The International Reference Ionosphere (IRI) shape parameters B0, B1, and D1 provide a representation of the shape of the F2 layer, the thickness of the F2 layer and the shape of the F1 layer of the ionosphere respectively. The aim of this study was to examine the variability of these parameters using Grahamstown, South Africa (33.3±S, 26.5±E) ionosonde data and determine their predictability by the IRI-2001 model. A further aim of this study was to investigate developing an alternative model for predicting these parameters. These parameters can be determined from electron density profiles that are inverted from ionograms recorded with an ionosonde. Data representing the B0, B1 and D1 parameters, with half hourly or hourly intervals, were scaled and deduced from the digital pulse sounder (DPS) ionosonde for the period April 1996 to December 2006. An analysis of the diurnal, seasonal, and solar variations of the behaviour of these parameters was undertaken for the years 2000, 2004 and 2005 using monthly medians. Comparisons between the observational results and that of the IRI model (IRI 2001 version) indicate that the IRI-2001 model does not accurately represent the diurnal and seasonal variation of the parameters. A preliminary model was thus developed using the technique of Neural Networks (NNs). All available data from the Grahamstown ionosonde from 1996 to 2006 were used in the training of the NNs and the prediction of the variation of the shape parameters. Inputs to the model were the day number, the hour of day, the solar activity and the magnetic index. Comparisons between the preliminary NN model and the IRI-2001 model indicated that the preliminary model was more accurate at the prediction of the parameters than the IRI-2001 model. This analysis showed the need to improve the existing IRI model or develop a new model for the South African region. This thesis describes the results from this feasibility study which show the variability and predictability of the IRI shape parameters.

This thesis presents my research on the development of a neural network (NN) based global empirical model of the ionospheric F2 region peak electron density using extended geophysically relevant inputs. The main principle behind this approach has been to utilize parameters other than simple geographic co-ordinates, on which the F2 peak electron density is known to depend, and to exploit the technique of NNs, thereby establishing and modeling the non-linear dynamic processes (both in space and time)associated with the F2 region electron density on a global scale. Four different models have been developed in this work. These are the foF2 NN model, M(3000)F2 NN model, short-term forecasting foF2 NN, and a near-real time foF2 NN model. Data used in the training of the NNs were obtained from the worldwide ionosonde stations spanning the period 1964 to 1986 based on availability, which included all periods of calm and disturbed magnetic activity. Common input parameters used in the training of all 4 models are day number (day of the year, DN), Universal Time (UT), a 2 month running mean of the sunspot number (R2), a 2 day running mean of the 3-hour planetary magnetic index ap (A16), solar zenith angle (CHI), geographic latitude (q), magnetic dip angle (I), angle of magnetic declination (D), angle of meridian relative to subsolar point (M). For the short-term and near-real time foF2 models, additional input parameters related to recent past observations of foF2 itself were included in the training of the NNs. The results of the foF2 NN model and M(3000)F2 NN model presented in this work, which compare favourably with the IRI (International Reference Ionosphere) model successfully demonstrate the potential of NNs for spatial and temporal modeling of the ionospheric parameters foF2 and M(3000)F2 globally. The results obtained from the short-term foF2 NN model and nearreal time foF2 NN model reveal that, in addition to the temporal and spatial input variables, short-term forecasting of foF2 is much improved by including past observations of foF2 itself. Results obtained from the near-real time foF2 NN model also reveal that there exists a correlation between measured foF2 values at different locations across the globe. Again, comparisons of the foF2 NN model and M(3000)F2 NN model predictions with that of the IRI model predictions and observed values at some selected high latitude stations, suggest that the NN technique can successfully be employed to model the complex irregularities associated with the high latitude regions. Based on the results obtained in this research and the comparison made with the IRI model (URSI and CCIR coefficients), these results justify consideration of the NN technique for the prediction of global ionospheric parameters. I believe that, after consideration by the IRI community, these models will prove to be valuable to both the high frequency (HF) communication and worldwide ionospheric communities.

Fourier spectral and pseudospectral methods are used in numerical modeling of ionospheric processes, namely macroscopic evolution of naturally and artificially created ionospheric density irregularities. The simulation model consists of two-dimensional electrostatic nonlinear fluid plasma equations that describe the plasma evolution. The spectral and pseudospectral methods are used to solve the spatial dependence of these self-consistent equations. They are chosen over the widely used finite difference and finite element techniques since spectral methods are straightforward to implement on nonlinear equations. They are at least as accurate as finite difference simulations. A potential equation solver is developed to solve the nonlinear potential equation iteratively. Time integration is accomplished using a combination of leapfrog and leapfrog-trapezoidal methods. A FORTRAN program is developed to implement the simulation model. All calculations are performed in the Fourier domain. The simulation model is tested by considering three types of problems. This is accomplished by specifying an initial density (Pedersen conductivity) profile that represents slab model density, density enhancement (due to releases such as barium), or density depletion (due to late times effects of electron attachment material releases) in the presence of a neutral wind. The evolution of the irregularities is monitored and discussed. The simulation results agree with similar results obtained using finite difference methods. A comparison is made between the ionospheric depletion and enhancement problems. Our results show that, given the same parameters and perturbation level, the depletion profiles bifurcate much faster than that of the enhancement. We argue that this is due to the larger growth rate in the E X B interchange instability of the density depletion case. / Master of Science