yes or No for Pers indicating whether 

lightning occurred the previous day during the same time period. Clicking the "Display 

Probabilities" button will produce the output form (Section 5.4). As with Speed in the initial input 

form, the initial values for the sounding stability predictors are -999. If any of these values are 

not changed, an error message form for that predictor will be displayed (Figure 10, right). The 

"New Input" button closes the form and returns control to the initial input form. 

PREDICTORS FOR MCO JUL 21.00 UTC EQUA110N 

--------------------~· iThere are four predictors for MCO lui 21-00 UTC: 

1. Three-Speed Flow Regime Probability, 

2. Precipitable Water (PW), 

3. 1-Day Persistence and 

4. Daily dimatology. 

Input the values for PW in inches and Persistence below, 

the GUI Will get the flow regime probability and daily 

climatology values automatically. 

~~K2~~~~~=-~~~~~· 

~ Yes was lightning obseM!d within 10 NM of MCO 

r No between 210CHIOOO UTC yesterday? 

Userforml 

OUT OF BOUNDS: Enter 

the Precipitable Water 

in inches from today's 

1000 UTC sounding 

ll oK ] 

Figure 10. The predictor input form for the July MCO 2100-0000 UTC equation (left). The user 

inputs values for the predictors, in this case Yes or No for Pers and the PW value in inches, and 

then clicks the "Display Probabilities" button to get the output form. If predictor values are not 

input and the user clicks the "Display Probabilities" button, the form on the right is displayed 

reminding the user to input a predictor value. The "New Input" button closes the form and 

returns control to the initial input form. 

24 
Mr. Volkmer asked for an option to display the probabilities for all time periods in the same 

output form. The AMU added "ALL" as a choice in the time period drop-down menu on the initial 

input form and made it the default choice (Figure 8). The predictor input form associated with 

choosing all time periods for the MCO July example is shown in Figure 11. The user chooses 

the values for the predictors in all four equations at once. As with the initial input and single 

equation predictor input forms, the initial values of the sounding predictors are -999. An error 

message form, similar to that in Figure 10, will be displayed for each value not entered. Figure 

11 shows values that were manually input. The user can use the up-down arrows or enter a 

value directly in the box. In order to not crowd the form, the specific predictors for each time 

period are not listed as in the individual time period equation input form (Figure 1 0). The specific 

predictors for each time period are 

• 1500-1800 UTC: Tl and FR2; 

• 1800-2100 UTC: Tl and Pers; 

• 2100-0000 UTC: FR3, PW, Pers, and DCiimo; and 

• 0000-0003 UTC: FR (not speed related), DCiimo, and Ll. 

25 
26 

....... PREDICTORS FOR ALL MCO JUL EQUATIONS 

QIOOtiiB Yes or flo In the llnle 9/TJUfiS below for 

1-Day Pelsistence: 

- Persistena! 18-21 UR: 

f.' Yes Was lightning observed within 10 NM of MCO 

f. No between 180G-2100 lJTC yesterday? 

- .Persistence 21-00 UR: 

r. Yes WBS lightning observed within 10 NM of MCO 

r No between 21oo-oooo lJTC yesterdBy? 

Obtlln the I.{. I'W and 7Ivat.!s,.. the aJnellt 

M1DDS 1IIIJIJ UTC Sier-Tpmduct: 

~ Lfted Index (1.1} 

LT: Enter the Lifted Index from this 

morning's 1000 lJTC XMR sounding 

r Predpltable Water (PW) 

I Jl Enter the Predpitzlble Wi!lter (inches) from 1. 92 this morning's 1000 lJTC XMR sounding 

r- Thonlpson Index {II) 

J 

~ ~ Enter the Thompson Index from this 

"" morning's 1000 lJTC XMR sounding ~ 

~ 

r IIIPOIO"AIIT Uand U -ORIII'6~ I 

New Input I It DispiBy ProiNibiMies ] 

Figure 11. The GUI input form for the predictor values in 

the July MCO equations for all four time periods. The user 

makes choices in each category and clicks the "Display 

Probabilities" button to open the output form. The "New 

Input" button closes the form and returns control to the 

initial input form (Figure 8). The "IMPORTANT Tl and Ll 

INFORMATION" button provides information about Ll 

calculation differences in AWIPS and MIDDS. 
The "IMPORT ANT Tl and Ll INFORMATION" button in Figure 11 outputs a message form 

(Figure 12) that provides information about differences in how Ll is calculated between AWIPS 

used by NWS MLB and MIDDS used by the 45 WS. AWIPS uses the forecast maximum 

temperature for the surface parcel and MIDDS uses the 1000 UTC observed values. The 

equations were developed using the MIDDS value. The difference in the calculations would 

result in a lower, or more unstable, Ll value in AWIPS. Use of the AWIPS Ll would result in 

higher lightning probability values than would be calculated with the MIDDS value. Since Tl is 

the difference between K-lndex and Ll, it would behave similarly. This is important information 

for forecasters when choosing Ll and Tl values. The form is closed by clicking the "OK" button 

and control is returned to the predictor input form. 

