In support of the National Climate Assessment (NCA) program, satellite Lightning Imaging Sensor (LIS) data is used to estimate lightning nitrogen oxides (LNOx) production over the southern portion of the conterminous US. The total energy of each flash is estimated by analyzing the LIS optical event data associated with each flash (i.e., event radiance, event footprint area, and derivable event range). The LIS detects an extremely small fraction of the total flash energy; this fraction is assumed to be constant apart from the variability associated with the flash optical energy detected across the narrow (0.909 nm) LIS band. The estimate of total energy from each flash is converted to moles of LNOx production by assuming a chemical yield of 10(17) molecules Joule(-1). The LIS-inferred variable LNOx production from each flash is summed to obtain total LNOx production, and then appropriately enhanced to account for LIS detection efficiency and LIS view time. Annual geographical plots and time series of LNOx production are provided for a 16 year period (1998-2013)

Koshak, W. J.

McCaul, E. W.

Peterson, H. S.

Vant-Hull, B.

NASA Technical Reports Server

XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
Variation of a Lightning NOx Indicator for National Climate
Assessment
W. J. Koshak1∗, B. Vant-Hull2, E. W. McCaul3, and H. S. Peterson3
1Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL, U.S.A.
2City College of New York, New York, NY, U.S.A.
3Universities Space Research Association, Huntsville, AL, U.S.A.
ABSTRACT: In support of the National Climate Assessment (NCA) program, satellite Lightning Imag-
ing Sensor (LIS) data is used to estimate lightning nitrogen oxides (LNOx) production over the southern
portion of the conterminous US. The total energy of each flash is estimated by analyzing the LIS optical
event data associated with each flash (i.e., event radiance, event footprint area, and derivable event range).
The LIS detects an extremely small fraction of the total flash energy; this fraction is assumed to be constant
apart from the variability associated with the flash optical energy detected across the narrow (0.909 nm) LIS
band. The estimate of total energy from each flash is converted to moles of LNOx production by assuming
a chemical yield of 1017 molecules Joule−1. The LIS-inferred variable LNOx production from each flash
is summed to obtain total LNOx production, and then appropriately enhanced to account for LIS detection
efficiency and LIS view time. Annual geographical plots and time series of LNOx production are provided
for a 16 year period (1998-2013).
INTRODUCTION
The intense heating of air by a lightning discharge, followed by rapid cooling, results in the pro-
duction of nitrogen oxides (NOx = NO + NO2) as discussed in Chameides [1979]. The LNOx indirectly
influences our climate since these molecules are important in controlling the concentration of ozone (O3)
and hydroxyl radicals (OH) in the atmosphere [Huntrieser et al., 1998]. Since climate is most sensitive to
O3 in the upper troposphere, and since lightning NOx is the most important source of NOx in the upper
troposphere at tropical and subtropical latitudes, lightning is a particularly useful parameter to monitor for
climate assessments [Schumann and Huntrieser, 2007].
In support of the Global Change Research Act (GCRA) of 1990, the National Climate Assessment
(NCA) program analyzes the effects of global change on the natural environment, human health and welfare,
human social systems, agriculture, energy production and use, land and water resources, transportation, and
biological diversity. Participants of the NCA program analyze natural and human-induced trends in global
change, and project major trends 25 to 100 years out.
During the past few years, a software tool was developed at the NASA Marshall Space Flight Center
(MSFC) to conduct NCA-related analyses [Koshak et al., 2014]. The tool monitors and examines long-term
changes in lightning characteristics over the conterminous US (CONUS).
In this study, we have expanded the capability of the tool so that it can provide a unique estimate
of LNOx production, thereby further supporting the climate assessment process. The estimate is computed
using data from the Tropical Rainfall Measuring Mission Lightning Imaging Sensor (TRMM/LIS; Christian
et al. [1999]; Cecil et al. [2014]). Despite the 16+ years operational life of LIS thus far, the information
content of the LIS data has not been fully exploited to gain valuable insight on LNOx production. Hence,
inferring LNOx production on a flash-by-flash basis using LIS observations, as performed in this study,
represents key progress. Since the trend in lightning NOx production is sought over a long (i.e., 16 year)
∗Corresponding author, email: william.koshak@nasa.gov, Earth Science Office, NASA Marshall Space Flight Center,
