J Geod (2011) 85:909–920
DOI 10.1007/s00190-010-0427-x

REVIEW

The international reference ionosphere today and in the future
Dieter Bilitza · Lee-Anne McKinnell · Bodo Reinisch ·Tim Fuller-Rowell

Received: 10 June 2010 / Accepted: 16 November 2010
© Springer-Verlag 2010

Abstract The international reference ionosphere (IRI) is
the internationally recognized and recommended standard
for the specification of plasma parameters in Earth’s ionosphere. It describes monthly averages of electron density,
electron temperature, ion temperature, ion composition, andseveral additional parameters in the altitude range from 60 to
1,500 km. A joint working group of the Committee on Space
Research (COSPAR) and the International Union of Radio
Science (URSI) is in charge of developing and improving the
IRI model. As requested by COSPAR and URSI, IRI is anempirical model being based on most of the available andreliable data sources for the ionospheric plasma. The paper
describes the latest version of the model and reviews efforts
towards future improvements, including the development of
new global models for the F2 peak density and height, and a
D. Bilitza (B)Space Weather Laboratory, George Mason University, Fairfax,
VA 22030, USAe-mail: dbilitza@gmu.edu; dieter.bilitza-1@nasa.gov
D. Bilitza
Heliospheric Laboratory, NASA Goddard Space Flight Center,Greenbelt, MD 20771, USA
L.-A. McKinnell
Hermanus Magnetic Observatory, PO Box 32,
Hermanus 7200, South Africa
e-mail: lmckinnell@hmo.ac.za
B. ReinischCenter for Atmospheric Research, University of Massachusetts
Lowell, 600 Suffolk Street, Lowell, MA 01854, USAe-mail: Bodo_Reinisch@uml.edu
T. Fuller-Rowell
CIRES University of Colorado and NOAA Space Weather Prediction
Center, 325 Broadway, Boulder, CO 80305, USAe-mail: Tim.Fuller-Rowell@noaa.gov

new approach to describe the electron density in the topsideand plasmasphere. Our emphasis will be on the electron density because it is the IRI parameter most relevant to geodetic
techniques and studies. Annual IRI meetings are the main
venue for the discussion of IRI activities, future improvements, and additions to the model. A new special IRI task
force activity is focusing on the development of a real-time
IRI (RT-IRI) by combining data assimilation techniques with
the IRI model. A first RT-IRI task force meeting was held in2009 in Colorado Springs. We will review the outcome ofthis meeting and the plans for the future. The IRI homepage
is at http://www.IRI.gsfc.nasa.gov.
Keywords Ionosphere · IRI · Empirical model ·F2 peak models · Topside

1 Introduction
Empirical models play an important role in all parts of theSun–Earth environment. They give the scientist, engineer,and educator easy access to a condensed form of the available empirical evidence for a specific parameter, optimally,
being based on all reliable data sources that exist for theparameter. Examples of such widely used models are theinternational geomagnetic reference field (IGRF) model for
Earth’s magnetic field (IGRF 2010) and the Mass spectrometer and incoherent scatter (MSIS) model for Earth’s atmosphere (Picone et al. 2002). The ionospheric equivalent tothese models is the international reference ionosphere (IRI).
Since initiated by the Committee on Space Research(COSPAR) and the International Union of Radio Science(URSI) in 1969, IRI has been steadily improved with newer
data and better modeling techniques leading to the release of
a number of key editions of the model including the IRI-78

