PUBLICATIONSSpace Weather
RESEARCH ARTICLE
10.1002/2016SW001593
Special Section:
Reprise of 2001 Space Weather
Monograph

International Reference Ionosphere 2016:
From ionospheric climate to real-time
weather predictions
D. Bilitza1,2, D. Altadill3, V. Truhlik4, V. Shubin5, I. Galkin6, B. Reinisch6,7, and X. Huang7,8

1

Key Points:• New models for the F2 peak heighthmF2 in IRI-2016• Development of the Real-TimeInternational Reference Ionosphere(IRI)• Improved description of IRI ion
composition at low and high solar
activities

Correspondence to:
D. Bilitza,
dbilitza@gmu.edu

Citation:
Bilitza, D., D. Altadill, V. Truhlik,
V. Shubin, I. Galkin, B. Reinisch, andX. Huang (2017), International
Reference Ionosphere 2016: From
ionospheric climate to real-time
weather predictions, Space Weather, 15,
418–429, doi:10.1002/2016SW001593.
Received 28 DEC 2016
Accepted 8 FEB 2017
Accepted article online 13 FEB 2017
Published online 27 FEB 2017
Corrected 10 APR 2017
This article was corrected on 10 APR
2017. See the end of the full text for
details.

Department of Physics and Astronomy, George Mason University, Fairfax, Virginia, USA, 2Heliospheric Laboratory, NASA,
Goddard Space Flight Center, Greenbelt, Maryland, USA, 3Observatori de l’Ebre, (OE), CSIC -Universitat Ramon Llull,
Roquetes, Spain, 4Department of upper atmosphere, Institute of Atmospheric Physics CAS, Prague, Czech Republic,
5
Pushkov Institute of Terrestrial Magnetism, Ionosphere and Radio-Wave Propagation (IZMIRAN), Russian Academy of
Science, Moscow, Russia, 6Space Science Laboratory, University of Massachusetts Lowell, Lowell, Massachusetts, USA,
7
Lowell Digisonde International, LLC, Lowell, Massachusetts, USA, 8Deceased 15 October 2016

Abstract The paper presents the latest version of the International Reference Ionosphere model (IRI-2016)
describing the most important changes and improvements that were included with this version anddiscussing their impact on the IRI predictions of ionospheric parameters. IRI-2016 includes two new model
options for the F2 peak height hmF2 and a better representation of topside ion densities at very low and highsolar activities. In addition, a number of smaller changes were made concerning the use of solar indicesand the speedup of the computer program. We also review the latest developments toward a Real-Time IRI.
The goal is to progress from predicting climatology to describing the real-time weather conditions in
the ionosphere.

1. Introduction
The International Reference Ionosphere (IRI) is a joint undertaking by the Committee on Space Research(COSPAR) and the International Union of Radio Science (URSI) with the goal of developing and improving
an international standard for the parameters in Earth’s ionosphere [Bilitza et al., 2014]. This endeavor was originally triggered by the need for an ionosphere model for the satellite/experiment design and satellite data
analysis (COSPAR) and for radio propagation studies (URSI) but has meanwhile found a much broader range
of users with space weather concerns. In April 2014 IRI became the ofﬁcial ISO standard for the ionosphere;
ISO is the International Standardization Organization. As requested by these international unions, IRI was
built as an empirical model representing the syntheses of most of the available ground and space measurements of ionospheric characteristics. IRI represents monthly averages of electron and ion densities and temperatures in the altitude range of 50 km–2000 km. It also provides the vertical total electron content (TEC)from the lower boundary (60–80 km) to a user-speciﬁed upper boundary. Additional IRI output includes
the ion drift near the magnetic equator and the probability for the occurrence of a F1 layer and of spread F.
The IRI Working Group consists of 60+ ionospheric experts that includes representatives of the different
ground and space ionospheric measurement techniques and the different countries worldwide. The ongoingevaluation and improvement of the IRI model is discussed during biannual workshops that focus on a speciﬁc
aspect of the modeling problem. Each workshop is generally attended by 100–120 participants (see irimodel.
org for more details). The most recent workshop was held as a COSPAR Capacity Building Workshop at theKing Mongkut’s Institute of Technology Ladkrabang in Bangkok, Thailand, 2–13 November 2015 on the special topic of “Improved Accuracy in the Equatorial Region and Progress Towards a Real-time IRI Model.”Presentations from these workshops and other IRI-related papers are published in special issues ofAdvances in Space Research (ASR), the last one on the topic “International Reference Ionosphere andGlobal Navigation Satellite Systems” in volume 55, number 8. More details about the IRI project can be foundon the IRI homepage at irimodel.org including workshop summaries, references and a list of the special ASR
issues, the IRI model code, and related links.

©2017. American Geophysical Union.
All Rights Reserved.

BILITZA ET AL.

