J. Space Weather Space Clim. 4 (2014) A07
DOI: 10.1051/swsc/2014004
 D. Bilitza et al., Published by EDP Sciences 2014

OPEN

RESEARCH ARTICLEACCESS

The International Reference Ionosphere 2012 – a modelof international collaborationI
Dieter Bilitza1,2,*, David Altadill3, Yongliang Zhang4, Chris Mertens5, Vladimir Truhlik6, Phil Richards1,
Lee-Anne McKinnell7, and Bodo Reinisch8
1

George Mason University, School of Physics Astronomy and Computational Science, 4400 University Drive, Fairfax, Virginia,
USA*
Corresponding author: dbilitza@gmu.edu
2
NASA Goddard Space Flight Center, Heliospheric Physics Laboratory, Greenbelt, Maryland, USA
3
Observatori de l’Ebre, CSIC, Uni. Ramon Llull, Roquetes, Spain
4
The Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, USA
5
NASA Langley Research Center, Science Directorate, Mail Stop 401B, Hampton, VA 23681-2199, USA
6
Institute of Atmospheric Physics, ASCR, Prague 14131, Czech Republic
7
SANSA Space Science, PO Box 32, Hermanus 7200, South Africa
8
Center for Atmospheric Research, University of Massachusetts, Lowell, MA, USA
Received 5 October 2013 / Accepted 15 January 2014
ABSTRACTThe International Reference Ionosphere (IRI) project was established jointly by the Committee on Space Research (COSPAR) andthe International Union of Radio Science (URSI) in the late sixties with the goal to develop an international standard for the speciﬁcation of plasma parameters in the Earth’s ionosphere. COSPAR needed such a speciﬁcation for the evaluation of environmental
effects on spacecraft and experiments in space, and URSI for radiowave propagation studies and applications. At the request of
COSPAR and URSI, IRI was developed as a data-based model to avoid the uncertainty of theory-based models which are only
as good as the evolving theoretical understanding. Being based on most of the available and reliable observations of the ionosphericplasma from the ground and from space, IRI describes monthly averages of electron density, electron temperature, ion temperature,
ion composition, and several additional parameters in the altitude range from 60 km to 2000 km. A working group of about
50 international ionospheric experts is in charge of developing and improving the IRI model. Over time as new data became available and new modeling techniques emerged, steadily improved editions of the IRI model have been published. This paper gives abrief history of the IRI project and describes the latest version of the model, IRI-2012. It also brieﬂy discusses efforts to develop areal-time IRI model. The IRI homepage is at http://IRImodel.org.

1. Introduction
The successful launch and operation of satellites in space, starting in the late ﬁfties, gave us an in situ view of space conditions
and initiated a technological revolution that is still ongoing. It
brought us every-day conveniences like satellite-TV and the
Global Positioning System (GPS) related services and a uniquevantage point for looking at our blue planet for a better understanding of its weather and climate. From early on there was aneed to better understand and model the environment in whichthese satellites were ﬂying. These predictions are required during the planning phase, to evaluate environmental effects on
spacecraft and experiment, and to develop an optimal mission
and instrument design. This became painfully clear with early
miscalculations of the solar cycle variations of neutral densities.
Underestimating the large densities reached during solar maximum led to early de-orbiting of spacecraft most prominently of
the Skylab space station. In the mid-1960s it became clear thatinternational standard models were needed for the upper atmosphere (thermosphere) and the ionosphere. The international
body charged with this task was the newly established Committee on Space Research (COSPAR), a union of the space agencies of all space-faring nations. First order of business was theI

Dedicated to Karl Rawer, the father of IRI, on the occasion of his
100th birthday.

development of a model for the upper atmosphere to get a better
estimate of the expected lifetime of satellites in space. The
COSPAR International Reference Atmosphere (CIRA) working
group was established and issued steadily improved models
starting with CIRA-1961 (CIRA 1961). As requested by
COSPAR this was an empirical model based on all available
ground and space observations, and avoiding as much as possible dependence on the still evolving theoretical understandingof the thermosphere. Following the success and great beneﬁtof the CIRA project, COSPAR initiated a similar endeavor
for the ionosphere establishing the International Reference
Ionosphere (IRI) project in 1968. Shortly thereafter the International Union of Radio Science (URSI) became a co-sponsor of
the IRI project. With COSPAR and URSI the IRI project had
the backing from the major international unions representing
space-based ionospheric observations (COSPAR) and groundbased ionospheric observations (URSI). IRI development has
beneﬁtted greatly from the synergism between these two communities which are represented about evenly in the IRI Working
Group and during annual IRI Workshops.
The goals and intent of the IRI project are summarized in its
COSPAR/URSI Terms of Reference as follows:
The Task Group was established to develop and improvea standard model of the ionospheric plasma parameters.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

J. Space Weather Space Clim. 4 (2014) A07

Fig. 1. Global distribution of members of the URSI/COSPAR Working Group for the International Reference Ionosphere.

