The International Reference Ionosphere – Status 2013
Introduction
The International Reference Ionosphere (IRI) is an international project that was initiated by the Committee on Space Research (COSPAR) and the International Union of Radio Science (URSI) with the goal of establishing a standard representation of the plasma parameters in Earth’s ionosphere. Such a model of the ionosphere is important for the many applications that rely on electromagnetic waves traveling through the ionosphere including telecommunication, GPS, earth observation from space (e.g., satellite altimetry), radio astronomy and many more, because all of these applications need to correct for the retarding and refractive effect of the ionosphere on the probing signal. At the request of COSPAR and URSI, IRI is an empirical model being based on the majority of the available and reliable ground and space observations and avoiding as much as possible dependence on the still evolving theoretical understanding. But theoretical considerations can be helpful in bridging data gaps and for internal consistency checks. COSPAR’s prime interest in IRI is as 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. With COSPAR and URSI the IRI project has the backing from the major international unions representing space-based ionospheric observations (COSPAR) and ground-based ionospheric observations (URSI). IRI development has benefitted greatly from the synergism between these two communities that are represented about evenly in the IRI Working Group and during bi-annual IRI Workshops. The current membership roster of the IRI Working Group is listed in Table 1. Progress of the IRI project is documented in several dedicated issues of the journal Advances in Space Research with selected papers from these workshops (see http://irimodel.org/docs/asr_list.html for references). The model has undergone a continuous improvement process that resulted in the release of major new editions of the model about every five years (Rawer et al., 1975, Rawer et al., 1978, Rawer et al., 1978, Rawer et al., 1981, Bilitza, 1986, Bilitza, 1990, Bilitza, 1997, Bilitza, 2001, Bilitza and Reinisch, 2008).
An important goal of geospace science is to develop predictions and forecast capabilities in support of human presence and technical endeavors in geospace. Data-based models like IRI are an important element of this undertaking because they compress the large volume of observational evidence in mathematical form combining different data sources and different techniques. One particular advantage over theoretical models is the very fact that they do not depend on the evolving theoretical understanding of the heliospheric environment. There are several examples where effects were already included in IRI before they were fully understood and included in theoretical models. One example is the distinct longitudinal variation with 4 maxima (wave number 4 structure) of the F-peak electron density that was first reported by Benkova et al. (1990) based on Interkosmos 19 topside sounder data and later with IMAGE/EUV observations (Immel et al., 2006), and then confirmed with data from CHAMP (Lühr et al., 2007) and TOPEX (Scherliess et al., 2008). While theoretical models still struggle to include this phenomenon in their modeling framework, IRI already includes a smoothed version of this phenomenon (McNamara et al., 2010); a smoothing effect is to be expected since IRI is based on monthly averages. Other examples are the midlatitude evening anomaly (Weddell Sea anomaly) observed by the Super Dual Auroral Radar Network (SuperDARN) radars that is well captured by IRI (de Larquier et al., 2011) and the occurrence of ionospheric plasma caves under the Equatorial Ionization Anomaly (EIA) (Lee et al., 2012).
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 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 and more extended than earlier minima and as a result IRI being built with the data from earlier minima overestimated the plasma densities during the minimum period (Lühr and Xiong, 2010, Bilitza et al., 2012).
In this article we will first present the changes that led to IRI-2012 version of the model and then will discuss two important activities towards future improvements: a better representation of the F peak height hmF2 and the development of a Real-Time IRI.
Section snippets
IRI-2012
The latest version of the IRI model, IRI-2012, includes several important improvements and new additions that lead to a more accurate representation of (1) the electron density and ion composition in the region from the F2 peak down to the E peak, (2) the solar cycle variation of the electron temperature, (3) the storm effects in the auroral E-region, and will for the first time include the representation of auroral oval boundaries and their magnetic storm induced movement to lower latitudes.
Ongoing activities towards future improvements of IRI
Comparisons with IRI are often one of the first data evaluation tasks when a new data source becomes available. The goal is to compare the new ground or space data set with the prior empirical evidence accumulated in the IRI model. When consistent discrepancies are found and the reliability of the data is confirmed then efforts begin to improve IRI with this new data source. A number of IRI improvement efforts are currently underway we will focus here only on the two most important ones: a more
Conclusions
This paper is based on a status report given at the 2013 IRI Workshop in Olsztyn, Poland. It reviews the changes that were introduced with the release of the IRI-2012 version of the model and discusses two important ongoing activities, an improved representation of the F-peak height hmF2 and the development of a Real-Time IRI. IRI continues to evolve and IRI users will benefit from the many improvements introduced with IRI-2012:
- (a)
A new model for the bottomside electron density resulting in a 32%
Acknowledgments
IRI is the result of modeling efforts by IRI Working Group members. All these contributions are essential for the IRI success and are highly acknowledged by the author. I also want to acknowledge the many users of the model who have provided valuable feedback. A special thank you to Man-Lian Zhang who provided the Hainan ionosonde data used in Fig. 1.
References (87)
- et al.
Proposal of new models of the bottom-side B0 and B1 parameters for IRI
Adv. Space Res.
(2009) - et al.
Global empirical models of the density peak height and of the equivalent scale height for quiet conditions
Adv. Space Res.
(2013) - et al.
Longitudinal features shown by topside sounde data and their importance in ionospheric mapping
Adv. Space Res.
(1990) Including auroral boundaries in the IRI model
Adv. Space Res.