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(Rawer et al. 1978), IRI-85 (Bilitza 1986), IRI-1990(Bilitza 1990), IRI-2000 (Bilitza 2001), and IRI-2007 (Bilitza and Reinisch 2008). The model progressed from a setof tables of representative values to an analytical representation of densities and temperatures on the whole globe. It is adata-based model, as requested by COSPAR and URSI, andwas developed making use of all available and reliable data
sources for the ionospheric plasma. This includes the worldwide network of ionosondes that has monitored ionosphericelectron densities at and below the F-peak for more than halfa century, the powerful incoherent scatter radars that measureplasma densities, temperatures, and velocities throughout thewhole ionosphere, but unfortunately only at a few selected
locations (∼8 in operation currently), the topside soundersatellites that have provided the global distribution of electron density from the satellite altitude down to the F-peak, insitu satellite measurements of ionospheric parameters along
the satellite orbit, and rocket observations of the lower ionosphere, currently the only reliable method to obtain plasma
parameters in the D-region.
Being an empirical model IRI has the advantage that it
does not depend on the evolving theoretical understandingof the processes that shape the ionospheric plasma. A good
example is the recently discovered four maxima structurein the longitudinal variation of F-peak electron density andionospheric electron content that was first observed with
IMAGE/EUV observations (Immel et al. 2006), and thenconfirmed with data from CHAMP (Lühr et al. 2007) and
TOPEX (Schmidt et al. 2008) and that is thought to be caused
by non-migrating, diurnal atmospheric tides that are, in turn,driven mainly by weather in the tropics. Although theoreticalmodels still grabble with including this phenomenon in theirmodeling 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 longitudinal variations is generally smaller in IRI than what is observed. However, that isunderstandable, because IRI is based on monthly averages
and the averaging process smoothes out some of these longitude structures.
A disadvantage of empirical models is the strong dependence on the underlying data base. Regions and time periods
not well covered by the data base will result in diminishedreliability of the model in these areas. So, for example, therepresentation of the F-peak density (NmF2; see Sect. 4) in
IRI is based on the data from the global network of ionosondes. The model performs best at continental northern midlatitudes because that is the region with the highest densityof ionosondes stations and many of these stations have been
operating over a long time period. Most difficult are the ocean
areas, where only a few island stations exist and an interpolation scheme has to be applied to cover this part of the globe.
The two major models in use for NmF2, the CCIR (1966)

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and URSI-88 (Rush et al. 1989), differ primarily in the interpolation scheme used for the ocean areas. IRI offers use ofboth models and based on the data background recommends
the CCIR model for the continents and the URSI-88 modelfor the ocean areas. A radically new modeling approach forthis important parameter will be described in Sect. 4 of thispaper.
The main venue for improvements of the IRI model are
annual workshops during which the latest results are presented and discussed and decisions are made regarding model
updates and additions. A new effort was started in 2009 thathas as its goal the development of the real-time IRI (RT-IRI).
A first RT-IRI meeting was held at the US Air Force Academy in Colorado Springs, Colorado, USA in May 2009. The
results from the meeting and plans for the future regarding
this activity will be discussed in Sect. 6.

2 IRI relevance to geodetic techniques
From the start applications of the model have been an important driver for the IRI development and improvement activities. IRI applications range across a wide user community andthis importance was recognized by the International
Standardization Organization (ISO) by voting it the recommended technical specification for ionospheric parameters(ISO 2009). Here, we will focus on applications of IRI in
the realm of geodetic techniques. We will also discuss thepotential impact of ionospheric data deduced from geodetic
techniques towards improvements of IRI.
One important role of IRI is as a background ionospherefor validating the reliability and accuracy of a particularapproach for deducing ionospheric parameters from geodetic
measurements. Hernandez-Pajares et al. (2002) and Niranjan et al. (2007) have used IRI for testing algorithms thatconvert GPS measurements into global total electron content (TEC) maps. Many tomographic techniques and radio
occultation inversion algorithms got their real first test in an
IRI background ionosphere (Bust et al. 2004; Lee et al. 2008;
Garcia and Crespon 2008). Starting out with an IRI groundtruth ionosphere, the simulated geodetic measurements arecomputed for this ionosphere and than the tomographic algorithm is applied to reconstruct the underlying ionosphere,
which is then compared against the original IRI ground truth.
Hocke and Igarashi (2002) used the same approach to validate
their GPS/MET occultation measurement algorithm and Dearand Mitchell (2006) for testing their MIDAS GPS technique.
Most of these techniques rely on an iterative mathematical process and many use IRI to define the initial conditions for this process (e.g., Bust et al. 2004). Another area
where IRI has helped these techniques is with interpolating in regions where no GPS measurements are available(e.g.,Orús et al. 2002). Interesting also is the work of