The model should be primarily based on experimental
evidence using all available ground and space data
sources; theoretical considerations can be helpful inbridging data gaps and for internal consistency checks.
Where discrepancies exist between different data
sources, the IRI team should promote critical discussion
to establish the reliability of the different databases. IRI
should be updated as new data become available andas old data sources are fully evaluated and exploited.
IRI is a joint working group of COSPAR and URSI.
COSPAR’s prime interest is in a general description of
the ionosphere as part of the terrestrial environment for
the evaluation of environmental effects on spacecraft
and experiments in space. URSI’s prime interest is in
the electron density part of IRI for deﬁning the background ionosphere for radiowave propagation studies
and applications.
The IRI Working Group has grown over the years to ateam of now 58 experts providing a balanced representation
both in terms of global presence (see Fig. 1) as well as in covering the different ground and space techniques used toacquire ionospheric data. This good coverage technique-wiseand globally has been a great asset for the IRI project because
it helped gaining access to all essential ground and space data
sets for the ionosphere. Other ingredients for the IRI successstory include the annual IRI meetings where improvements
and additions to the model are discussed and decided(http://irimodel.org/docs/iri_workshops.html), the publicationof a selection of the papers from these workshops in dedicatedissues of Advances in Space Research (http://irimodel.org/
docs/asr_list.html), and from early on open source availabilityof the IRI Fortran code, ﬁrst on punched tape and cards, thenon 9-track magnetic tape, then on ﬂoppy disk, then on CD,
and ﬁnally a web interface for direct online computation.
Important was also, of course, the continuous improvement
process that resulted in the release of major new editions of

the model about every 5 years. Some of the more important
IRI milestones are listed in Table 1.
As an empirical model IRI has the advantage that it does
not depend on the evolving theoretical understanding of theprocesses that shape the ionospheric plasma. A good example
is the recently discovered four maxima structure in the longitudinal variation of F-peak electron density and of ionosphericelectron content that was ﬁrst observed with IMAGE/EUV
observations (Immel et al. 2006), and then conﬁrmed with datafrom CHAMP (Lühr et al. 2007) and TOPEX (Scherliess et al.
2008), and that is thought to be caused by nonmigrating, diurnal atmospheric tides that are driven by tropospheric weather inthe tropics. While theoretical models still struggle to include
this phenomenon in their modeling framework, inspection of
the longitudinal variation of the F-peak density value NmF2
in IRI revealed that IRI already reproduces this phenomenon(McNamara et al. 2010). The amplitude of these longitudinalvariations is generally smaller in IRI than what is observed.
But that is understandable, because IRI is based on monthly
averages and the averaging process smoothes out some of thelongitude structures.
A disadvantage of empirical models is the strong dependence on the underlying database. Regions and time periods
not well covered by the database will result in diminished reliability of the model in these areas. So, for example, conditions
during the most recent solar minimum in 2008/2009 were very
different from earlier minima. The minimum was lower andmore extended than earlier minima and as a result IRI, being
built with the data from earlier minima, overestimated theplasma densities during the minimum period (Lühr & Xiong2010).

2. Latest version of the model – IRI-2012
The latest version of the IRI model, IRI-2012, includes several
important improvements and new additions that will be

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D. Bilitza et al.: IRI-2012
Table 1. IRI milestones.
Year
1968

Event (reference)
COSPAR establishes
IRI Working Group

Description
Database
K. Rawer, Chair; WG members predominantly from spacecommunity (satellite and rocket measurements)

1969

URSI joins IRI project

New WG members from ground observation community

1975

IRI-75: Set of Tables(Rawer et al. 1975)

Representative values for equat., low,
mid latitudes

Ionosonde, ICS, AEROS

1978

IRI-79: URSI Special
Rep.: (Rawer et al. 1978a)IRI-79: WDC-A-STP Rep.
(Rawer et al. 1981)
IRI-86: ﬂoppy disk for PC(Bilitza 1985, 1986)

Global coverage using CCIR
maps for peak parameters,
modiﬁed for foF1 and foE;using IG12 with foF2

Global ionosonde network– Improved Ne at low latitudes– Global Te, Ti models

AEROS-A,-B, AE-C,-D,-E, ISIS-1,-2, ICS data

1990

IRI-90: NSSDC Report(Bilitza 1990)– URSI maps for foF2
– Improved NmE model– More global ionosonde data
– Incoherent scatter (ICS) data

1995

IRI-95: online (IRIWeb)(Bilitza 1997)

Improvements at low latitudes

DE-2 data

1999

URSI resolution

IRI recognized as the international standard for the ionosphere2001

IRI-2001 (Bilitza 2001)– Two new options for Ne D-region– new models for F1 and B0, B1– STORM model– New model for Te– Rocket compilations– Ionsonde network– Stormtime ionosonde data
– Intercosmos 19, 24, 25

2007

IRI-2007 (Bilitza &Reinisch 2008)– Two new options for Ne topside– new ion composition model– spread-F occurrence model– TS: Alouette 1, 2, ISIS 1, 2
– AE-C,-E, Intercosmos 24– Brazilian ionosonde data

2012

IRI-2012 (this article)

See Section 2.

1981
1986

explained in this section. These changes involve not only the
representations of electron density, but also the description ofelectron temperature and ion composition. These improvements
are the result of modeling efforts since the last major release,
IRI-2007. Modeling progress is documented in several special
issues of Advances in Space Research: Volume 39, Number
5, 2007; Volume 42, Number 4, August 2008; Volume 43,
Number 11, June 2009; Volume 44, Number 6, September
2009; Volume 46, Number 8, October 2010; and two issuesof Earth, Planets, and Space: Volume 63, Number 4, 2011
and Volume 64, Number 6, 2012.
2.1. Electron density

The ionospheric electron density proﬁle reaches its highest
density NmF2 at the F2 peak height hmF2. This point divides
the electron density proﬁle into its bottomside and topside parts.
In IRI both proﬁle parts are normalized to the F2 peak density andheight. In the region below the F2 peak the proﬁle structure ismore complex because parts of the density proﬁle are also normalized to the F1 or E peak. The buildup of the IRI electron density proﬁle is illustrated in Figure 2 and is described in detail in the
IRI-1990 report (Bilitza 1990). The region between the F2 and F1heights is of special interest because of its effect on HF radio wave
propagation and because of its non-negligible contribution to theionospheric Total Electron Content (TEC). IRI-2012 includes a
new model for this region that will be explained in the next
section. With IRI-2012 the model will for the ﬁrst time include
a representation of the auroral oval boundaries and a descriptionof storm effects in the auroral E region. These IRI additions will
be explained in Sections 2.1.2 and 2.1.3, respectively.