IMPORTANT lHOMPSON INDEX INFORMAllON 

TI = KI - U. The U used in developng the 

equations was cak:ueted using the lowest 

1DO-mb layer observed mean temperature. 

AWIPS uses the forecast maxinum 

temperatwe, resuting in a more Lllstable U. 

This wl produce a higher probabky vakJe than 

using u calculated wth actual observamns. 

Figure 12. The message form displayed when the 

"IMPORTANT Tl and Ll INFORMATION" button is 

clicked. It is closed by clicking the "OK" button. 

27 
5.4 Output Forms 

After making choices in either predictor input form, the user clicks the "Display Probabilities" 

button to display the probability output form. There are two output forms, one for a single time 

period choice and the other for the "ALL" time period choice. 

The single time period output form for 1 July at MCO under 10 kt of SW-1 flow during 2100-

0000 UTC is shown in Figure 13. The left panel repeats the information from the initial input 

form, and the DCiimo, FR, FR2, FR3, and equation lightning occurrence probabilities are shown 

in the right panel. The first four values in the right panel are extracted from Excel PivotTables 

and are always the same for the same values chosen in the initial input form. DCiimo will 

change with the month, day, station, and time period. The three flow regime probabilities will 

change with the month, station, flow regime, and speed for FR2 and FR3. The equation output 

will change as the sounding predictor values from the predictor input form are likely to be 

different on each individual day. The output form also contains the same two buttons found in 

the initial input form that display descriptions of the flow regimes, and the wind speed categories 

and their ranges (Figure 9). 

28 

CLIMATOLOGIES AND EQUAllON OUTPUT (1995-2011) 

I..BIIIIRi PROIIAIIII..IIl 

- Input - output 

DATE IJulltJI DAD..Y aJMO I 42% II 

STATIIXI I_ MCO Jl R.OWREGIME t 50% J! 

TIME PERlDD (UJC) L ~1-oo J R.OW REGIME 2-SPO o/oj] 

R.OW ltE6DIE ~ R.OW REGIME 3-SPO I 56% Jj 

SPEED L to ~ EQUAllDR I 56% 

Row Regi-e Del"• .... 

Oloose New Input Speed~ and ltanges 

~-

With the exception of the equation output. the dinHitologk:BI sbltistics shown 

here reflect hisloricallightning occ:wrence for the period 1995-2011. They are 

not necessBiily indicative of fWn lightning oc:aHTence. 

Figure 13. The single time period output form for July 2100-0000 UTC at 

MCO. The input choices from Figure 9 (left) are in the left panel and the 

climatological and computed probabilities are in the right panel. The equation 

probability was calculated from the values chosen in Figure 1 0 (left) with 

PW=1 .92. The "Choose New Input" button closes the form and returns 

control to the predictor input form. The "Flow Regime Definitions" and "Speed 

Categories and Ranges" buttons open message forms that provide definitions 

of these parameters (see Figure 9). 
Figure 14 shows the output form after choosing "ALL" for the time period in Figure 9 and the 

predictor values in Figure 11 . It is similar to the single time period output form, the left panel 

repeats the information from the initial input form. The right panel, however, displays the 

probabilities for all four time periods. This allows the forecasters to see the progression of 

probabilities throughout the day. In this example, the probabilities increase from the first time 

period to the second (1100-1400 to 1400-1700 EDT), remain relatively steady or decrease a 

small amount from the second to the third time period (1400-1700 to 1700-2000 EDT), and drop 

dramatically into the last period, which occurs past sunset (2000-2300 EDT). Also, while the 

equation probability is similar to the climatological probabilities in the third time period, the 

equation probabilities for the other three time periods are significantly different than the 

climatological values. This could provide added value for the forecasters. The equation 

probabilities are calculated from current day values and would be more representative of the 

current environment than the climatological values. 

CUMATOI.DGIES AND EQUAmN OUTPUT (1995-2011) 

Input output 

DATE "Ane (UTC) 15-18 18-21 21-00 110-03 

STATJml DAJLYQJMO 

1DIE (UR:) R.OWREGDIE 

R.OWRE6DIE R.OW RE6DIE 2-SPD 

SPEED 

R.OW RE6DIE 3-SPD 

EQUATJml 

Speed categories 

~·--~==~--~~~----------~~~~~. ~~==============~ 

With the exception of the equation output, the cllmatologlall statistics shown 

here reflect hlstoriallllghtnlng occurrence for the period 1995-2011. They are 

not necessarily Indicative of future lightning occurrence. 

