TEC derived from some GPS stations in Nigeria and comparison with the IRI and NeQuick models

Total electron content (TEC) measured simultaneously using Global Positioning System (GPS) ionospheric monitors installed at some locations in Nigeria during the year 2011 (Rz = 55.7) was used to study the diurnal, seasonal, and annual TEC variations. The TEC exhibits daytime maximum, seasonal variation and semiannual variations. Measured TEC were compared with those predicted by the improved versions of the International Reference Ionosphere (IRI) and NeQuick models. The models followed the diurnal and seasonal variation patterns of the observed values of TEC. However, IRI model produced better estimates of TEC than NeQuick at all locations.     

Comparison of ionospheric F2 peak parameters foF2 and hmF2 with IRI2001 at Hainan

Monthly median values of foF2, hmF2 and M(3000)F2 parameters, with quarter-hourly time interval resolution for the diurnal variation, obtained with DPS4 digisonde at Hainan (19.5°N, 109.1°E; Geomagnetic coordinates: 178.95°E, 8.1°N) are used to investigate the low-latitude ionospheric variations and comparisons with the International Reference Ionosphere (IRI) model predictions. The data used for the present study covers the period from February 2002 to April 2007, which is characterized by a wide range of solar activity, ranging from high solar activity (2002) to low solar activity (2007). The results show that (1) Generally, IRI predictions follow well the diurnal and seasonal variation patterns of the experimental values of foF2, especially in the summer of 2002. However, there are systematic deviation between experimental values and IRI predictions with either CCIR or URSI coefficients. Generally IRI model greatly underestimate the values of foF2 from about noon to sunrise of next day, especially in the afternoon, and slightly overestimate them from sunrise to about noon. It seems that there are bigger deviations between IRI Model predictions and the experimental observations for the moderate solar activity. (2) Generally the IRI-predicted hmF2 values using CCIR M(3000)F2 option shows a poor agreement with the experimental results, but there is a relatively good agreement in summer at low solar activity. The deviation between the IRI-predicted hmF2 using CCIR M(3000)F2 and observed hmF2 is bigger from noon to sunset and around sunrise especially at high solar activity. The occurrence time of hmF2 peak (about 1200 LT) of the IRI model predictions is earlier than that of observations (around 1500 LT). The agreement between the IRI hmF2 obtained with the measured M(3000)F2 and the observed hmF2 is very good except that IRI overestimates slightly hmF2 in the daytime in summer at high solar activity and underestimates it in the nighttime with lower values near sunrise at low solar activity.     

New input to IRI from the worldwide digisonde network

Electron density profiles derived from Digisonde ionograms at Argentia, Millstone Hill, Wallops Island, Bermuda, Dourbes and Karachi are compared with IRI model prediction. Four months of data for 1989/90 were analyzed. For a number of station/months N(h) profiles were available every 15 or 30 minutes providing a good statistical database for the evaluation of the IRI model in terms of diurnal and seasonal variations. The data presented here are part of the VIM study (Validation of Ionospheric Models) initiated by the URSI Working Group G3 on Ionospheric Informatics.     

Assessment of improvement of the IRI model over Ethiopia for the modeling of the variability of TEC during the period 2013–2016

This paper discusses the monthly and seasonal variation of the total electron content (TEC) and the improvement of performance of the IRI model in estimating TEC over Ethiopia during the solar maximum (2013–2016) phase employing as reference the GPS derived TEC data inferred from four GPS receivers installed in different regions of Ethiopia; Assosa (geog 10.05°N, 34.55°E, Geom. 7.01°N), Ambo (8.97°N, 37.86°E, Geom. 5.42°N), Nazret (8.57°N, 39.29°E, Geom. 4.81°N) and Arba Minch (6.06°N, 37.56°E, Geom. 2.62°N). The results reveal that, in the years 2013–2016, the highest peak GPS-derived diurnal VTEC is observed in the March equinox in 2015 over Arba Minch station. Moreover, both the arithmetic mean GPS-derived and modelled VTEC values, generally, show maximum and minimum values in the equinoctial and June solstice months, respectively in 2014–2015. However, in 2013, the minimum and maximum arithmetic mean GPS-derived values are observed in the March equinox and December solstice, respectively. The results also show that, even though overestimation of the modelled VTEC has been observed on most of the hours, all versions of the model are generally good to estimate both the monthly and seasonal diurnal hourly VTEC values, especially in the early morning hours (00:00–03:00?UT or 03:00–06:00?LT). However, it has also been shown that the IRI 2007 and IRI 2012 versions generally perform best in matching the diurnal GPS derived TEC values as compared to that of the IRI 2016 version. In addition, the IRI 2012 version with IRI2001 option for the topside electron density shows the highest overestimation of the VTEC as compared to the other options. None of the versions of the IRI model are proved to be able to capture the effects of geomagnetic storms.     

