Available online at www.sciencedirect.com

Advances in Space Research 46 (2010) 1055–1063
www.elsevier.com/locate/asr

Near real-time assimilation in IRI of auroral peak E-region densityand equatorward boundary
Yongliang Zhang a,*, Larry J. Paxton a, Dieter Bilitza b, Rick Doe ca

The Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, USAb
George Mason University, 4400 University Drive, Fairfax, VA 22030, USAc
SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA
Received 10 September 2009; received in revised form 11 June 2010; accepted 15 June 2010

Abstract
The paper describes the method and initial results of assimilating the auroral peak E-region density (NmE) and the auroral equatorward boundary (EB) into the International Reference Ionosphere (IRI). The NmE and EB are obtained using a FUV based auroralmodel or FUV measurements in near real-time. Initial results show that the auroral NmE is often signiﬁcantly larger than the NmE
due to the solar EUV. This indicates the importance of including the contribution of precipitating electrons in IRI. The global equatorialboundary helps to improve the speciﬁcation of the sub-auroral ionosphere trough in IRI. An IDL software package has been developed
to interactively display the IRI parameters with assimilated NmE and EB. It can serve as an operational tool for space weather
monitoring.
Ó 2010 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: IRI; Ionosphere; Auroral peak E-region density; Auroral equatorward boundary

1. Introduction
The International Reference Ionosphere (IRI) is a
widely used empirical model for ionospheric parameters
and the de facto international standard for the ionosphere.
IRI describes monthly averages of electron and ion densities and temperatures in the ionospheric altitude range
from about 50 km to 1500 km. The development of themodel was initiated in the late sixties by the international
scientiﬁc unions in charge of ionospheric physics, the International Union of Radio Science (URSI) and the Committee on Space Research (COSPAR). Since then the model
has steadily evolved from reference tables for speciﬁc times
and locations to a global model that describes the variationof the ionospheric plasma in space and time. By charter IRI
is an empirical model being based primarily on measure-*

Corresponding author.
E-mail address: Yongliang.Zhang@jhuapl.edu (Y. Zhang).

ments from ground and space; theoretical considerations
are used to establish appropriate mathematical functions
to describe the dominant variation patterns and they arealso used to ﬁll data gaps. The latest version of the modelis IRI-2007 as described by Bilitza and Reinisch (2008).
The prime focus of the early development was on the middle latitudes because that is where most of the IRI users
were in need of a reliable representation of ionosphericcharacteristics. Combining data from almost all available
ground and space data sources the model has now reacheda high level of reliability in the mid-latitude ionosphere as
documented in extensive comparisons with newer data andin many successful applications ranging from ionosphericcorrections for remote sensing from Earth-observing satellite to background ionosphere for testing tomographic
techniques and global navigation satellite system (GNSS)
mappings to list just two of the many applications of themodel. With mid-latitudes well represented eﬀorts are
now directed towards an accurate representation of the
low and high latitudes. In both regions the modeling pro-

0273-1177/$36.00 Ó 2010 COSPAR. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.asr.2010.06.029

