1. 2004 NASA FACULTY FELLOWSHIP PROGRAM JOHN F. KENNEDY SPACE
CENTER UNIVERSITY OF CENTRAL FLORIDA STATISTICAL FORECASTING OF
LIGHTNING CESSATION James Ervin Glover, Ph.D. Associate Professor
Department of Computer Science and Mathematics Oral Roberts
University KSC Colleague: Francis J. Merceret CCAFS/45WS Colleague:
William P. Roeder ABSTRACT This study developed new methods for
applying statistical curve fitting to slowing lightning flash rates
in decaying thunderstorms. The curves are then utilized to estimate
the probability of one or more lightning flashes occurring by
observing cloud-to-ground lightning in selected thunderstorms
within a ten nautical mile radius of Cape Canaveral Air Force
Station. H-1
2. STATISTICAL FORECASTING OF LIGHTNING CESSATION Jim E. Glover
1. INTRODUCTION Problem/Objective: Forecasting the end of lightning
is one of the most important operational weather challenges at Cape
Canaveral Air Force Station (CCAFS) and NASA Kennedy Space center
(KSC). The Air Forces 45th Weather Squadron (45 WS) provides
comprehensive meteorological services to operations at these
locations. There is little objective guidance on how to predict the
end of lightning, so 45 WS must be conservative in ending their
lightning advisories to ensure personnel safety. Unfortunately,
this costs a lot of money and delays processing to meet space
launch schedules. One of the 45 WS strategic goals is to end
lightning advisories more quickly, while maintaining safety. The 45
WS teamed with KSC to begin the Statistical Forecasting of
Lightning Cessation project, which was funded under the NASA
Faculty Fellowship Program of 2004. Phase-1 of the project had two
goals: 1) create a climatology of the times between last and
next-to-last lightning flashes, and 2) determine if curve fitting
to lightning flash rates in the decaying phase of thunderstorms
could be used to predict the time when the probability of another
flash drops below some low operational threshold. The first goal
was designed to provide some immediate object guidance to the
forecasters on ending lightning advisories. The second goal was
designed as a proof-of-concept for future techniques. The long-term
vision is a system that will automatically analyze lightning
flashes from individual storms in real-time, identify which are
decaying and display the time until the probability of another
flash falls below some low operational threshold, flagging which
ones have already met that threshold, so that their lightning
advisories should be considered for cancellation. 2. Project
Summary 45 WS needs to end lightning advisories sooner, while
maintaining safety. This is a very tough, technical challenge. For
safety, 45WS has to be conservative in ending lightning advisories.
This proves to be costly to CCAFS/KSC ground processing operations
and solving the problem is one of the strategic goals of 45WS for
2004. Long-Term Vision: Development of software that will
automatically analyze lightning flash-rates for local storms in
real-time and flag the storms that are decaying and the storms
whose advisories should be considered for canceling. 3. Methodology
Restricted analysis to CGLSS data (only cloud-to-ground lightning
was considered) 58 convective season storms were analyzed A 59th
composite storm was created, consisting of the mean of all 58 storm
strike-rates over each unit time interval Sample Years Year Number
of Storms 1999 12 2000 13 2001 14 2002 7 2003 12 H-2
3. Two Approaches 1. Climatological distribution of times
between last flash and next to last flash 2. Curve fitting of
slowing flash rates in the decaying phase of a thunderstorm
Climatological Distribution Method Times between last two flashes
Generates empirical chart Provides probability that most recent
flash is last one, given time history Also gives time interval
corresponding to a specified probability of another flash
Forecaster can use to determine when to cancel Phase II
Curve-Fitting Method Slowing flash rates in decaying phase Derive
probability that there will be at least one more flash in a
specified time Derive probability that most recent flash is the
last one 4. Theoretical Background Let the continuous random
variable T with probability density function f(t) represent the
time to failure (cessation) of lightning strikes in a storm. The
reliability at time t, R(t) of the storm is the probability that it
will continue to last (strike) for at least a specified time. ),(1
)(1)( )()( 0 tF dttfdttf tTPtR t t = == >= (1) where F(t) is the
cumulative distribution of T, with F(t)=f(t). )( )()( )( tR tFttF
ttimetosurvivedstormtheift]t[t,inceasewillstormP + =+ (2) The
failure rate, Z(t), is given by: H-3
4. . )( )( )(1 )( )( )(' )( 1)()( lim )( )()( lim 0 0 tR tf tF
tf tR tF tRt tFttF t tR tFttF Z(t) t t = = = + = + = (3) This
expression shows the failure rate in terms of the distribution of
the time to failure. Since R(t)=1-F(t), and R(t)= F(t), we get the
differential equation ( ))(ln )( )(' )( tR dt d tR tR tZ == . (4)
Solving by integration yields: ,)( )( ln)()(ln )( ln)( = = += +
dttZ cdttZ ectR etR cdttZtR (5) where R(0)=1, F(0)=1-R(0)=0. Hence,
knowledge of either the failure rate or the density function
uniquely determines the other. CG Strike Flash Rate - 30 Jul 03 0 1
2 3 4 5 6 7 8 9 Time(Z) CG Strikes Figure 1. Cloud to Ground Strike
Rates H-4
5. KSC FACULTY FELLOWSHIP SUMMER 2004 STATISTICAL FORECASTING
OF LIGHTNING (CESSATION) (TLast T2nd Last) NumberofEvents Best-Fit
Curve (Family, Coefficients, Goodness of Fit) % Chance Of Another
Flash 25% 10% 5% 01% Average Time (integrate best-fit curve) 10 min
15 min 20 min 25 min % Chance Of Another Flash 25% 10% 5% 01%
Average Time (integrate best-fit curve) 10 min 15 min 20 min 25 min
Hypothetical Integration form of best-fit equation t-to-infinity
Figure 2. Hueristic Time Displacements PDF of DELTA-t y =
-0.0469Ln(x) + 0.1493 R2 = 0.7509 0 0.02 0.04 0.06 0.08 0.1 0.12
0.14 0.16 0.18 0 5 10 15 20 25 DELTA-t P(DELTA-t) Series1 Log.
