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 new global version of M(3000)F2 prediction model based on artificial neural networks

A new version of global empirical model for the ionospheric propagation factor, M(3000)F2 prediction is presented. Artificial neural network (ANN) technique was employed by considering the relevant geophysical input parameters which are known to influence the M(3000)F2 parameter. This new version is an update to the previous neural network based M(3000)F2 global model developed by Oyeyemi et al. (2007), and aims to address the inadequacy of the International Reference Ionosphere (IRI) M(3000)F2 model (the International Radio Consultative Committee (CCIR) M(3000)F2 model). The M(3000)F2 has been found to be relatively inaccurate in representing the diurnal structure of the low latitude region and the equatorial ionosphere. In particular, the existing hmF2 IRI model is unable to reproduce the sharp post-sunset drop in M(3000)F2 values, which correspond to a sharp post-sunset peak in the peak height of the F2 layer, hmF2. Data from 80 ionospheric stations globally, including a good number of stations in the low latitude region were considered for this work. M(3000)F2 hourly values from 1987 to 2008, spanning all periods of low and high solar activity were used for model development and verification process. The ability of the new model to predict the M(3000)F2 parameter especially in the low latitude and equatorial regions, which is known to be problematic for the existing IRI model is demonstrated.     

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.     

Variation in the ionospheric propagating factor M(3000)F2 at Ouagadougou,Burkina Faso

We use hourly monthly median values of propagation factor M(3000)F2 data observed at Ouagadougou Ionospheric Observatory (geographic12.4°N, 1.5°W; 5.9^o dip), Burkina Faso (West Africa) during the years Januar1987–December1988 (average F10.7 < 130 × 10⁻²² W/m²/Hz, representative of low solar flux conditions) and for January 1989–December1990 (average F10.7 ? 130 × 10⁻²² W/m²/Hz, representative of high solar epoch) for magnetically quiet conditions to describe local time, seasonal and solar cycle variations of equatorial ionospheric propagation factor M(3000)F2 in the African region. We show that that seasonal trend between solar maximum and solar minimum curves display simple patterns for all seasons and exhibits reasonable disparity with root mean square error (RMSE) of about 0.31, 0.29 and 0.26 for December solstice, June solstice and equinox, respectively. Variability Σ defined by the percentage ratio of the absolute standard deviation to the mean indicates significant dissimilarity for the two solar flux levels. Solar maximum day (10–14 LT) and night (22–02 LT) values show considerable variations than the solar minimum day and night values. We compare our observations with those of the IRI 2007 to validate the prediction capacity of the empirical model. We find that the IRI model tends to underestimate and overestimate the observed values of M(3000)F2, in particular, during June solstice season. There are large discrepancies, mainly during high solar flux equinox and December solstice between dawn and local midnight. On the other hand, IRI provides a slightly better predictions for M(3000)F2 between 0900 and 1500 LT during equinox low and high solar activity and equinox high sunspot number. Our data are of great importance in the area of short-wave telecommunication and ionospheric modeling.     

Evaluation of global modeling of M(3000)F2 and hmF2 based on alternative empirical orthogonal function expansions

Global modeling of M(3000)F2 and hmF2 based on three alternative EOF (empirical orthogonal function) expansion methods is described briefly. Data used for the model construction is the monthly median hourly values of M(3000)F2 from the ionosonde/digisonde stations distributed around the world for the period of 1975–1985 and the hmF2 data of the same period converted from the measured M(3000)F2 based on the strong anti-correlation existing between them. Independent data of a low (1965) and a high (1970) solar activity year are used to validate the three alternative models based on different EOF expansion methods. Comparisons between the modeled results and observed data for both the low (1965) and high (1970) solar activity years showed good agreement for both M(3000)F2 and hmF2 parameters. Statistical analysis on the differences between model values and observed data showed that all the three alternative models (Model A, B and C) based on the different EOF expansion methods have better agreement with the observed data than the models currently used in IRI. All three alternative EOF based models have almost the same accuracy. Discussion on the preference of the three alternative EOF based models is given.     