(1995)International reference ionosphere – status 1995/96
Adv. Space Res.
(1997)- et al.
International reference ionosphere 2007: improvements and new parameters
Adv. Space Res.
(2008) - et al.
Modelling of ionospheric temperature profiles
Adv. Space Res.
(1985) - et al.
Improved IRI predictions for the GEOSAT time period
Adv. Space. Res.
(1997) - et al.
New B0 and B1 models for IRI
Adv. Space. Res.
(2000) - et al.
solar cycle variation of mid-latitude electron density and temperature: satellite measurements and model calculations
Adv. Space Res.
(2007)
Measurements and IRI model predictions during the recent solar minimum
J. Atmos. Sol. Terr. Phys.
Improvement of IRI B0, B1 and D1 at mid-latitudes using MARP
Adv. Space Res.
Global empirical models of ionospheric electron temperature in the upper F-region and plasmasphere based on in situ measurements from atmosphere explorer C, ISIS 1 and ISIS 2 satellites
J. Atmos. Terr. Phys.
A different method to determine the height of the F2 peak
Adv. Space Res.
Relative ion composition model at midlatitudes
J. Atmos. Terr. Phys.
Improving the 75 to 300 km ion composition model of the IRI
Adv. Space Res.
A new model of the ion composition at 75 to 1000 km for IRI
Adv. Space Res.
Progress in ionospheric informatics based on electron density profile analysis of ionograms
Adv. Space Res.
Towards a new reference model of hmF2 for IRI
Adv. Space Res.
A revised corrected geomagnetic coordinate system for epochs 1985 and 1990
J. Atmos. Terr. Phys.
Combining GPS measurements and IRI model values for space weather specification
Adv. Space Res.
Quiet-condition hmF2, NmF2, and B0 variations at Jicamarca and comparison with IRI-2001 during solar maximum
J. Atmos. Sol. Terr. Phys.
Quiet-time variation of F2-layer parameters at Jicamarca and comparison with IRI-2001 during solar minimum
J. Atmos. Sol. Terr. Phys.
The variability and IRI2007-predictability of hmF2 over South Africa
Adv. Space Res.
A neural network-based ionospheric model for the auroral zone
J. Atmos. Sol. Terr. Phys.
A new approach to modeling the daytime lower ionosphere at auroral latitudes
Adv. Space Res.
Empirical STORM-E model: I. Theoretical and observational basis
Adv. Space Res.
Empirical STORM-E model: II. Geomagnetic corrections to nighttime ionospheric e-region electron densities
Adv. Space Res.
The equatorial electrojet and the profile parameters B0 and B1 around midday
J. Atmos. Sol. Terr. Phys.
Equatorial F2-layer peak height and correlation with vertical ion drift and M(3000)F2
Adv. Space Res.
Regional 4-D modeling of the ionospheric electron density
Adv. Space Res.
Bottomside profile shape parameters during low solar activity and comparison with IRI-2007 model
J. Atmos. Sol. Terr. Phys.
Global model of the F2 layer peak height for low solar activity based on GPS radio-occultation data
J. Atmos. Sol. Terr. Phys.
Atmospheric ultraviolet radiance integrated code (AURIC): theory, software architecture, inputs, and selected results
J. Quant. Spectrosc. Radiat. Transfer
An empirical model of ion composition in the outer ionosphere
Adv. Space Res.
New advances in empirical modeling of ion composition in the outer ionosphere
Adv. Space Res.
Latitudinal variation of the topside electron temperature at different levels of solar activity
Adv. Space Res.
An empirical Kp-dependent global auroral model based on TIMED/GUVI data
J. Atmos. Sol. Terr. Phys.
Variability of the behavior of the bottomside (B0, B1) parameters obtained from the ground-based ionograms at China’s low latitude station
Adv. Space Res.
Near real-time assimilation of auroral peak E-region density and equatorward boundary in IRI
Adv. Space Res.
Development of an HF selection tool based on the electron density assimilative model near-real-time ionosphere
Radio Sci.
A new magnetic coordinate system for conjugate studies at high latitudes
J. Geophys. Res.
Cited by (53)
A comprehensive review of Electric Solar Wind Sail concept and its applications
2022, Progress in Aerospace SciencesA proposed method for improving the IRI2016 model by means of Swarm over the American Sector during the event of 5–11 September 2017
2021, Advances in Space ResearchCitation Excerpt :The major data sources are the worldwide network of ionosondes, the powerful incoherent scatter radars (Jicamarca, Arecibo, Millstone Hill, Malvern, St. Santin), the ISIS and Alouette topside sounders, and in situ instruments on several satellites and rockets. The IRI model is updated periodically and has evolved over several years (Bilitza, 2015). Swarm is a satellite mission of the European Space Agency (ESA).
Comparison and evaluation of a bottom-up GPS-RO electron density retrieval for D and E regions using radar observations and models
2020, Journal of Atmospheric and Solar-Terrestrial PhysicsModeling of GPS-TEC using QR-decomposition over the low latitude sector during disturbed geomagnetic conditions
2019, Advances in Space ResearchCitation Excerpt :During this process, new editions of the IRI model emerged and have steadily improved. The first version of the IRI model was released in 1978 and followed by several regularly improved versions of the IRI model (Rawer et al., 1978; Bilitza, 2015). The latest version of the IRI-2016 model is an improved version of the IRI-2012 model (Bilitza et al., 2017).