(Series1) Figure 3. Probability Density Function for Real-Time
Displacements H-5
6. Z-Score of Ln(Displacement) y = 0.7604x - 1.3254 R2 = 0.997
-1.5 -1 -0.5 0 0.5 1 1.5 0 1 2 3 4 Ln( Displacment ) Z-Score
Z-Score Linear (Z-Score) Figure 4. Linearization of Time
Displacements Curve-Fitting Method Slowing flash rates in decaying
phase Derive probability that there will be at least one more flash
in a specified time Derive probability that most recent flash is
the last one time flashrate 1% Chance Of >= One More Flash
(integrate best-fit curve) Time to consider canceling advisory
(%-threshold TBD) Current Time When should I cancel Lightning
Advisory Figure 5. Hueristic Decaying Flash Rates H-6
7. Figure 6. Reliability Curve for Strike Rates Figure 7.
Reliability Curve for Composite Storm H-7
8. Figure 8. Probability Density Function for Strike Rates 5.
Summary 58 local thunderstorms analyzed, leading to a composite
storm Climatological distributions of Delta-t developed Available
for immediate operational use Probability thresholds vs.
wait-timedetermined Preliminary proof-of-concept for curve-fitting
of flash rates in decaying thunderstorms to predict probabilities
and timing of last flash 6. Future Plans Test techniques on
independent cases Increase sample size Filter better for individual
thunderstorms and their decay phase Include complex multi-cellular
organized storms Expand study to utilize LDAR (all types of
lightning, including lightning aloft, not just cloud-to-ground
lightning as detected by CGLSS) Hope for Phase II and Phase III in
summers of 2006 and 2007. H-8
10. REFERENCES [1] Holle, Ronald L., Murphy, Martin, Lopez,
Raul E, ,Distances and Times Between Cloud-to-Ground Flashes in a
Storm, KMI 103-79, (2003) [2] Kedem, B and Pfeiffer, R.,Short,
D.A.,Variability of Space-Time Mean Rain Rate ((1997) [3] Walpole,
R.E., Myers, R.H., Probability and Statistics for Engineers and
Scientists, Fourth Edition, MacMillan Publishing Company (1989)
H-10
11. Appendix A: Weibull Distribution (content found at
http://www.itl.nist.gov/div898/handbook/eda/section3/eda3668.htm)
Probability Density Function The formula for the probability
density function of the general Weibull distribution is (A1) where
is the shape parameter, is the location parameter and is the scale
parameter. The case where = 0 and = 1 is called the standard
Weibull distribution. The case where = 0 is called the 2-parameter
Weibull distribution. The equation for the standard Weibull
distribution reduces to (A2) Since the general form of probability
functions can be expressed in terms of the standard distribution,
all subsequent formulas in this section are given for the standard
form of the function. The following is the plot of the Weibull
probability density function. Figure A1. The Weibull probability
density function. Cumulative Distribution Function The formula for
the cumulative distribution function of the Weibull distribution is
(A3) The following is the plot of the Weibull cumulative
distribution function with the same values of as the pdf plots
above. H-11
12. Figure A2. Plot of the Weibull cumulative distribution
function. H-12
13. Appendix B: Lognormal Distribution (content found at
http://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm)
Probability Density Function A variable x is lognormally
distributed if y = ln(x) is normally distributed with ln denoting
the natural logarithm. The general formula for the probability
density function of the lognormal distribution is (B1) where is the
shape parameter, is the location parameter and m is the scale
parameter. The case where = 0 and m = 1 is called the standard
lognormal distribution. The case where equals zero is called the
2-parameter lognormal distribution. The equation for the standard
lognormal distribution is (B2) Since the general form of
probability functions can be expressed in terms of the standard
distribution, all subsequent formulas in this section are given for
the standard form of the function. The following is the plot of the
lognormal probability density function for four values of . Figure
B1. The lognormal probability density function. Cumulative
Distribution Function The formula for the cumulative distribution
function of the lognormal distribution is (B3) H-13
14. where is the cumulative distribution function of the normal
distribution. The following is the plot of the lognormal cumulative
distribution function with the same values of as the pdf plots
above. Figure B2. Plot of the lognormal cumulative distribution
function. The formulas below are with the location parameter equal
to zero and the scale parameter equal to one. Mean Median Scale
parameter m (= 1 if scale parameter not specified). Mode Range Zero
to positive infinity Standard Deviation Skewness Kurtosis
Coefficient of Variation H-14
15. Appendix C: Developed Software Descriptions
======================== PROGRAM A-STAR ========================
A-STAR READS A SEQUENCE OF NUMBERS OF STRIKES AT A SEQUENCE OF
TIMES, t, IN UNIT INCREMENTS OF MINUTES AND DETERMINES STRIKE RATES
AND TIME DISPLACEMENTS BETWEEN THE (2ND-TO-LAST AND LAST) STRIKES
AND BETWEEN THE (3RD-TO-LAST AND 2ND-TO-LAST) STRIKES, TO GENERATE
A CLIMATOLOGY FOR TIME DISPLACEMENTS BETWEEEN STRIKES, AND TO
DETERMINE OPTIMAL TIMES FOR CANCELLING ADVISORIES, BASED UPON
EMPIRICALLY COMPUTED RELIABILITIES DERIVED FROM STORM SIGNATURES.