2.1.1. New models for B0 and B1

The dependence of electron density Ne on height h in the region
between the F2 and F1 heights is described in IRI by the function
N e ðhÞ ¼ NmF 2 

exp ðZ B1 Þ;coshðZ ÞZ¼

hmF 2  h
;
B0

ð1Þ

with the bottomside thickness parameter B0 and the shapeparameter B1. It is important to note that B0 is different fromthe often used half-density thickness parameter Ym that isdeﬁned as Ym = hmF2-h0.5 with the half-density height h0.5
deﬁned as N(h0.5) = 0.5 · NmF2. In our case the height hx,
where B0 = hmF2  hx (or Z = 1), is the height where thedensity proﬁle has in fact dropped down to 0.24 · NmF2.
For the description of the global and temporal variations of
the B0 and B1 parameters IRI offers two model options andis now adding a third one with IRI-2012 that is the recommended (default) option. All three models use as their database proﬁles obtained by ionosonde data analysis; they varyin the volume and global coverage of the data used and in
the functions used to represent the variation patterns.
Gulyaeva (1987) used mainly data from mid-latitude stations
and her model consists of a functional description of theobserved correlation between hmF2 and h0.5; using equation(1) this can then be converted into a model for B0. A muchwider range of conditions in terms of global, seasonal, andsolar cycle conditions was covered by the B0 model of Bilitza
et al. (2000) that grew out of an IRI Task Force Activity held

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J. Space Weather Space Clim. 4 (2014) A07

Fig. 2. Buildup of the IRI electron density proﬁle and its separation
into different regions.

at the International Centre of Theoretical Physics (ICTP) in
Trieste, Italy. The model consists of a table of representativevalues and an interpolation scheme for intermediate conditions. It describes variations with Local Time (LT), season,
solar activity (R-12), and modiﬁed magnetic latitude (modip).
Bilitza et al. (2000) also provide a model for the shape parameter B1 which shows a marked change from day to night.
Epstein step-functions that vary from the daytime value of
1.9 to the nighttime value of 2.6 with smooth 1-hr transitions
at sunset and sunrise are used to model this behavior.
With improvements in ionosonde design and data analysis
technique, the volume of bottomside proﬁles deduced from ionograms has been increasing steadily. Researchers have used thisdatabase to evaluate the two existing model options with thegoal of determining which one provides the better results. It
was found, however, that both model options have their shortcomings as was shown by Lee & Reinisch (2006), Lee et al.
(2008), Chen et al. (2006), Blanch et al. (2007), Zhang et al.
(2008), and Sethi et al. (2009) with ionosonde data from a wide
range of ground stations. Most importantly, marked discrepancies were found in the representation of seasonal and solar
activity trends of B0, with the Bilitza et al. (2000) model generally providing better results during daytime, while the Gulyaeva(1987) model performed better during nighttime. This was also
conﬁrmed by Lei et al. (2004) with Millstone Hill incoherent
scatter data. Additionally, like the ionosonde comparisons theyfound that for B1 diurnal variations are well represented by the
Bilitza et al. (2000) model. But they also noted a 15% change in
B1 over a solar cycle that is currently not included in the IRI
model. All these shortcomings are most likely due to the seasonal and solar cycle coverage limitations of the databases used
to develop the earlier models. As a result of these studies the
IRI team made it its highest priority to upgrade the B0 and

Fig. 3. Comparisons of the root mean square errors (RMSE)
between the ionosonde B0 data (27 stations) and the Bilitza et al.
(2000) model (Standard), the Gulyaeva (1987) model (Gulyaeva)
and the Altadill et al. (2009) B0 model (SH) for the years 1998–2006.
The plot at the top shows the solar activity levels corresponding toeach year, the middle plot shows the RSME for B0 and the bottom
plot shows the RSME for B1 (Altadill et al. 2009).

B1 models. Altadill et al. (2009) took up the challenge and succeeded in developing a signiﬁcantly improved model based ondata from 27 globally distributed ionosonde stations (DGS or
DPS systems) for the years 1998–2006. The model applies
spherical harmonics analysis and describes variations with
modiﬁed dip latitude (modip), LT, month, and sunspot number.
Using their B0 database they ﬁnd an improvement of up to 32%
over the Bilitza et al. (2000) model and up to 40% over the
Gulyaeva (1987) model and an improvement of up to 20%
for the B1 values. Figure 3 illustrates the signiﬁcant improvement achieved with the new model in relation to the former bottomside parameter models of IRI. This will also translate into
improvements of ionospheric TEC although to a smaller
amount because the bottomside contributes only about
15%–40% to the total TEC. The parameter B0 is now widelyused for deﬁning the bottomside proﬁle thickness. Obrou
et al. (2003) ﬁnd a close correlation of B0 from two stations
near the magnetic equator with the strength of the equatorial
electrojet. Lee (2011) pointed to the anomalous behavior ofB0 during the recent extreme solar minimum, noting that, while
the F2 peak density and height continue to decrease through2008, B0 only decreased until 2007 and then started increasing
again.
2.1.2. Auroral boundaries

At high-latitudes the inﬂux of energetic solar wind and magnetospheric particles results not only in the beautiful display of the