Huntsville, AL USA.
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https://ntrs.nasa.gov/search.jsp?R=20140011690 2019-08-31T19:20:06+00:00Z
XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
period, it is suggested that the estimate here has some benefit over the commonly used flash extrapolation
method described in Lawrence et al. [1995]. The methodology applied to compute the LIS-inferred LNOx
estimate is described, and results (i.e., geographical distributions, time series) are provided, along with a
summary.
METHODOLOGY
Trending LNOx
The flash extrapolation method [Lawrence et al., 1995] is commonly employed to estimate LNOx
production. In such an approach, the LNOx production rate takes the form G = γP¯F [Liaw et al., 1990].
Here, F is a (typically global) flash rate, P¯ is the average NOx production per flash (e.g., a constant 250
moles/flash), and γ is a constant coefficient that converts the units of G into Teragrams of nitrogen per year
(Tg(N) yr−1). A closely related expression that integrates the production rate over time is
P =
N∑
k=1
Pk = NP¯ . (1)
Here, P is the total LNOx production (in moles), and Pk is the LNOx production from the kth flash of the
set of N flashes that occur in a specified period of time. By definition, P¯ is the mean LNOx production
per flash. Suppose the period of interest is a year, and one wanted to trend P from year to year. Whereas
N would vary from year to year in general, P¯ is restricted to one’s assumption about the mean LNOx
production per flash (i.e., P¯ would be fixed at the commonly employed value of 250 moles/flash). In reality
however, the value of P¯ also likely changes from year-to-year. Therefore, the flash extrapolation method is
not an optimal approach for trending LNOx production.
LIS-Inferred Flash NOx Production
LIS data not only provide total lightning flash count (a variable obviously important to LNOx pro-
duction), but also information about the optical brightness and spatial extent of individual lightning flashes.
This additional information is key for better understanding flash energetics, which in turn is fundamental to
flash LNOx production.
To overcome the over-restrictive nature associated with assuming a fixed mean LNOx production per
flash, LIS lightning optical event energies from each flash are examined to estimate the total flash energy,
Ek, of each kth flash observed.
P =
N∑
k=1
Pk =
N∑
k=1
(
Y
NA
)
Ek =
(
Y
NA
) N∑
k=1
Qk
βk
. (2)
Here, Y is the NOx yield and is assigned a value of 1017 molecules J−1 (see for example Borucki and
Chameides [1984]). The factor NA = 6.022× 1023 molecules mole−1 is Avogadro’s number. The quantity
Qk is the amount of optical energy emitted by the kth flash that is detected by LIS, and βk = Qk/Ek is the
fraction of the total flash energy detected by LIS.
With a flash exciting n pixels in the LIS charge coupled device (CCD) array across m LIS frames
(each frame is 2 ms in duration), the value of Qk can be expressed as (see Koshak [2010])
Qk = CA∆λ
m∑
i=1
n∑
j=1
∆ωjkξ¯λijk = CA∆λ
m∑
i=1
n∑
j=1
[
ajk cosαjk
r2jk
]
ξ¯λijk . (3)
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XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
Here, A = 2.9225 × 10−3m2 is the area of the LIS entrance aperture, and ∆λ = 0.909 × 10−3µm is the
LIS spectral bandwidth. The quantity ξ¯λijk is the LIS event “radiance” product which is actually in units
of µJ/m2/sr/µm, and C = 10−6 for converting µJ to Joules. The solid angle ∆ωjk subtended by the
event footprint at the LIS detector can be obtained in a straight-forward manner and is given in the square
brackets in the last equation of (3). The LIS event footprint area product (in units of km2) is given by ajk.
The quantity αjk is the foreshortening angle; i.e., the angle between the normal vector of the event footprint
area and the unit vector pointing from LIS to the event footprint, so that ajk cosαjk is the projected area.
The range rjk is the distance from LIS to the event footprint. These two quantities are given by
αjk = sin
−1
[(
R+ z
R+H
)
sin θjk
]
, rjk = (R+H)
sin(αjk − θjk)
sin θjk
. (4)
Here, the mean Earth radius R = 6371 km, the cloud top height H = 11 km, and the LIS orbital altitude is
z = 350 km (prior to the August 2001 orbital boost) and z = 402.5 km (following the August 2001 orbital
boost). The lens boresight angle θjk associated with the optical event is obtained by using the event CCD
address LIS data product (xpixel, ypixel) in conjunction with the LIS lens transfer function obtained from the
LIS calibration [Koshak et al., 2000].
In general, βk varies with changes in: cloud scattering properties, lightning properties, and LIS in-
strument properties. For example, the location of the flash within the thundercloud and the optical scattering
characteristics of the thundercloud represent complicating factors. A lightning flash that is embedded deeply