1056

Y. Zhang et al. / Advances in Space Research 46 (2010) 1055–1063

cess is complicated by the multitude of processes and themany characteristic structures observed in these parts ofthe globe.
At high latitude or auroral regions, energetic electron
precipitation enhances ionospheric electron densities. During magnetic storms, the ionospheric density due to theprecipitating electrons could be signiﬁcantly larger thanthe electron density due to the solar EUV ﬂux at high latitudes. The enhanced electron density leads to an increaseof ionospheric conductance which aﬀects the Joule heating
and plays a very important role in the magnetosphere–ionosphere (M–I) coupling. The magnetospheric energy input(Joule heating and particle heating) is the major source for
disturbance in the ionosphere and thermosphere during
magnetic storms (Prölss and Craven, 1998; Zhang et al.,2003, 2004). Speciﬁcation of the ionosphere condition in
the auroral region is urgently needed.
A representation of auroral characteristics in IRI has
long been an important issue on the agenda of IRI developers (Bilitza, 1995) and users (Szuszczewicz et al., 1993). Asa ﬁrst step towards this goal a model description of auroralboundaries needs to be included in IRI to specify the location of the auroral oval on the globe and to describe theexpansion of the oval with increasing magnetic activity.
The ﬁrst generation of auroral oval representations was
based on all sky imager observations, e.g., Holzworthand Meng (1975). Next came a series of models that
described the precipitating particle ﬂux distribution in theauroral oval based on satellite in situ particle measurements (Wallis and Budzinski, 1981; Spiro et al., 1982;
Hardy et al., 1987; Fuller-Rowell and Evans, 1987). Using
appropriate ﬂux threshold values one can deduce auroralboundaries from these ﬂux maps. Szuszczewicz et al.
(1993) and Bilitza (1995) compared some of these boundary models with data and with each other and made recommendations regarding their use in IRI, but no clear favorite
evolved. The most complete representation of auroral electron ﬂuxes and boundaries can be achieved with FUV
imagers like GUVI and SSUSI that simultaneously measure UV intensities in several wavelength bands including
N2 LBHS (140–150 nm) and LBHL (165–180 nm). The
model of Zhang and Paxton (2008) is the latest and mostadvanced of these models and will be the base model for
introducing auroral boundaries into IRI. Introduction of
the boundaries will also not only allow the inclusion of arealistic auroral E-peak model but also other typicalhigh-latitude features like the sub-auroral trough (Prölss,2007) and the co-located enhancement in electron temperature (Prölss, 2006a,b).
The current version of IRI (Bilitza, 2001; Bilitza andReinisch, 2008) does not include the contribution from theparticle precipitation (mainly electrons (Hardy et al.,1987)) in the auroral oval. The shortcomings in the speciﬁcation of the high latitude ionosphere, especially the E-region
in the auroral oval, are becoming a signiﬁcant obstacle in
improving our understanding and ability to accurately
forecast disturbances in the ionosphere and thermosphere

during magnetic storms. The IRI community also has
already recognized the need for auroral oval information(e.g., auroral boundary) in IRI for many years (Bilitza,
1995). The availability of a large volume of optical andparticle data sets from recent satellite missions allows nearreal-time global auroral speciﬁcation and the developmentof a new auroral model (Zhang and Paxton, 2008). In thispaper, we focus on assimilating auroral products (peakauroral E-region density and equatorward auroral boundary) from global spectrographic FUV auroral measurementsor the FUV based auroral model into IRI. The FUV (or
GUVI) based empirical auroral model can be driven by
Kp or measured equatorward auroral boundary. Inclusionof the FUV auroral model allows ﬂexibility in auroral
speciﬁcation in IRI.
2. Auroral data and model
2.1. Auroral data
Global FUV spectrograph instruments, such as
TIMED/GUVI (Paxton and Meng, 1999; Christensen
et al., 2003) and DMSP/SSUSI (Paxton et al., 2002), simultaneously measure FUV emissions in a few bands including
N2 LBHS (140–150 nm) and LBHL (165–180 nm) at same
locations with a high spatial resolution (7–9 km at nadir).
The simultaneous measurements provide a reliable andunbiased way to retrieve the characteristics (mean energy
and energy ﬂux) of precipitating electrons. Techniques
have been developed to estimate the above two key auroralelectron parameters, mean electron energy (Eo) and electron energy ﬂux (Q), from the FUV auroral data. Strickland et al. (1983) proposed use of the N2 LBHS and
LBHL emissions to estimate Eo and Q. Since the emissionsof the LBHS and LBHL originate from the same species,
their ratio is nearly independent of compositional changes.
The key step in transforming the observed auroral LBHS
and LBHL intensities into Eo and Q of precipitating electrons is to establish a relationship between the observed
intensities and these parameters. We ﬁrst run the Boltzman
Three Constituent (B3C) auroral transport code (Daniell,1993) for Gaussian-distributed precipitating electrons with
a ﬁxed energy ﬂux of 1 erg/(cm2 s) and mean energies varying from 2 to 20 keV. Neutral atmospheric density proﬁleswere taken from the MSIS86 model at a latitude of 60°(typical auroral latitude) with ﬁxed F10.7 (150) and Ap(1) values. The Atmospheric Ultraviolet Radiance Integrated Code (AURIC) (Strickland et al., 1999) is then used
to calculate the atmospheric radiance from the proﬁles ofthe volume emission rate generated by B3C. The GUVI
Eo and Q products have been routinely produced andposted on the GUVI website (http://guvi.jhuapl.edu).
These products were used to develop a FUV based auroralmodel that covers the whole Kp range (0–9) (Zhang andPaxton, 2008). The same technique also produces peakauroral E-region density (NmE) and its associated height(hmE). The SSUSI instrument is nearly identical to GUVI.