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D. Bilitza et al.: IRI-2012

aurora borealis and australis, but can also have serious detrimental effects on technology in space. A demarcation of thisregion in IRI has therefore been an important goal of the
IRI team (Bilitza 1995). Aurorae are primarily seen within
the auroral oval, a region roughly centered at the magnetic poles
and typically between 60 and 80 degrees magnetic latitude. The
oval is slightly elongated toward the night-side and has its largest extent at local midnight. During magnetic storms the oval
expands and moves to lower latitudes. Different methods havebeen used to deﬁne and model auroral boundaries. Feldstein &Starkov (1967) summarized all-sky imager observations fromthe International Geophysical Year (IGY) 1957–1958 in a setof representative ovals for seven levels of magnetic activity
and Holzworth & Meng (1975) used a third-order Fourier formula to establish a simple mathematical representation of theseovals. A number of statistical models were developed for theauroral precipitating electron ﬂux and the resulting auroral zone
conductances (height-integrated Hall and Pedersen conductivities) based on in situ ﬂux measurements from different satellites(ISIS-2: Wallis & Budzinski 1981; AE-C, D, E: Spiro et al.
1982; DMSP-F2, F4, P78-1: Hardy et al. 1987; NOAA-TIROS:
Fuller-Rowell & Evans 1987). Auroral boundaries are obtained
from such models simply (and also somewhat arbitrarily) bychoosing a minimum ﬂux or conductance level. An often used
estimate is a conductance of 1 mho or a minimum integral
energy ﬂux of 0.25 ergs cm2 s1. Szuszczewicz et al.
(1993) and Bilitza (1995) discussed the usage of these models
in the IRI framework and present comparisons of the different
approaches.
Zhang & Paxton (2008) recently presented a new approachthat is based on global far ultraviolet (FUV) observations by the
Global Ultraviolet Imager (GUVI) of the Thermosphere
Ionosphere Mesosphere Energetics and Dynamics (TIMED)
satellite. The imager provides a much larger database and betterglobal and local time coverage than in situ measurements.
GUVI provides cross-track scanned images of the Earth’s ultraviolet airglow and FUV auroral emissions. Radiances measured
in the N2 Lyman-Birge-Hopﬁeld (LBH) bands (LBHS:
140.0–150.0 nm and LBHL: 165.0–180.0 nm) can be used toobtain estimates of the mean energy (E0) and energy ﬂux (Q)of precipitating electrons (Strickland et al. 1999). Theoreticalcomputations generally proceed in the opposite direction. Starting with E0 and Q (and an assumed spectral distribution) an
auroral transport code and airglow code are combined to obtain
atmospheric radiances. Zhang & Paxton (2008) used the Atmospheric Ultraviolet Radiance Integrated Code (AURIC) of
Strickland et al. (1999) and the Boltzmann Three Constituent(B3C) of Daniell (1993) to construct tables that relate LBH
radiances to the ﬂux characteristics (E0, Q) of precipitating
electrons. Based on GUVI data from 2002 to 2005 (~44,000
images) they developed E0 and Q models describing variations
with magnetic latitude and magnetic local time (MLT) for different levels of magnetic activity. Fortuitously, this time period
included some of the most intense super-storms of the solar
cycle and as a result the Zhang & Paxton (2008) model covers
a much wider range of magnetic activity conditions (Kp = 0–9)
than the Hardy et al. (1987) model (Kp = 0–6). IRI-2012 uses
the equatorward auroral boundary obtained from the Zhang &Paxton (2008) model at the threshold ﬂux value of0.25 ergs cm2 s1. Figure 4 shows the predicted decrease of
the equatorward boundary and increase of the poleward boundary as the oval expands with increasing magnetic activity.

Fig. 4. Equatorward and poleward auroral boundaries and peak ﬂux
location obtained with the Zhang & Paxton (2008) model fordifferent levels of magnetic activity (Kp).

Two adjustments had to be made before the model could be
included in IRI. The ﬁrst simply required converting the modelfrom Kp dependence to ap dependence because IRI uses the
3-hourly ap index for the description of magnetic storm effects.
This is trivial because the two scales are closely correlated. The
second adjustment involves the coordinate systems used. The
magnetic coordinates used by Zhang & Paxton (2008) are theAltitude Adjusted CGM coordinates (AACGM) of Baker &Wing (1989) and Bhavnani & Hein (1994) while IRI uses theCorrected Geo-Magnetic (CGM) coordinates of Gustafsson
et al. (1992). Both coordinate systems use the International
Geomagnetic Reference Field (Finlay et al. 2010) to trace froma point in space to the dipole geomagnetic equator and thentrace back down along the dipole ﬁeld line returning to thesame altitude (CGM) or to the ground (AACGM) and use theso found geographic coordinates as the CGM or AACGM coordinates for the original point in space. CGM and AACGM
coordinates are identical at the Earth surface but differences
between the two increase with increasing altitude. The problem
is easily resolved by using the Zhang-Paxton model in IRI with
the CGM coordinates for altitude zero.
Including auroral boundaries in IRI is a ﬁrst step toward abetter representation of density and temperature features in IRI
that are related to these boundaries such as the subauroral density trough and correlated temperature peak. With the availability of real-time GUVI-type measurements, e.g., from TIMED/GUVI or DMSP/SSUSI, a real-time speciﬁcation of auroralboundaries can be assimilated into IRI (Zhang et al. 2010).
2.1.3. Storm-time model for auroral E-region

Increased particle precipitation during geomagnetic storms can
produce signiﬁcant electron density enhancements in the auroral E-region and lead to HF communication interruptions. A
ﬁrst attempt to describe these changes for IRI, was made by
McKinnell et al. (2004) and McKinnell & Friedrich (2007) with
their Ionospheric Model for Auroral Zone (IMAZ). IMAZ wasdeveloped with EISCAT incoherent scatter radar measurementsfrom 1984 and 1998, plus about 50 rocket ﬂights that measured
the electron density using rocket borne radio wave propagation

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J. Space Weather Space Clim. 4 (2014) A07
Table 2. Storm-to-quiet electron density ratios in the auroral E-region.
Incoherent scatter radar
EISCAT-Tromso
EISCAT-Tromso
EISCAT-Tromso
EISCAT-Longyearbyen
EISCAT-Longyearbyen
Sondrestorm
Sondrestorm
Sondrestorm
Sondrestorm