Choose New Input 

Figure 14. The output form as in Figure 13 but for all four time periods. The input choices from 

Figure 9 are in the left panel and the climatological and computed probabilities for all four time 

periods are in the right panel. The equation probabilities were calculated from the values chosen 

in Figure 11 . The "Choose New Input" button closes the form and returns control to the predictor 

input form for the four time periods (Figure 11 ). The "Flow Regime Definitions" and "Speed 

Categories" buttons open message forms that contain definitions of these parameters as in 

Figure 9. 

29 
6 Summary and Conclusions 

The AMU developed a tool to assist NWS MLB forecasters in making the lightning 

probability forecasts for airports in east-central Florida. The tool is a GUI developed using VBA 

in Excel that outputs climatological and forecast equation probabilities. Forecasters will input 

values for the predictors and the GUI outputs the probabilities of lightning occurrence for the 

airfield and time period of interest. The GUI also outputs the DCiimo, FR, FR2, and FR3 

climatological probabilities for comparison. 

Work in this task combined the warm season equation development done for the 45 WS 

with the area around airfields and time period stratifications for NWS MLB. Specifically, the 

equations forecast the probability of lightning occurrence within 10 NM the airfield center and 

within four three-hour periods: 1500-1800, 1800-2100, 2100-0000, and 0000-0300 UTC. These 

are the times when lightning is most likely to occur in east-central Florida during the warm 

season. The airfields are MCO, MLB, and TIX. The 10 NM radius and three-hour time 

increments reflect the requirements at the airfields for which NWS MLB has forecast 

responsibility. An equation was developed for each warm season month, station, and three-hour 

time period. With five months, two stations (MCO and MLB), and four time periods, that resulted 

in 40 equations. Equations were not developed for TIX due to time constraints. 

The performance of the equations compared to the forecast benchmarks of DCiimo, FR, 

FR2, and FR3 was mixed with just a slight improvment. They did outperform Pers by 20% to 

over 50%. Even after several attempts to improve performance by modifying predictors and 

changing the POR, equation performance did not improve. Nonetheless, NWS MLB requested 

that equations be developed and their output displayed since the observed sounding 

parameters used in the equations may provide added value to the equation output. The 

equation probability value is displayed in the GUI along with the associated DCiimo, FR, FR2, 

and FR3. The AMU delivered the GUI to NWS MLB and they are now using it in daily warm 

season operations. 

6.1 XMR 1000 UTC Sounding 

In late March 2013, the Air Force stopped releasing rawinsondes at 1000 UTC from CCAFS 

due to budget issues. This is effective until the end of the 2013 fiscal year on 30 September. 

The forecasters at NWS MLB understand that the tool may not perform optimally with other 

data, but they plan to use high temporal resolution model analysis soundings as close to 1000 

UTC and CCAFS as possible, and will also use the Cape Canaveral Global Positioning System 

PW data available from the site http://www.suominet.ucar.edu/. 

When the 1000 UTC sounding is reinstated, they will resume using that data. It is important 

to note the climatological values output with the equation probabilities are still valid as they do 

not depend on current sounding data. 

6.2 Future Work 

It is possible more data from a longer POR would improve equation performance. The POR 

for this task was 17 years, 1995-2011 . The first tool the AMU created for the 45 WS was also 

developed using a warm season POR of 17 years, and was successful in significantly 

outperforming every forecast benchmark. The main difference between the two tasks was the 

time periods. The spatial area used for the 45 WS tool was larger, but not significantly. In the 45 

WS tool, the forecast was valid anytime between 0700 to midnight local time, a 17 -hour period. 

In this task, the data was stratified into four three-hour time periods in a 12-hour period. The 

lightning occurrences had to be separated into the four time periods, reducing the number with 

which to develop relationships between the predictors and lightning occurrence within each time 

period. The AMU also found this result in their task to relate GPS PW to lightning occurrence 

(Huddleston 2012) within shorter time periods. The data were stratified to the point of reducing 

30 
the number of observations in each stratification such that robust statistical relationships could 

not be realized. A longer POR would provide more lightning occurrences for each stratification, 

and it may also help to increase the time periods. 

