Ionospheric research and space weather services

Ionospheric research and space weather services

ARTICLE IN PRESS Journal of Atmospheric and Solar-Terrestrial Physics 70 (2008) 1870– 1878 Contents lists available at ScienceDirect Journal of Atmo...

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ARTICLE IN PRESS Journal of Atmospheric and Solar-Terrestrial Physics 70 (2008) 1870– 1878

Contents lists available at ScienceDirect

Journal of Atmospheric and Solar-Terrestrial Physics journal homepage:

Ionospheric research and space weather services Ljiljana R. Cander Rutherford Appleton Laboratory, Chilton, OX11 0QX, UK

a r t i c l e i n f o


Article history: Accepted 17 May 2008 Available online 29 May 2008

This paper concentrates on those major areas where our current physical understanding and recent advances can lead to positive predictions of the expected effects of ionospheric activity on the near-Earth space environment and on technological systems which operate within this environment. It briefly describes some of the key links between solar activity and the various physical processes, which govern ionospheric plasma structure that has been under scientific examination over past several decades but has lately received significant importance in relation to the space weather services. Specific examples during extremely intense solar event show how ionospheric monitoring techniques that have contributed immense data sets and related empirical and theoretical formulations have been incorporated in different ionospheric specification and prediction models for real-time operational applications. Finally, the general question of what might be expected as a result of current activities within different European cooperative projects is addressed. & 2008 Elsevier Ltd. All rights reserved.

Keywords: Ionosphere Space weather Storms Prediction and forecasting

1. Introduction The Earth’s ionosphere is a highly dynamic plasma medium that continues to command much interest in both purely scientific and applications communities. It exhibits the long-term (ionospheric climatology) and the short-term (ionospheric weather) variability at all latitudes and longitudes. Scientific research has the goal to explore the characteristics, causes, and consequences of ionospheric structures and dynamics during quiet and disturbed conditions and diverse irregularities by focusing on the extremes of space weather. Proper understanding of Earth’s ionosphere is of fundamental practical importance because it is an essential part of telecommunication and navigation systems that use the ionosphere to operate or would operate much better in its absence. In Europe, the need for increased reliability of technological systems whose performance depends on the state of the ionosphere has significantly increased in the last few years and it is strongly related to a broader European collaboration

E-mail address: [email protected] 1364-6826/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jastp.2008.05.010

within different frameworks (Cander, 2003; Zolesi and Cander, 2004; Belehaki et al., 2006). Studies of Earth’s ionospheric for several decades attempted to investigate large-scale daytime electrodynamics, structure of E- and F-region irregularities in relation to gravity waves, auroral activity, and tropospheric weather systems. Particularly important have been the studies related to the causes and behaviour of significant changes in electron densities during geomagnetic storms which increase and decay over time scales of a few minutes, and their overall influence on the radio propagation environment. More recent observations including space-based in situ and remote and ground-based measurements together with modelling and theoretical results provide new science of both large-scale and smallscale ionospheric processes. Complementing these new observations are advances in data assimilation models and models of the coupled ionosphere–magnetosphere and of the thermosphere–ionosphere circulation. This science is now widely engaged in providing space weather services from several communities including heliospheric, magnetospheric, ionospheric and atmospheric scientists as well as space weather experts to the users of the space environment affecting life and society.

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16 Chilton (358.7 E, 51.6 N) 28,29,30 and 31 October 2003 14 12 foF2 (MHz)

In the following section, the ionospheric weather aspects will be introduced by using a few examples of the ionospheric behaviour during the October 2003 storm. The following brief overview of the existing and ionospheric models in development provide some basic information on the ionospheric research required for space weather services (Sections 3 and 4). It aims to emphasize the complex and interdependent nature of investigations that seek a broad international participation and support. Some examples of the space weather services in Europe will be presented in Section 5. Questions of what might be expected as a result of current activities within different European cooperative projects are addressed in Section 6.