Storm period
2003 10/21–11/02
2004 11/10–11/13
2006 12/14–12/16
2003 10/21–11/02
2004 11/06–11/13
2003 10/28–10/31
2004 11/10–11/13
2005 01/16–01/21
2006 12/14–12/16

IRI RMSE
1.12
1.07
1.21
1.23
1.37
1.27
1.15
1.15
1.20

instruments. A Neural Network (NN) was trained with thesedata evaluating various inﬂuencing parameters. Best results
were obtained when allowing variations with magnetic local
time, riometer absorption, local magnetic K index, F10.7 solar
radio ﬂux, and the neutral pressure. Neutral pressure provides arepresentation of the altitudinal and seasonal variations. The
riometer absorption and K index track storm-related changes
in the E-region. This modeling approach depends on the availability of ground-based radio wave absorption (riometer) measurements and was difﬁcult to seamlessly integrate with theexisting quiet-time IRI model. It was provided as a separate,
independent software code in IRI-2001 and IRI-2007.
A new model for describing auroral E-region storm effects
in IRI was developed by Mertens et al. (2013a, 2013b) basedon TIMED SABER data. SABER is the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER)
instrument that measures limb radiances in several channels.
Mertens et al. (2013a, 2013b) recognized that one of the channels, the 4.3 lm radiance measurement, can be a good proxyfor characterizing the nighttime E-region electron density inresponse to auroral precipitation. During daytime emissions at
this wavelength are dominated by CO2 (v1, v2, v3) vibrationrotation bands during daytime thus providing a CO2 density
measurement (the main aim of the SABER 4.3 lm measurement). But during nighttime, emissions from vibrationallyexcited NO+ become important and can be reliably separated
from the background CO2 (v3) contribution. In comparisonswith simultaneous precipitating electron energy ﬂux measurements by the NOAA/POES satellite Mertens et al. (2013a)
showed that the NO+(v) Volume Emission Rates (VER) arean excellent proxy for the incoming energy ﬂux. The stormtime to quiet-time ratio for the electron density is obtained fromthe corresponding VERs for NO+(v):r ¼

VERStorm ½NOþ Storm ½NeStorm:
VERQuiet½NeQuiet½NOþ Quiet

ð2aÞ

A power-law function is used to describe the dependence
on the 3-hourly ap index
rðap; km Þ ¼ C1  apC2 þ C3;

ð2bÞ

with the coefﬁcients C1, C2, and C3 varying with magnetic
latitude km. Comparisons with nighttime and twilight incoherent scatter radar measurements from EISCAT and Sondrestrom during storm events show good agreement (Mertens
et al. 2013b). Table 2 was generated based on these comparisons. One important shortcoming of this model is the limitation to nighttime and twilight hours. Usage in IRI requires acontinuous model that does not abruptly start/end at the transition to nighttime/daytime. As a preliminary ﬁrst-order

STORM-E RMSE
0.67
0.35
0.68
0.67
0.89
0.79
0.86
0.81
0.86

approach we assume that the nighttime r(ap, km) dependence
can be also used throughout the day.
2.2. Electron temperature

IRI provides two options for the electron temperature Te. In
both cases the model built-up is the same, using a spherical harmonics representation of the global temperature variations at
ﬁxed heights and Epstein functions to describe the altitudinalvariation. When this approach was ﬁrst applied by Bilitza(1985) and Bilitza et al. (1985), it was based on the AE-C,
ISIS-1, and ISIS-2 global parameterizations of Brace & Theis(1981) at 300 km, 400 km, 1400 km, and 3000 km and the
AEROS-A model of Spenner & Plugge (1979) at 600 km.
Truhlik et al. (2000) more recently utilized the global measurements of the Interkosmos 19, 24, and 25 satellites to develop
global models at 350, 550, 650, 1400, and 2000 km with amore detailed description of the diurnal variation including
the early morning overshoot that was not well represented in
the earlier models. Both of these models describe the variations
with magnetic latitude and magnetic local time (MLT) providing different sets of coefﬁcients for different seasons. Thesemodels have performed well in comparisons with satellite andincoherent scatter radar measurements. Their main shortcomingis the fact that they do not include variations with solar activity.
There are a number of reasons why modeling of the solar activity variations of the electron temperature is a difﬁcult task: (1)While most ionospheric parameters increase with solar activity
Te can increase, decrease, or stay constant depending on the altitude, latitude, time of day, or season. This is because Te is determined by the balance of heating through photoelectrons that are
created by the solar EUV irradiance, cooling through collisions
with neutrals and ions, and heat conduction along magnetic
ﬁeld lines. All three terms increase with solar activity due tothe increase in EUV ﬂux, neutral density, neutral temperature,and electron and ion densities. Since the three terms compensate
each other the net result can be a Te increase, decrease, or nochange at all. An example is shown in Figure 5 where Te
averages from satellite in situ measurements are plotted versus
invdip latitude for three levels of solar activity (low, medium,high) at 550 km (±50 km) for solstice (left) and equinox (right).
For equinox Te increases with solar activity at high and low latitudes, decreases at middle latitudes, and is near constant in
between these regions. In the summer hemisphere the Te correlation with solar activity is positive while on the winter side it isnegative. (2) Inconsistencies remain among various data sets
especially in regimes of low electron density because most Te
measurement techniques depend critically on the presence ofa sufﬁcient number of electrons. An example is shown in
Figure 6 where the difference between DMSP Langmuir probe
measurements and simultaneous measurements by the groundA07-p6

D. Bilitza et al.: IRI-2012

Fig. 5. Equinox (left) and solstice (right) noontime electron temperatures versus invdip latitude at 550 km for three levels of solar activity (blue,
low, PF10.7 < 110; green, medium, 110 < PF10.7 < 180; red, high, PF10.7 > 180). Data shown are from satellite in situ measurements giving
the median plus upper and lower quartiles. Np is total number of measurements for each curve.