This paper brieﬂy summarizes the improvements that are being introduced with the 2016 version of the IRI
model. Most importantly, IRI-2016 presents two new models for the F2 peak electron density height, hmF2,
and improvements of the ion composition at low and high solar activity. In addition, a number of changes

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Figure 1. (left) M(3000)F2 ionosonde measurements (dot) and CCIR model predictions (minus) over Ouagadougou, Burkina
Faso (latitude: 12.4, longitude: 1.5, dip: 6) in July of 1991; (right) hmF2 ionosonde measurements (circle), IRI predictions
using CCIR-M(3000)F2 (minus), and using the measured M(3000)F2 (solid triangle) for the same location and time; The redcircles highlight the evening drop in M(3000)F2 that is not reproduced in the CCIR model and therefore leads to a signiﬁcantunderestimation of the ionosonde hmF2 by IRI.

were made regarding the input and use solar and ionospheric indices, and changes were made to theprogram code (FORTRAN) to speedup the computation of IRI values. We will also review the progress thatis being made toward the development of the Real-Time IRI.

2. New hmF2 Models
The height of the F2 peak, hmF2, is of great importance for many radio propagation studies and applications
because it marks the point of highest electron density in the ionosphere. hmF2 has also proven to be a valuable source for deriving the neutral wind at middle latitudes [Miller et al., 1986; Richards, 1991; Dyson et al.,1997], and IRI hmF2 has been used to produce the global scale meridional wind climatology [Miller et al.,1997]. IRI like most other empirical models has relied on the Consultative Committee on International
Radio (CCIR) model [Consultative Committee on International Radio (CCIR), 1967] for the propagation factor
M(3000)F2 and on the close correlation between hmF2 and M(3000)F2 [Shimazaki, 1955; Bilitza and Eyfrig,
1978; Bilitza et al., 1979; Dudeney, 1983]. M(3000)F2 is deﬁned as MUF(3000)/foF2 where MUF(3000) is the highest frequency that, refracted in the ionosphere, can be received at a distance of 3000 km. Like foF2, M(3000)F2
is routinely scaled from ionograms and therefore a large database exists for this parameter covering several
solar cycles. Obtaining hmF2 from ionograms requires a more complex analysis involving the scaling andinversion of the ionogram trace. For these reasons CCIR modeling has focused on foF2 and M(3000)F2 rather
than hmF2. The CCIR [1967] models for foF2 and M(3000)F2 use the same formalism, and they are based onmonthly median values obtained by the worldwide network of ionosondes (about 150 stations) during theyears 1954 to 1958. Limitations of this approach for hmF2 modeling have been noted in a number of studies[Bilitza, 1985; Adeniyi et al., 2003; Lee and Reinisch, 2006; Brum et al., 2011; Magdaleno et al., 2011; AraujoPradere et al., 2013; Ezquer et al., 2014]. Adeniyi et al. [2003] pointed out the three main error sources: (1)the limited data volume available at the time of the model development; (2) the limits in reproducing small
scale features in the diurnal variation because of the chosen functional representation (harmonics of up toorder 4 only); and (3) the uncertainty introduced with the formula describing the relationship between
hmF2 and M(3000)F2. Because of error source (2) the current model is not able to reproduce the observed
sharp evening peak of hmF2 at equatorial latitudes that is related to the reversal in ion drift [Lee andReinisch, 2006; Abdu et al., 2010]. An example of such a misrepresentation is shown in Figure 1. Regarding
error source (3), it is clear that the relationship between hmF2 and M(3000)F2 depends on the distributionof ionization below the F2 peak. Functional descriptions of the relationship have relied on the ratio foF2/foE
to characterize this distribution. Nevertheless, later work has shown that this is not sufﬁcient and that differences in distribution due to magnetic latitude and solar activity have also to be considered leading to a newmodel for the relationship [Bilitza et al., 1979] that is used in IRI. However, one has to keep in mind that either
one of these models for the hmF2-M(3000)F2 relationship is just an approximation depending on several
assumptions [see Dudeney, 1983]. Figure 2 shows an example of a breakdown of the relationship leading
to large differences between the observed and IRI model values. Furthermore, during the recent

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Figure 2. (left) M(3000)F2 ionosonde measurements (dot) and CCIR model predictions (minus) over Ouagadougou, Burkina
Faso (latitude: 12.4, longitude: 1.5, dip: 6) in January of 1991; (right) hmF2 ionosonde measurements (circle), IRI predictions
using CCIR-M(3000)F2 (minus), and using the measured M(3000)F2 (solid triangle) for the same location and time; the yellow
squares highlight a time period where the measured M(3000)F2 is well represented by the CCIR model, but the observed
hmF2 values are signiﬁcantly overestimated by IRI indicating a breakdown of the relationship between M(3000)F2 and hmF2.