10 8 6 4 2

2. Ionospheric weather

12 October 2003

0 00:00






time (UT) 16 Rome (12. 5 E, 41.8 N) 28,29,30 and 31 October 2003 14 12 foF2 (MHz)

It is well known that the Earth’s ionosphere is a plasma environment whose state at any given time and any specified location results from the interactions of multiple physical and chemical processes that occur simultaneously and/or sequentially in different solar-terrestrial domains. The various radiative, chemical, and transport processes in the ionosphere–thermosphere system together with the effects of solar, interplanetary, magnetospheric processes above and mesospheric processes below generate: (1) background ionosphere (climatology-created and affected by incident EUV radiation, processes of ion/electron loss due to neutral constituent chemical reactions, ionospheric electrodynamics, and ion drag from thermospheric wind); and (2) disturbed ionosphere (weather-created and affected by the short-term variable impact of the Sun’s protons, solar wind particles and/or geomagnetic field). As a consequence, the ionosphere demonstrates significant variations on timescales ranging from 11 years of a solar cycle and longer to a few seconds, from large-scale ionospheric climate, variability, storms, auroral region, equatorial regions to local and/or regional effects such as sporadic E, tilts and gradients, small-scale ionospheric irregularities. A general pattern of ionospheric space weather clearly emerged from F-layer storm behaviour (Pro¨lss, 1995; Mendillo and Klobuchar, 2006). The density gradients and associated density irregularities produced during the ionospheric storm of 28–31 October 2003 show an interesting picture of ionospheric dynamics and the connection to the solar wind and magnetosphere that were influencing the high-precision navigation by Global Positioning System (GPS) for commercial air travel for two intervals of more than 20 h (Garner et al., 2006). Systematic measurements of the F-layer critical frequency (foF2) at Chilton (358.7E, 51.6N) and Rome (12.5E, 41.8N), derived from the network of European ionosondes, and the vertical Total Electron Content (TEC) at Hailsham (0.3E, 50.9N) and Matera (16.7E, 40.6N), derived from arrays of European GPS receiver network, show that these measurements are essential in sampling vast movements of ionization across midlatitudes (Figs. 1a and b, and 2a and b). In October 2003, the solar activity changed from low levels in the first half of the month to high activity in the


10 8 6 4 2 0 00:00

12 October 2003






time (UT) Fig. 1. (a) Pattern of ionospheric variability in foF2 at Chilton during October 2003 storm, (b) Pattern of ionospheric variability in foF2 at Rome during October 2003 storm.

last 10 days. On 28–31 October, geomagnetic observatories worldwide registered extensive geomagnetic disturbances (Cander and Mihajlovic, 2005) that during daytime on 29 October created a predominantly negative ionization perturbation at Chilton, but not at Rome. On 30 and 31 October, however, foF2 is significantly reduced at both Chilton and Rome declining by more than 50% relative to the foF2 values on the geomagnetically quiet day of 12 October 2003 (Figs. 1a and b). The ionosonde measurements were then compared to vertical TEC data from the nearby Heilsham and Matera IGS GPS sites. From Figs. 2a and b which display the 10 min vertical TEC values on 28–31 October 2003, it can be easily seen that the total ionospheric content was highly disturbed. There is a large-scale positive phase prior to the storm commencement during daytime hours on 28 October that


L.R. Cander / Journal of Atmospheric and Solar-Terrestrial Physics 70 (2008) 1870–1878

Fig. 2. (a) Pattern of ionospheric variability in TEC at Hailsham during October 2003 storm, (b) Pattern of ionospheric variability in TEC at Matera during October 2003 storm.

are not so pronounced in the foF2 variations. It continues during afternoon and night-time hours on 29 October. As expected, the negative phase of the ionospheric storm for the TEC is detected from daytime hours of 30 and 31 October. However, a large TEC enhancement on the nightside, not seen in foF2 data because of missing

data in the scaling procedure (Piggott and Rawer, 1972), appeared in the evening sector of 29 and 30 October at the high mid-latitude site Hailsham, lasting for a few hours. These examples suggest that vertical TEC data obtained from a worldwide network of ground-based GPS

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50 28 - 31 October 2003


10 min values 12 October

MUF (3000)F2 (MHz)

40 35 30 25 20 15 10 5 0 28


30 days


Fig. 3. Diurnal MUF(3000)F2 values at Chilton during 28–31 October 2003 together with MUF(3000)F2 values for the quiet day of 12 October 2003.