within an optically thick thundercloud would not produce as bright of an optical cloud-top illumination as if
the same flash occurred closer to cloud-top. In addition, a horizontally propagating flash at a given altitude
would illuminate a larger area of cloud-top than had the flash instead propagated downward, all else being
equal. But, given the large sampling of flashes and the myriad of different thundercloud morphologies en-
countered with the 16 years of LIS data employed here, we expect that many of these complications average
out. In addition, the LIS instrument has been found to be remarkably stable [Buechler et al., 2014]. There-
fore, we fix the value of βk = β = 1.8451×10−19. This is the value required such that the mean production
in the 73,292 flashes observed by LIS over CONUS in the year 1998 (an arbitrarily selected reference year)
is 250 moles/flash. With this simplification, and substituting (3) into (2), the LNOx production inferred by
LIS becomes
P =
CY A∆λ
βNA
N∑
k=1
m∑
i=1
n∑
j=1
[
ajk cosαjk
r2jk
]
ξ¯λijk , (5)
where the foreshortening angle and range are as given in (4).
Because LIS detection efficiency is under 100% and because LIS does not continually view a geo-
graphical region, the LIS flash counts are appropriately corrected (i.e., increased); see for example Cecil
et al. [2014]. Therefore, for any given 0.5 × 0.5 degree latitude/longitude bin over CONUS, there will be
No LIS counts (i.e., observed flashes), and an associated much larger projected total count Nt due to these
corrections. Hence, the total number of flashes assumed, but unobserved, is Nu = Nt − No. Even though
there is no LIS event data for the unobserved flashes, the large number of observed flashes No obtained
throughout a year (and across all seasons and the diurnal cycle) provide a reasonable estimate of the mean
LNOx production per flash for the year. Hence, a reasonable way to correct (5) for LIS detection efficiency
and view time is to express the total production Pt for a given region as
Pt =
No∑
k=1
Pk +Nu
(
1
No
No∑
k=1
Pk
)
, Pk =
CY A∆λ
βNA
m∑
i=1
n∑
j=1
[
ajk cosαjk
r2jk
]
ξ¯λijk . (6)
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XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
Table 1: Summary of flash counts, associated LNOx production, and LNOx production per flash.
Year No Po Λo Nt Pt Λt
(megamoles) (moles) (×106) (gigamoles) (moles)
1998 73,293 18.32 250.0 49.25 12.08 245.2
1999 71,806 19.79 275.6 45.88 12.41 270.5
2000 61,701 16.69 270.4 40.03 10.50 262.2
2001 71,226 16.80 235.9 43.11 9.96 231.0
2002 79,530 17.64 221.8 42.67 9.28 217.5
2003 100,090 21.42 214.1 50.44 10.72 212.5
2004 100,695 21.89 217.4 51.83 11.15 215.2
2005 96,522 20.11 208.4 47.84 9.91 205.0
2006 78,787 17.33 220.0 40.51 8.71 215.0
2007 87,181 18.25 209.4 44.37 9.12 205.5
2008 90,307 19.30 213.8 44.77 9.44 210.8
2009 95,793 18.70 195.3 48.72 9.28 190.6
2010 93,751 17.65 188.3 49.25 8.94 181.5
2011 96,680 17.05 176.3 48.99 8.43 172.1
2012 86,766 17.71 204.1 44.14 8.92 202.0
2013 80,431 15.59 193.8 40.96 7.80 190.4
RESULTS
In this section, we apply (6) to obtain geographical variations of LNOx production over CONUS
(upto 38oN latitude, the northern limit of LIS viewing) from year-to-year, and the associated total LNOx
production time-series.
Fig. 1 provides the year-to-year geographical variability of the total LNOx production, Pt, for the 8
year period 1998-2005. Fig. 2 continues the geographical series for the follow-on 8 year period 2006-2013.
The values of Pt are in units of megamoles. In addition, Table 1 summarizes the values of the variables
(No, Po, Nt, Pt), along with the average LNOx production per flash (Λ = P/N ) using the observed and
projected total values. Again, note that the value Λo ≡ 250 moles in the year 1998 since this is the reference
value employed in the calibration of βk. Finally, Fig. 3 summarizes the variability in (No, Po, Nt, Pt) as
time-series plots.
The average (given to a precision of one decimal place) of the flash count Nt in the first 8 yr period
(1998-2005) is 46.4 million flashes, and the average in the following 8 yr period (2006-2013) is 45.2 million,
a drop of only 2.5%. However, the average LNOx production Pt in the first 8 yr period is 10.7 gigamoles,
and 8.8 gigamoles in the following 8 yr period, a more substantial drop of 17.8%. On a per flash basis, the
average LNOx per flash for the respective periods is 232.4 and 196.0 moles, a drop of 15.7%.
SUMMARY
A method was introduced for estimating the LNOx production on a flash-by-flash basis using LIS data
products, and the LIS lens transfer function obtained from the laboratory calibration of the LIS. By summing
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XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
up the optical energy from the set of LIS-observed optical events in a flash, appropriately scaling the sum to