THE ROUTINE GENERATES A CUMULATIVE DISTRIBUTION F(t) AS A FUNCTION
OF TIME t AND A CORRESPONDING RELIABILITY FUNCTION R(t) FOR THE
RANDOM VARIABLE T, WHERE T IS THE EXPECTED OR MEAN TIME TO FAILURE
(E.G., CESSATION OF LIGHTNING STRIKES) IN A MEASURED STORM. THE
RELIABILITY AT TIME t IS GIVEN BY: R(t) = 1 - F(t) = P( T > t ).
THE ANALYST IS QUERIED FOR A DESIRED MAXIMUM PROBABILITY (%) FOR
THE EXPECTED OCCURRENCE OF ONE OR MORE LIGHTNING STRIKES AND THE
ROUTINE DETERMINES THE TIME t CORRESPONDING TO THE RELIABILITY
R(t). A SUMMARY FILE IS GENERATED INTO A-STAR.OT0, CONSISTING OF:
STORM #, DATE OF STORM, DURATION TIME PER STORM, AND CUMULATIVE
NUMBER OF STRIKES PER STORM.
==================================================================
======================== PROGRAM B-STAR ========================
B-STAR READS A SEQUENCE < t > OF VALUES OF SOME RANDOM
VARIABLE T (e.g., LIGHTNING DISPLACEMENTS IN MINUTES BETWEEN
2ND-TO-LAST AND LAST STRIKES OR BETWEEN 3RD-TO-LAST AND 2ND-TO-LAST
STRIKES, GENERATING A CLIMATOLOGY FOR TIME DISPLACEMENTS BETWEEEN
STRIKES AND DETERMINING OPTIMAL TIMES FOR CANCELLING ADVISORIES,
BASED UPON EMPIRICALLY COMPUTED RELIABILITIES DERIVED FROM STORM
SIGNATURES.) THE ROUTINE GENERATES FREQUENCY/CUMULATIVE FREQUENCY
DISTRIBUTIONS, BASED UPON TACIT SORTING, BINNING, AND
NORMALIZATION. A RELIABILITY DISTRIBUTION R(t) IS GENERATED FOR THE
RANDOM VARIABLE T, WHERE t IS THE EXPECTED OR MEAN DISPLACEMENT
TIME TO FAILURE ( e.g., THE OCCURRENCE OF AT LEAST ONE MORE STRIKE
TIME DISPLACEMENT HAVING SIZE EXCEEDING g(T) ) FOR SOME FUNCTION g.
MOREOVER, THE ANALYST IS PROMPTED FOR A DESIRED MAXIMUM PROBABILITY
(%) FOR THE EXPECTED OCCURRENCE OF AT MOST ONE MORE VALUE OF g(T)
FOR WHICH T EXCEEDS t AND THE ROUTINE DETERMINES THE VALUE t
CORRESPONDING TO THE RELIABILITY R(t). (NOTE, E.G., THAT IS IS
NATURALLY EXPECTED THAT DECREASING NUMBERS OF STORMS WILL HAVE
INCREASING NUMBERS OF TIME DISPLACEMENTS BETWEEN SUCCESSIVE
LIGHTNING STRIKES.)
==================================================================
H-15
16. ======================== PROGRAM C-STAR
======================== C-STAR READS A SEQUENCE OF BLOCKS OF
EVENTS, EACH CONSISTING OF A SEQUENCE OF NUMBERS OF LIGHTNING
STRIKES AT A SEQUENCE OF TIMES, t, IN UNIT INCREMENTS OF MINUTES
AND DETERMINES STRIKE RATES AND TIME DISPLACEMENTS BETWEEN THE
(2ND-TO-LAST AND LAST) STRIKES OR BETWEEN THE (3RD-TO-LAST AND
2ND-TO-LAST) STRIKES, TO GENERATE A CLIMATOLOGY FOR TIME
DISPLACEMENTS BETWEEEN STRIKES, AND TO DETERMINE OPTIMAL TIMES FOR
CANCELLING ADVISORIES, BASED UPON EMPIRICALLY COMPUTED
RELIABILITIES DERIVED FROM STORM SIGNATURES. A RELIABILITY
DISTRIBUTION IS ALSO GENERATED FOR THE RANDOM VARIABLE T, WHERE T
IS THE EXPECTED OR MEAN TIME TO FAILURE (CESSATION OF LIGHTNING
STRIKES) IN EACH MEASURED STORM. THIS PROCESS IS CYCLICALLY
REPEATED FOR EACH STORM IN THE SEQUENCE. THE ROUTINE THEN GENERATES
A MATRIX OF TIMES t CORREESPONDING TO A DESIRED SEQUENCE OF
RELIABILITIES, R(t), AS WELL AS THE AVERAGE VALUE OF t ASSOCIATED
WITH EACH VALUE OF R(t), WHERE R(t) = 1 - F(t) = P( T > t) AND
F(t) IS THE CUMULATIVE DISTIBUTION OF T. R(t), THE RELIABILITY OF T
AT TIME t, IS THE PROBABILITY (%) OF THE EXPECTED OCCURRENCE OF ONE
OR MORE LIGHTNING STRIKES AFTER TIME t.
==================================================================
======================== PROGRAM D-STAR ========================
D-STAR READS A SEQUENCE OF PMAX STORM EVENTS CONSISTING OF Q0
VARYING LIGHTNING STRIKE RATES AS A FUNCTION OF TIME t IN UNIT
INCREMENTS OF MINUTES AND DETERMINES A COMPOSITE STORM, CONSISTING
OF V0 COMPUTED MEAN STRIKE RATES AT EACH TIME t , WHERE THE TIME
DURATION OF STORMS VARIES. THE ROUTINE THEN DETERMINES TIME
DISPLACEMENTS BETWEEN THE (2ND-TO-LAST AND LAST) STRIKES AND
BETWEEN THE (3RD-TO-LAST AND 2ND-TO-LAST) STRIKES,GENERATING TIME
DISPLACEMENTS BETWEEEN STRIKES FOR THE COMPOSITE STORM AND
DETERMINING OPTIMAL TIMES FOR CANCELLING ADVISORIES, BASED UPON
EMPIRICALLY COMPUTED RELIABILITIES DERIVED FROM THE STORM
SIGNATURE. THE ROUTINE GENERATES A CUMULATIVE DISTRIBUTION F(t) AS
A FUNCTION OF TIME t AND A CORRESPONDING RELIABILITY FUNCTION R(t)
FOR THE RANDOM VARIABLE T, WHERE T IS THE EXPECTED OR MEAN TIME TO
FAILURE (E.G., CESSATION OF LIGHTNING STRIKES) IN A MEASURED STORM.