measurements can be an important tool for retrieving ionospheric features that appear during storm conditions (Leitinger et al., 2004). While the large variations in TEC are interpreted as significant enhancement or depletion characteristic of a storm behaviour, the explanation of the physical mechanisms that lead to such a complex behaviour could be found in a number of papers, e.g., Kersley et al. (2004); Cander and Mihajlovic (2005); and Mendillo and Klobuchar (2006). At mid-latitudes, thermospheric winds and electromagnetic fields are believed to play a dominant role in ionospheric plasma changes seen here in foF2 and TEC temporal and spatial variations (Figs. 1 and 2). Negative storm phases have been attributed to changes in the atomic to molecular neutral density ratio. Positive phases are generally believed to be caused by uplifting of the F-region by equatorward winds in the early hours of a storm development. The dayside plasma content is significantly reduced in the regions adjacent to the storm-enhanced density structure, which enhances the electron content. In that context, To´th et al. (2007) recently reported particularly interesting sun-tothermosphere simulation results of the 28–30 October 2003 storm with the Space Weather Modelling Framework. The observed structure in the ionospheric/ plasmaspheric ionization during this extreme space weather event display some common features as well as distinctly different irregularities making it difficult to match the rapid foF2 and TEC variations in ionospheric specification and forecasting models and observational data as the storm develops. During decades of ionospheric measurements, studies and modelling, knowledge about the morphology of the ionospheric response to severe space weather events has accumulated, and the physical mechanisms that drive storms have been understood in significant detail (Pro¨lss, 1995 and the references therein). These depressions of critical frequencies need to be anticipated by HF communicators. With the current

capabilities and the immense power of digital signal processing and flexible equipment, it is possible to design and operate systems that can react by modifying operational parameters adaptively in response to changing propagation conditions. For example, the basic MUF(3000), which is the highest frequency at which a radio wave can propagate between given terminals 3000 km apart by ionospheric refraction alone, displayed in Fig. 3 shows that during the 29–31 October storm event, ionospheric HF radio propagation was drastically reduced to values below 15 MHz. However, periods of severe disturbances will affect more than the MUFs. Irregularities in the ionosphere result in signals travelling by more than one path producing fading and consequently serious difficulties in communications. Positioning errors make navigation operations extremely difficult. Accordingly, one of the greatest challenges in developing accurate and reliable satellite-based augmentation systems (SBAS) is modelling of ionospheric effects during storm events of such a magnitude. Firstly, because ionosphere models can suffer degraded performance in regions where large spatial gradients in TEC exist. Secondly, because the observed feature of storm-enhanced density, associated with large TEC gradients at mid-latitudes, is a significant source of error in the SBAS correction models (Skone et al., 2004). It is therefore reasonable to conclude that such conditions can be specified successfully by space weather forecasting techniques based on real-time data availability in conjunction with mathematical algorithms to extrapolate to near-future conditions (Wilkinson, 2006).

3. Existing models A comprehensive knowledge of ionospheric structure and dynamic demands a space weather forecasting


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system that can be based either on (i) advanced models coupled across the space weather environment leading to detailed knowledge of conditions and good prediction facilities, or on (ii) specific well-targeted services that provide products like specification data, post-event analysis, predictions, nowcasts and forecasts generated in near real time in response to available observations. However, it is very well known that major factors limiting our ability to accurately model a wide range of ionospheric effects from storms to scintillation are: (1) current ground-based sensing capability; (2) density and frequency of ionospheric observations; (3) sophistication and accuracy of available ionospheric models; and (4) current scientific understanding of the physics of ionosphere–thermosphere–magnetosphere coupling mechanisms. As a result, most existing modelling techniques only successfully describe long-term ionospheric variations, such as those directly related to the sunspot cycle. Empirical models, like the global IRI model, the NeQuick model, the European region COST PRIME, and COSTPROF family of models, attempt to extract systematic ionospheric variations from past data records (Bilitza, 1992; Bradley, 1995; Hanbaba, 1999; Zolesi and Cander, 2004 and references therein). However, long-term trends are less well known and understood (e.g., Lastovicka et al., 2006). Hence these models describe average conditions of the non-auroral, non-disturbed ionosphere. Based on measurements, these empirical models are realistic in providing electron density profiles in those areas sufficiently covered by observations. Their results are useful in predicting monthly median values of basic ionospheric parameters for a given place and time of day. It is