Comparison of GPS-TEC measurements with NeQuick2 and IRI model predictions in the low latitude East African region during varying solar activity period (1998 and 2008–2015)

This paper examines the performances of NeQuick2, the latest available IRI-2016, IRI-2012 and IRI-2007 models in describing the monthly and seasonal mean total electron content (TEC) over the East African region. This is to gain insight into the success of the various model types and versions at characterizing the ionosphere within the equatorial ionization anomaly. TEC derived from five Global Positioning System (GPS) receivers installed at Addis Ababa (ADD, 5.33°N, 111.99°E Geog.), Asab (ASAB, 8.67°N, 116.44°E Geog.), Ambo (ABOO, 5.43°N, 111.05°E Geog.), Nairobi (RCMN, ?4.48°N, 108.46°E Geog.) and Nazret (NAZR, 4.78°N, 112.43°E Geog.), are compared with the corresponding values computed using those models during varying solar activity period (1998 and 2008–2015). We found that different models describe the equatorial and anomaly region ionosphere best depending on solar cycle, season and geomagnetic activity levels. Our results show that IRI-2016 is the best model (compared to others in terms of discrepancy range) in estimating the monthly mean GPS-TEC at NAZR, ADD and RCMN stations except at ADD during 2008 and 2012. It is also found that IRI-2012 is the best model in estimating the monthly mean TEC at ABOO station in 2014. IRI show better agreement with observations during June solstice for all the years studied at ADD except in 2012 where NeQuick2 better performs. At NAZR, NeQuick2 better performs in estimating seasonal mean GPS-TEC during 2011, while IRI models are best during 2008–2009. Both NeQuick2 and IRI models underestimate measured TEC for all the seasons at ADD in 2010 but overestimate at NAZR in 2009 and RCMN in 2008. The periodic variations of experimental and modeled TEC have been compared with solar and geomagnetic indices at ABOO and ASAB in 2014 and results indicate that the F10.7 and sunspot number as indices of solar activity seriously affects the TEC variations with periods of 16–32?days followed by the geomagnetic activity on shorter timescales (roughly periods of less than 16?days). In this case, NeQuick2 derived TEC shows better agreement with a long term period variations of GPS-TEC, while IRI-2016 and IRI-2007 show better agreement with observations during short term periodic variations. This indicates that the dependence of NeQuick2 derived TEC on F10.7 is seasonal. Hence, we suggest that representation of geomagnetic activity indices is required for better performance over the low latitude region.     

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.     

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.     

A regional model for the prediction of M(3000)F2 over East Asia

Research on empirical or physical models of ionospheric parameters is one of the important topics in the field of space weather and communication support services. To improve the accuracy of predicting the monthly median ionospheric propagating factor at 3000 km of the F2 layer (identified as M(3000)F2) for high frequency radio wave propagation, a model based on modified orthogonal temporal–spatial functions is proposed. The proposed model has three new characteristics: (1) The solar activity parameters of sunspot number and the 10.7-cm solar radio flux are together introduced into temporal reconstruction. (2) Both the geomagnetic dip and its modified value are chosen as features of the geographical spatial variation for spatial reconstruction. (3) A series of harmonic functions are used to represent the M(3000)F2, which reflects seasonal and solar cycle variations. The proposed model is established by combining nonlinear regression for three characteristics with harmonic analysis by using vertical sounding data over East Asia. Statistical results reveal that M(3000)F2 calculated by the proposed model is consistent with the trend of the monthly median observations. The proposed model is better than the International Reference Ionosphere (IRI) model by comparison between predictions and observations of six station, which illustrates that the proposed model outperforms the IRI model over East Asia. The proposed method can be further expanded for potentially providing more accurate predictions for other ionospheric parameters on the global scale.     

Evaluation of the improvement of IRI-2016 over IRI-2012 at the India low-latitude region during the ascending phase of cycle 24