Y. Zhang et al. / Advances in Space Research 46 (2010) 1055–1063(a) Kp = 3.0(a) Kp = 3.0

12

12
65 o

65 o

75 o

75 o
85 o

18

06

85 o

18

00

0

2

06

00

4

6

8

0

1

2

3

4

5

Mean Energy (keV)

Energy Flux (ergs/s/cm2)

(a)
= 3.0
KpKp
= 3.0

(a)
= 3.0
KpKp
= 3.0

12

12
65 o

65 o

75 o

75 o

85 o

18

06

85 o

18

00

100

1057

110

120

06

00

130

140

150

HmE (km)01•10 5 2•10

5

3•10

5

5

4•10

5

5•10

NmE (cm-3)

Fig. 1. Results from the GUVI based auroral model: (a) mean energy and (b) energy ﬂux of precipitating electrons, (c) height (hmE) of the peak E-region
density, and (d) NmE for Kp = 3. The plots are in magnetic coordinates (noon at top, mid-night at bottom, dawn at right, dusk at left). The red line shows
the auroral equatorward boundary at a ﬁxed ﬂux threshold (0.2 ergs/s/cm2). (For interpretation of the references to colour in this ﬁgure legend, the reader
is referred to the web version of this article.)

GUVI and SSUSI auroral products (Eo, Q, hmE, NmE andboundary) have being routinely produced in near real-time.

Therefore, it is ideal to use the GUVI and SSUSI auroral
products in updating IRI.

Fig. 2. Auroral ionospheric E-layer electron density proﬁles obtained from the Sondrestrom Incoherent Scattering Radar between 22:27 and 22:30 UT on
January 13, 2005.

1058

Y. Zhang et al. / Advances in Space Research 46 (2010) 1055–1063

Fig. 3. Left panel: DMSP F16 SSUSI auroral image over Greenland. The white bar over intense aurora (indicated by a solid red arrow) shows the scan
track of the Sondrestrom Incoherent Scatter Radar. Right: NmE, hmE along the white bar from SSUSI (blue line) and the radar (red line). (Forinterpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)

2.2. Auroral model
The FUV based auroral model (Zhang and Paxton,
2008) will also be used in updating IRI. The model is also
called GUVI based model as it uses GUVI data. Details of
the model description can be found in the reference. Here
we show one example of the model outputs for a given

Kp. The outputs (Fig. 1, Kp = 3) include Eo, Q, hmE,
NmE and the equatorward boundary in the magnetic latitude (Mlat) and local time (MLT) coordinates. Fig. 1a indicates that the mean energy gradually increases from thedusk side to the night side and the dawn side. The meanenergy reaches its minimum on the dayside. On the other
hand, the energy ﬂux (Fig. 1b) peaks on the nightside
and reaches its minimum on the dayside. These features
are consistent with dynamics and the drift path eﬀect ofhigh energy electrons in the central plasmas sheet and dayside cusp electrons with low energy and ﬂux. The ﬂux map
also allows us to ﬁnd the equatorward boundary (see redline in Fig. 1) of the auroral oval at a ﬁxed ﬂux threshold(0.2 ergs/s/cm2). The corresponding hmE and NmE are
shown in Figs. 1c and d. It is clear that the mean energy
determines the hmE (e.g. Eo > 6 keV leads to low hmE(110 km)). The NmE is mostly controlled by the energy
ﬂux and thus Fig. 1b and d is similar with each other.
Using the FUV based auroral model in IRI provides the
beneﬁt of global speciﬁcation of the aurora in IRI as measurements often have limited spatial coverage. In addition
to Kp, the auroral model can be driven by near real-timemeasured equatorward auroral boundary.
3. Validation of auroral NmE, hmE and equatorward
boundary

Fig. 4. Individual image of electron energy ﬂux obtained from DMSP F17
SSUSI around 08:53 UT on July 14, 2007 (day of year: 195) in thesouthern hemisphere. The red line highlights the equatorward boundary.
The blue line is the FUV model boundary that has the best ﬁt to the
SSUSI boundary. The three small blue circles show the equatorward
boundary determined from simultaneous DMSP particle measurements.
(For interpretation of the references to colour in this ﬁgure legend, thereader is referred to the web version of this article.)