The international reference ionosphere today and in the future

Datta-Barua et al. (2008), who estimated the range ofhigh-order ionospheric errors for the dual-frequency GPS
user based on an IRI ionosphere.
Global TEC maps represent the vertical TEC while GPS
measurements provide the slant TEC along the path fromground station to satellite. Conversion from slant TEC tovertical TEC is in most cases performed using a thin shell
approach which assumes that the ionospheric plasma is compressed into a thin shell at height h ts . The conversion factor
depends on the elevation angle of the slant path and on h ts .
Many techniques still use a constant (often 300 km) height h ts
for this conversion, even though it has been shown that using
the varying height of maximum density from IRI, hmF2 produces superior results (Komjathy et al. 1998; Brunini et al.
2003).
Ionospheric parameters deduced from geodetic measurements are an excellent resource for improving the IRI model.
Of greatest interest are electron density profiles deduced
through tomography and occultation. Validation of thesetechniques is progressing successfully, but a few open questions still remain regarding inherent limitation of these techniques in regions of steep gradients and areas of sparse groundstation coverage (e.g., the ocean areas). Integral measurements (TEC), on the other hand, are widely used for space
weather applications and only a few small issues remain having to do with the inherent instrument bias (Garner et al. 2008)
and the conversion algorithm as noted earlier in this section.
Bias uncertainties remain also for vertical TEC measurements from dual altimeter instruments like TOPEX, Jason
and ERS. Altimeter data complement the GPS data because
they provide TEC over the oceans, but not over land. They
are an excellent data source for model validations, especiallyin relation to the relative variation of model parameters thus
avoiding bias uncertainties.
Komjathy et al. (1998) were one of the first to assimilate
global TEC maps deduced from GPS measurements into the
IRI model and thus updating the monthly average model tothe daily and hourly ionospheric conditions monitored by
GPS. An even more direct approach by Hernandez-Pajares
et al. (2002) made use of individual slant TEC measurements. They determine an equivalent solar index by adjusting
IRI-slant-TEC to the measured slant-TEC and than use IRI
with this modified solar index. More recently, Schmidt et al.
(2008) and Zeilhofer et al. (2009) have developed a 4-D representation of the ionospheric electron density using IRI as
reference model and using an expansion in B-spline functions
to describe the difference term between the reference modeland the data (see article by Dettmering et al. in this issue).
Other IRI-GPS assimilation schemes have been developedby Fuller-Rowell et al. (2006) using IRI deduced EOF functions to represent TEC over the continental USA (US-TEC)
and by Angling et al. (2009) using IRI and GPS data in theirelectron density assimilative model (EDAM).

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3 Status and plans
The newest version of the IRI model, IRI-2011, will include
significant improvements not only for the representations ofelectron density, but also for the description of electron temperature and ion composition. These improvements are theresult of modeling efforts, since the last major release, IRI2007. Modeling progress is documented in several special
issues of Advances in Space Research: Volume 39, Number 5,
2007; Volume 42, Number 4, Aug 2008; Volume 43, Number
11, June 2009; Volume 44, Number 6, September 2009.
Our focus here will be only on those improvements thatwill have an impact on the electron density which is theparameter of most interest for geodetic techniques and data
analysis. One of the most important changes is the inclusionof new neural network-based models for the point of highest
density which are explained in Sect. 4.
In the bottomside the IRI electron density profile is normalized to the E and F2 peaks and the shape of the profile
is determined by the bottomside thickness parameter B0 andthe shape parameter B1 . Currently, two options are given
for these parameters: (i) the standard option consisting ofa table of values and associated interpolation scheme (Bilitza et al. 2000) and (ii) the Gulyaeva option based on themodel of Gulyaeva (1987) utilizing the half-density point
h 0.5 where the topside density has dropped down to half thepeak density. Shortcomings of these older models are theirlimited data base and the resulting misrepresentation of variations with season, latitude and solar activity. Altadill et al.
(2008, 2009) have applied spherical harmonic analysis to datafrom 27 globally distributed ionosondes stations obtaining a
new model for B0 and B1 that more accurately describes theobserved variations with latitude, local time, month, and sunspot number. Overall, the improvements over the older IRI
model are of the order of 15–35%. The largest improvements
are seen at low latitudes.
At high-latitudes the off-set of the magnetic pole fromthe geographic pole and its rotation around the geographic(rotation axis) pole together with the influx of energetic solar
wind particles results in the formation of the auroral oval at
the boundary between closed and open magnetic field lines.
Ionospheric densities and temperatures exhibit characteristic
variations in and near the oval region and, therefore, the inclusion of an oval description in IRI has long been a high priorityof the IRI team (e.g., Szuszczewicz et al. 1993 and Bilitza
1995) and is the first step towards including high-latitude
characteristics in IRI. Zhang and Paxton (2008) have recently
developed a model of the auroral electron energy flux basedon the global far ultraviolet (FUV) measurements with theglobal ultraviolet imager (GUVI) on the thermosphere ionosphere mesosphere energetics and dynamics (TIMED) satellite. The model also describes the expansion of the oval
during magnetic storms. Using a threshold flux value