important to note that on the regional scale, it is possible to effectively predict these values with sufficient accuracy by using only the sunspot number as required input parameter. An example is given in Fig. 4 where one of the regional SIRM family of models generates the long-term forecast map over Europe for February 2007 (Belehaki et al., 2006). These climatological models often provide very accurate ionospheric quiet-day reference conditions and many space weather forecasts in the past relied on climatology. Over the years, different modelling techniques have been used in a variety of space weather applications. Physics-based theoretical and/or numerical models attempt to solve a set of first-principles equations for the ionospheric plasma, starting from the continuity, energy, and momentum equations for electrons and ions (Schunk, 1988; Anderson, 1993). Some of the most important advanced models are listed in Table 1. Theoretical ionospheric models have proved their capabilities in reproducing selected sets of non-auroral observations. All these models are essentially confined to modern day supercomputers because of their complexity. The main problem of using theoretical models for operational space weather prediction and forecasting is the large amount of computer time needed. In addition, an extensive preparation of inputs is needed to obtain meaningful results (Sojka, 1989). For most operational ionospheric weather applications, this would be a significant limitation. Parametric models simplify the theoretical models by expressing them in terms of solarterrestrial parameters and geographical locations, giving a realistic representation of the ionospheric spatial and temporal structure using a limited number of numerical

Fig. 4. Long-term prediction map over European area for February 2007 at 1200 UT derived from the SIRM model developed under the DIAS project.

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coefficients. Some of the best-known models available at present are given in Table 2. It is important to emphasize that parametric models allow realistic ionospheric models to be adjusted in real time and to provide an accurate specification of the instantaneous ionosphere and then to be incorporated in three-dimensional ray tracing programs for HF propagation purposes (Reilly et al., 1991). However, it is clear that parametric models based on theoretical considerations are suitable only for well-specified geophysical problems. 4. Model in development Some of the advanced models now being developed will have a major impact on the space weather services Table 1 Some of the best-known physics-based theoretical and/or numerical models Institution



Utah State University

Time Dependent Ionospheric Model (TDIM)

University College London and Sheffield University National Center for Atmospheric Research—NCAR

Coupled Thermosphere–Ionosphere Model (CTIM)

Schunk et al. (1986) A detailed review of observation model comparisons is given by Sojka (1989) Fuller-Rowell et al. (1987) and Quegan et al. (1982)

University of Alabama

Field Line Interhemispheric Plasma Model (FLIP)

Phillips Laboratory

Global Theoretical Ionospheric Model (GTIM)

Thermosphere–Ionosphere Global Circulation Model (TIGCM)

Roble et al. (1988) More advanced version is TIMEGCM (M for mesosphere, E for electrodynamics) Torre et al. (1990), Richards et al. (1994a, b). It is used by Richards and Wilkinson (1998) in a comparison of measurements and modelling during the November 1993 ionospheric storm Anderson (1973), Moffett (1979) and Decker et al. (1994)


expansion during the coming years (Tobiska et al., 2002; Wilkinson, 2006). These are the physics-based datadriven models that use data assimilation techniques to specify the ionospheric plasma distributions. The terrestrial weather community as well as oceanographers have used such techniques for decades. Currently, the bestknown model of the kind is the Global Assimilation of Ionospheric Measurements (GAIM)—a physics-based data assimilation model of the ionosphere and neutral atmosphere (Schunk et al., 2004; Scherliess et al., 2006 and references therein). It is a four-level system providing time-dependent electron density Ne distributions by using: (1) Time-dependent climatology from the physics-based Ionospheric Forecast Model; (2) Observational database to adjust the empirical drivers so that they are consistent with measurements, then run Ionosphere– Plasmasphere Model with the adjusted drivers for simulation; (3) Kalman filters that combine the simulation results with the available real-time data; and (4) GAIM forecast mode that provides specifications and forecasts for 3D Ne distribution from 90 to 25,000 km on a global, regional, and local grid (25 km  25 km) depending on the operational demands and available data sets. In addition, GAIM will provide when finished the global distributions for the ionospheric drivers (neutral winds, electric fields, and particle precipitation), and quantitative estimates for the accuracy of the reconstructed ionospheric densities. In order to be effective, space weather specification and forecast models must add value to operational missions. Therefore, the most obvious challenges in the assimilation are related to the large and disparate data sets to assimilate, the model drivers (like electric fields and wind), the integrated quantities (e.g., TEC and radiance), the multiple sensors, and the sub-model that must be included. For any forecasting procedure involved, the greatest importance is related to the accurate physicsbased model, the accurate empirical model that is used, and the accurate forecast of drivers. Although models are already available in this area, further development is essential (Basu and Pallamraju, 2006). An excellent source for a number of available atmospheric, ionospheric, plasmaspheric, and other solarterrestrial models is at