exceptionally low solar minimum in 2008–2009 conditions were reached for which the relationship model
used in IRI no longer produced realistic hmF2 values. A lower limit of 1.7 was introduced for the ratio foF2/
foE to keep IRI hmF2 values dropping too low. And ﬁnally, the use of an M(3000)F2-based model makes itvery difﬁcult to assimilate measured hmF2 values, e.g., from incoherent scatter radars, into the IRI model for
real-time updating.
Because of these limitations of the IRI hmF2 model, the IRI group had given high priority to the developmentof new hmF2 models, and this led to renewed community-wide focus on modeling this important quantity.
Gulyaeva et al. [2008] developed a model based on topside sounder data from ISIS-1, ISIS-2, IK-19, andCosmos-1890 from the period 1969–1987 (~90,000 values) describing variations with local time, season, geomagnetic latitude, geodetic longitude, and solar radio ﬂux. Older topside sounder proﬁle data have the shortcoming that they do not reach F2 peak altitudes and an extrapolation scheme has to be applied to recover the
peak density and height. Gulyaeva et al. [2008] applied an extrapolation of the ﬁrst derivative of the topside
electron density proﬁle and obtained the layer height where dln[Ne]/dln[h] = 0. Zhang et al. [2009] applied
empirical orthogonal functions for a global representation of the IRI hmF2 values. Hoque and Jakowski[2012] developed their model based on data from 69 ionosondes and radio occultation data from CHAMP,
Gravity Recovery and Climate Experiment (GRACE), and Constellation Observing System for Meteorology,
Ionosphere, and Climate (COSMIC). The model uses just 13 coefﬁcients to describe diurnal, annual, semiannual, (magnetic) latitudinal, and solar cycle variations. The models of Gulyaeva et al. [2008] and Hoque andJakowski [2012] did well in representing the main variation patterns of hmF2. However, these models are
not able to describe the detailed spatial and temporal structure of hmF2 variations because of the small number of coefﬁcients used. Brunini et al. [2013] took a different approach. They recognized the success and wideuse of the CCIR [1967] model for the F2 peak plasma frequency, foF2, and applied the same formalism to hmF2
values computed with IRI. This turned out to be an important precursor for the real-time hmF2 model that will
be discussed later in this paper. The CCIR [1967] mapping is based on the pioneering work of Jones and Gallet[1962] using a special set of geographic functions in combination with harmonics in UT. A purely ionosondebased modeling effort was undertaken by Magdaleno et al. [2011] and Altadill et al. [2013]. Additionally,
Shubin et al. [2013] presented a hmF2 model based on radio occultation data from COSMIC, GRACE, andCHAMP. The last two models have been selected for inclusion in the 2016 version of the IRI model and will
be discussed in greater detail in the following sections.
All of these models now represent hmF2 directly and no longer depend on the propagation factor M(3000)F2
and its relationship with hmF2, therefore avoiding the error source connected with the M(3000)F2 approach. In
addition to these models, which represent the quiet time behavior of hmF2, a number of studies haveattempted to describe the changes during storm times including the very promising models of Blanch andAltadill [2012] and of Gulyaeva [2012] based on digisonde data from the Ebro station in Spain and topsidesounder data (from ISIS, IK-19, and COSMOS-1809), respectively.