THE RELIABILITY AT TIME t IS GIVEN BY: R(t) = 1 - F(t) = P( T >
t ). THE ANALYST IS QUERIED FOR A DESIRED MAXIMUM PROBABILITY (%)
FOR THE EXPECTED OCCURRENCE OF ONE OR MORE LIGHTNING STRIKES AND
THE ROUTINE DETERMINES THE TIME t CORRESPONDING TO THE RELIABILITY
R(t). THE ANALYST MAY ENTER A DESIRED MAXIMUM VALUE FOR THE
INTEGER-VALUED DURATION TIMES IN MINUTES FOR EACH STORM TO BE
SELECTED FROM D-STAR.INP AND ANALYZED. THE MAXIMUM DIMENSIONS OF
P(N,M) P(10,400), WHERE N IS THE NUMBER OF STORMS AND M IS THE
NUMBER OF MINUTES OF DURATION. A FILE OF THE STORM WITH MEAN VALUES
OF NUMBER OF STRIKES AT TIME t IS GENERATED INTO D-STAR.OT8. D-STAR
IS A VARIANT OF A-STAR.
==================================================================
H-16
17. ======================== PROGRAM E-STAR
======================== E-STAR READS A SEQUENCE OF PMAX STORM
EVENTS CONSISTING OF Q0 VARYING LIGHTNING STRIKE RATES AS A
FUNCTION OF TIME t IN UNIT INCREMENTS OF MINUTES AND DETERMINES A
COMPOSITE STORM, CONSISTING OF V0 COMPUTED MEAN STRIKE RATES AT
EACH TIME t , WHERE THE TIME DURATION OF STORMS VARIES. THE ROUTINE
THEN DETERMINES TIME DISPLACEMENTS BETWEEN THE (2ND-TO-LAST AND
LAST) STRIKES AND BETWEEN THE (3RD-TO-LAST AND 2ND-TO-LAST)
STRIKES,GENERATING TIME DISPLACEMENTS BETWEEEN STRIKES FOR THE
COMPOSITE STORM AND DETERMINING OPTIMAL TIMES FOR CANCELLING
ADVISORIES, BASED UPON EMPIRICALLY COMPUTED RELIABILITIES DERIVED
FROM THE STORM SIGNATURE. THE ROUTINE GENERATES A CUMULATIVE
DISTRIBUTION F(t) AS A FUNCTION OF TIME t AND A CORRESPONDING
RELIABILITY FUNCTION R(t) FOR THE RANDOM VARIABLE T, WHERE T IS THE
EXPECTED OR MEAN TIME TO FAILURE (E.G., CESSATION OF LIGHTNING
STRIKES) IN A MEASURED STORM. THE RELIABILITY AT TIME t IS GIVEN
BY: R(t) = 1 - F(t) = P( T > t ). THE ANALYST IS QUERIED FOR A
DESIRED MAXIMUM PROBABILITY (%) FOR THE EXPECTED OCCURRENCE OF ONE
OR MORE LIGHTNING STRIKES AND THE ROUTINE DETERMINES THE TIME t
CORRESPONDING TO THE RELIABILITY R(t). THE DETERMINES THE MAXIMUM
STORM DURATION, DMAX, AND UTILIZES THIS DURATION TIME IN MINUTES
FOR EACH STORM TO BE SELECTED FROM E-STAR.INP AND ANALYZED. THE
MAXIMUM DIMENSIONS OF P(N,M) P(40,400), WHERE N IS THE NUMBER OF
STORMS AND M IS THE NUMBER OF MINUTES OF DURATION. A CUMULATIVE
FILE OF STORMS WITH MEAN VALUES OF NUMBER OF STRIKES AT TIME t IS
GENERATED INTO E-STAR.OT9. E-STAR IS A VARIANT OF D-STAR.
==================================================================
======================== PROGRAM F-STAR ========================
F-STAR READS A SEQUENCE OF PMAX STORM EVENTS CONSISTING OF Q0
VARYING LIGHTNING STRIKE RATES AS A FUNCTION OF TIME t IN UNIT
INCREMENTS OF MINUTES AND FILTERS THOSE STORMS FROM THE SEQUENCE
FOR WHICH TM LIES IN THE DESIRED INTERVAL [ T1,T2 ] AND CS LIES IN
THE DESIRED INTERVAL [ CS1,CS2 ] , WHERE TM IS THE TOTAL DURATION
OF EACH STORM (IN MINUTES) AND CS IS THE CUMULATIVE NUMBER OF
STRIKES OF EACH STORM. FILTERED STORMS ARE PRINTED IN F-STAR.OT2
WITH HEADERS FOR FURTHER ANALYSIS.
==================================================================
======================== PROGRAM G-STAR ========================
G-STAR READS A SINGLE STORM EVENT CONSISTING OF Q0 VARYING
LIGHTNING STRIKE RATES AS A FUNCTION OF TIME t IN UNIT INCREMENTS
OF MINUTES AND PRINTS THE MEASUREMENTS AS EITHER A-STAR.INP OR
B-STAR.INP.