Total electron content at low latitudes and its comparison with the IRI90

We used total electron content (TEC) data measured by Faraday rotation technique over Cachoeira Paulista (22.5°S, 45°W), in Brazil, to study the TEC variations with the solar flux at 10.7 cm (F10.7) and to compare the results with the IRI90 predictions. The data were divided into summer, equinox and winter. During the analysed period F10.7 varied from 66 up to 330. Our data shows that the observed TEC at 1600 LT (around the diurnal maximum) and at 0500 LT (around the diurnal minimum) increases with F10.7 until saturation is reached which occurs at F10.7≈210 to 220 for equinox and summer, and at F10.7≈180 for winter months. Comparison with the IRI90 predictions shows that IRI overestimates the TEC at 0500 LT for all solar flux values. At 1600 LT, IRI overestimates the observed TEC for low solar flux but underestimates it for high solar flux values.     

Testing the improvement of performance of the IRI model in the estimation of TEC over the mid-latitude American regions

This paper mainly discusses the improvement of performance of the International Reference Ionosphere (IRI) model in estimating the variation of the Vertical Total Electron Content (VTEC) over the mid latitude American regions during the relatively low (2008–2010) and relatively high (2012) solar activity years. This has been conducted employing the VTEC values obtained from the dual frequency ground based Global Positioning System (GPS) receivers located at Mineral Area Community College, MACC (37.85°N, 269.52°W) and Mississippi County Airport, MAIR (36.85°N, 270.64°W), and the latest versions of the IRI online model (IRI 2007, IRI 2012 and IRI 2016). The study mainly focuses to compare the trend of variability of the monthly and seasonal modeled VTEC values (IRI 2007 VTEC, IRI 2012 VTEC and IRI 2016 VTEC) with the corresponding measured VTEC values (GPS VTEC). The overall results show that the IRI VTEC values (almost in all versions of the model) are generally smaller than the GPS VTEC except after about 15:00 UT (09:00 LT) in the December solstice when the Sun shifts to the high solar activity. On the contrary, overestimations of the VTEC values by the model are observed in traversing from the low solar activity (2008) to high solar activity (2012) phase, especially after about 15:00 UT (09::00 LT) with the IRI 2016 version showing the highest. In general, the IRI 2007 and IRI 2012 versions show similar monthly and seasonal underestimations or overestimations showing that the two versions have almost similar performance. The IRI 2016 version is generally better in capturing both the diurnal and arithmetic mean GPS VTEC values with some exceptional months and seasons as compared to those of the IRI 2007 and IRI 2012 versions.     

Equatorial anomaly according to the Interkosmos-19 data and IRI model: A comparison

The comparison of the IRI model with the foF2 distribution in the equatorial anomaly region obtained by topside sounding onboard the Interkosmos-19 satellite has been carried out. The global distribution of foF2 in terms of LT-maps was constructed by averaging Intercosmos-19 data for summer, winter, and equinox. These maps, in fact, represent an empirical model of the equatorial anomaly for high solar activity F10.7 ~ 200. The comparison is carried out for the latitudinal foF2 profiles in the characteristic longitudinal sectors of 30, 90, 210, 270, and 330°, as well as for the longitudinal variations in foF2 over the equator. The largest difference between the models (up to 60%) for any season was found in the Pacific longitudinal sector of 210°, where there are a few ground-based sounding stations. Considerable discrepancies, however, are sometimes observed in the longitudinal sectors, where there are many ground-based stations, for example, in the European or Indian sector. The discrepancies reach their maximum at 00 LT, since a decay of the equatorial anomaly begins before midnight in the IRI model and after midnight according to the Interkosmos-19 data. The discrepancies are also large in the morning at 06 LT, since in the IRI model, the foF2 growth begins long before sunrise. In the longitudinal variations in foF2 over the equator at noon, according to the satellite data, four harmonics are distinguished in the June solstice and at the equinox, and three harmonics in the December solstice, while in the IRI model only two and one harmonics respectively are revealed. In diurnal variations in foF2 and, accordingly, in the equatorial anomaly intensity, the IRI model does not adequately reproduce even the main, evening extremum.     