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2.1. The Altadill et al. [2013] hmF2 Model (Called AMTB from Hereon)
The AMTB model is based on data from 26 digisonde stations from the Global Ionosphere Radio Observatory(GIRO) network [http://giro.uml.edu] [Reinisch and Galkin, 2011] for the time period 1998–2006. In order toobtain monthly averages of quiet time hmF2 the Monthly Averaged Representative Proﬁle (MARP) technique[Huang and Reinisch, 1996] was applied. MARP excludes the quarter (25%) of individual N(h) proﬁles thatshow the largest deviations from the monthly average and MARP thus removes the proﬁles most likely
related to disturbed ionospheric conditions. The global hmF2 variations are represented by spherical harmonics in a reference frame deﬁned by modiﬁed dip latitude and longitude. Because the limited number of stations is not sufﬁcient for the spherical harmonics analysis, Altadill et al. [2013] had to ﬁll out the global data
coverage with ﬁctitious data points. These data points were established by assuming that differences in local
time are equivalent to differences in longitude. So starting from each station, additional data points wereadded at 15° intervals in longitude (1 h LT) along the station’s modiﬁed dip latitude. AMTB applies harmonics
terms up to order 4 in longitude and 8 in latitude and uses a Fourier expansion of the spherical harmonics
coefﬁcients up to order 2 to represent the annual variation. Model coefﬁcients are provided for low and highsolar activity (12 month running mean of sunspot number R12 = 15 and 120), and linear interpolation andextrapolation is suggested for other solar activities. In total the AMTB model consists of 610 coefﬁcients.
Compared to the old IRI model, the AMTB model improves the ﬁt to its underlying database by 10% on average and by up to 30% at high and low latitudes. Because of this good performance, AMTB is the recommended hmF2 option in IRI-2016.
2.2. The Satellite and Digisonde Model of the F2 Layer Height (SDMF2)The SDMF2 model was developed by Shubin et al. [2013] and then extended by Shubin [2015] with a large
amount of radio occultation (RO) data from CHAMP (years: 2001–2008; ~300,000 values), GRACE (2007–2011; ~100,000) and COSMIC (2006–2012; ~3,500,000) and with hmF2 data from 62 digisondes for the years
1987–2012 from the Digital Ionogram Data Base (http://ulcar.uml.edu/DIDBase/). Like other models, SDMF2
describes global variations with spherical harmonics using terms up to order 12 in modiﬁed dip latitude
and up to order 8 in longitude. A Fourier expansion up to order 3 is used to describe the UT variation of
the spherical harmonics coefﬁcients. This is done for each month and for two levels of solar activity(F10.7A < 80 and F10.7A > 120 where F10.7A is the 81 day average of solar index F10.7) leading to a total of
85,824 coefﬁcients. SDMF2 assume a logarithmic dependence on solar activity, which is different from thelinear approach of most other models and helps to represent a saturation effect observed at high solar activities. Although the RO technique has made great progress over the last years the use of these data stillrequires a ﬁltering process to eliminate unrealistic and clearly wrong proﬁles. Shubin [2015] applies such a
process focused on the F2 peak region of the proﬁles eliminating 10% of the data. Another problem theSDMF2 developers faced was the uneven distribution in solar activity with the low solar activity subset being
much larger than the high one. This was partially compensated by ﬁlling out data empty months of the highsolar activity subset with seasonal averages. Evaluating his model and IRI with the digisonde data that had
not been used for the modeling (from solar activity range 80 < F10.7A < 120), Shubin [2015] ﬁnds an improvement of up to 6% over IRI with the largest improvements in the South African sector. The SDMF2 model isintroduced in IRI-2016 as the second new option for hmF2. This will give the IRI user community the ability
to compare the different hmF2 model predictions against various data sources and eventually help to improve
the accuracy of these models and measurement techniques. It also gives an indication how much uncertainty
still exists in determining the true hmF2 value.
2.3. First Comparisons of the IRI hmF2 Model Options
With the inclusion of two new model options for hmF2 the ﬁrst questions are to what extent do these newmodels overcome the problems of the older M(3000)F2-based model and how much do these models differ
between each other. Figures 3a and 3b show the diurnal variation of the three options for a midlatitude
example for summer and winter of a high solar activity year. All three models agree with each other fairly wellwith the largest differences (20–30 km) at nighttime. During winter the SMDF2 model (called Shubin-2015 inFigures 3–5) is 20–40 km lower than the other two models. In Figures 4a and 4b we take a look at one of the
problem areas of the M(3000)F2-based hmF2 model. As noted earlier, the current IRI model does not reproduce the evening peak in hmF2 that is observed at the magnetic equator. Figure 4 shows that both new
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Figure 3. Comparison of the diurnal variation of the three IRI options for hmF2 at middle latitudes (geographic latitude/
longitude = 45°/0°) for a (left) summer (15 July) and (right) winter (15 January) example for a year of high solar activity(2000).

models predict evening peaks and thus are a clear improvement over the older model. We note, however,that the two models predict the peak occurrence at different local times most clearly visible in the wintercase in Figure 4b. Comparisons with measurements from techniques other than digisonde and RO arerequired to ﬁnd out which one of these model predictions is closer to the real ionosphere. Figures 5a and5b highlight differences in the solar cycle variation predicted by these three hmF2 models for a midlatitude
location. The differences among these three models are largest in the solar minima and maxima reachingvalues of up to 40–50 km. At the solar minimum, the AMTB model is consistently higher than the othertwo models by about 30 km. At solar maximum, the SMDF2 model produces the largest values during
nighttime and the smallest values during daytime. In particular, these last ﬁgures hint at the signiﬁcantdiscrepancies that still exist between the hmF2 values measured from the ground with digisondes andthose measured from a satellite with the RO technique. By offering both options, IRI lets users explore theeffect that these uncertainties may have on their speciﬁc application. In the case of the ionosonde theassumption of a monotonously increasing bottomside proﬁle can lead to errors of 10–20 km if a valley ispresent in the E region. RO errors can be even larger especially in the low-latitude region where theassumption of spherical symmetry breaks down. Because of the lower error estimates for ionosondemeasurements IRI recommends the Altadill et al. [2013] model as its ﬁrst choice.