==================================================================
H-17
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
1. 2004 NASA FACULTY FELLOWSHIP PROGRAM JOHN F. KENNEDY SPACE
CENTER UNIVERSITY OF CENTRAL FLORIDA STATISTICAL FORECASTING OF
LIGHTNING CESSATION James Ervin Glover, Ph.D. Associate Professor
Department of Computer Science and Mathematics Oral Roberts
University KSC Colleague: Francis J. Merceret CCAFS/45WS Colleague:
William P. Roeder ABSTRACT This study developed new methods for
applying statistical curve fitting to slowing lightning flash rates
in decaying thunderstorms. The curves are then utilized to estimate
the probability of one or more lightning flashes occurring by
observing cloud-to-ground lightning in selected thunderstorms
within a ten nautical mile radius of Cape Canaveral Air Force
Station. H-1
2. STATISTICAL FORECASTING OF LIGHTNING CESSATION Jim E. Glover
1. INTRODUCTION Problem/Objective: Forecasting the end of lightning
is one of the most important operational weather challenges at Cape
Canaveral Air Force Station (CCAFS) and NASA Kennedy Space center
(KSC). The Air Forces 45th Weather Squadron (45 WS) provides
comprehensive meteorological services to operations at these
locations. There is little objective guidance on how to predict the
end of lightning, so 45 WS must be conservative in ending their
lightning advisories to ensure personnel safety. Unfortunately,
this costs a lot of money and delays processing to meet space
launch schedules. One of the 45 WS strategic goals is to end
lightning advisories more quickly, while maintaining safety. The 45
WS teamed with KSC to begin the Statistical Forecasting of
Lightning Cessation project, which was funded under the NASA
Faculty Fellowship Program of 2004. Phase-1 of the project had two
goals: 1) create a climatology of the times between last and
next-to-last lightning flashes, and 2) determine if curve fitting
to lightning flash rates in the decaying phase of thunderstorms
could be used to predict the time when the probability of another
flash drops below some low operational threshold. The first goal
was designed to provide some immediate object guidance to the
forecasters on ending lightning advisories. The second goal was
designed as a proof-of-concept for future techniques. The long-term
vision is a system that will automatically analyze lightning
flashes from individual storms in real-time, identify which are
decaying and display the time until the probability of another
flash falls below some low operational threshold, flagging which
ones have already met that threshold, so that their lightning
advisories should be considered for cancellation. 2. Project
Summary 45 WS needs to end lightning advisories sooner, while
maintaining safety. This is a very tough, technical challenge. For
safety, 45WS has to be conservative in ending lightning advisories.
This proves to be costly to CCAFS/KSC ground processing operations
and solving the problem is one of the strategic goals of 45WS for
2004. Long-Term Vision: Development of software that will
automatically analyze lightning flash-rates for local storms in
real-time and flag the storms that are decaying and the storms
whose advisories should be considered for canceling. 3. Methodology
Restricted analysis to CGLSS data (only cloud-to-ground lightning
was considered) 58 convective season storms were analyzed A 59th
composite storm was created, consisting of the mean of all 58 storm
strike-rates over each unit time interval Sample Years Year Number
of Storms 1999 12 2000 13 2001 14 2002 7 2003 12 H-2
3. Two Approaches 1. Climatological distribution of times
between last flash and next to last flash 2. Curve fitting of
slowing flash rates in the decaying phase of a thunderstorm
Climatological Distribution Method Times between last two flashes
Generates empirical chart Provides probability that most recent
flash is last one, given time history Also gives time interval
corresponding to a specified probability of another flash
Forecaster can use to determine when to cancel Phase II
Curve-Fitting Method Slowing flash rates in decaying phase Derive
probability that there will be at least one more flash in a
specified time Derive probability that most recent flash is the
last one 4. Theoretical Background Let the continuous random
variable T with probability density function f(t) represent the
time to failure (cessation) of lightning strikes in a storm. The
reliability at time t, R(t) of the storm is the probability that it
will continue to last (strike) for at least a specified time. ),(1
)(1)( )()( 0 tF dttfdttf tTPtR t t = == >= (1) where F(t) is the
cumulative distribution of T, with F(t)=f(t). )( )()( )( tR tFttF
ttimetosurvivedstormtheift]t[t,inceasewillstormP + =+ (2) The
failure rate, Z(t), is given by: H-3
4. . )( )( )(1 )( )( )(' )( 1)()( lim )( )()( lim 0 0 tR tf tF
tf tR tF tRt tFttF t tR tFttF Z(t) t t = = = + = + = (3) This
expression shows the failure rate in terms of the distribution of
the time to failure. Since R(t)=1-F(t), and R(t)= F(t), we get the
differential equation ( ))(ln )( )(' )( tR dt d tR tR tZ == . (4)
Solving by integration yields: ,)( )( ln)()(ln )( ln)( = = += +
dttZ cdttZ ectR etR cdttZtR (5) where R(0)=1, F(0)=1-R(0)=0. Hence,
knowledge of either the failure rate or the density function
uniquely determines the other. CG Strike Flash Rate - 30 Jul 03 0 1
2 3 4 5 6 7 8 9 Time(Z) CG Strikes Figure 1. Cloud to Ground Strike
Rates H-4
5. KSC FACULTY FELLOWSHIP SUMMER 2004 STATISTICAL FORECASTING
OF LIGHTNING (CESSATION) (TLast T2nd Last) NumberofEvents Best-Fit
Curve (Family, Coefficients, Goodness of Fit) % Chance Of Another
Flash 25% 10% 5% 01% Average Time (integrate best-fit curve) 10 min
15 min 20 min 25 min % Chance Of Another Flash 25% 10% 5% 01%
Average Time (integrate best-fit curve) 10 min 15 min 20 min 25 min
Hypothetical Integration form of best-fit equation t-to-infinity
Figure 2. Hueristic Time Displacements PDF of DELTA-t y =
-0.0469Ln(x) + 0.1493 R2 = 0.7509 0 0.02 0.04 0.06 0.08 0.1 0.12
0.14 0.16 0.18 0 5 10 15 20 25 DELTA-t P(DELTA-t) Series1 Log.