To validate the FUV auroral NmE and hmE, the NmE
and hmE from coincident incoherent scattering radar(ISR) measurements were compared with DMSP F16 SSUSI
NmE and hmE. Fig. 2 shows one example of the Sondrestrom ISR electron density proﬁles between 22:27 and22:30 UT, January 13, 2005. During the period, the ISR
scans from South to North (see the white bar in the left panelof Fig. 3 for the scan track). The left panel of Fig. 3 indicatespart of the SSUSI auroral image over Greenland. The Son-

Y. Zhang et al. / Advances in Space Research 46 (2010) 1055–1063

1059

Fig. 5. IRI peak E-region electron density with assimilated auroral NmE based on the GUVI auroral model. The model was driven by measured Kp index.
The display was realized through the IDL interactive software.

drestrom ISR was scanning through intense auroras. The
right panels in Fig. 3 show the comparison between SSUSIand ISR NmE and hmE along the ISR scanning track. Considering the diﬀerent time and spatial resolutions and techniques used in SSUSI and ISR, and the dynamics of thediscrete auroras, the SSUSI NmE and hmE agree remarkably well with those from the ISR. This is a typical example
among a number SSUSI and ISR coincident events.
The DMSP F16 SSUSI calibration and validation eﬀort
has concluded that FUV based NmE and hmE values havea fairly good agreement with simultaneous independent
incoherent scatter radar measurement during nighttime
when the solar contribution is negligible and E-layer ionization is predominantly due to electron precipitation. All these
indicate that the FUV based NmE and hmE are reliable
products that can be used to update IRI in auroral regions.
The location of the auroral oval is also important andneeds to be speciﬁed as it aﬀects the global ionosphere andthermosphere response to the heating in the auroral region.
The oval location is best speciﬁed by its equatorward boundaries. There are a number of ways to determine the boundaries. Here, we use a ﬁxed ﬂux threshold (0.2 ergs/s/cm2)
to ﬁnd the boundaries as this is the most meaningful way
in view of geophysical eﬀectiveness. Fig. 4 shows one example of the electron energy ﬂux map in Mlat-MLT coordinates
based on DMSP F17 SSUSI data around 08:53 UT on July

14, 2007 in the southern hemisphere. Note the SSUSI data or
swath just covered half of the auroral oval. The red line is theequatorward boundary of the SSUSI swath. It was calledswath boundary, and is used to ﬁnd the global boundary(blue line) by ﬁtting the GUVI model based boundary (seeexample in Fig. 1) and the swath boundary. The small blue
circles show the location of the equatorward boundary
based on simultaneous DMSP particle data. It is clear that
there is a very good agreement among the swath, global
and independent particle boundaries. A statistical study
done as part of the SSUSI calibration and validation eﬀort
also concluded that the SSUSI equatorward auroral boundary agrees well with the boundary determined from coincident particle measurements for relatively bright auroras
with sharp boundaries.
4. Assimilating NmE and auroral boundary in IRI
4.1. Assimilating modeled NmE in IRI
The IRI outputs are usually speciﬁed in geographic
coordinates. The GUVI auroral model, however, is basedon magnetic latitude and local time. The ﬁrst step is totransform the modeled NmE and boundary into geographic coordinates. We use AACGM (Altitude AdjustedCGM) (Baker and Wing, 1989) to do the transform by1060

Y. Zhang et al. / Advances in Space Research 46 (2010) 1055–1063

Fig. 6. Similar to Fig. 5 but for diﬀerent date in the southern hemisphere.

assuming an altitude of 110 km, a typical height of auroral
E-region peak density. The second step is to merge theNmE due to electron precipitation and solar EUV ﬂux.
In the auroral ionosphere, the electron density (Ne) is determined by the continuity equation (see the Eq. (1)), where
Ve is the electron bulk velocity. The Peuv and Pe are theelectron production rates due to solar EUV ﬂux and electron precipitation, respectively. The aN 2e is the electron loss
rate due to ion–electron recombination. In the ionosphereE-region, the strong ion-neutral collision allows to neglect
the convection term. We further assume that the ionization
process quickly reaches local equilibrium, thus the time
variation of Ne is 0. The electron density can then be determined by Eq. (2)@N eþ r  ðN e V~e Þ ¼ P euv þ P e  aN 2e@t
aN 2e ¼ P euv þ P e