3. Improvement of the on Composition Model in the Topside Ionosphere
The topside ion composition model in IRI provides the percentage of O+, H+, He+, and N+ ions from the F2peak up to 2000 km. Two options are given, with the older Danilov and Yaichnikov [1985] model based on
a compilation of Russian high-altitude rocket measurements and the newer model of Třisková et al. [2003](TTS-03; the recommended default) based on in situ satellite measurements from IK-24, AE-C, and AE-E.
The relatively small database used in the development of these model options limits their accuracy
and reliability in areas not well covered by the database. Progress toward a better representation ofion composition in IRI has been slow because of calibration and contamination issues with some of theearlier satellite measurements and because of remaining discrepancies between ground and space-based
observations [see Bilitza et al., 2014, Figure 6]. The main problem of global modeling of ion composition is

Figure 4. Comparison of the diurnal variation of the three IRI options for hmF2 at the magnetic equator (geographic
latitude/longitude = 11°/0°) for a (left) summer (15 July) and (right) winter (15 January) example for a year of high solar
activity (2000).

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Figure 5. Comparison of the solar cycle variation of the three IRI options for hmF2 at middle latitudes (geographic latitude = 45°/0°) for a (right) summer midnight (15 July, LT = 0:00) and (right) winter noon (15 January, LT = 12:00) example.

the relatively small database in comparison with other parameters like the electron density. The modelinghas to rely on few mass spectrometer or retarding potential analyzer satellite experiments. Unfortunately,
ion composition measurements by incoherent scatter radars or by rocket experiments are quite rare andtheir temporal and geographical coverage is insufﬁcient for development of a global model. However,with the launch of the Communications/Navigation Outage Forecasting System (C/NOFS) satellite [de La
Beaujardière and the C/NOFS Deﬁnition Team, 2004] in April 2008 a valuable new data source became
available for modeling the ionospheric ion composition in the low-latitude region covered by this lowinclination satellite. The Coupled Ion Neutral Dynamics Instrument (CINDI) measured the ion compositionin the latitude range 400–800 km from April 2008 to November 2015 when the satellite ceased operations. When comparing these new data with IRI predictions, Heelis et al. [2009] and Coley et al. [2010]
found that IRI overestimated the percentage of O+ ions in the topside ionosphere and underestimate
the percentage of H+ ions. The discrepancies were most pronounced during the solar minimum years
2008–2009. Klenzing et al. [2011] conﬁrmed these results and further investigated the impact these misrepresentations have on the upper transition height ht; ht is the height where the percentage of O+ is
equal to the percentage of light ions (50%), below ht O+ is the dominant ion, and above ht H+ andHe+ become the dominant ions. Efforts are underway to include this important transition parameter as
one of the anchor points for the IRI ion composition model because of its added signiﬁcance as marker
for the transition from the ionosphere to the plasmasphere [Bilitza, 1991]. The 2008–2009 solar minimum
was highly unusual in that it lasted longer than previous solar minima and reached a very low level ofsolar activity. IRI was built based on the existing data record, and since the 2008–2009 minimum conditions were so different from prior solar minima, it is not surprising, therefore, that the IRI model
performed poorly.
For IRI-2016 a number of improvements of the TTS-03 model were implemented partly as a result of more oldand newer data becoming available and partly in response to user-found errors and misrepresentations.
Truhlik et al. [2015] were able to correct the model at low solar activities with the help of data from the
C/NOFS-CINDI experiment. Improvements were also achieved at high solar activity with a more effective
exploitation of the IK-24, AE-C, and AE-D database. Based on their analysis, the limits of allowable F10.7 solar
index values were extended from 85–200 in IRI-2012 to 65–260 in IRI-2016. The Truhlik et al. [2015] (TBT-15)
model uses a similar formalism as TTS-03 for the representation of global variations, but instead of the linear
F10.7 dependence, it uses a logarithmic approach and instead of the percentages TBT-15 ﬁrst models theabsolute densities and then normalizes to 100%. The new model is also based on more data for solar minimum (two times more than in TTS-03) and for solar minimum uses 4 anchor points versus 3 in TTS-03. The
impact of these improvements is documented in Figure 6. Shown here is the ratio between IRI predictions
and the CINDI measurements of O+ and H+ ions versus solar activity averaged over the years 2008–2012.
The dashed curves are using IRI-2012, and the solid curves are based on the new IRI-2016 ion composition
model. For middle and high solar activities, the ratios are close to one underlining the good performanceof the IRI model. With decreasing solar activity, the ratios for IRI-2012 deviate signiﬁcantly from 1 reachinga ratio of 2 for the O+ ions and of 0.6 for H+ ions at the lowest level of solar activity. With IRI-2016 the ratios
are now close to 1 over the whole solar activity range. Figure 7 highlights the success of the new model inlowering the transition height ht from close to 1000 km down to almost 600 km which is closer to the ht value
reported with the CINDI measurements [Klenzing et al., 2011].