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Zhang et al. (2010) define the poleward and equatorward
boundaries of the oval and their movement with magnetic
activity. This boundary parameterization is now scheduled
for inclusion in IRI.
During night time infrared emissions measured by another
TIMED instrument, the sounding of the atmosphere using
broadband emission radiometry (SABER) instrument, will
help us to represent in IRI the storm induced enhancementsof the E-region electron density that is caused by increased
particle precipitation. Mertens et al. (2007) and Fernandez
et al. (2010) have developed a model for the E-peak enhancement for different levels of magnetic activity using a formalism similar to the one used in the F-region STROM modelof Fuller-Rowell et al. (2000). Comparisons with incoherent
scatter radar measurements show good agreement (Fernandez
et al. 2010).
In addition, IRI-2010 will make use of the latest version of
the International Geomagnetic Reference Field (IGRF 2010)
for its computation of magnetic coordinates.
These changes will result in significant improvements of
IRI electron densities and total electron content (TEC) and,
therefore, will benefit the many applications of the IRI modelfor geodetic techniques and data analysis.
4 New global models for the F2 peak parameters
The point of highest density in the ionosphere, the F2 peak,is defined by two parameters the peak height (hmF2) and thepeak plasma frequency (foF2) which is directly related to thesquare-root of the F2 peak density. A third parameter alsoof interest here is the propagation factor M(3000)F2, which
can be monitored from the ground with the help of ionosondes and which is inversely related to hmF2. Currently, newneural network (NN) based global models for the F2 peakparameters (foF2 and M(3000)F2) are being developed andevaluated as a possible replacement for the CCIR (1966) and
URSI (Rush et al. 1989) models presently used as IRI F2 peakparameter prediction tools. The NN is the technique, whereby
a computer is trained to learn the relationship between a given
set of inputs and a known output. This technique is very useful in predicting non-linear relationships, and makes use of
the history of what has come before. Therefore, it is crucial tohave a large database of archived data from which to develop
the NN based model. The NNs used in this work make use of
the feed forward back propagation algorithm and the reader
is referred to Haykin (1994) for more details on the workingof NNs.

D. Bilitza et al.

1986. Data from various resources of the World Data Centre(WDC) archives (Space Physics Interactive Data Resource
SPIDR, the Digital Ionogram Database, DIDBase, and IPSRadio and Space Services) have been used in the development of the model. To satisfy all requirements for the model,
data from all latitude regions with different levels of magnetic
and solar activity and season were used to test for the predictive ability of the NN model and the results were comparedwith the URSI and CCIR predictions of the IRI model. As
shown in Oyeyemi and McKinnell (2008), a large percentageof the data used is from the northern hemisphere, while few
of the data are from the southern hemisphere with not mucharound the equatorial region. A significant paucity of datafrom the polar sectors (north and south) was also found. The
database consisted of a mixture of manually edited and automatically scaled data. Because it was too man-hour intensive
to manually edit all of the data, a general trend analysis was
performed on all of the data to ensure that there were no
extreme outliers and obvious defects in the scaling. The NN
technique is able to cope with minor problems in the datain that the technique aims for the best average solution, andwill ignore a few serious outliers. The final NN input space
for the first version of the foF2 global model consisted of
day number, universal time, solar zenith angle, solar activity,
magnetic activity, day-of-year, geographic latitude, magnetic
inclination and declination. For detailed information on the
inputs to the NN the reader is referred to Oyeyemi (2005)
and Oyeyemi et al. (2005).
The NN model for predicting the global foF2 value has
been tested extensively to judge its ability to meet the requirements for the replacement of the IRI foF2 maps. Figure 1
shows the bar graphs illustrating the RMSE differences
between observed foF2 values and predictions by the NN
model and the IRI model (URSI and CCIR coefficients) for
the foF2 hourly values for a few stations. Further discussion on the testing procedure can be found in McKinnell andOyeyemi (2009).
Currently, work is progressing on the development andevaluation of this model. Additional data have been acquired
to address the gaps in the distribution, particularly at thepolar and equatorial regions, and this data will be added tothe database and the NN re-trained. The input space is also
being assessed since the current input space is not compatible
with the current IRI code, and a more compatible input spaceis required. At each stage of the development, the model istested and compared with IRI to ensure that the replacement
solution for the foF2 maps predicts to a greater accuracy thanthe current IRI solution.