Fig. 6. Difference between DMSP probe Te measurements andsimultaneous Millstone Hill incoherent scatter measurements versus
the solar activity index PF10.7 = (F10.7D + F10.7_81)/2 with
F10.7D the daily solar radio ﬂux index F10.7 and F10.7_81 the 81
day average of F10.7. The 540 coincidences from the time period
1996 to 2006 are separated into daytime (+) and night-time (e) andalso include a least-square ﬁtted linear approximation (broken curve)with the formula given at the bottom.

incoherent scatter radar at Millstone Hill are plotted versus solar
activity, revealing large discrepancies at low solar activities(and therefore low electron density) with the probe data exceeding the radar data by about 1000 K (100%). Bilitza et al. (2007)
and Truhlik et al. (2009) have studied this complex solar activity behavior of the electron temperature with a large database ofsatellite in situ measurements and with the help of the Field
Line Interhemispheric Plasma (FLIP) physical model (Richards
2001) and the empirical model of Zhang et al. (2005) that
represents many years of Millstone Hill incoherent scatter radar(ISR) measurements. Their work resulted in a new model for
Te (Truhlik et al. 2012) that now describes solar activity variations and is included in the latest version of the IRI model.
2.3. Ion composition

The ion composition has been the step-child of IRI development activities, partly because of the limited amount of available data and still existing discrepancies between ground andspace observations, and partly because of the much smaller
community of IRI users that require information about the ionospheric ion composition. As with many of its parameters, theIRI user can choose between two options for the ion composition. The older version is based on the work of Danilov &Smirnova (1995) in the bottomside and of Danilov & Yaichnikov(1985) in the topside ionosphere using a compilation of Russian
rocket measurements including high-altitude rockets that cover
topside altitudes. The models give the percentage of O+, H+,
N+, He+, NO+, O2+, and Cluster ions as a function of solar
zenith angle, latitude, season, and solar activity. Considering
the limited database the models have performed quite welland gave IRI users a ﬁrst estimate of the distribution of ionsin the ionosphere. A newer topside version was developed by
Triskova et al. (2003) and is included in IRI as the recommended option since IRI-2007. This newer model takes advantage of the better global coverage provided by satellite ion mass
spectrometer measurements (Interkosmos-24, AE-C, AE-E) anduses the invdip latitude coordinate that is close to the magnetic
inclination (dip) near the magnetic equator and gets close toinvariant latitude at higher latitudes, and thus correlates wellwith the observed variation patterns of the topside ions (Truhlik
et al. 2004). Compared to the older Danilov & Yaichnikov(1985) model, the newer model also provides a more detailed
description of variations over the year, and uses the more appropriate Magnetic Local Time (MLT) to describe diurnal changes
instead of the solar zenith angle that is more suited for the lower
ionosphere. With IRI-2012 a newer ion composition model is
now also introduced for the bottomside ionosphere. The modelof Richards et al. (2010) makes use of the fact that the photochemistry in the lower ionosphere is well-established as comparisons between model calculations and measurements have
proven. So by normalizing the ion densities obtained with theFLIP model photochemistry to the IRI electron densities,
Richards et al. (2010) obtain a new description of the composition of molecular ions and O+ in the lower ionosphere. Their
comparisons with AE-C ion density measurements show good
agreement and also highlight the signiﬁcant improvement the
new model achieves over the older Danilov & Smirnova(1995) model. Model caveats are, that it does not account forauroral particle precipitation nor for rapid convection. Partly
these limitations will be overcome by the normalization witha total ion density (= electron density) that includes theseeffects.
2.4. Other improvements in IRI-2012

As requested by COSPAR the IRI plasma temperatures should
be in thermal equilibrium with the CIRA neutral temperature at
120 km and below, and should not fall below the neutral temperature at any altitude. The IRI program therefore had toinclude the computation of the CIRA neutral temperature. With

A07-p7

J. Space Weather Space Clim. 4 (2014) A07

Fig. 7. World maps of plasma frequency foF2 obtained with IRI RTAM by assimilating data from the Global Ionospheric Radio Observatory(GIRO) into the IRI model for foF2. The left panel shows the ﬁnal map. The right panel shows the GIRO digisonde stations (circles) that
contributed and the color in the circle percentage difference between IRI and the GIRO measurements. The color in the rest of the right panel
shows the percentage difference between RTAM and IRI.

IRI-2012 the CIRA model has now been upgraded to the
NRLMSIS-00 model (Picone et al. 2002) in concurrence withthe decisions made by the CIRA Working Group. This is alsoof importance for the new model for the bottomside ion composition of Richards et al. (2010) that requires neutral densities for
its photochemistry computations.
IRI-2010 makes use of the latest version of the International
Geomagnetic Reference Field (Finlay et al. 2010) for its computation of magnetic coordinates, using the latest sets of coefﬁcients for 2010 and beyond to accurately represent the changes
in the Earth’s magnetic ﬁeld. For a better representation of thelatitudinal changes of ionospheric parameters at high-latitudes
IRI-2012 now also includes the Corrected Geomagnetic Coordinates (CGM) of Gustafsson et al. (1992) that are needed, forexample, for the representation of auroral boundaries.
3. Real-time IRI
IRI represents the monthly average behavior of the ionosphereat a given place and time, for a given level of solar activity. So
IRI can predict the changes from one month to the next but notfrom day to day. More and more applications, however, requirea description of the day-to-day variations. In some cases an estimate of the expected range of variability is all that is needed
and there is an IRI effort underway to include a quantitativedescription of ionospheric variability in IRI in the form of themonthly standard deviation (Araujo-Pradere et al. 2004, 2005;
Bilitza et al. 2004). A more accurate description of the changes
from day to day requires the use of real-time data and of updating or assimilation techniques to combine IRI with these dataand in the process produce the Real-Time IRI (IRI-RT).