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4. Solar and Ionospheric Indices
Used in IRI

Figure 6. Ratio between IRI predictions and C/NOFS-CINDI measure++ments of O (blue) and H (red) densities versus the F10.7 monthly
solar index for IRI-2012 and the new IRI-2016. Note the large
improvement at low solar activities.

The IRI model is driven by several solar andionospheric indices: the sunspot number R,
the solar radio ﬂux at 10.7 cm wavelength
F10.7, and the ionosonde-based ionosphericglobal (IG) index. An IRI user can rely oneither the indices ﬁles that are part of the
IRI distribution and that are updated twicea year or the user can enter his/her own
values for these indices. We will brieﬂy discuss these indices, recent changes in theirproduction, and their role in the IRI model.

The sunspot number R that is provided by
the Solar Inﬂuences Data analysis Center(SIDC, http://sidc.oma.be) in Brussels,
Belgium, was the ﬁrst solar index to be widely used in ionospheric modeling. Statistical studies showed highest correlation between R and ionosonde-measured foF2 (the F2 peak plasmas frequency) when averaged
with a 12 month sliding window (denoted R12). R12 is the solar activity index recommended by theInternational Telecommunication Union to describe the solar activity variation of its widely used CCIR
[1967] model for foF2 and the propagation factor M(3000)F2. A recent review of the way R is determined fromobservations of spots on the Sun resulted in a recalibration of the R computation [Clette et al., 2014]. The newsunspot number is now internationally accepted and distributed by SIDC. It exceeds the old index by about afactor of 1.4. This revision of the sunspot index causes a problem for IRI and other models that were developed using the old R index. Using the new R with these models would result in a misrepresentation of themodeled parameters. To avoid this misrepresentation, IRI will use the new index with a scaling factor of
0.7. For the 12 month running mean 0.7 is a very good estimate for the ratio of these two indices[Gulyaeva, 2014]. In IRI the R12 index is used
for the F2 peak height hmF2, for the F1
region plasma frequency foF1, for the bottomside thickness parameter B0, and forthe electron density at the D region
inﬂection point.

Figure 7. Topside ion composition with the IRI-2007 (dotted curves),
IRI-2012 (dashed curves), and the new IRI-2016 models (solid curves).
Note the steady decrease in upper transition height ht from close
to 1000 km in IRI-2007 to close to 700 km in IRI-2016.

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The global daily value of the F10.7 index ismeasured at local noon at the Penticton
Radio Observatory in Canada since 1947.
F10.7 has been slowly replacing R in ionospheric modeling because it correlates better with the extreme ultraviolet (EUV)
irradiance of the Sun that is responsible
for the ionization of ions in the ionosphere.
It has the added advantage that it can bemeasured directly at the ground and does
not require an analysis scheme like the sunspot number. It is a very robust long-term
measurement of solar activity with few
gaps or calibration issues. Modelers haveused different averages of the F10.7 index
from the daily index F10.7 to the 81 day(three solar rotation) average F 10:7 81 to a
12 month running mean F 10:7 12 . IRI makes
use of all three indices. F10.7 for the424Space Weather

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Figure 8. Correlation between the indices IG12 and R12 (+) over thetime period from 1958 to 2016 and curves ﬁtted:
2
IG12 = 12.349 + 1.468 × R12 0.00268 × (R12) (red; used in IRI),
2
IG12 = 11.563 + 1.533 × R12 0.0031 × (R12) (green; recommended by Gulyaeva [2014]).

previous day is needed as input for the
NRL-MSISE00 model [Picone et al., 2002] ofthe neutral temperatures and densities thatare required as lower limits for the plasma
temperatures and as input for the ion composition model [Richards et al., 2010]. For
the electron temperature the best results
have been obtained with the composite
index PF10.7 = (F10.7 + F 10:7 81 )/2 and thisindex is used in IRI for the electron temperature model. PF10.7 is also used for the
IRI topside ion composition by the TTS-03
model [Třisková et al., 2003], while the older
DY-85 option [Danilov and Yaichnikov,
1985] describes solar activity variations
with the F 10:7 12 index.

The IG index was introduced by Liu et al.
[1983]. It is obtained by adjusting the CCIR [1967] model for foF2 to the noontime measurements of several
reference ionosonde stations. This is achieved by changing the R12 index that describes the solar activity variations in the CCIR model. The ﬁnal step is taking the average over all stations to generate the global IG index.
It is produced and distributed by the UK Solar System Data Centre in Slough, England. The original index was
produced based on 11 reference stations; however, not all of these stations have remained in operation orcontinued to be able to share their data. Currently the index is determined with four reference stations:two from the Southern Hemisphere (Port Stanley/UK and Canberra/Canada) and two from the Northern
Hemisphere (Kokubunji/Japan and Chilton/UK). This has limited the reliability of this index in representing
the global ionospheric conditions; nevertheless, it is still superior to the R and F10.7 indices in describing
the solar cycle changes in the F region ionosphere. In IRI the 12 month running mean IG12 is used with the
CCIR [1967] foF2 model and therefore has a strong impact on the whole electron density proﬁle since it is normalized to the F2 peak and since the ionosphere reaches its highest densities at the F2 peak. As for the otherindices, an IRI user has the option to use the internal indices ﬁle or to enter his/her own index value for IG12.
The long-term correlation function between IG12 and R12 (Figure 8) is used to automatically adjust IG12 if only
R12 is entered and vice versa.