4.1 foF2 model
4.2 M(3000)F2 model
Initially, a global foF2 model was developed using hourly
values of foF2 from 85 global ionospheric stations, spanningthe period 1995–2005 and for a few stations from 1976 to123

Similarly to the above-described method for developing a
new foF2 model, a model for the M(3000)F2 parameter has

The international reference ionosphere today and in the future913

rotations) running mean of the F10.7 cm radio flux value(F10.781 ).
Figure 2 shows the measured and predicted M(3000)F2
values for selected example stations and years using the current version of the global model at 10h00 UT. The examples
have been chosen to show the prediction ability of the newmodel for the three latitude zones (equatorial, mid and high)value. Improvements of the IRI predictions vary according tolatitude which is expected due to the limited representationof some latitudes within the database. In general, the conclusion was that the new input space variables did not makea significant difference and, therefore, will be taken as thechosen input space for the global peak parameters model due
to its compatibility with the existing IRI code.
4.3 Future work

Fig. 1 Bar graph illustrations of RMSE differences between observedand URSI, CCIR and NN model predictions for all daily hourly values of foF2 for each verification station for the years indicated (after
Oyeyemi and McKinnell 2007)

been developed using the NN technique. A total of 51 global
stations were used to create a database of hourly M(3000)F2
values from the period 1964–1986. The original development of this model, which made use of the same input structure as the foF2 model is described in Oyeyemi (2007).
Recently, a new version of the M(3000)F2 model was produced with a modified input space that was designed to moreeasily integrate with the existing IRI code. This modified
M(3000)F2 model was presented at the IRI 2009 meeting in
Kagoshima, Japan. The model has two major differences tothe original version; (i) the magnetic inclination, magnetic
declination and angle of meridian were replaced with themodip angle, and (ii) the sunspot number index was replacedwith the P10.7 solar flux index. The modip angle was calculated using the IGRF model as is done in IRI. The P10.7
index is calculated as the average of the combination of thedaily value of the F10.7 cm flux and the 81 day (3 solar

Although the intention is to also develop a global model for
hmF2, this work is still in the early stages. Variability studies over the Southern African region have been undertaken(Adewale et al. 2009), and a major study has commenced
involving various stations around the globe. The global study
involves accessing data from various stations, performing ageneral trend analysis on the data, and creating a database of
hmF2 values that can be used for developing the model.
Regarding the foF2 model, the current version is being
updated to accommodate additional data for the polar andequatorial regions that were acquired recently. At the sametime, new input space variables will be incorporated into the
foF2 model along similar lines as was done for the new version of the M(3000)F2 global model.
Extensive tests on the suitability for inclusion of thesemodels into IRI as replacement modules for the current peakparameter prediction models are being carried out, and theresults have been presented at recent IRI workshops. In particular, concerns regarding the predictability of peak parameters at high and equatorial latitudes are being addressed.

5 Representing the topside electron density for IRI
Using newly available satellite data sources, e.g., ISIS-2 andIMAGE/RPI, a new representative model of the topside electron density distribution is being developed. One major
challenge for topside Ne modeling is finding a suitable mathematical representation of the topside vertical Ne profiles.
Representations that have been proposed include exponential functions (e.g., Bent et al. 1972; Llewellyn and Bent
1973), Epstein function (e.g., Rawer 1988; Radicella andLeitinger 2001; Depuev and Pulinets 2004), Sech-squaredfunction (e.g., Kutiev and Marinov 2007), Chapman function
with constant scale height (e.g., Reinisch et al. 2004), Chapman function with two constant scale heights for O+ and H+