This paper investigated the performance of the latest International Reference Ionosphere model (IRI-2016) over that of IRI-2012 in predicting total electron content (TEC) at three different stations in the Indian region. The data used were Global Positioning System (GPS) data collected during the ascending phase of solar cycle 24 over three low-latitude stations in India namely; Bangalore (13.02°N Geographic latitude, 77.57°E Geographic longitude), Hyderabad (17.25°N Geographic latitude, 78.30°E Geographic longitude) and Surat (21.16°N Geographic latitude, 72.78°E Geographic longitude). Monthly, the seasonal and annual variability of GPS-TEC have been compared with those derived from International Reference Ionosphere IRI-2016 and IRI-2012 with two different options of topside electron density: NeQuick and IRI01-corr. It is observed that both versions of IRI (i.e., IRI-2012 and IRI-2016) predict the GPS-TEC with some deviations, the latest version of IRI (IRI-2016) predicted the TEC similar to those predicted by IRI-2012 for all the seasons at all stations except for morning hours (0500 LT to 1000?LT). This shows that the effect of the updated version is seen only during morning hours and also that there is no change in TEC values by IRI-2016 from those predicted by IRI-2012 for the rest of the time of the day in the Indian low latitude region. The semiannual variations in the daytime maximum values of TEC are clearly observed from both GPS and model-derived TEC values with two peaks around March-April and September-October months of each year. Further, the Correlation of TEC derived by IRI-2016 and IRI-2012 with EUV and F10.7 shows similar results. This shows that the solar input to the IRI-2016 is similar to IRI 2012. There is no significant difference observed in TEC, bottom-side thickness (B0) and shape (B1) parameter predictions by both the versions of the IRI model. However, a clear improvement is visible in hmF2 and NmF2 predictions by IRI-2016 to that by IRI-2012. The SHU-2015 option of the IRI-2016 gives a better prediction of NmF2 for all the months at low latitude station Ahmedabad compare to AMTB 2013.     

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM,IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTEC_GPS ? VTEC_Model| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.     

On the performance of IRI-2016 to predict the North-South asymmetry of the equatorial ionization anomaly around 73°E longitude

The ionospheric total electron content (TEC) in both northern and southern Equatorial anomaly regions are examined by using the Global Positioning System (GPS) based TEC measurements around 73°E Longitude in the Asian sector. The TEC contour charts obtained at SURAT (21.16°N; 72.78°E; 12.9°N Geomagnetic Lat.) and DGAR (7.27°S; 72.37°E; 15.3°S Geomagnetic Lat.) over 73°E longitude during a very low solar activity phase (2009) and a moderate solar activity (2012) phase are used in this study. The results show the existence of hemispheric asymmetry and the effects of solar activity on the EIA crest in occurrence time, location and strength. The results are also compared with the TEC derived by IRI-2016 Model and it is found that the North-South asymmetry at the EIA region is clearly depicted by IRI-2016 with some discrepancies (up to 20% in the northern hemisphere at SURAT and up to 40% in the southern hemisphere at DGAR station for June Solstice and up to 10% both for SURAT and DGAR for December Solstice). This discrepancy in the IRI-2016 model is found larger during the year 2012 than that during the solar minimum year 2009 at both the hemispheres. Further, an asymmetry index, (A_i) is determined to illustrate the North-South asymmetry observed in TEC at EIA crest. The seasonal, annual and solar flux dependence of this index are investigated during both solstices and compared with the TEC derived by IRI.     

A comparison of ionosonde measured foF2 and IRI-2016 predictions over China

A comparison of the ionospheric F-region critical frequency (foF2) between ionosonde measurements and IRI-2016 predictions is studied over China during the period from January 2008 to October 2016. Four stations are selected, and the latitude coverage starts at 49.4°N and ends at 23.2°N with a sequential latitude interval of about 10°, the corresponding geomagnetic latitudes are from 39.5°N to 13.2°N. The results show that the variability of the observed foF2 versus latitudes, seasons, local time and levels of solar activity could be well reproduced by IRI-2016. However, the daily lowest value of foF2 from the IRI-2016 prediction occurs earlier than that from the ionosonde. Around the sunrise, the IRI-2016 prediction shows a very sharp rise and grows much faster than the observed foF2 in every month. The foF2 difference between the two options (URSI and CCIR) in IRI-2016 increases as the F10.7 index decreases. During 2008–2009, the annual average deviations of URSI and CCIR range from ?5% to ?10% and from 5% to ?5%, respectively. Generally, the CCIR performs better than URSI during postsunset under low solar activity or in Equatorial Ionization Anomaly (EIA) region over China, while it shows no large difference in performance with URSI in other locations or for other time.     

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.     