5. Real-Time IRI
Good progress has been made in the development of an IRI Real-Time model. The goal is to progress from theclimatology provided by the standard IRI model to a description of real-time weather conditions based on theingestion of real-time measurements into the IRI model. A number of studies have used ionosonde and/or
GPS data to update the IRI model by adjusting the ionospheric and/or solar indices used in IRI with real-time
ionosonde or TEC data [Bilitza et al., 1997; Komjathy et al., 1998; Hernandez-Pajares et al., 2002; Pezzopane et al.,2011; Ssessanga et al., 2015]. Another group of studies have used the IRI as background ionosphere applying a
Kalman ﬁlter or variational method to assimilate real-time data into IRI [Fridman et al., 2006; Schmidt et al.,2008; Angling et al., 2009; Yue et al., 2012; Galkin et al., 2012]. These activities were discussed during several
dedicated IRI Real-Time Workshop (see irimodel.org for more details) and have led to considerable improvements of these real-time algorithms.
The most advanced IRI Real-Time model is the IRI Real-Time Assimilative Modelling (IRTAM) system developed by Galkin et al. [2012] that assimilates digisonde data from the Global Ionospheric Radio Observatory(GIRO) network into the IRI model. The IRTAM approach is based on the CCIR [1967] models for the F2 peakplasma frequency foF2 and the propagation factor M(3000)F2 that are being used in IRI. IRTAM uses the
CCIR set of functions to describe the global and spatial variation of the difference between the digisondemeasurement and the IRI prediction of foF2. The method consists of three steps: (1) expand the 24 h historyof ΔfoF2 at each station in sixth-order harmonics (13 coefﬁcients ΔCi); (2) interpolate each ΔCi into (36 × 36)
spatial grid using a multicell iterative optimization for interpolation smoothness via a neural network

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Figure 9. The measured and IRI-predicted electron density proﬁle at Kirkland AFB on 17 January 2014 at 07:00 UT. Besides
the densities measured by the digisonde (dots) the ﬁgure shows the standard IRI (blue solid curve), IRI with IRTAM foF2 andhmF2 (red dashed curve) and IRI with IRTAM foF2, hmF2, and B0 and B1 (red solid curve).

computation with fading synoptic weights; (3) ﬁt each of the grids with the CCIR [1967] set of geographic
functions (76 coefﬁcients) to obtain 13 × 76 = 988 corrections ΔCik. Adding the correction terms to theoriginal CCIR coefﬁcients produces the IRI Real-Time model of foF2. The same procedure is used for theheight of the F2 peak, hmF2. There is, however, an important difference, the parameter mapped in CCIR is
M(3000)F2 and not hmF2. IRI determines hmF2 with the help of the Bilitza et al. [1979] model that describes

Figure 10. IRTAM global predictions for (top row) foF2, hmF2, and B0 for 24 June 2016 at 12:15:00 UT. The circles in Figure 10 (top row) mark the locations of digisonde
stations, and their color gives the magnitude of the measured parameters. (bottom row) The global differences maps between measured and predicted parameters.
The solid curve in all panels marks the terminator.

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the correlation between M(3000)F2
and hmF2. Here the work of Brunini
et al. [2013] was of great help
because they had already used the
CCIR formalism to represent the
IRI-hmF2 directly. Starting from this
CCIR-type representation of hmF2,
the process is then the same as for
foF2. The latest and newest addition
to the IRI parameters that are being
Figure 11. Monthly accesses to the online IRIweb service. Only accesses to provided in real time is the bottomthe SPDF site are counted. Accesses to the CCMC site are not included.
side thickness B0 and the bottomside
Note the logarithmic scale indicating an exponential increase in accesses
shapeparameter B1. The approach isover the last 6 years.
the same as for hmF2; the ﬁrst step,
however, is the representation of the IRI-B0 and IRI-B1 in CCIR-type models that were then updated withreal-time digisonde measurements in the same way as foF2. Figure 9 shows that assimilation of B0 and B1 iscritical to the accuracy of the IRTAM electron density proﬁles. Using only foF2 and hmF2 assimilation without
also assimilating the measured B0, B1 values can produce a wrong bottomside electron density proﬁle.
In addition, the latest version of IRTAM introduces a new trend term (fourteenth coefﬁcient) this was necessary to relax the 24 h periodicity requirement of the CCIR [1967] basis model. An example of IRTAM predictions for foF2, hmF2 and B0 is shown in Figure 10 including also the location of the digisondes used and thedifferences between measurements and predictions at each station (color in station circle). A ﬁrst preliminary
evaluation including 15 million points found an average improvement factor of ~2 when using IRI with IRTAM
versus the standard IRI without IRTAM.

