This thesis creates a basic framework and provides the information necessary to create a more accurate description of the topside ionosphere in terms of the altitude variation of the electron density (Ne) over the South African region. The detailed overview of various topside ionospheric modelling techniques, with specific emphasis on their implications for the efforts to model the South African topside, provides a starting point towards achieving the goals. The novelty of the thesis lies in the investigation of the applicabilityof three different techniques to model the South African topside ionosphere: (1) The possibility of using Artificial Neural Network (ANN) techniques for empirical modelling of the topside ionosphere based on the available, however irregularly sampled, topside sounder measurements. The goal of this model was to test the ability of ANN techniques to capture the complex relationships between the various ionospheric variables using irregularly distributed measurements. While this technique is promising, the method did not show significant improvement over the International Reference Ionosphere (IRI) model results when compared with the actual measurements. (2) Application of the diffusive equilibrium theory. Although based on sound physics foundations, the method only operates on a generalised level leading to results that are not necessarily unique. Furthermore, the approach relies on many ionospheric variables as inputs which are derived from other models whose accuracy is not verified. (3) Attempts to complement the standard functional techniques, (Chapman, Epstein, Exponential and Parabolic), with Global Positioning System (GPS) and ionosonde measurements in an effort to provide deeper insights into the actual conditions within the ionosphere. The vertical Ne distribution is reconstructed by linking together the different aspects of the constituent ions and their transition height by considering how they influence the shape of the profile. While this approach has not been tested against actual measurements, results show that the method could be potentially useful for topside ionospheric studies. Due to the limitations of each technique reviewed, this thesis observes that the employment of an approach that incorporates both theoretical onsiderations and empirical aspects has the potential to lead to a more accurate characterisation of the topside ionospheric behaviour, and resulting in improved models in terms of reliability and forecasting ability. The point is made that a topside sounder mission for South Africa would provide the required measured topside ionospheric data and answer the many science questions that this region poses as well as solving a number of the limitations set out in this thesis.

The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.

The South African Global Navigation Satellite System (GNSS) network of dual frequency receivers provide an opportunity to investigate solar cycle effects on ionospheric Total Electron Content (TEC) over the South Africa region by taking advantage of the dispersive nature of the ionospheric medium. For this task, the global University of New Brunswick Ionospheric Modelling Technique (UNB-IMT) was adopted, modified and applied to compute TEC using data from the southern African GNSS Network. TEC values were compared with CODE International GNSS services TEC predictions and Ionosonde-derived TEC (ITEC) measurements to test and validate the UNB-IMT results over South Africa. It was found that the variation trends of GTEC and ITEC over all stations are in good agreement and show pronounced seasonal variations with high TEC values around equinoxes for a year near solar maximum and less pronounced around solar minimum. Signature TEC depletions and enhanced spikes were prevalently evident around equinoxes, particularly for a year near solar maximum. These observations were investigated and further discussed with an analysis of the midday Disturbance Storm Time (DST) index of geomagnetic activity. The residual GTEC – ITEC corresponding to plasmaspheric electron content and equivalent ionospheric foF2 and total slab thickness parameters were computed and comprehensively discussed. The results verified the use of UNB-IMT as one of the tools for ionospheric research over South Africa. The UNB-IMT algorithm was applied to investigate TEC variability during different epochs of solar cycle 23. The results were investigated and further discussed by analyzing the GOES 8 and 10 satellites X-ray flux (0.1 – 0.8 nm) and SOHO Solar Extreme Ultraviolet Monitor higher resolution data. Comparison of UNB-IMT TEC derived from collocated HRAO and HARB GNSS receivers was undertaken for the solar X17 and X9 flare events, which occurred on day 301, 2003 and day 339, 2006. It was found that there exist considerable TEC differences between the two collocated receivers with some evidence of solar cycle dependence. Furthermore, the daytime UNB TEC compared with the International Reference Ionosphere 2001 predicted TEC found both models to show a good agreement. The UNB-IMT TEC was further applied to investigate the capabilities of geodetic Very Long Baseline Interferometry (VLBI) derived TEC using the Vienna TEC Model for space weather monitoring over HartRAO during the CONT02 and CONT05 campaigns conducted during the years 2002 (near solar maximum) and 005 (near solar minimum). The results verified the use of geodetic VLBI as one of the possible instruments for monitoring space weather impacts on the ionosphere over South Africa.

In this thesis an ionospheric tomographic algorithm called Multi-Instrument Data Anal- ysis System (MIDAS) is used to reconstruct electron density profiles using the Global Positioning System (GPS) data recorded from 53 GPS receivers over the South African region. MIDAS, developed by the Invert group at the University of Bath in the UK, is an inversion algorithm that produces a time dependent 3D image of the electron density of the ionosphere. GPS receivers record the time delay and phase advance of the trans- ionospheric GPS signals that traverse through the ionosphere from which the ionospheric parameter called Total Electron Content (TEC) can be computed. TEC, the line integral of the electron density along the satellite-receiver signal path, is ingested by ionospheric tomographic algorithms such as MIDAS to produce a time dependent 3D electron density profile. In order to validate electron density profiles from MIDAS, MIDAS derived NmF2 values were compared with ionosonde derived NmF2 values extracted from their respective 1D electron density profiles at 15 minute intervals for all four South African ionosonde stations (Grahamstown, Hermanus, Louisvale, and Madimbo). MIDAS 2D images of the electron density showed good diurnal and seasonal patterns; where a comparison of the 2D images at 12h00 UT for all the validation days exhibited maximum electron concentration during the autumn and summer and a minimum during the winter. A root mean square error (rmse) value as small as 0.88x 10¹¹[el=m³] was calculated for the Louisvale ionosonde station during the winter season and a maximum rmse value of 1.92x 10¹¹[el=m³] was ob- tained during the autumn season. The r² values were the least during the autumn and relatively large during summer and winter; similarly the rmse values were found to be a maximum during the autumn and a minimum during the winter indicating that MIDAS performs better during the winter than during the autumn and spring seasons. It is also observed that MIDAS performs better at Louisvale and Madimbo than at Grahamstown and Hermanus. In conclusion, the MIDAS reconstruction has showed good agreement with the ionosonde measurements; therefore, MIDAS can be considered a useful tool to study the ionosphere over the South African region.