Due to the amount of time the AMU spent trying methods to improve equation performance, 

including changing the POR that resulted in the need to recalculate some of the predictors, 

there was not enough time to create equations for TIX or other airfields. The AMU also did not 

have time to conduct other equation performance metrics such as reliability and the ability of the 

final equations to distinguish between lightning and non-lightning days. The AMU did create 

charts to determine the ability of the original MCO equations to distinguish between lightning 

and non-lightning days. In order to have enough samples, the AMU combined data from all 

months to evaluate each time period and created the charts in Figure 15. For good 

performance, the blue curves should have a maximum occurrence in the lower probability 

values decreasing to a minimum at higher probability values, and the red curves should have a 

minimum in the lower probability values increasing to a maximum at the higher values. The 

equations were able to distinguish non-lightning days in all periods, but performance was best in 

the 1500-1800 and 0000-0300 UTC periods representing the beginning and end of the day, 

respectively. They were able to distinguish lightning days better in the afternoon time periods of 

1800-2100 and 2100-0000 UTC than during 1500-1800 and 0000-0300 UTC. Still, the curves 

were spread over most of the range of forecast probabilities indicating ambiguous performance. 

15-1800 UTC 18-2100 UTC 

0.6 !\ 

0.6 

0.5 0.5 Ql I \ Ql 

~0 4 ~0.4 

1 

....... Non-Lightning Days t I!! I \& 1-+- Non-Lightning Days ~ I!! (\ ~0.3 ~0.3 ---Lightning Days ... I !\ '\ 1--- Lightning Days ... I \ ... ... ...--... 00.2 00.2 

~ I / '---..---.._ ~ I \ / \ 0.1 0.1 ..-:::--

0 v ~ ~ 0 

I ~ _..::::::- ~ 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

Forecast Probabilities Forecast Probabilities 

21-0000 UTC 00-0300 UTC 

0.6 0.6 

0.5 0.5 !\ Ql Ql 

~ 0.4 I \ ., I:-Non-Lightning Days[ ~0.4 

A I!! I \/""'\ .. ---Lightning Days ~ 0.3 ~0 3 ... I \ ... I / \ \ ... !-+- Non-Lightning Days ~ ~ 

... 0 0.2 00.2 ~ I '- ~ ""- ~ 1/ "' \ ---Lightning Days 

0.1 I------- - ~ 0.1 

0 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

0 

0 

v 

0.1 0.2 0.3 

~ 

0.4 0.5 0.6 0.7 0.8 0.9 1 

Forecast Probabilities Forecast Probabilities 

Figure 15. Forecast probability distributions for lightning (red) and non-lightning (blue) 

days in the verification data in the four time periods for MCO, all months, and for the 

original POR 1989-1995. The y-axis values are the frequency of occurrence of each 

probability value, and the x-axis values are the forecast probability values output by the 

equations. 

Based on these results, future work should involve collecting more data over a longer POR, 

developing equations for more airfields, and conducting other verification tests of the equations 

to determine all aspects of their performance. This will create equations that provide a good 

estimate of lightning occurrence at the airfields and in the time periods of interest and give the 

forecasters a good first guess from which to build a forecast from other data sources. 

31 
References 

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33 
Acronyms 

45WS 45th Weather Squadron NWS MLB National Weather Service in 

AMU Applied Meteorology Unit Melbourne, FL 

AWIPS Advanced Weather Interactive Pers One-Day Persistence 

Processing System POR Period of Record 

CCAFS Cape Canaveral Air Force Station PW Precipitable Water 

CT Cross Totals RH Relative Humidity 

CWA County Warning Area RHas Mean RH in the 825-525 mb layer 

DCiimo Daily Climatology RHas Mean RH in the 800-600 mb layer 

DE Detection Efficiency SE Southeast FR 

FR Flow Regime SE-1 Southeast 1 FR 

FR2 2-speed FR SE-2 Southeast 2 FR 

FR3 3-speed FR Sl Showalter Index 

GUI Graphical User Interface ss Skill Score 

JAX Jacksonville, FL 3-letter identifier sw Southwest FR 

Kl K-lndex SW-1 Southwest 1 FR 

KSC Kennedy Space Center SW-2 Southwest 2 FR 

Ll Lifted Index SWEAT Severe Weather ThrEAT Index 

MCO Orlando International Airport Tsoo Temperature at 500mb 

MFL Miami, FL 3-letter identifier TBW Tampa, FL 3-letter identifier 

MIDDS Meteorological Interactive Data Tl Thompson Index 

Display System TIX Space Coast Regional Airport 

MLB Melbourne International Airport TT Total Totals 

NE Northeast FR VT Vertical Totals 

NLDN National Lightning Detection WMO World Meteorological Organization Network 

NW Northwest FR XMR CCAFS rawinsonde 3-letter 

identifier 

34 
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. 

35