Highlights about the performances of storm-time TEC modelling techniques for low/equatorial and mid-latitude locations

A statistical evaluation of storm-time total electron content (TEC) modelling techniques over various latitudes of the African sector and surrounding areas is presented. The source of observational TEC data used in this study is the Global Navigation Satellite Systems (GNSS), specifically the Global Positioning Systems (GPS) receiver networks. For each selected receiver station, three different storm-time models based on empirical orthogonal functions (EOF) analysis, non-linear regression analysis (NLRA) and Artificial neural networks (ANN), were implemented. Storm-time GPS TEC data used for both development and validation of the models was selected based on the storm criterion of $\mathit{Dst}?-50$ nT or $K_p?4$ to take into account both coronal mass ejections (CMEs) and co-rotating interaction regions (CIRs) driven storms, respectively. To make an independent test of the models, storm periods considered for validation were excluded from datasets used during the implementation of the models and results are compared with observations, monthly median values, and International Reference Ionosphere (IRI-2016) predictions. Considering GPS TEC as reference, a statistical analysis performed over six storm periods reserved for validation revealed that ANN model is about 10%, 26%, and 58% more accurate than EOF, NLRA, and IRI models, respectively. It was further found that, EOF model performs 15%, and 44% better than NLRA, and IRI models, respectively, while NLRA is 25% better than IRI. On the other hand, results are also discussed referring to the background ionosphere represented by monthly median TEC (MM TEC) and statistics are provided. Moreover, strengths and weaknesses of each model are highlighted.     

Traveling ionospheric disturbances observed at South African midlatitudes during the 29–31 October 2003 geomagnetically disturbed period

This paper presents traveling ionospheric disturbances (TIDs) observations from GPS measurements over the South African region during the geomagnetically disturbed period of 29–31 October 2003. Two receiver arrays, which were along two distinct longitudinal sectors of about 18°-20° and 27°-28° were used in order to investigate the amplitude, periods and virtual propagation characteristics of the storm induced ionospheric disturbances. The study revealed a large sudden TEC increase on 28 October 2003, the day before the first of the two major storms studied here, that was recorded simultaneously by all the receivers used. This pre-storm enhancement was linked to an X-class solar flare, auroral/magnetospheric activities and vertical plasma drift, based on the behaviour of the geomagnetic storm and auroral indices as well as strong equatorial electrojet. Diurnal trends of the TEC and foF2 measurements revealed that the geomagnetic storm caused a negative ionospheric storm; these parameters were depleted between 29 and 31 October 2003. Large scale traveling ionospheric disturbances were observed on the days of the geomagnetic storms (29 and 31 October 2003), using line-of-sight vertical TEC (vTEC) measurements from individual satellites. Amplitude and dominant periods of these structures varied between 0.08–2.16 TECU, and 1.07–2.13 h respectively. The wave structures were observed to propagate towards the equator with velocities between 587.04 and 1635.09 m/s.     

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.     

The ionospheric response to high-intensity long duration continuous AE activity (HILDCAA) event (13–15 April 2005) over mid-latitude African region

The ionospheric responses to High-Intensity Long Duration Continuous Auroral Electrojet Activity (HILDCAA) event which happened following the CIR-driven storm were studied over the southern hemisphere mid-latitude in the African sector. The 13–15 April 2005 event was analysed to understand some of the mechanisms responsible for the ionospheric changes during HILDCAA event. The ionosonde critical frequency of F2 layer (foF2) and Global Navigation Satellite System (GNSS) Total Electron Content (TEC) were used to analyse the ionospheric responses. The daytime increase in foF2 and TEC values were observed on 13 April 2005. The TEC and foF2 enhancement could be attributed to Large Scale Traveling Ionospheric Disturbances (LSTIDs), increase in thermospheric neutral composition changes, Prompt Penetration Electric Field (PPEF) and an expansion of Equatorial Ionization Anomaly (EIA) to the mid-latitude.     

Modelling ionospheric vertical drifts over Africa low latitudes using Empirical Orthogonal functions and comparison with climatological model

For the first time, empirical model of daytime vertical $\mathbf{E}\times \mathbf{B}$ drift based on Empirical Orthogonal functions (EOF) decomposition technique is presented. Day-to-day variability of $\mathbf{E}\times \mathbf{B}$ drift inferred from horizontal (H) geomagnetic field data around dip latitude for the period of 2008–2013 is used to both develop and validate the model. Results show that the EOF technique is promising with modelled values and data giving correlation coefficient values of at least 0.90 for geomagnetic conditions of both $K_p?3$ and $K_p>3$ within 2008–2013. Independent model validation shows that in situ $\mathbf{E}\times \mathbf{B}$ values from ion velocity meter (IVM) instrument on-board C/NOFS satellite are closer to model $\mathbf{E}\times \mathbf{B}$ estimates than the climatological Scherliess-Fejer (SF) model incorporated within the International Reference Ionosphere (IRI).     