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44Grahamstown
1988

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Argentine Islands
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Grahamstown
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Day Number

Fig. 2 Measured and predicted M(3000)F2 values for four example stations and years representing three latitude zones at 10h00 UT(e.g., Kutiev et al. 2006), and the Vary–Chap function (i.e.,
modified Chapman function for continuously varying scaleheight) (Reinisch et al. 2007).
The general α-Chapman electron density profile N(h) proposed by Rishbeth and Garriott (1969) has a neutral scaleheight H(h) that varies with height h, and we, therefore, callit the Vary–Chap function:
Hm 1/2
1exp
Ne (h) = Nm
(1 − y − exp (−y)) ,
H (h)
2
h

dz
y=
(1)
H (z)
hm

where Nm and h m are the density and height at the F2 peak.
Representation of a measured N (h) profile as a Vary–Chapfunction requires knowledge of H (h). Huang and Reinisch(2001) have shown that Eq. (1) can be solved for H (h) as
function of N (h)
N (h) −2
X (h) [1 − lnX (h)](2a)
H (h) = Hm
Nm

h 
N (z) 21
where X (h) = 1 +
dz
(2b)
Hm
Nm
hm

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It is easy to show that for H to be real and positive for allh >h m the value for Hm must satisfy the condition Hm ≥h
0.6 s [N (z)/Nm ]2 dz and in our analysis we selected Hm =
 hs hm2h m [N (z)/Nm ] dz. Here h s is the height of the satellite; h s ≈1,400 km for the ISIS-2 satellite. Figure 3 illustrates the duality relation of N(h) and H (h). The left panel shows a measured topside profile (black dots), and the right panel thederived H (h) function. Of course, by inserting this derived
H (h) function into Eq. (1), the measured N (h) function isreproduced (red curve in left panel). Of the 80,000 topside
electron density profiles obtained from digitized ISIS-2 ionograms (Huang et al. 2002; Bilitza et al. 2004; Benson and
Bilitza 2009; Jackson 1969). Reinisch et al. (2007) used alldata from magnetically quiet periods (K p ≤ 3) to derive
median normalized scale height profiles Hn = H (h)/H m asa function of season, latitude, and local time. The examples inFig. 4 for the summer at low mid-latitudes show remarkably
consistent behavior with not much diurnal variation exceptthat the inter-quartile spread is larger at night as compared
to daytime. The normalized scale height function Hn characterizes the shape of the topside profile without dependence
on the F2 peak characteristics hmF2 and NmF2. The N (h)profiles that are calculated from Hn using Eq. (1) are, ofcourse, controlled by these peak values. Because of the large

The international reference ionosphere today and in the future915

Fig. 3 left ISIS-2 Ne profile(black dots), right calculated
scale height H (h)/H m profile
from the N (h) profile shown on
the left. The Vary–Chap profile(red line) calculated with Hn (h)shown on the right issuperimposed on the measured
profile on the left. The two
curves match so well that theylook like a single line

Fig. 4 Median Hn functions (black line) for magnetic latitudes between 20◦ and 30◦ for summer at different magnetic local times (MLT). The redand blue dots are the lower and upper quartiles

variation of the F2 peak characteristics, it would not havebeen meaningful trying to define “median” topside profiles.
Nsumei et al. (2010) have used hyperbolic tangent functions
for the modeling of Hn (h).
A transition function is used to extend the topside profile toplasmaspheric heights. In Fig. 5a, the measured bottomside
profile is extended to the topside using the Vary–Chap topsidemodel constructing the profile up to 1,400 km. The empirical plasmasphere model derived from IMAGE/RPI measurements (Reinisch et al. 2001a,b; Huang et al. 2004) gives theN (h) profile for this location and time in the altitude range

from 2,500 to 4,000 km. Suitable transition functions havebeen developed that seamlessly connect the ionosphere andplasmasphere profiles as illustrated in Fig. 5b (Reinisch et al.
2007).