Most updating algorithms use the computation of an ionospheric-effective solar index by adjusting IRI predictions to ionosonde measurements (Bilitza et al. 1997), or to GPS Total
Electron Content (TEC) maps (Komjathy et al. 1998), or to
GPS slant TEC data (Hernandez-Pajares et al. 2002). Different
techniques have been used to assimilate data into IRI. Fridman
et al. (2006) used the Tikhonov methodology with IRI and GPS
data. The Electron Density Assimilative Model (EDAM)
approach of Angling et al. (2009) is based on a weighted,
damped least mean squares estimation (also referred to as Best
Linear Unbiased Estimation – BLUE) and assimilates mostly
GPS data into IRI-2007. Good results were obtained by Yue
et al. (2012) using a Kalman ﬁlter technique to assimilate
GPS data, radio occultation data (CHAMP, GRACE, COSMIC,
SAC-C, Metop-A, and TerraSAR-X), and Jason-1 and 2 altimeter TEC measurements into IRI-2007. Schmidt et al. (2008)
represent the difference between GPS data and IRI globallyand regionally with a multidimensional expansion in B-spline
functions. Combing assimilation and updating techniques,Pezzopane et al. (2011) ﬁrst determine a ionospheric-effective
sunspot number from comparing IRI to ionosonde F2 peakparameter (foF2, M(3000)F2) measurements and then after
fully analyzing the ionograms applied a Kriging technique toassimilate the full electron density proﬁle into IRI-2007. The
IRI Real-Time Assimilative Mapping (IRTAM) of Galkin
et al. (2012) is based on plasma frequency (foF2) measurements
by the worldwide network of Digisonde stations (the Global
Ionospheric Radio Observatory – GIRO) and employs a linear
optimization technique to obtain an improved global representation of foF2 for IRI every 15 min (http://giro.uml.edu/
RTAM). An example is shown in Figure 7. Zhang et al.
(2010) developed an algorithm for updating the IRI auroral

A07-p8

D. Bilitza et al.: IRI-2012
Table 3. Percentage of JGR, GRL, SW, and RS papers using IRI.
Percentage of papers using IRI
2009
2010
2011
2012

JGR
5.0%
5.6%
7.1%
8.0%

GRL
3.6%
4.7%
1.6%
1.7%

SW
0.0%
5.6%
8.1%
9.5%

RS
10.5%
11.8%
14.2%
15.1%

JGR = Journal Geophysical Research-Space, SW = Space Weather
journal, GRL = Geophysical Research Letters, RS = Radio Science.

boundaries (see Sect. 2.1.2) with real-time measurements by the
GUVI instrument on the TIMED satellite and the SSUSI instrument on the DMSP satellite.
Special IRI Real-Time workshops were held in 2009 and2012 to discuss the feasibility of the different methods and to
decide on the best approach toward the IRI-RT. These workshops discussed two related but distinct objectives of the concept of ‘‘real-time’’. The ﬁrst is to provide an historical
record of the state of the ionosphere. This is a post-processing
activity that does not require the analysis to be done in realtime, but is more akin to the concept of ‘‘re-analysis’’ thathas been applied to tropospheric weather. The second objective
is to perform the assimilation in real-time, pushing the IRI climatological state toward the actual state using all available
observations. The two activities are related because they arebuilt upon the same concept, to combine model and data withdata assimilation techniques. There are several motivations
for these two activities. Providing an accurate record of the stateof the ionosphere over an extended period would contribute a
tremendous resource for scientiﬁc studies. There is also theneed for real-time ionospheric speciﬁcation and forecast forspace weather applications. By using IRI at the heart of areal-time data assimilation system, it beneﬁts from the yearsof development work that are behind this internationally recognized ionospheric reference model.

4. Measures of success
IRI has become a household name in ionospheric physics. It isinternationally accepted as the standard for ionospheric parameters and is used by a wide spectrum of novice and expert users
for applications in science, engineering, and education. Theinternational recognition of IRI is documented by the 1999
URSI resolution (see Table 1) and by the fact that IRI is thestandard for the ionosphere for the European Cooperation forSpace Standardization (ECSS 2008) and for the International
Standardization Organization (ISO; Gulyaeva & Bilitza 2011).
The wide scientiﬁc usage of the IRI model can be measured
by the number of scientiﬁc publications that acknowledge theuse of IRI. Table 3 shows the percentage of papers that used
IRI in the years 2009 through 2012 in the science journals Journal of Geophysical Research (JGR) – Space, GeophysicalResearch Letters (GRL), Space Weather (SW) journal, andRadio Science (RS). These numbers were obtained by an automated keyword search of the full text of all articles and then amanual inspection of the found articles, to make sure IRI wasused and not only referenced. Except for GRL, the percentageof papers using IRI is increasing from year to year, reaching 8%

Fig. 8. Monthly accesses to the IRIweb online interface fromJanuary 2009 to July 2013.