total flash energy, and employing an acceptable NOx chemical yield per Joule of flash energy, an estimate of
the production of LNOx from the LIS-observed flash is obtained. Thus, this study has emphasized that LIS
does not just simply count and locate lightning flashes, but also provides important additional information
that can be related to flash energetics, and hence LNOx production.
The method was applied to analyze the 16 year period (1998-2013) over the region of CONUS viewed
by LIS, and geographical and time-series plots of the variation of total LNOx production have been provided.
Because LNOx is an important component of climate variation, this study supports the National Climate
Assessment (NCA). Although there is a modest (2.5%) drop in total lightning count (obtained by comparing
the mean count in the first 8 yr period with the mean count in the subsequent 8 yr period), there is a more
substantial (17.8%) drop in the total LNOx production between these same two periods. Hence, lightning
energetics should not be ignored when estimating long-term trends in LNOx production. In other words,
trending lightning count alone is inadequate for monitoring the impact of lightning chemistry on climate.
Finally, the method introduced here can be applied to analyze future Geostationary Lightning Mapper
(GLM; Goodman et al. [2013]) data. Because GLM will continuously view a region (whereas LIS view
time is limited), application of the method to GLM will provide even better LNOx estimates. The method
can also be applied to analyze future International Space Station Lightning Imaging Sensor (ISS/LIS) data;
the higher inclination orbit of the ISS will allow for global LNOx estimation.
ACKNOWLEDGMENTS: This work was supported by NASA Headquarters through NASA Research
Announcement (NRA) NNH12ZDA001N under Dr. Jack Kaye, and Dr. Lucia Tsaoussi (Deputy Associate
Director for Earth Science Research Science Mission Directorate NASA Headquarters), and under Dr. James
Smoot (detailed to NASA Headquarters for NCA activities).
References
Borucki, W. J., and W. L. Chameides, Lightning: estimates of the rates of energy dissipation and nitrogen fixation,
Rev. Geophys. Space Phys., 22, 363–372, 1984.
Buechler, D. E., W. J. Koshak, H. J. Christian, and S. J. Goodman, Assessing the performance of the Lightning Imaging
Sensor (LIS) using deep convective clouds, Atmos. Res., 135-136, 397–403, 2014.
Cecil, D. J., D. E. Buechler, and R. J. Blakeslee, Gridded lightning climatology from TRMM-LIS and OTD: dataset
description, Atmos. Res., 135-136, 404–414, 2014.
Chameides, W. L., Effect of variable energy input on nitrogen fixation in instantaneous linear discharges, Nature, 277,
123–125, 1979.
Christian, H. J., et al., The Lightning Imaging Sensor, in 11th Int. Conf. on Atmospheric Electricity, pp. 746–749,
ICAE, Guntersville, AL, 1999.
Goodman, S. J., et al., The GOES-R Geostationary Lightning Mapper (GLM), Atmos. Res., 125-126, 34–49, 2013.
Huntrieser, H., H. Schlager, C. Feigl, and H. Holler, Transport and production of NOx in electrified thunderstorms:
Survey of previous studies and new observations at midlatitudes, J. Geophys. Res., 103, 28,247–28,264, 1998.
Koshak, W. J., Optical characteristics of OTD flashes and the implications for flash-type discrimination, J. Atmo.
Oceanic Technol., 27, 1822–1838, 2010.
Koshak, W. J., M. F. Stewart, H. J. Christian, J. W. Bergstrom, J. M. Hall, and R. J. Solakiewicz, Laboratory calibration
of the Optical Transient Detector and the Lightning Imaging Sensor, J. Atmo. Oceanic Technol., 17, 905–915, 2000.
Koshak, W. J., K. L. Cummins, D. E. Buechler, B. Vant-Hull, R. J. Blakeslee, E. R. Williams, and H. S. Peterson,
Variability of CONUS lightning in 2003-2012 and associated impacts, submitted to J. Appl. Meteorol. Clim., 2014.
Lawrence, M. G., W. L. Chameides, P. S. Kasibhatla, H. L. II, and W. Moxim, Lightning and atmospheric chemistry:
The rate of atmospheric NO production, in Handbook of Atmospheric Electrodynamics, 1995.
Liaw, Y. P., D. L. Sisterson, and N. L. Miller, Comparison of field, laboratory, and theoretical estimates of global
nitrogen fixation by lightning, J. Geophys. Res., 95, 22,489–22,494, 1990.
Schumann, U., and H. Huntrieser, The global lightning-induced nitrogen oxides source, Atmos. Chem. Phys., 7, 3823–
3907, 2007.
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XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
1998 
2005 
2004 
2003 
2002 
1999 
2000 
2001 
Figure 1: LIS-inferred LNOx production (megamoles) for the period 1998-2005.
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XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
2006 
2013 
2012 
2011 
2010 
2007 
2008 
2009 
Figure 2: LIS-inferred LNOx production (megamoles) for the period 2006-2013.
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XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma, U.S.A.
P o
 