Table 2 Some of the best-known parametric models Name




Low-latitude model, based on theoretically obtained grid values for electron density profiles normalized to the F2 peak and then represented by modified Chapman function Low- and mid-latitude model, which uses the formalism of the Chiu model with coefficients fitted to the SLIM model profiles Global with improved performance in the high latitude, controlled by the sunspot number (SSN) and geomagnetic Q index and conceived to allow for real-time updates of the input parameters from a number of sensors Global, generated as an amalgam of a number of other models by using either the foF2 CCIR coefficients for normalization of the electron density profiles or coefficients produced by the TDIM The URSI/COSPAR standard for the ionosphere

Anderson et al. (1987)



IRI—International Reference Ionosphere

Anderson et al. (1989) Tascione et al. (1988)

Daniell et al. (1993a, b)

Bilitza (2003) Wilkinson (2004) for ionospheric variability study


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5. Space weather services In an effort to minimize or circumvent the detrimental ionospheric effects in specific applications, a number of space weather services have been developed and made publicly available. They provide ionospheric specifications and forecasts on a spatial grid that can be regional or local. As an introduction to the currently available facilities and data sources a few examples are listed here. The shortterm ionospheric forecasting tool at RAL, UK and the supporting network of contributing ionosonde stations provides maps of forecasts up to 24 h ahead of the critical frequency foF2, maximum usable frequency for 3000 km range MUF(3000)F2, and TEC for the European area at each UT hour. It was available as an on-line interactive 24/7 service at (Cander et al., 2004). IPS Radio and Space Services, Australia makes hourly area predictions using some real-time data. In addition, there are T Index maps available that indicate differences between long-term predicted and real-time HF propagation conditions available at http://www.ips. In the USA there are two systems that regularly provide real-time data: SPIDR site in Boulder at http://, and DIDBase at the University of Massachusetts Lowell that holds all digisonde prompt ionospheric sounding data collected all over the world, including Europe (Reinisch et al., 2004; Galkin et al., 2006). Some of the DISS network data from the US Air Force are available at Even from these very limited examples, it is obvious that some of the basic data needed to support ionospheric research and associated space weather services are: (I) bottomside electron density profiles from ionosondes; (ii) in situ and remote electron density measurements from satellites; (iii) vertical TEC measurements between ground-based receivers and the GNSS satellites; (iv) scintillation data from scintillation receivers; and (v) radio occultation data from satellite constellations. The ionospheric community has a long tradition of providing global observations of Earth’s ionosphere through the use of ground- as well as space-based remote sensing. In addition to providing global data sets for scientific research, this community has pioneered in facilitating the use of these data by operational agencies, and in incorporating them through their inclusion in data assimilation systems to improve environmental forecasting. In particular, these space weather services, where models are tools, generated end-user applications in: (1) climatology (statistical/empirical); (2) specification (nowcasting), and (3) forecasting up to 24 h ahead, using real or near-time data. Some examples of European experiences in these areas are given in Table 3. In this context it is appropriate to mention the European Digital Upper Atmosphere Server (DIAS) which is a comprehensive, regional, and timely ionospheric specification and forecasting service based on real-time information and historical data collections observed by operating ionospheric stations in Europe and appropriate ionospheric models (Belehaki et al., 2006). It provides among many other ionospheric added value products like

Table 3 Some of the best-known European space weather services Name

Full Name

Detailed description available at website


ESA Space Weather European Network Space Weather Impact on Precise Positioning Applications of GNSS European Digital Upper Atmosphere Server swenet/index.html index.htm See also Jakowski et al. (2005)


DIAS See also Belehaki et al. (2005)

the ionospheric activity indices for foF2 and M(30000)F2 to monitor ionospheric propagation conditions over Europe in real time for customer’s warning purposes (Bremer et al., 2006). When prominent large-scale disturbances occurred in the European area during the severe space weather event in October 2003, variations of the ionospheric activity indexes foF2AI (%) and M(3000)F2AI (%) at Chilton during the 28–31 October 2003 were used to describe the temporal and spatial storm evolution process. Fig. 5 shows a complex ionospheric F-region storm morphology that was dominated by overall negative disturbances during the main phase of the storm and the usefulness of these indexes in their identification.