Low solar activity variability and IRI 2007 predictability of equatorial Africa GPS TEC

Diurnal, seasonal and latitudinal variations of Vertical Total Electron Content (VTEC) over the equatorial region of the African continent and a comparison with IRI-2007 derived TEC (IRI-TEC), using all three options (namely; NeQuick, IRI01-corr and IRI-2001), are presented in this paper. The variability and comparison are presented for 2009, a year of low solar activity, using data from thirteen Global Positioning System (GPS) receivers. VTEC values were grouped into four seasons namely March Equinox (February, March, April), June Solstice (May, June, July), September Equinox (August, September, October), and December Solstice (November, December, January). VTEC generally increases from 06h00 LT and reaches its maximum value at approximately 15h00–17h00 LT during all seasons and at all locations. The NeQuick and IRI01-corr options of the IRI model predict reasonably well the observed diurnal and seasonal variation patterns of VTEC values. However, the IRI-2001 option gave a relatively poor prediction when compared with the other options. The post-midnight and post-sunset deviations between modeled and observed VTEC could arise because NmF2 or the shape of the electron density profile, or both, are not well predicted by the model; hence some improvements are still required in order to obtain improved predictions of TEC over the equatorial region of the Africa sector.     

Ionospheric behavior of the F2 peak parameters foF2 and hmF2 at Hainan and comparisons with IRI model predictions

Monthly median values of foF2, hmF2 and M(3000)F2 parameters, with hourly time interval resolution for the diurnal variation, obtained with DPS-4 digisonde observations at Hainan (19.4°N, 109.0°E) are used to study the low latitude ionospheric variation behavior. The observational results are compared with the International Reference Ionospheric Model (IRI) predictions. The time period coverage of the data used for the present study is from March 2002 to February 2005. Our present study showed that: (1) In general, IRI predictions using CCIR and URSI coefficients follow well the diurnal and seasonal variation patterns of the experimental values of foF2. However, CCIR foF2 and URSI foF2 IRI predictions systematically underestimate the observed results during most time period of the day, with the percentage difference ΔfoF2 (%) values changing between about −5% and −25%, whereas for a few hours around pre-sunrise, the IRI predictions generally overestimate the observational ones with ΔfoF2 (%) sometimes reaching as large as ∼30%. The agreement between the IRI results and the observational ones is better for the year 2002 than for the other years. The best agreement between the IRI results and the observational ones is obtained in summer when using URSI coefficients, with the seasonal average values of ΔfoF2 (%) being within the limits of ±10%. (2) In general, the IRI predicted hmF2 values using CCIR M(3000)F2 option shows a poor agreement with the observational results. However, when using the measured M(3000)F2 as input, the diurnal variation pattern of hmF2 given by IRI2001 has a much better agreement with the observational one with the detailed fine structures including the pre-sunrise and post-sunset peaks reproduced reasonably well. The agreement between the IRI predicted hmF2 values using CCIR M(30,000)F2 option and the observational ones is worst for the afternoon to post-midnight hours for the high solar activity year 2002. During daytime hours the agreement between the hmF2 values obtained with CCIR M(30,000)F2 option and the observational ones is best for summer season. The discrepancy between the observational hmF2 and that obtained with CCIR M(30,000)F2 option stem from the CCIR M(3000)F2 model, which does not produce the small scale structures observed in the measured M(3000)F2.     

A prediction model of short-term ionospheric foF2 based on AdaBoost

In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.     