P
t 
N
t 
N
o
 
ANNUAL  VARIATIONS 
Figure 3: Time-series plots of flash counts and associated LNOx production.
8


Variation of a Lightning NOx Indicator for National Climate Assessment

Lightning nitrogen oxides (LNOx) indirectly influences our climate since these molecules are important in controlling the concentration of ozone (O3) and hydroxyl radicals (OH) in the atmosphere [Huntrieser et al., 1998]. In support of the National Climate Assessment (NCA) program, satellite Lightning Imaging Sensor (LIS; Christian et al. [1999]; Cecil et al. [2014]) data is used to estimate LNOx production over the southern portion of the conterminous US for the 16 year period 1998-2013

Koshak, William

                                                                                                                  William Koshak1 ,  B. Vant-Hull2, E. W. McCaul3, and H. S. Peterson3  
 
                                                                                                                                                     1Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL  USA. 
                                                                                                                                                                     2 Steinman Hall, City College of New York, New York, NY, USA 
                                                                                                               3Universities Space Research Association, National Space Science & Technology Center, Huntsville, AL, USA. 
1.  INTRODUCTION 
Lightning nitrogen oxides (LNOx) indirectly influences our climate since these molecules are important in controlling the concentration of 
ozone (O3) and hydroxyl radicals (OH) in the atmosphere [Huntrieser et al., 1998]. In support of the National Climate Assessment (NCA) 
program, satellite Lightning Imaging Sensor (LIS; Christian et al. [1999]; Cecil et al. [2014]) data is used to estimate LNOx production over the 
southern portion of the conterminous US for the 16 year period 1998-2013.  
 
2.  METHODOLOGY                                                                                                                                                                  
 
   
3. ANNUAL GEOGRAPHICAL VARIATIONS 
Below is the annual geographical variations of the total LNOx production Pt  described in the previous section. The value of beta has been 
calibrated so that the mean LNOx production per flash in 1998 (the reference year) is 250 moles.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4. LNOx  TREND 
The trend in the total LNOx (summed up across the entire southern  
CONUS region) and associated flash counts are provided here.  
Keeping in mind that LIS is regarded as a very stable instrument  
[Buechler et al., 2014], note that there is a downward trend in the  
LIS-inferred total LNOx production.  
 
 
   
 
 
 
  
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
15th International Conference on Atmospheric Electricity (ICAE), Norman, OK   June 15-20, 2014.  
5.   REFERENCES 
 
Buechler, D. E. W. J. Koshak, H. J. Christian, and S. J. Goodman, Assessing the performance of the Lightning Imaging Sensor (LIS) using deep convective clouds, Atmos. Res., 135-136, 397-403, 2014. 
Cecil, D. J., D. E. Buechler, and R. J. Blakeslee, Gridded lightning climatology from TRMM-LIS and OTD: dataset description, Atmos. Res., 135-136, 404-414, 2014. 
Christian, H. J., et al., The Lightning Imaging Sensor, in 11th Conf. on Atmospheric Electricity, pp. 746-749, ICAE, Guntersville, AL, 1999. 
Huntrieser, H., H. Schlager, C. Feigl, and H. Holler, Transport and production of NOx in electrified thunderstorms: Survey of previous studies and new observations at midlatitudes, J. Geophys. Res., 103, 28247-28264, 1998. 
 
   
                                                                    Variation of a Lightning NO
x
 Indicator for National Climate Assessment   
 
 
 
Downward Trend in LNOx 
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Variation of a Lightning NOx Indicator for National Climate Assessment

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