6. Summary
This article introduces and discusses the 2016 version of the International Reference Ionosphere model. The
most important improvements are as follows:
1. There are two new model options for the F2 peak height hmF2, one based on digisonde data [Altadill et al.,2013] and one based on radio occultation data [Shubin, 2015]. Most signiﬁcantly, these new options are
now modeling hmF2 directly and no longer through its relationship to the propagation factor M(3000)
F2. The digisonde-based model is the recommended choice in IRI-2016. Our comparisons of these models
highlight areas and time periods where signiﬁcant differences exist that warrant further study and a callfor more direct measurements of hmF2.
2. There is a more accurate and reliable description of the topside ion composition at low solar activities
based on C/NOFS-CINDI data.
3. IRI-2016 accounts for the newly revised sunspot number index in the IRI model parts that depend on thisparameter.

Table 1. Percentage of Papers That Acknowledge the Use of IRI in the
Journal of Geophysical Research (JGR), the Geophysical Research Letters(GRL), the Space Weather (SW) Journal, and Radio Science (RS)BILITZA ET AL.

Year

JGR

GRL

SW

RS

2009

5.0%

3.6%

0.0%

10.5%

2010
2011

5.6%
7.1%

4.7%
1.6%

5.6%
8.1%

11.8%
14.2%

2012

7.6%

2.7%

4.8%

13.8%

2013
2014

5.1%
6.6%

1.7%
0.5%

2.3%
5.7%

8.2%
10.7%

2015

8.3%

2.3%

1.6%

9.6%

2016

6.8%

0.8%

2.2%

13.2%

IRI-2016

Signiﬁcant advances have been
made in the development of a RealTime IRI model. The IRI Real-Time
Assimilative Modeling (IRTAM) now
assimilates real-time measurementsof foF2, hmF2, B0, and B1 from 40+ digisonde stations into IRI and ﬁrst test
have shown an improvement of afactor of 2 compared to the standard
IRI and even larger during disturbed
time periods. The next steps will be
the inclusion of real-time measurements of hmF1, foF1, C1, hmE, and foE
in the IRTAM process.

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More information about the IRI activities including publications and workshops can be found on the homepage irimodel.org, which also provides access to the IRI FORTRAN code and the IRIweb service. The continued
wide usage of the IRI model is documented in Figure 11 and Table 1. Figure 11 plots the monthly accesses to
IRIweb since January 2009. IRIweb is an online service for the computation and plotting of IRI parameter proﬁles for user-provided location and time. The services can be also accessed via web services. Figure 11 illustrates the steady increase in IRI usage reaching now 5 million accesses per month. Actual usage of this service
is even higher because we are not counting the accesses to the parallel site maintained by the Community
Coordinated Modeling Center (CCMC).
Table 1 reports the acknowledged use of the IRI model in the scientiﬁc journals Journal of Geophysics-Space
Physics (JGR-SP), Geophysical Research Letters, Radio Science (RS), and Space Weather. The numbers in
Table 1 are the percentage of papers of a speciﬁc year that made use of the IRI model. In 2015 a remarkable
8% of JGR-SP papers and 10% (!) of RS papers relied on IRI for their studies.
Acknowledgments
We like to acknowledge the IRI Working
Group with all its members for theirsupport of the IRI activities. We like todedicate this paper to our coauthor
Xueqin Huang who passed away on 15
October 2016. Xueqin was a member of
the IRI Working Group and contributed
key IRI improvements. We thank NASA’s
Space Physics Data Facility (SPDF) andCommunity Coordinated ModelingCenter (CCMC) for the IRIweb online
computation service. B.R., X.H., I.G., andD.B. were in part supported by USAF
contracts FA9453-14-C-0305 andFA865016C9104. D.A. is in part supported by Universitat Ramon Llull project 2016-URL-Trac-001 funded by“Obra Social la Caixa” and by Spanish
Ministry MINECO through project
CTM2014-52182-C3-1-P. V.T. was supported, in part, by grant 15-07281J of
the Grant Agency of the Czech Republic.
V.S. was supported by the RussianFoundation for Basic Research throughproject 17-05-00427. The IRI model code
in Fortran is available from the IRI
homepage at irimodel.org.

BILITZA ET AL.

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Erratum
In the originally published version of this article, several instances of text were incorrectly typeset. The
following have since been corrected and this version may be considered the authoritative version of record.
In Table 1, in the year column “2014” was repeated 3 times. It now reads “2014, 2015, 2016”.

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