6 Real-time IRI
IRI has traditionally been designed to represent the averagebehavior of the ionosphere at a given place and time, for given
levels of solar and geomagnetic activity. The model is not

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Fig. 5 a The Vary–Chap
topside model (with quartiles)
extends the measured
bottomside profile to 1,400 km
altitude. RPI plasmaspheremodel profile for the same
location and time for altitudes
above 2,500 km. b Connecting a
Vary–Chap topside profile (solid
black line) to the RPI
plasmasphere profile (dashedblack line). Green line is IRI
model

currently designed to predict day-to-day variability beyond
that characterized by the indices. For some parameters (e.g.
NmF2), models have been proposed for IRI that describe
estimates of uncertainty or variability in the predicted values(Araujo-Pradere et al. 2005). These uncertainties are based on
the standard deviation about the fit to the data, so are unable
to characterize whether a particular day is likely to be higher
or lower than climatology at any given location. A workshop was held at the Air Force Academy in Colorado Springs
6–8 May 2009 (http://www.ionospheres.org/rtiri2009/) to
discuss the feasibility of transitioning from IRI as a climatological reference model to IRI as an ionospheric weathermodel. The workshop 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 postprocessing activity does not strictly require the analysis to bedone in real time, but is more akin to the concept of “re-analysis” that has been applied to tropospheric weather (Kalnay123

et al. 1996), where the operational data assimilation procedure is re-calculated for past years. The re-analysis is usuallytriggered by new analysis techniques and the need to re-do
previous analyses to ensure a continuous improved uniform
record. The second objective is to perform the assimilationin real time, pushing the IRI climatological state towards theactual state using all available observations. The two activities are related because they are built upon the same concept,
to combine model and data with data assimilation techniques.
There are several motivations for these two activities. Providing an accurate record of the state of the ionosphere overan extended period would contribute a tremendous resource
for scientific studies. There is also the need for real-time ionospheric specification and forecast for space weather
applications. Using IRI at the heart of a real-time data assimilation system, it utilizes the huge investment over the years
to develop an internationally recognized ionospheric reference model, IRI. The activity is timely due to the recent

The international reference ionosphere today and in the future

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Fig. 6 Illustration of thesignificant deviation ofionospheric weather fromclimatology. The figure showson the left an example of a TEC
map that is close to IRI
reference climatology. The mapon the right shows the following
day, when the structure is very
different from climatology, as
produced by the assimilation of
GPS data

development of data assimilation techniques in the space
weather discipline, and the ever increasing availability ofionospheric data from ground and space-based observations,a significant portion of which are available in real time. The
motivation has been reinforced further by the increasingly
closer connection between meteorology (terrestrial weather)and space weather.
Advances in specification and forecasting troposphere
weather has come from the larger increase in the availabilityof data (primarily from new satellite observations) and theability to combine the information with a model using optimal data assimilation techniques. The number of real-time
ionosondes is increasing (Reinisch and Galkin 2010) anddata are available from an ever-increasing global network ofdual-frequency GNSS receivers providing slant path electron content. Data are also available from constellations ofsatellites providing a dense global distribution of radio occultation measurements. The promise of increasing real-timedata sources has spawned the development of data assimilation techniques by the space physics community. Two of theearly thrusts in this area proceeded under a MURI program,with teams at Utah State University (USU) and the University of Southern California (USC) both using the same acronym: GAIM. The GAIM acronym stood for either Global
Assimilation of Ionospheric Measurements in the USU team(Schunk 2004; Scherliess et al. 2006), and Global Assimilative Ionospheric Model in the USC team (Wang et al. 2004;
Komjathy et al. 2010).
There are now several operational centers that haveadopted data assimilation techniques for ionospheric specification. Maps of real-time TEC for Australia, North America,
Europe, and Japan are provided by the Australian IonosphericPrediction Service (IPS, http://www.ips.gov.au/Satellite/2/
1). Other examples are the MIDAS GPS system (Dear and
Mitchell 2006) and the EDAM- GPS system (Angling et al.
2009). NOAA’s Space Weather Prediction Center(SWPC, http://www.swpc.noaa.gov/) transitioned a regional
ionospheric data assimilation model to specify total electron
content (TEC) over the contiguous United States (CONUS).
The US-TEC model (Fuller-Rowell et al. 2006 uses a realtime network of about 100 ground-based, dual-frequency,