Monthly mean foF2 model for an African low-latitude station and comparison with IRI

We have employed the hourly values of the ionospheric F-region critical frequency (foF2) obtained from Ouagadougou ionosonde, Burkina Faso (geographic coordinates 12° N, 1.8° W) during the interval of 1985–1995 (solar cycle 22) and solar radio flux of 10 cm wavelength (F10.7) to develop a local model (LM) for the African low-latitude station. The model was developed from regression analysis method, using the two-segmented regression analysis. We validated LM with foF2 data from Korhogo observatory, Cote d’Ivorie (geographical coordinates 9.3° N, 5.4° W). LM as well as the International Reference Ionosphere (IRI) agrees well with observations. LM gave some improvement on the IRI-predicted foF2 values at the sunrise (06 LT) at all solar flux levels and in all seasons except June solstice. The performance of the models at the representing the salient features of the equatorial foF2 was presented. Considering daytime and nighttime performances, LM and IRI are comparable in low solar activity (LSA), LM performed better than IRI in moderate solar activity (MSA), while IRI performed better than LM in high solar activity (HSA). CCIR has a root mean square error (r.m.s.e), which is only 0.10 MHz lower than that of LM while LM has r.m.s.e, which is about 0.05 MHz lower than that of URSI. In general, our result shows that performance of IRI, especially the CCIR option of the IRI, is quite comparable with the LM. The improved performance of IRI is a reflection of the numerous contributions of ionospheric physicists in the African region, larger volume of data for the IRI and the diversity of data sources, as well as the successes of the IRI task force activities.     

Statistical ionospheric E layer properties measured with the Cyprus Digisonde and comparisons with IRI predictions

This paper reports the diurnal, seasonal, and long term variability of the E layer critical frequency (foE) and peak height (hmE) derived from Digisonde measurements from 2009 to 2016 at the low-middle latitude European station of Nicosia, Cyprus (geographical coordinates: 35°N, 33°E, geomagnetic lat. 29.38°N, I = 51.7°). Manually scaled monthly median values of foE and hmE are compared with IRI-2012 predictions with a view to assess the predictability of IRI. Results show that in general, IRI slightly overestimates foE values both at low and high solar activity. At low solar activity, overestimations are mostly limited to 0.25?MHz (equivalent electron density, 0.775?×?10³?el/m^?3) but can go as high as 0.5?MHz (equivalent electron density, 3.1?×?10³?el/m^?3, during noon) around equinox. In some months, underestimations, though sporadic in nature, up to 0.25?MHz are noted (mostly during sunrise and sunset). At high solar activity, a similar pattern of over-/underestimation is evident. During the entire period of study, over-/under estimations are mostly limited to 0.25?MHz. In very few cases, these exceed 0.25?MHz but are limited to 0.5?MHz. Analysis of hmE reveals that: (1) hmE remains almost constant during ±2 to ±4?h around local noon, (2) hmE values are higher in winter than in spring, summer and autumn, (3) there are two maxima near sunrise and sunset with a noontime minimum in between. During the entire period of study, significant differences between observed hmE and the IRI predictions have been noted. IRI fails to predict hmE and outputs a constant value of 110?km, which is higher than most of the observed values. Over- and under estimations range from 3 to 13?km and from 0 to 3?km respectively.     

Comparing the improved Di Giovanni/Radicella model with sounding-based electron density profiles and with the IRI model

The Di Giovanni/Radicella model (DGR) /1/ determines a bottom side electron densty profile alone from the set of routinely scaled ionogram parameters foE, foF1, foF2 and M(3000)F2 and the total electron content; the smoothed sunspot number R12 appears in the calculation. Present designations are DGR2/2/ and DRR3 /3/ [see Appendix]; they are valid in the northern hemisphere. DGR is compared with electron density profiles derived from ionograms obtained at Juliusruh (54.6°N, 13.4°E), and with the (URSI-based) IRI90 at different conditiones. Experimental total electron content (TEC) data are compared to both models. At the considered station, the profiles obtained by both models are reasonably in agreement amongst themselves and with the experimental data.

The TEC derived from the DGR3 model is in good agreement with experimental TEC, whereas, at high solar activity, IRI90 gives too high TEC values, especially during daytime.     