6. Discussion and conclusions As modern society increasingly relies on technological systems in space, the contribution of space weather research to society is becoming more important. The following activities and system are particularly affected by space weather: satellite operations, manned space flights, radiation in space and aviation, navigation systems, highfrequency communications, short-wave broadcasting, surveillance radars, electrical control systems, electricity power grid distribution, long pipeline corrosion, and insurance. Thus space weather conditions affect public safety, information services, defence, industrial processes, and transport. The programme elements of any space weather services include: fundamental research, modelling, observations, technology transition, operational specification and forecasting, and education. All these elements are vital and the programme can succeed only if all the different elements work together. While studies continue to search for the actual physical mechanisms that explain the spatial and temporal morphologies over various space weather events and to test forecasting algorithms for practical ionospheric applications, it is valuable to have at least a well-defined pattern of a storm, and a technique to forecast it in the near future. The short-term forecasting (a few hours in advance) of operational parameters for ionospheric and trans-ionospheric radio communications is required to improve the quality and reliability of radio communication services, including frequency-adaptive applications at

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80 28 - 31 October 2003 Chilton

foF2AI (%), M (3000) F2

60 40 20 0 28




-20 -40 -60 -80 days

Fig. 5. Examples of ionospheric alert indexes for foF2AI and M9(3000)F2AI at Chilton ionospheric station displaying 10 min data.

MF and HF, and trans-ionospheric radiodetermination. A contribution in that direction is the 4-year COST296 Action on ‘‘Mitigation of Ionospheric Effects on Radio Systems’’ that started in February 2005 (www.cost296. The impact of this Action’s results on ionospheric space weather research and application in telecommunication and navigation is expected to be significant (Cander and Zolesi, 2008). Finally, it is suggested that the progress of the ionospheric research and space weather services should be built on: (1) improvements in understanding the physical processes of geospace in numerical models across the full range of its domain starting from micro-scale to helioscale; (2) increase in the availability of data with greater precision from both ground-based and new satellite observations; (3) ability to combine the two using most advantageous data assimilation techniques; and (4) operational use of models and their validation.

Acknowledgements The investigation is carried out in the framework of COST296 Action. The author would like to express appreciation to the International GPS Service for Geodynamics (IGS) for providing data regularly as well as to Dr. L. Ciraolo for using his ‘‘TECmake’’ software for TEC evaluation. References Anderson, D.N., 1973. A theoretical study of the ionospheric F region equatorial anomaly, I, Theory, Planet. Space Science 21, 409. Anderson, D.N., 1993. Global Ionospheric modelling 1993. In: Matsumoto, H. (Ed.), Modern Radio Science. Oxford University Press, Oxford, p. 159. Anderson, D.N., Mendillo, M., Herniter, B., 1987. A semi-empirical low latitude ionospheric model. Radio Science 22, 292. Anderson, D.N., Forbes, J.M., Codrescu, M., 1989. A fully analytical, lowand middle-latitude ionospheric model. Journal of Geophysical Research 94, 1520.