(Series1) Figure 3. Probability Density Function for Real-Time
Displacements H-5
6. Z-Score of Ln(Displacement) y = 0.7604x - 1.3254 R2 = 0.997
-1.5 -1 -0.5 0 0.5 1 1.5 0 1 2 3 4 Ln( Displacment ) Z-Score
Z-Score Linear (Z-Score) Figure 4. Linearization of Time
Displacements Curve-Fitting Method Slowing flash rates in decaying
phase Derive probability that there will be at least one more flash
in a specified time Derive probability that most recent flash is
the last one time flashrate 1% Chance Of >= One More Flash
(integrate best-fit curve) Time to consider canceling advisory
(%-threshold TBD) Current Time When should I cancel Lightning
Advisory Figure 5. Hueristic Decaying Flash Rates H-6
7. Figure 6. Reliability Curve for Strike Rates Figure 7.
Reliability Curve for Composite Storm H-7
8. Figure 8. Probability Density Function for Strike Rates 5.
Summary 58 local thunderstorms analyzed, leading to a composite
storm Climatological distributions of Delta-t developed Available
for immediate operational use Probability thresholds vs.
wait-timedetermined Preliminary proof-of-concept for curve-fitting
of flash rates in decaying thunderstorms to predict probabilities
and timing of last flash 6. Future Plans Test techniques on
independent cases Increase sample size Filter better for individual
thunderstorms and their decay phase Include complex multi-cellular
organized storms Expand study to utilize LDAR (all types of
lightning, including lightning aloft, not just cloud-to-ground
lightning as detected by CGLSS) Hope for Phase II and Phase III in
summers of 2006 and 2007. H-8
10. REFERENCES [1] Holle, Ronald L., Murphy, Martin, Lopez,
Raul E, ,Distances and Times Between Cloud-to-Ground Flashes in a
Storm, KMI 103-79, (2003) [2] Kedem, B and Pfeiffer, R.,Short,
D.A.,Variability of Space-Time Mean Rain Rate ((1997) [3] Walpole,
R.E., Myers, R.H., Probability and Statistics for Engineers and
Scientists, Fourth Edition, MacMillan Publishing Company (1989)
H-10
11. Appendix A: Weibull Distribution (content found at
http://www.itl.nist.gov/div898/handbook/eda/section3/eda3668.htm)
Probability Density Function The formula for the probability
density function of the general Weibull distribution is (A1) where
is the shape parameter, is the location parameter and is the scale
parameter. The case where = 0 and = 1 is called the standard
Weibull distribution. The case where = 0 is called the 2-parameter
Weibull distribution. The equation for the standard Weibull
distribution reduces to (A2) Since the general form of probability
functions can be expressed in terms of the standard distribution,
all subsequent formulas in this section are given for the standard
form of the function. The following is the plot of the Weibull
probability density function. Figure A1. The Weibull probability
density function. Cumulative Distribution Function The formula for
the cumulative distribution function of the Weibull distribution is
(A3) The following is the plot of the Weibull cumulative
distribution function with the same values of as the pdf plots
above. H-11
12. Figure A2. Plot of the Weibull cumulative distribution
function. H-12
13. Appendix B: Lognormal Distribution (content found at
http://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm)
Probability Density Function A variable x is lognormally
distributed if y = ln(x) is normally distributed with ln denoting
the natural logarithm. The general formula for the probability
density function of the lognormal distribution is (B1) where is the
shape parameter, is the location parameter and m is the scale
parameter. The case where = 0 and m = 1 is called the standard
lognormal distribution. The case where equals zero is called the
2-parameter lognormal distribution. The equation for the standard
lognormal distribution is (B2) Since the general form of
probability functions can be expressed in terms of the standard
distribution, all subsequent formulas in this section are given for
the standard form of the function. The following is the plot of the
lognormal probability density function for four values of . Figure
B1. The lognormal probability density function. Cumulative
Distribution Function The formula for the cumulative distribution
function of the lognormal distribution is (B3) H-13
14. where is the cumulative distribution function of the normal
distribution. The following is the plot of the lognormal cumulative
distribution function with the same values of as the pdf plots
above. Figure B2. Plot of the lognormal cumulative distribution
function. The formulas below are with the location parameter equal
to zero and the scale parameter equal to one. Mean Median Scale
parameter m (= 1 if scale parameter not specified). Mode Range Zero
to positive infinity Standard Deviation Skewness Kurtosis
Coefficient of Variation H-14
15. Appendix C: Developed Software Descriptions
======================== PROGRAM A-STAR ========================
A-STAR READS A SEQUENCE OF NUMBERS OF STRIKES AT A SEQUENCE OF
TIMES, t, IN UNIT INCREMENTS OF MINUTES AND DETERMINES STRIKE RATES
AND TIME DISPLACEMENTS BETWEEN THE (2ND-TO-LAST AND LAST) STRIKES
AND BETWEEN THE (3RD-TO-LAST AND 2ND-TO-LAST) STRIKES, TO GENERATE
A CLIMATOLOGY FOR TIME DISPLACEMENTS BETWEEEN STRIKES, AND TO
DETERMINE OPTIMAL TIMES FOR CANCELLING ADVISORIES, BASED UPON
EMPIRICALLY COMPUTED RELIABILITIES DERIVED FROM STORM SIGNATURES.