for JGR, 9.5% for SW and an amazing 15.1% for RS in 2012;
so every 7th RS paper in 2012 relied on IRI to achieve its science goals. In addition to these four journals there are manyother journals that have articles describing work that relies on
the IRI model. A wider literature search reveals 133 citationsof the IRI-2007 paper (Bilitza & Reinisch 2008) in 2011 and138 citations in 2012 across a wide spectrum of journals,
23 in all, including GPS Solutions, Computer Physics Communications, Solar Physics, Applied Optics, Plasma Science andTechnology, Computers & Geosciences, to name just a few,
underlining the wide spectrum of IRI applications. Finally,
we can also see a remarkable increase in the number of accesses
to the IRIWeb, the web interface for computing IRI parametersthat is shown in Figure 8. Accesses show roughly a 10-foldincrease from year to year, recently reaching the 1 million permonth mark.
The IRI model has gained this high level of acceptance andtrust because it has been extensively evaluated with large volumes of data from ground and space measurement techniques.
When discrepancies were found, the IRI team made it its highest priority to improve the model to accurately represent the
new data source. Many of these comparisons and if necessaryconsecutive improvement studies can be found in the seriesof special IRI-related issues of Advances in Space Research thatwere mentioned in the Introduction. Recently the Coupling,
Energetics, and Dynamics of Atmospheric Regions (CEDAR)
program of the National Science Foundation (NSF) initiatedthe Electrodynamics Thermosphere Ionosphere (ETI) Challenge to assess the accuracy of ionosphere/thermosphere models in reproducing ionosphere and thermosphere parameters. A
total of nine events and ﬁve physical parameters were selected
to compare observations with the predictions by 10 of the mostwell established ionosphere/thermosphere models. IRI was the
only data-based model, all others were physics-based models
and two of these in addition employed data assimilation techniques with GPS data for the respective event periods. IRI performed very well in this challenge and was best or one of thethree best in all categories considered by this challenge(Shim et al. 2011, 2012). It is noteworthy that in several cases
IRI performed better than the physics-based models with data
assimilation capabilities. As mentioned in the previous section
the accuracy of IRI can be increased signiﬁcantly with data
assimilation techniques and IRI with data assimilation would
therefore have done extremely well in the CEDAR ETI
Challenge.

A07-p9J. Space Weather Space Clim. 4 (2014) A07

Success of an international science collaboration should not
only be measured by the applicability, accuracy, and wide usageof the scientiﬁc results of the endeavor, that we presented
above, but also in as much as it succeeded in involving scientists from many countries worldwide including developed anddeveloping countries, and in as much as it helped initiate multilateral science projects and provided an opportunity for emerging science programs to participate in an international program.
The IRI project and program has been highly successful in allof these areas. Many of its bi-annual workshops were held indeveloping countries providing scientists from these countries
with the opportunity to get involved in IRI-related science projects with other countries. The IRI project, on the other hand,beneﬁtted by getting access to new data sources from thesecountries.

access to essentially all reliable ground and space data
sets for ionospheric parameters.
2. Annual IRI Workshops that are the main venue for discussing improvements and enhancements to the modeland that are the catalyst for a multitude of international
collaborations whose goal was to improve speciﬁc
aspects of the model. A trademark of these informal
meetings is the ‘‘Final Discussion’’ session where the
IRI team decides on the improvements and additions to
be included in the next version of the model, and where‘‘volunteers’’ are enlisted to investigate new data sources
and speciﬁc modeling questions for future model updates.
3. Publication of the workshop papers initially in Space
Research, and later in Advances in Space Research thathas resulted in an excellent record of the IRI activity
and has produced a unique series of articles documenting
the international efforts in ionospheric modeling.
4. An easy to use, fast, open source software and webinterface.

5. Conclusions
Empirical models play an important role in all parts of the SunEarth environment. They give the scientist, engineer, and educator easy access to a condensed form of the available empiricalevidence for a speciﬁc parameter, optimally, being based on all
reliable data sources that exist for the parameter. Examples ofsuch widely used models are the International Geomagnetic
Reference Field (IGRF) model for Earth’s magnetic ﬁeld
(Finlay et al. 2010) and the Mass Spectrometer and Incoherent
Scatter (MSIS) model for Earth’s Atmosphere (Picone et al.
2002). The ionospheric equivalent to these models is the International Reference Ionosphere (IRI). The IRI project is an
excellent example of a successful international science collaboration that has operated for several decades and has resulted inan ionospheric model that is widely acknowledged as the standard ionospheric model and now also close to becoming an ISO
standard (Gulyaeva & Bilitza 2011). It has also helped a large
multitude of both novice and expert users ranging from hamradio operators to satellite/rocket instrument designers to operators of Earth observing satellites.
Since initiated by COSPAR and URSI in 1969, IRI has
been steadily improved with newer data and better modelingtechniques leading to the release of a number of key editionsof the model including the IRI-78 (Rawer et al. 1978a,1978b), IRI-85 (Bilitza 1986), IRI-1990 (Bilitza 1990), IRI2000 (Bilitza 2001), and IRI-2007 (Bilitza & Reinisch 2008).
In the preceding sections we have presented the latest version
IRI-2012 and brieﬂy discussed a new effort to develop a
Real-Time IRI by combining IRI with real-time data using
updating or assimilation techniques. The IRI success story is
documented by the increasing rate of accesses to the IRIweb
interface, recently reaching 1 million per month, and by theincreasing number of science papers that acknowledge theuse of the IRI model, e.g., 8% of all 2012 Journal of Geophysical Research papers and 15% of all 2012 Radio Sciencepapers. The special ingredients that made the IRI such a successstory are:
1. Working group members who provide a good, balanced
cross-section in terms of the representation of different
countries and continents (see Fig. 1) as well as in termsof the representation of different measurement techniques. This turned out to be a great asset in gaining

Acknowledgements. We acknowledge the contributions of IRI
Working Group members to the IRI effort and the many users ofthe model who have provided valuable feedback.

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Cite this article as: Bilitza D, Altadill D, Zhang Y, Mertens C, Truhlik V, et al.: The International Reference Ionosphere 2012 – amodel of international collaboration. J. Space Weather Space Clim., 2014, 4, A07.

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