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.     

Climatology of ionospheric scintillations and TEC trend over the Ugandan region

This study presents results on the investigation of the diurnal, monthly and seasonal variability of Total Electron Content (TEC), phase (_σΦ${\sigma }_{\mathrm{\Phi }}$) and amplitude (S4) scintillation indices over Ugandan (Low latitude) region. Scintillation Network Decision Aid (SCINDA) data was obtained from Makerere (0.34°N, 32.57°E) station, Uganda for two years (2011 and 2012). Data from two dual frequency GPS receivers at Mbarara (0.60°S, 30.74°E) and Entebbe (0.04°N, 32.44°E) was used to study TEC climatology during the same period of scintillation study. The results show that peak TEC values were recorded during the months of October–November, and the lowest values during the months of July–August. The diurnal peak of TEC occurs between 10:00 and 14:00 UT hours. Seasonally, the ascending and descending phases of TEC were observed during the equinoxes (March and September) and solstice (June and December), respectively. The scintillations observed during the study were classified as weak (0.1≤$\le$S4,_σΦ≤${\sigma }_{\mathrm{\Phi }}\le$0.3) and strong (0.3<$<$S4,_σΦ≤${\sigma }_{\mathrm{\Phi }}\le$1.0). The diurnal scintillation pattern showed peaks between 17:00 and 22:00 UT hour, while the seasonal pattern follows the TEC pattern mentioned above. Amplitude scintillation was more dominant than phase scintillation during the two years of the study. Scintillation peaks occur during the months of March–April and September–October, while the least scintillations occur during the months of June–July. Therefore, the contribution of this study is filling the gap in the current documentation of amplitude scintillation without phase scintillation over the Ugandan region. The scintillations observed have been attributed to wave-like structures which have periods of about 2–3 h, in the range of that of large scale travelling ionospheric disturbances (LSTIDs).     

Multi-instrument observations of large-scale atmospheric gravity waves/traveling ionospheric disturbances associated with enhanced auroral activity over Svalbard

This study reports on observations of large-scale atmospheric gravity waves/traveling ionospheric disturbances (AGWs/TIDs) using Global Positioning System (GPS) total electron content (TEC) and Fabry–Perot Interferometer’s (FPI’s) intensity of oxygen red line emission at 630?nm measurements over Svalbard on the night of 6 January 2014. TEC large-scale TIDs have primary periods ranging between 29 and 65?min and propagate at a mean horizontal velocity of $～$749–761?m/s with azimuth of $～$345–347$°$ (which corresponds to poleward propagation direction). On the other hand, FPI large-scale AGWs have larger periods of $～$42–142?min. These large-scale AGWs/TIDs were linked to enhanced auroral activity identified from co-located all-sky camera and IMAGE magnetometers. Similar periods, speed and poleward propagation were found for the all-sky camera ($～$60–97?min and $～$823?m/s) and the IMAGE magnetometers ($～$32–53?min and $～$708?m/s) observations. Joule heating or/and particle precipitation as a result of auroral energy injection were identified as likely generation mechanisms for these disturbances.     

The variability and IRI2007-predictability of hmF2 over South Africa

This paper presents an investigation into the variability and predictability of the maximum height of the ionospheric F2 layer, hmF2 over the South African region. Data from three South African stations, namely Madimbo (22.4°S, 26.5°E, dip angle: −61.47°), Grahamstown (33.3°S, 26.5°E, dip angle: −64.08°) and Louisvale (28.5°S, 21.2°E, dip angle: −65.44°) were used in this study. The results indicate that hmF2 shows a larger variability around midnight than during the daytime for all seasons. Monthly median hmF2 values were used in all cases and were compared with predictions from the IRI-2007 model, using the URSI (Union Radio-Scientifique Internationale) coefficient option. The analysis covers the diurnal and seasonal hourly hmF2 values for the selected months and time sectors e.g. January, July, April and October for 2003 and 2005. The time ranges between (03h00–23h00 UT; LT = UT + 2h) representing the local sunrise, midday, sunset and midnight hours. The time covers sunrise, midday, sunrise, and midnight hours (03–06h00 UT, 07–11h00 UT, sunrise 16–18h00 UT and 22–23h00 UT; LT = UT + 2h). The dependence of the results on solar activity levels was also investigated. The IRI-2007 predictions follow fairly well the diurnal and seasonal variation patterns of the observed hmF2 values at all the stations. However, the IRI-2007 model overestimates and underestimates the hmF2 value during different months for all the solar activity periods.