GPS receivers operated by the US Coast Guard, which arepart of the Continuously Operating Reference Stations(CORS) network. The data is combined with an empiricalionospheric reference model using a Kalman filter. One of
the important aspects of US-TEC is that the reference modelis the IRI. Two examples of the TEC maps are shown in Fig. 6
illustrating the significant deviation of ionospheric weather
from climatology that can arise. The figure shows on the left,
an example of a TEC map that is close to the IRI reference
climatology. The map on the right shows the following day
during a storm, when the structure is very different from theclimatology. These very large deviations from climatology
are not typical but do occur during storms; normal day-today variability would tend to be smaller. The STORM modelin IRI (Fuller-Rowell et al. 2000), which uses the time history of the geomagnetic index Ap, is not able to capture thistype of structure. One of the goals of the RT-IRI is to be able
to provide this type of information regionally and globally,and not only for TEC, but for all ionospheric parameters,
now and for the recent past. The air force weather agency(AFWA), on the other hand, is currently running the USU
global Gauss–Markov GAIM model. Other operational centers, such as the international GNSS service (IGS) have chosen to average four different ionospheric data assimilation
models to characterizing the ionosphere. IGS require the ionosphere specification for second-order corrections for global
navigation and positioning applications (Orús et al. 2002).
The workshop participants, representing activities in 11
countries, reviewed past activities in this area, including therange of data assimilation techniques, some of which arebeing employed in the operation centers. These include
Gauss-Markov Kalman filters, Ensemble Kalman filters, and
3-D and 4-D variational techniques (Kalman 1960; Daley
1991). The participants also reviewed the available data
sources suitable for use by a RT-IRI initiative, and the important issue of data quality and error estimation. One of theimportant components of data assimilation is the need tohave accurate estimates of observation error, which in thepast have been poorly quantified.
The consensus of the participants was that the initial focusof the Task Force should be on regional retrospective

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analysis for selected periods, and eventually combine theregions to produce a global specification in real time. The
parameters chosen to target were the 2-D maps of NmF2,
hmF2, and TEC, plus estimates of uncertainty, gradually
migrating to the 3D ionospheric structure later. The data chosen for this first study would include hand-scaled ionograms
and ground-based GNSS receivers. Just as in troposphericnumerical weather prediction (NWP), new datasets will be
added after careful evaluation of their impact on the analysis (Cucurull et al. 2006). This will include radio occultation
data from the COSMIC satellite constellation (Anthes et al.
2008). In the future, the assimilation period will be extended
and the results made available to the community on the IRI
web page for scientific studies. The group will also explore
methodologies to combine the regional maps to produce a
global high-resolution analysis, recognizing the limitationsin the accuracy in data sparse regions. The availability ofspace based radio occultation data will be particularly important to improve the accuracy over the oceans.

7 Conclusions
We have presented the current status of IRI modeling activities and plans for the future with special emphasis on theparameters and regions that are most important for geodetic
measurement techniques and their data analysis schemes. IRI
and geodetic techniques have benefitted each other. We havepresented many examples of applications of IRI in geodetic
techniques. This includes the use of the height of largest
density from IRI for the conversion of slant GPS-TEC tovertical TEC. IRI has been widely used as a backgroundmodel to test tomographic and radio occultation algorithms
related to signals from high and low orbiting GPS satellites.
In turn ionospheric measurements from geodetic techniques
are a promising new resource for improvements of IRI and an
excellent candidate for data assimilation into the IRI model.
With data assimilation the IRI model can progress from themonthly average conditions (climatology) that it is representing now to daily and hourly conditions if a good set of global
observations is available. The ultimate goal is the development of a Real Time IRI that assimilates all available andreliable ionospheric measurements into the model including
ionosonde and incoherent scatter radar measurements frombelow, satellite in situ measurements from within, and GPS
and radio occultation measurements from above. The IRI
community is ideally suited to perform this new extension to
the capabilities of this internationally recognized ionosphericreference model. The IRI science community has a long history of working together efficiently and productively. With
the increase in the number of data sources and the development of data assimilation techniques in the ionospheric community this is the right time to make this important advance,

123

D. Bilitza et al.

and follow the lead from the tropospheric weather forecasting
community.
Acknowledgments We acknowledge the contributions of IRI Working Group members to the IRI effort and the many users of the model
who have provided valuable feedback. This work was supported throughNSF grant ATM-0819440 and NASA Grants NNX09AJ74G, NNX07AG38G, and NNX07AO65G.

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