Basu, S., Pallamraju, D., 2006. Science rationale for CAWSES (Climate and Weather of the Sun–Earth System): SCOSTEP’s interdisciplinary program for 2004-2008. Advances in Space Research 38, 1781. Belehaki, A., Cander, Lj.R., Zolesi, B., Bremer, J., Juren, C., Stanislawska, I., Dialetis, D., Hatzopoulos, M., 2005. DIAS project: the establishment of a European digital upper atmosphere server. Journal of Atmospheric and Solar-Terrestrial Physics. 67 (12), 1092. Belehaki, A., Cander, Lj.R., Zolesi, B., Bremer, J., Juren, C., Stanislawska, I., Dialetis, D., Hatzopoulos, M., 2006. Monitoring and forecasting the ionosphere over Europe: the DIAS project. Space Weather 4, S12002. Bilitza, D., 1992. Solar-terrestrial models and application software. Planet Space Science 40, 541. Bilitza, D., 2003. International reference ionosphere 2000: examples of improvements and new features. Advances in Space Research 31 (3), 757. Bradley, P.A., 1995. PRIME (Prediction Regional Ionospheric Modelling over Europe). COST Action 238 Final Report, Commission of the European Communities, Brussels. Bremer, J., Cander, Lj.R., Mielich, J., Stamper, R., 2006. Derivation and test of ionospheric activity indices from real-time ionosonde observations in the European region. Journal of Atmospheric and SolarTerrestrial Physics 68 (18), 2075. Cander, Lj.R., 2003. Toward forecasting and mapping ionospheric space weather under the COST actions. Advances in Space Research 31 (4), 957. Cander, Lj.R., Mihajlovic, S.J., 2005. Ionospheric spatial and temporal variations during the 29–31 October 2003 storm. Journal of Atmospheric and Solar-Terrestrial Physics 67 (12), 1118. Cander, Lj.R., Zolesi, B., 2008. Science rational for MIERS/IRI. Advances in Space Research, in press. Cander, Lj.R., Hickford, J., Tsagouri, I., Belehaki, A., 2004. Real-time dynamic system for monitoring ionospheric propagation conditions over Europe. Electronics Letters 40 (4), 224. Daniell, R.E., Whartenby, W.G., Brown, L.D., 1993a. Parameterised real time ionospheric specification model. PRISM version 1.2. Algorithm description, Computational Physics, Inc., 385 Elliot St, Newton, MA, USA. Daniell, R.E., Whartenby, W.G., Brown, L.D., 1993b. Parameterised real time ionospheric specification model, PRISM version 1.2. Validation Report, Computational Physics, Inc., 385 Elliot St, Newton, MA, USA. Decker, D.T., Valladares, C.E., Sheehan, R., Basu, S., Anderson, D.N., Heelis, R.A., 1994. Modeling daytime F layer patches over Sonderstrom. Radio Science 29, 249. Fuller-Rowell, T.J., Rees, D., Quegan, S., Moffett, R.J., Bailey, G.J., 1987. Interaction between neutral thermosphere composition and the polar ionosphere using a coupled ionosphere–thermosphere model. Journal of Geophysical Research 92, 7744. Galkin, I.A., Khmyrov, G., Kozlov, A., Reinisch, B.W., Huang, X., Kitrosser, D.F., 2006. Ionosonde networking, databasing, and web serving. Radio Science 41 (5), RS5S33.


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Garner, T.W., Bust, G.S., Gaussiran II, T.L., Straus, P.R., 2006. Variations in the midlatitude and equatorial ionosphere during the October 2003 magnetic storm. Radio Science 41, RS6S08. Hanbaba, R., 1999. Improved quality of service in ionospheric telecommunication systems planning and operation. COST Action 251 Final Report, Space Research Centre, Warsaw. Jakowski, N., Stankov, S.M., Klaehn, D., 2005. Operational space weather service for GNSS precise positioning. Annales Geophysicae 23 (9), 3071. Kersley, L., Malan, D., Pryse, S.E., Cander, Lj.R., Bamford, .A., Belehaki, A., Leitinger, R., Radicella, S.M., Mitchel, C.N., Spencer, P.S.J., 2004. Total electron content—a key parameter in propagation: measurement and use in ionospheric imaging. Annals of Geophysics 47 (2/3, Suppl.), 1067. Lastovicka, J., Akmaev, R.A., Beig, G., Bremer, J., Emmert, J.T., 2006. Global change in the upper atmosphere. Science 314 (5803), 1253. Leitinger, R., Ciraolo, L., Kersley, L., Kouris, S.S., Spalla, P., 2004. Relations between electron content and peak density: regular and extreme behaviour. Annals of Geophysics 47 (2/3, Suppl.), 1093. Mendillo, M., Klobuchar, J.A., 2006. Total electron content: synthesis of past storm studies and needed future work. Radio Science 41, RS5S02. Moffett, R.J., 1979. The equatorial anomaly in the electron distribution of the terrestrial F region. Fundamental of Cosmic Physics 4, 313. Piggott, W.R., Rawer, K., 1972. URSI handbook of ionogram interpretation and reduction. World Data Center A for Solar-Terrestrial Physics, Report UAG-23, Environmental Data Service, Asheville, North Carolina, US Department of Commerce, National Oceanic and Atmospheric Administration. Pro¨lss, G.W., 1995. Ionospheric F region storms. In: Volland, H. (Ed.), Handbook of Atmospheric Electrodynamics. CRC Press, Boca Raton, FL, pp. 195–248. Quegan, S., Bailey, G.J., Moffett, R.J., Heelis, R.A., Fuller-Rowell, T.J., Rees, D., Spiro, R.W., 1982. A theoretical study of the distribution of ionization in the high-latitude ionosphere and the plasmasphere: first results on the mid-latitude trough and the light-ion trough. Journal of Atmospheric and Terrestrial Physics 44, 619. Reilly, M.H., Rhoads, F.J., Goodman, J.M., 1991. Updated climatological model predictions of ionospheric and HF propagation parameters. Radio Science 26, 1017. Reinisch, B.W., Galkin, I.A., Khmyrov, G., Kozlov, A., Kitrosser, D.F., 2004. Automated collection and dissemination of ionospheric data from the digisonde network. Advances in Radio Science 2, 241. Richards, P.G., Wilkinson, P.J., 1998. The ionosphere and thermosphere at southern midlatitudes during the November 1993 ionospheric storm: a comparison of measurement and modelling. Journal of Geophysical Research 103 (A3), 9373. Richards, P.G., Torr, D.G., Reinisch, B.W., Gamache, R.R., Wilkinson, P.J., 1994a. F2 peak electron density at Millstone Hill and Hobart:

comparison of theory and measurement at solar maximum. Journal of Geophysical Research 99, 15,005. Richards, P.G., Torr, D.G., Buonsanto, M.J., Sipler, D.P., 1994b. Ionospheric effects of the March 1990 magnetic storm: comparison of theory and measurement. Journal of Geophysical Research 99, 23,359. Roble, R.G., Ridley, E.C., Richmond, A.D., Dickinson, R.E., 1988. A coupled thermosphere/ionosphere general circulation model. Geophysical Research Letters 15, 1325. Scherliess, L., Schunk, R.W., Sojka, J.J., Thompson, D.C., Zhu, L., 2006. Utah State University Assimilation of Ionospheric Measurements Gauss– Markov Kalman filter model of the ionosphere: model description and validation. Journal of Geophysical Research 111, A11315. Schunk, R.W., 1988. A mathematical model of the middle and high latitude ionosphere. Pure and Applied Geophysics 127, 255. Schunk, R.W., Sojka, J.J., Bowline, M.D., 1986. Theoretical study of the electron temperature in the high-latitude ionosphere for solar maximum and winter conditions. Journal of Geophysical Research 91, 12041. Schunk, R.W., Scherliess, L., Sojka, J.J., Thompson, D.C., Anderson, D.N., Codrescu, M., Minter, C., Fuller-Rowell, T.J., Heelis, R.A., Hairston, M., Howe, B.M., 2004. Global Assimilation of Ionospheric Measurements (GAIM). Radio Science 39, RS1S02. Skone, S., Yousuf, R., Coster, A., 2004. Performance evaluation of the wide area augmentation system for ionospheric storm events. Journal of Global Positioning Systems 3 (1–2), 251. Sojka, J.J., 1989. Global scale, physical models of the F region ionosphere. Reviews of Geophysics 27, 371. Tascione, T.F., Kroehl, H.W., Creiger, R., Freeman Jr., J.W., Wolf, R.A., Spiro, R.W., Hilmer, R.V., Shade, J.W., Hausman, B.A., 1988. New ionospheric and magnetospheric specification models. Radio Science 23, 211. Tobiska, K.W., Cameron, G., Davenport, G., Goodman, J., Hand, G., Papitashvili, V., Pi, X., 2002. Improved polar HF propagation using nowcast and forecast space weather parameters. In: 10th International Ionospheric Effects Symposium, May 7–9, USA, 2002, p. 364. Torr, M.R., Torr, D.G., Richards, P.G., Yung, S.P., 1990. Mid- and lowlatitude model of thermospheric emissions, 1, O+ (2 P) 7320 A and N2 (2P) 3371 A. Journal of Geophysical Research 95, 21,147. To´th, G., De Zeeuw, D.L., Gombosi, T.I., Manchester, W.B., Ridley, A.J., Sokolov, I.V., Roussev, I.I., 2007. Sun-to-thermosphere simulation of the 28–30 October 2003 storm with the Space Weather Modeling Framework. Space Weather 5, S06003. Wilkinson, P.J., 2004. Ionospheric variability and the international reference ionosphere. Advances in Space Research 34 (9), 1853. Wilkinson, P.J., 2006. The future of the ionosphere. In: IET Conference on Ionospheric Radio Systems and Techniques, 18–21 July 2006, London, UK. Zolesi, B., Cander, Lj.R., 2004. COST271 Action ‘‘Effects of the upper atmosphere on terrestrial and Earth-space communications’’. Final Report, Annals of Geophysics, 47(2/3, Suppl.), 2004.