Variability of the behavior of the bottomside (B0, B1) parameters obtained from the ground-based ionograms at China’s low latitude station

In this paper, data of (B0, B1) parameters deduced from the electron density profiles that are inverted from the ionograms recorded at Hainan (19.4°N, 109.0°E), China during a three year period from March 2002 to February 2005 are used to study the diurnal and seasonal variation of (B0, B1) parameters at low latitude. The observational results are compared with the IRI2001 model predictions. Variability study of (B0, B1) in terms of percentage ratio of the inter-quartiles to the median values and correlative analysis between (B0, B1) parameters and other ionospheric characteristics such as hmF2 and M(3000)F2 are also made. Our present study showed that: (1) for daytime hours, the IRI2001 model results with new table option (B0_Tab) is in a better agreement with the observational results (B0_Obs) than the IRI2001 model results with Gulyaeva option (B0_Gul) for summer season, whereas B0_Gul is in a better agreement with B0_Obs than B0_Tab for winter season. For nighttime, in general, B0_Gul is in a better agreement with B0_Obs than B0_Tab. For other occasions, both B0_Tab and B0_Gul showed some systematic deviations from the observational ones. Moreover, the deviations of B0_Tab and B0_Gul from B0_Obs showed opposite trends; (2) the monthly upper (lower) quartiles of (B0, B1) parameter showed a good linear relationship with the monthly median values, this makes it possible to do the regression analysis between the monthly upper (lower) quartiles and the monthly median values, which can give a measure of the variability of these parameters. In terms of the percentage ratio of the inter-quartiles to the median values, the variability of B0 showed a diurnal variation ranging between 22% and 36% with maximum value occurring at pre-sunrise hours, whereas the variability of B1 showed a diurnal variation ranging between 15% and 30% with higher value by daytime than at night; (3) B0 shows high linear correlative relationships with hmF2 and M(3000)F2 for most of the local time period of a day except for a few hours around midnight, whereas B1 showed high linear correlations with B0, hmF2 for daytime hours, but not for nighttime hours. This suggests that it maybe is possible to obtain the synthetic database of (B0, B1) parameter or to construct the model of (B0, B1) using database of hmF2 or M(3000)F2 which is much easier to obtain from experimental measurements.     

IRI 2001/90 TEC predictions over a low latitude station

Total electron content (TEC) over Tucumán (26.9°S, 294.6°W) measured with Faraday technique during the high solar activity year 1982, is used to check IRI 2001 TEC predictions at the southern crest of the equatorial anomaly region. Comparisons with IRI 90 are also made. The results show that in general IRI overestimates TEC values around the daily minimum and underestimates it the remaining hours. Better predictions are obtained using ground ionosonde measurements as input coefficients in the IRI model. The results suggest that for hours of maximum TEC values the electron density profile is broader than that assumed by the model. The main reason for the disagreement would be the IRI shape of the electron density profile.     

Cross-hemisphere comparison of mid-latitude ionospheric variability during 1996–2009: Juliusruh vs. Hobart

Analysis of a long-time series of hourly median characteristics of the ionospheric plasma at two mid-latitude locations in the Northern and Southern hemisphere, Juliusruh (54.6N; 13.4E) and Hobart (42.9S; 147.3E), reveals patterns of their synchronous and independent variability. We studied timelines of GPS vTEC, ionogram-derived F2-layer peak electron density NmF2, ionospheric equivalent slab thickness τ, and their ratios at two locations during the complete 23rd solar cycle and its following period of the extremely low solar activity in 2008–2009. This study has also involved the comparative analysis of the observed data versus the model predictions by IRI-2012. During the high solar activity in 2000–2002, seasonal variations show a complicated cross-hemisphere behavior influenced by the winter and semi-annual anomalies, with the largest noon-time values of TEC and NmF2 observed around equinoxes. Strength of the winter anomaly in NmF2 was significantly greater at Juliusruh in comparison with Hobart. The winter anomaly in GPS vTEC values was much weaker than in NmF2 for the Northern hemisphere mid-latitudes and was entirely absent at the Southern hemisphere. Cross-hemisphere analysis of the equivalent slab thickness shows its clear seasonal dependence for all levels of solar activity: the day-time maximum τ_max is observed during local summer, whereas the day-time minimum τ_min is observed during local winter. The night-time values of τ were higher compared to the day-time values during the winter and equinox seasons. Comparative model-data study shows rather good IRI performance of the day-time NmF2 for mid-latitudes of both hemispheres and rather noticeable overestimations for the mid-night NmF2 values during high solar activity. Analysis of IRI vTEC demonstrates the model limitations, related with the absence of the plasmaspheric part, and actual demand in a reliable and standard ionosphere–plasmasphere model for analysis of GPS vTEC.