THE ROUTINE GENERATES A CUMULATIVE DISTRIBUTION F(t) AS A FUNCTION
OF TIME t AND A CORRESPONDING RELIABILITY FUNCTION R(t) FOR THE
RANDOM VARIABLE T, WHERE T IS THE EXPECTED OR MEAN TIME TO FAILURE
(E.G., CESSATION OF LIGHTNING STRIKES) IN A MEASURED STORM. THE
RELIABILITY AT TIME t IS GIVEN BY: R(t) = 1 - F(t) = P( T > t ).
THE ANALYST IS QUERIED FOR A DESIRED MAXIMUM PROBABILITY (%) FOR
THE EXPECTED OCCURRENCE OF ONE OR MORE LIGHTNING STRIKES AND THE
ROUTINE DETERMINES THE TIME t CORRESPONDING TO THE RELIABILITY
R(t). A SUMMARY FILE IS GENERATED INTO A-STAR.OT0, CONSISTING OF:
STORM #, DATE OF STORM, DURATION TIME PER STORM, AND CUMULATIVE
NUMBER OF STRIKES PER STORM.
==================================================================
======================== PROGRAM B-STAR ========================
B-STAR READS A SEQUENCE < t > OF VALUES OF SOME RANDOM
VARIABLE T (e.g., LIGHTNING DISPLACEMENTS IN MINUTES BETWEEN
2ND-TO-LAST AND LAST STRIKES OR BETWEEN 3RD-TO-LAST AND 2ND-TO-LAST
STRIKES, GENERATING A CLIMATOLOGY FOR TIME DISPLACEMENTS BETWEEEN
STRIKES AND DETERMINING OPTIMAL TIMES FOR CANCELLING ADVISORIES,
BASED UPON EMPIRICALLY COMPUTED RELIABILITIES DERIVED FROM STORM
SIGNATURES.) THE ROUTINE GENERATES FREQUENCY/CUMULATIVE FREQUENCY
DISTRIBUTIONS, BASED UPON TACIT SORTING, BINNING, AND
NORMALIZATION. A RELIABILITY DISTRIBUTION R(t) IS GENERATED FOR THE
RANDOM VARIABLE T, WHERE t IS THE EXPECTED OR MEAN DISPLACEMENT
TIME TO FAILURE ( e.g., THE OCCURRENCE OF AT LEAST ONE MORE STRIKE
TIME DISPLACEMENT HAVING SIZE EXCEEDING g(T) ) FOR SOME FUNCTION g.
MOREOVER, THE ANALYST IS PROMPTED FOR A DESIRED MAXIMUM PROBABILITY
(%) FOR THE EXPECTED OCCURRENCE OF AT MOST ONE MORE VALUE OF g(T)
FOR WHICH T EXCEEDS t AND THE ROUTINE DETERMINES THE VALUE t
CORRESPONDING TO THE RELIABILITY R(t). (NOTE, E.G., THAT IS IS
NATURALLY EXPECTED THAT DECREASING NUMBERS OF STORMS WILL HAVE
INCREASING NUMBERS OF TIME DISPLACEMENTS BETWEEN SUCCESSIVE
LIGHTNING STRIKES.)
==================================================================
H-15
16. ======================== PROGRAM C-STAR
======================== C-STAR READS A SEQUENCE OF BLOCKS OF
EVENTS, EACH CONSISTING OF A SEQUENCE OF NUMBERS OF LIGHTNING
STRIKES AT A SEQUENCE OF TIMES, t, IN UNIT INCREMENTS OF MINUTES
AND DETERMINES STRIKE RATES AND TIME DISPLACEMENTS BETWEEN THE
(2ND-TO-LAST AND LAST) STRIKES OR BETWEEN THE (3RD-TO-LAST AND
2ND-TO-LAST) STRIKES, TO GENERATE A CLIMATOLOGY FOR TIME
DISPLACEMENTS BETWEEEN STRIKES, AND TO DETERMINE OPTIMAL TIMES FOR
CANCELLING ADVISORIES, BASED UPON EMPIRICALLY COMPUTED
RELIABILITIES DERIVED FROM STORM SIGNATURES. A RELIABILITY
DISTRIBUTION IS ALSO GENERATED FOR THE RANDOM VARIABLE T, WHERE T
IS THE EXPECTED OR MEAN TIME TO FAILURE (CESSATION OF LIGHTNING
STRIKES) IN EACH MEASURED STORM. THIS PROCESS IS CYCLICALLY
REPEATED FOR EACH STORM IN THE SEQUENCE. THE ROUTINE THEN GENERATES
A MATRIX OF TIMES t CORREESPONDING TO A DESIRED SEQUENCE OF
RELIABILITIES, R(t), AS WELL AS THE AVERAGE VALUE OF t ASSOCIATED
WITH EACH VALUE OF R(t), WHERE R(t) = 1 - F(t) = P( T > t) AND
F(t) IS THE CUMULATIVE DISTIBUTION OF T. R(t), THE RELIABILITY OF T
AT TIME t, IS THE PROBABILITY (%) OF THE EXPECTED OCCURRENCE OF ONE
OR MORE LIGHTNING STRIKES AFTER TIME t.
==================================================================
======================== PROGRAM D-STAR ========================
D-STAR READS A SEQUENCE OF PMAX STORM EVENTS CONSISTING OF Q0
VARYING LIGHTNING STRIKE RATES AS A FUNCTION OF TIME t IN UNIT
INCREMENTS OF MINUTES AND DETERMINES A COMPOSITE STORM, CONSISTING
OF V0 COMPUTED MEAN STRIKE RATES AT EACH TIME t , WHERE THE TIME
DURATION OF STORMS VARIES. THE ROUTINE THEN DETERMINES TIME
DISPLACEMENTS BETWEEN THE (2ND-TO-LAST AND LAST) STRIKES AND
BETWEEN THE (3RD-TO-LAST AND 2ND-TO-LAST) STRIKES,GENERATING TIME
DISPLACEMENTS BETWEEEN STRIKES FOR THE COMPOSITE STORM AND
DETERMINING OPTIMAL TIMES FOR CANCELLING ADVISORIES, BASED UPON
EMPIRICALLY COMPUTED RELIABILITIES DERIVED FROM THE STORM
SIGNATURE. THE ROUTINE GENERATES A CUMULATIVE DISTRIBUTION F(t) AS
A FUNCTION OF TIME t AND A CORRESPONDING RELIABILITY FUNCTION R(t)
FOR THE RANDOM VARIABLE T, WHERE T IS THE EXPECTED OR MEAN TIME TO
FAILURE (E.G., CESSATION OF LIGHTNING STRIKES) IN A MEASURED STORM.