ð1Þ
ð2Þ

represent the electron density due to solar EUV
Let N euveonly and N ee due to electron precipitation only. They satisfy
following conditions, similar to Eq. (2)2aðN euve Þ ¼ P euv

ð3Þ

2aðN ee Þ ¼ P e

ð4ÞCombining Eqs. (2)–(4) gives:

Ne ¼

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
2e 2
ðN euve Þ þ ðN e Þð5ÞAs the ionosphere E-region peak heights due to solar EUV
or typical precipitating electrons (a few keV) are roughlyaround 110 km, the Eq. (5) can be applied to the NmE.
IRI provides solar EUV based NmE and the auroral model
gives electron precipitation based NmE. Eq. (5) combinesthem to provide assimilated NmE (see one example inFig. 5 in the northern hemisphere). The display in Fig. 5
was created using IDL widgets that call the IRI and theauroral model, assimilate the NmE, and interactively show
the location and associated NmE value at the computer
mouse position. The IDL widgets also allow free rotationand zoom of the earth for either global or local view. In
additional to the model NmE, the near real-time GUVI
and SSUSI auroral products have been successfully assimilated to IRI using the IDL software.
Fig. 6 shows another example of model NmE that was
assimilated into IRI for diﬀerent date and in the southernhemisphere. Both of Figs. 5 and 6 indicate that the NmE
due to precipitating electrons is much larger than the
NmE due to solar EUV radiation at the auroral latitudes.
This conﬁrms the importance of including contribution of
the precipitating electrons in IRI. The equatorward boundary from the auroral model (see Fig. 1) was not included in
Figs. 5 and 6. But the boundary was included with nearreal-time FUV aurora data (see examples later).

Y. Zhang et al. / Advances in Space Research 46 (2010) 1055–1063

1061

Fig. 7. Near real-time assimilation of SSUSI NmE into IRI on the northern hemisphere. The yellow line is the ﬁtted global equatorial model boundary.
The enhanced NmE in polar region indicates the swath of SSUSI measurements. (For interpretation of the references to colour in this ﬁgure legend, thereader is referred to the web version of this article.)

4.2. Assimilating near real-time measured NmE andboundary in IRI
Using the same method described in Section 4.1, thenear real-time or historical auroral products (NmE andboundary) from SSUSI and GUVI has been assimilated
into IRI. Fig. 7 shows one example of IRI with assimilated
SSUSI NmE and global boundary (yellow line). Despite oflimited spatial coverage of SSUSI data, the SSUSI NmE
provide an indicator of magnitude of auroral NmE as wellas the global auroral oval location through the boundary.
The example in Fig. 7 also demonstrates that the FUV
spectrographic imaging technique allows SSUSI and GUVI
to detect auroral emissions and generate auroral products
under both nightside and sunlit conditions. Fig. 8 shows
another example at diﬀerent time on the southern hemisphere. Again, these examples indicate that the auroral
NmE can be much larger than the solar EUV NmE at highlatitudes.
5. Discussion
The advanced FUV spectrographic imaging techniqueused in SSUSI and GUVI provides accurate atmosphericemission intensities simultaneously from same locations

over diﬀerent wavelength bands. This technique eliminates
the errors due to out of band contribution, non-simultaneous measurements and diﬀerent ﬁelds of view in traditional ﬁlter based FUV imagers. With the B3C and
AURIC modeling tools, the SSUSI and GUVI data provide unbiased estimation of the characteristics of precipitating electrons and associated ionospheric parameters(NmE, hmE and boundary). The validation of the auroral
ionospheric products demonstrated the value of the SSUSIand GUVI auroral products. One important feature fromthe NmE assimilation is that the auroral NmE is signiﬁcantly larger than the NmE due to solar EUV. This conﬁrms the urgent need to include auroral contribution in
IRI as accurate speciﬁcation of E-region electron density
is very important for a better estimation and understandingof storm time Joule heating, the major driver of the ionosphere and thermosphere space weather.
The above results support the early simulation conclusions. Comparison between TGCM runs (Roble and Ridley, 1987) with and without auroral precipitation showsthat relatively soft particles (1-2 keV) provide suﬃcientionization in the lower ionosphere to be competitive withsolar photo-ionization and can alter the dynamics in thelower thermosphere in the summer hemisphere. In the winter hemisphere, particle precipitation has a much larger