Description of the mean behaviour of ionospheric plasma temperatures

A status report on the empirical modeling of ionospheric electron and ion temperatures is given with special emphasis on the models used in the International Reference Ionosphere (IRI).Electron temperature models have now reached a state where reliable prediction of the mean altitudinal, latitudinal and diurnal variations is possible. These models are largely based on satellite measurements, but comparisons with incoherent scatter radar measurements have shown excellent agreement. Variations with season and magnetic and solar activity seem to be small and are not yet included consistently in these models.Similar to the electron temperature, the ion temperature shows the largest variations with altitude, latitude and local time. But due to the larger mass, these variations are smoother and more steady in the case of the ions and therefore easier to model. Nevertheless, very few ion temperature models exist. The IRI model takes advantage of the observed concurrence of the ion temperature with the neutral temperature at low altitudes and with the electron temperature at high altitudes.     

Ionospheric variation at Thailand equatorial latitude station: Comparison between observations and IRI-2001 model predictions

In this paper, the F2-layer critical frequency (foF2) and peak height (hmF2) measured by the FM/CW ionosonde at Thailand equatorial latitude station, namely Chumphon (10.72°N, 99.37°E, dip 3.22) are presented. The measurement data during low solar activity from January 2004 to December 2006 are analyzed based on the diurnal, seasonal variation. The results are then compared with IRI-2001 model predictions. Our study shows that: (1) In general, both the URSI and CCIR options of the IRI model give foF2 close to the measured ones, but the CCIR option produces a smaller range of deviation than the URSI option. The agreement during daytime is generally better than during nighttime. Overestimation mostly occurs in 2004 and 2006, while underestimation is during pre-sunrise hours in June solstice in 2005. The peak foF2 around sunset is higher during March equinox and September equinox than the other seasons, with longer duration of maximum levels in March equinox than September equinox. Large coefficients of variability foF2 occur during pre-sunrise hours. Meanwhile, the best agreement between the observed foF2 and the IRI model is obtained in June solstice. (2) In general, The IRI (CCIR) model predicts the observed hmF2 well during daytime in June solstice from 2004–2006, but it overestimates during March equinox, September equinox and December solstice. For nighttime, the model overestimates hmF2 values for all seasons especially during March equinox and September equinox. However, the model underestimates hmF2 values during September equinox and for some cases during June solstice and December solstice at pre-sunrise. The agreement between the IRI model and the hmF2(M3000_OBS) is worst around noontime, post-sunset and pre-sunrise hours. All comparative studies give feedback for new improvements of CCIR and URSI IRI models.     

Application of neural networks to South African GPS TEC modelling

The propagation of radio signals in the Earth’s atmosphere is dominantly affected by the ionosphere due to its dispersive nature. Global Positioning System (GPS) data provides relevant information that leads to the derivation of total electron content (TEC) which can be considered as the ionosphere’s measure of ionisation. This paper presents part of a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Vertical total electron content (VTEC) was calculated for four GPS receiver stations using the Adjusted Spherical Harmonic (ASHA) model. Factors that influence TEC were then identified and used to derive input parameters for the NN. The well established factors used are seasonal variation, diurnal variation, solar activity and magnetic activity. Comparison of diurnal predicted TEC values from both the NN model and the International Reference Ionosphere (IRI-2001) with GPS TEC revealed that the IRI provides more accurate predictions than the NN model during the spring equinoxes. However, on average the NN model predicts GPS TEC more accurately than the IRI model over the GPS locations considered within South Africa.     

Modelling of ionospheric temperature profiles

The amount of measured temperature data accumulated in recent years allows and asks for improvement and refinement of the rather crude temperature models employed in the International Reference Ionosphere 1979. By combining the mission-oriented models by BRACE, THEIS /2/ for the AE-C satellites and by SPENNER, PLUGGE /1/ for the AEROS-A satellite, a much better diurnal and latitudinal reliability can be obtained. It is also suggested that IRI should have the option to make use of the strong anti-correlation between electron temperature and density in cases where actual measured densities are available. For daytime condition, incorporation of a density dependent model into IRI can significantly enhance the prediction quality of IRI in the altitude range 300 to 600 km.Furtheron, the solar activity dependence of the electron temperature and of the density-temperature-relation are investigated by comparing the low solar activity models with more recent AE-C and -DE data.     