THE RELIABILITY AT TIME t IS GIVEN BY: R(t) = 1 - F(t) = P( T >
t ). THE ANALYST IS QUERIED FOR A DESIRED MAXIMUM PROBABILITY (%)
FOR THE EXPECTED OCCURRENCE OF ONE OR MORE LIGHTNING STRIKES AND
THE ROUTINE DETERMINES THE TIME t CORRESPONDING TO THE RELIABILITY
R(t). THE ANALYST MAY ENTER A DESIRED MAXIMUM VALUE FOR THE
INTEGER-VALUED DURATION TIMES IN MINUTES FOR EACH STORM TO BE
SELECTED FROM D-STAR.INP AND ANALYZED. THE MAXIMUM DIMENSIONS OF
P(N,M) P(10,400), WHERE N IS THE NUMBER OF STORMS AND M IS THE
NUMBER OF MINUTES OF DURATION. A FILE OF THE STORM WITH MEAN VALUES
OF NUMBER OF STRIKES AT TIME t IS GENERATED INTO D-STAR.OT8. D-STAR
IS A VARIANT OF A-STAR.
==================================================================
H-16
17. ======================== PROGRAM E-STAR
======================== E-STAR READS A SEQUENCE OF PMAX STORM
EVENTS CONSISTING OF Q0 VARYING LIGHTNING STRIKE RATES AS A
FUNCTION OF TIME t IN UNIT INCREMENTS OF MINUTES AND DETERMINES A
COMPOSITE STORM, CONSISTING OF V0 COMPUTED MEAN STRIKE RATES AT
EACH TIME t , WHERE THE TIME DURATION OF STORMS VARIES. THE ROUTINE
THEN DETERMINES TIME DISPLACEMENTS BETWEEN THE (2ND-TO-LAST AND
LAST) STRIKES AND BETWEEN THE (3RD-TO-LAST AND 2ND-TO-LAST)
STRIKES,GENERATING TIME DISPLACEMENTS BETWEEEN STRIKES FOR THE
COMPOSITE STORM AND DETERMINING OPTIMAL TIMES FOR CANCELLING
ADVISORIES, BASED UPON EMPIRICALLY COMPUTED RELIABILITIES DERIVED
FROM THE STORM SIGNATURE. THE ROUTINE GENERATES A CUMULATIVE
DISTRIBUTION F(t) AS A FUNCTION OF TIME t AND A CORRESPONDING
RELIABILITY FUNCTION R(t) FOR THE RANDOM VARIABLE T, WHERE T IS THE
EXPECTED OR MEAN TIME TO FAILURE (E.G., CESSATION OF LIGHTNING
STRIKES) IN A MEASURED STORM. THE RELIABILITY AT TIME t IS GIVEN
BY: R(t) = 1 - F(t) = P( T > t ). THE ANALYST IS QUERIED FOR A
DESIRED MAXIMUM PROBABILITY (%) FOR THE EXPECTED OCCURRENCE OF ONE
OR MORE LIGHTNING STRIKES AND THE ROUTINE DETERMINES THE TIME t
CORRESPONDING TO THE RELIABILITY R(t). THE DETERMINES THE MAXIMUM
STORM DURATION, DMAX, AND UTILIZES THIS DURATION TIME IN MINUTES
FOR EACH STORM TO BE SELECTED FROM E-STAR.INP AND ANALYZED. THE
MAXIMUM DIMENSIONS OF P(N,M) P(40,400), WHERE N IS THE NUMBER OF
STORMS AND M IS THE NUMBER OF MINUTES OF DURATION. A CUMULATIVE
FILE OF STORMS WITH MEAN VALUES OF NUMBER OF STRIKES AT TIME t IS
GENERATED INTO E-STAR.OT9. E-STAR IS A VARIANT OF D-STAR.
==================================================================
======================== PROGRAM F-STAR ========================
F-STAR READS A SEQUENCE OF PMAX STORM EVENTS CONSISTING OF Q0
VARYING LIGHTNING STRIKE RATES AS A FUNCTION OF TIME t IN UNIT
INCREMENTS OF MINUTES AND FILTERS THOSE STORMS FROM THE SEQUENCE
FOR WHICH TM LIES IN THE DESIRED INTERVAL [ T1,T2 ] AND CS LIES IN
THE DESIRED INTERVAL [ CS1,CS2 ] , WHERE TM IS THE TOTAL DURATION
OF EACH STORM (IN MINUTES) AND CS IS THE CUMULATIVE NUMBER OF
STRIKES OF EACH STORM. FILTERED STORMS ARE PRINTED IN F-STAR.OT2
WITH HEADERS FOR FURTHER ANALYSIS.
==================================================================
======================== PROGRAM G-STAR ========================
G-STAR READS A SINGLE STORM EVENT CONSISTING OF Q0 VARYING
LIGHTNING STRIKE RATES AS A FUNCTION OF TIME t IN UNIT INCREMENTS
OF MINUTES AND PRINTS THE MEASUREMENTS AS EITHER A-STAR.INP OR
B-STAR.INP.
==================================================================
H-17