1062

Y. Zhang et al. / Advances in Space Research 46 (2010) 1055–1063

Fig. 8. Similar to Fig. 7 but for diﬀerent time and southern hemisphere.

inﬂuence on the dynamical structure of both the upper andlower thermosphere. Roble and Ridley (1987) found that at
the F-region heights (300 km) both the polar cap temperature and wind velocity increased due to auroral precipitation. The dusk cell convection velocities are enhanced andreach a maximum velocity of 70 m/s. Enhanced Joule heating produces a temperature increase of about 50 K in thepolar cap. In addition, Roble and Ridley (1987) found thatauroral particle precipitation provides suﬃcient ionizationin the lower thermosphere to alter the circumpolar circulation. Such an eﬀect is more signiﬁcant in the winter hemisphere. These indicate that the IRI with assimilated
auroral NmE could also be useful to specify polar ionosphere conditions in global ionosphere, thermosphere, ormagnetosphere simulations.
In addition to the NmE, location of the enhanced ionization or auroral oval is also important. It is expected that theionosphere and thermosphere will have diﬀerent responses if
the enhanced NmE with the same magnitude is located at different latitudes. Inclusion of the equatorward auroralboundary in IRI (as shown in Figs. 7 and 8) will also improve
the speciﬁcation of auroral ionosphere in IRI and help all
applications that use IRI in high latitudes.
The interactive IDL display tool demonstrates that the
NmE and auroral boundary assimilation in IRI can bedone in near real-time. Furthermore, when a predicted

Kp is available, the FUV auroral model could be used toforecast the auroral products.
6. Summary and future work
The global IRI community has been continuously working on improving the International Reference Ionosphere.
Assimilation auroral NmE and equatorward boundary in
IRI is part of the improvement eﬀorts. The FUV auroral
data and products from SSUSI and GUVI as well as the
FUV based auroral model provide unique data sources
and tool to update the IRI in near real-time. The interactive IDL display package provides a useful way to monitor
the polar and global E-region peak density in near realtime. It can also be used as space weather operation tool.
The next step is to assimilate auroral E-region density
proﬁles in IRI. This will remove the requirement that thehmE due to precipitating electron is around 110 km, theIRI hmE. We also plan to translate the FUV auroral model(currently in IDL) to FORTRAN and make it a part of
future IRI.
Acknowledgements
The authors thank the reviewers for their helpful comments. This work was supported by NSF Grant ATM-

Y. Zhang et al. / Advances in Space Research 46 (2010) 1055–1063

0819771. The equatorward auroral oval boundaries fromthe DMSP particle data were provided by Dan Ober and
Gordon Wilson.
References
Baker, K.B., Wing, S. A new magnetic coordinate system for conjugate
studies at high latitudes. J. Geophys. Res. 94, 9134–9139, 1989.
Bilitza, D. Including auroral oval boundaries in the IRI model. Adv.
Space Res. 16 (1), 13–16, 1995.
Bilitza, D. International reference ionosphere 2000. Radio Sci. 36, 261–
275, 2001.
Bilitza, D., Reinisch, B.W. International reference ionosphere 2007:
improvements and new parameters. Adv. Space Res. 42, 599–610,
doi:10.1016/j.asr.2007.07.048, 2008.
Christensen, A.B., Paxton, L.J., Avery, A., craven, J., Crowley, G., Jumm,
D.C., Kil, H., Meier, R.R., Meng, C.-I., Morrison, D., Ogorzalek,
B.S., Straus, P., Strickland, D.J., Swenson, R.M., Walterscheid, R.L.,
Wolven, B., Zhang, Y. Initial observations with the Global Ultraviolet
Imager (GUVI) in the NASA TIMED satellite mission. J. Geophys.
Res. 108 (A12), 1451–1466, doi:10.1029/2003JA009918, 2003.
Daniell Jr., R.E. Modeling of optical signatures of electron spectra in theionospheric heating experiments, in: Proceedings and Program for theSeventh International Ionospheric Eﬀects Symposium, SRI-Int,
Arlington, VA, USA, 1993, p. 6B/5/1, 1993.
Fuller-Rowell, T.J., Evans, D.S. Height-integrated Pedersen and Hall
conductivity patterns inferred from the TIROS-NOAA satellite data.
J. Geophys. Res. 92, 7606–7618, 1987.
Hardy, D.A., Gussenhoven, M.S., Raistrick, R., McNeil, W.J. Statisticaland functional representations of the pattern of auroral energy ﬂux,
number ﬂux, and conductivity. J. Geophys. Res. 92, 12275–12294,
1987.
Holzworth, R.H., Meng, C.-I. Mathematical representation of the auroral
oval. Geophys. Res. Lett. 2, 377–380, 1975.
Paxton, L.J., Meng, C.-I. Auroral imaging and space-based optical remotesensing. J. Hopkins APL Tech. D. 20, 556–569, 1999.
Paxton, Larry J., Morrison, Daniel, Zhang, Yongliang, Kil, Hyosub,
Wolven, Brian, Ogorzalek, Bernard S., Humm, David, Meng, Ching-I.
Validation of remote sensing products produced by the special sensor
ultraviolet scanning imager (SSUSI) – a far-UV imaging spectrographon DMSP F16, in: Optical Spectroscopy Techniques, Remote Sensing,and Instrumentation for Atmospheric and Space Research IV, vol.
4485, pp. 338–348, 2002.