Ionospheric variability at taiwan low latitude station: Comparison between observations and IRI-2001 model

This paper presents the observed ionospheric F-region critical frequency, foF2, and peak height, hmF2, at northern crest of equatorial ionization anomaly (EIA) area station, namely Chung-Li (24.9°N, 121.1°E, dip 35°), and to be compared with International Reference Ionosphere model (IRI-2001) predictions for the period from 1994 to 1999, corresponding to half of the 23rd solar cycle. The diurnal and seasonal variation of foF2 and hmF2 are analyzed for different solar phases, respectively. The result shows the largest discrepancies were observed during nighttime for foF2 and hmF2, respectively. The value of foF2 both CCIR and URSI selected in the IRI model produced a good agreement during the daytime and underestimated during the noon time for high solar activities. The underestimation at noon time is mainly caused by the fountain effect from equator. Further, the peak height hmF2 shows a larger variability around the midnight than daytime in the equinox and winter seasons and reserved in summer, respectively. The study shows that the monthly median values of observed hmF2 is somewhat lower than those predicated by the IRI model, at night time in all the seasons except the period of 04:00–06:00 LT and reverse at daytime in summer. In general the IRI model predictions with respect to the observed in hmF2 is much better than foF2. The percentage deviation of the observed foF2 (hmF2) values with respect to the IRI model varies from 5% to 80% (0–25%) during nighttime and 2–17% (0–20%) at daytime, respectively. In general, the model generates good results, although some improvements are still necessary to implement in order to obtain better simulations for ionospheric low-latitudes region.     

Equatorial total electron content (TEC) at low and high solar activity

Total electron content (TEC) derived from ionosonde data recorded at the station of Korhogo (Lat = 9.33°N, Long = 5.43°W, Dip = 0.67°S) are compared to the International Reference Ionosphere (IRI) model predicted TEC for high (1999) and low (1994) solar activity conditions. The results show that the model represents the diurnal variation of the TEC as well as a solar activity and seasonal dependence. This variation is closer to that of the ionosonde-inferred TEC at high solar activity. However, at low solar activity the IRI overestimates the ionosonde-inferred TEC. The relative deviation ΔTEC is more prominent in the equinoctial seasons during nighttime hours where it is as high as 70%. At daytime hours, the relative deviation is estimated to 0–30%.     

The variability and predictability of the IRI B0, B1 parameters over Grahamstown,South Africa

The International Reference Ionosphere (IRI) parameters B₀ and B₁ provide a representation of the thickness and shape, respectively, of the F2 layer of the bottomside ionosphere. These parameters can be derived from electron density profiles that are determined from vertical incidence ionograms. This paper aims to illustrate the variability of these parameters for a single mid latitude station and demonstrate the ability of the Neural Network (NN) modeling technique for developing a predictive model for these parameters. Grahamstown, South Africa (33.3°S, 26.5°E) was chosen as the mid latitude station used in this study and the B₀ and B₁ parameters for an 11 year period were determined from electron density profiles recorded at that station with a University of Massachusetts Lowell Center for Atmospheric Research (UMLCAR) Digisonde. A preliminary single station NN model was then developed using the Grahamstown data from 1996 to 2005 as a training database, and input parameters known to affect the behaviour of the F2 layer, such as day number, hour, solar and magnetic indices. An analysis of the diurnal, seasonal and solar variations of these parameters was undertaken for the years 2000, 2005 and 2006 using hourly monthly median values. Comparisons between the values derived from measured data and those predicted using the two available IRI-2001 methods (IRI tables and Gulyaeva, T. Progress in ionospheric informatics based on electron density profile analysis of ionograms. Adv. Space Res. 7(6), 39–48, 1987.) and the newly developed NN model are also shown in this paper. The preliminary NN model showed that it is feasible to use the NN technique to develop a prediction tool for the IRI thickness and shape parameters and first results from this model reveal that for the mid latitude location used in this study the NN model provides a more accurate prediction than the current IRI model options.