1063

Prölss, G.W., Craven, J.J. Perturbations of the FUV dayglow andionospheric storm eﬀects. Adv. Space Sci. 22, 129–132, 1998.
Prölss, G.W. Subauroral electron temperature enhancement in thenighttime ionosphere. Ann. Geophys. 24, 1871–1885, 2006a.
Prölss, G.W. Electron temperature enhancement beneath the magnetospheric cusp. J. Geophys. Res. 111, A07304, 1–13, doi:10.1029/
2006JA011618, 2006b.
Prölss, G.W. The equatorward wall of the subauroral trough in theafternoon/evening sector. Ann. Geophys. 25, 645–659, 2007.
Roble, R.G., Ridley, E.C. An auroral model for the NCAR thermosphericgeneral circulation model (TGCM). Ann. Geophys. 5A, 369–382,
1987.
Spiro, R.W., Reif, P.H., Mather, L.J. Precipitating electron energy ﬂux
and auroral zone conductances: an empirical model. J. Geophys. Res.
87, 8215–8227, 1982.
Strickland, D.J., Jasperse, J.R., Whalen, J.A. Dependence of auroral FUV
emissions on the incident electron spectrum and neutral atmosphere. J.
Geophys. Res. 88, 8051–8062, 1983.
Strickland, D.J., Bishop, J., Evans, J.S., Majeed, T., Shen, P.M., Cox,
R.J., Link, R., Huﬀman, R.E. Atmospheric ultraviolet radianceintegrated code (AURIC): theory, software architecture, inputs, andselected results. J. Quant. Spectrosc. Radiat. Transfer 62, 689–742,
1999.
Szuszczewicz, E.P., Wilkinson, P., Swider, W., Pulinets, S., Abdu, M.A.,
Roelof, E., Fuller-rowell, T., Evans, D.S., Bateman, T., Blanchard, P.,
Gustafsson, G., Hanbaba, R., Joselyn, J., Kikuchi, T., Leitinger, R.,
Lester, M., Reddy, B., Ruohoniemi, M., Sands, M., Sobral, J., Walker,
G.O., Wickwar, V. Measurements and empirical model comparisons of
F-region characteristics and auroral oval boundaries during thesolstitial SUNDIAL campaign of 1987. Ann. Geophys. 11, 601–613,
1993.
Wallis, D.D., Budzinski, E.E. Empirical models of height-integrated
conductivities. J. Geophys. Res. 86, 125–137, 1981.
Zhang, Y., Paxton, L.J., Kil, H., Meng, C.-I., Mende, S.B., Frey, H.U.,
Immel, T.J. Negative ionospheric storms seen by the IMAGE
FUV instrument. J. Geophys. Res. 108, 1343–1353, doi:10.1029/
2002JA009797, 2003.
Zhang, Y., Paxton, L.J., Morrison, D., Wolven, B., Kil, H., Meng, C.-I.,
Mende, S.B., Immel, T.J. O/N2 changes during 1–4 October 2002
storms: IMAGE SI-13 and TIMED/GUVI observations. J. Geophys.
Res. 109, A10308, 1–16, doi:10.1029/2004JA010441, 2004.
Zhang, Y., Paxton, L.J. An empirical Kp-dependent global auroral model
based on TIMED/GUVI data. J. Atmos. Solar – Terr. Phys. 70, 1231–
1242, 2008.