Thesis (Ph. D. (Physics and Electronics))--Rhodes University, 2006.

This thesis describes the development and application of a neural network based ionospheric model for the bottomside electron density profile over Grahamstown, South Africa. All available ionospheric data from the archives of the Grahamstown (33.32ºS, 26.50ºE) ionospheric station were used for training neural networks (NNs) to predict the parameters required to produce the final profile. Inputs to the model, called the LAM model, are day number, hour, and measures of solar and magnetic activity. The output is a mathematical description of the bottomside electron density profile for that particular input set. The two main ionospheric layers, the E and F layers, are predicted separately and then combined at the final stage. For each layer, NNs have been trained to predict the individual ionospheric characteristics and coefficients that were required to describe the layer profile. NNs were also applied to the task of determining the hours between which an E layer is measurable by a groundbased ionosonde and the probability of the existence of an F1 layer. The F1 probability NN is innovative in that it provides information on the existence of the F1 layer as well as the probability of that layer being in a L-condition state - the state where an F1 layer is present on an ionogram but it is not possible to record any F1 parameters. In the event of an L-condition state being predicted as probable, an L algorithm has been designed to alter the shape of the profile to reflect this state. A smoothing algorithm has been implemented to remove discontinuities at the F1-F2 boundary and ensure that the profile represents realistic ionospheric behaviour in the F1 region. Tests show that the LAM model is more successful at predicting Grahamstown electron density profiles for a particular set of inputs than the International Reference Ionosphere (IRI). It is anticipated that the LAM model will be used as a tool in the pin-pointing of hostile HF transmitters, known as single-site location.

This thesis describes the development of an ionospheric map for the South African region using the current available resources. The International Reference Ionosphere (IRI) model, the South African Bottomside Ionospheric Model (SABIM), and measurements from ionosondes in the South African Ionosonde Network, were incorporated into the map. An accurate ionospheric map depicting the foF2 and hmF2 parameters as well as electron density profiles at any location within South Africa is a useful tool for, amongst others, High Frequency (HF) communicators and space weather centers. A major product of the work is software, written in MATLAB, which produces spatial and temporal representations of the South African ionosphere. The map was validated and demonstrated for practical application, since a significant aim of the project was to make the map as applicable as possible. It is hoped that the map will find immense application in HF radio communication industries, research industries, aviation industries, and other industries that make use of Earth-Space systems. A potential user of the map is GrinTek Ewation (GEW) who is currently evaluating it for their purposes

Modeling the ionosphere, where the effects of solar dynamo becomes more effective to space based and ground borne activities, has an undeniable importance for telecommunication and navigation purposes. Mid-latitude electron density trough is an interesting phenomenon in characterizing the behavior of the ionosphere, especially during disturbed conditions. Modeling the mid-latitude electron density trough is a very popular research subject which has been studied by several researchers until now. In this work, an operational technique has been developed for a probabilistic space weather forecast using fuzzy modeling and computer based detection of trough in two steps. First step is to detect the appropriate geomagnetical conditions for trough formation, depending on the values of 3-h planetary K index (Kp), magnetic season, latitude and local time, by using fuzzy modeling technique. Once the suitable geomagnetic conditions are detected, second step is to find the lower latitude position (LLP) and minimum position (MP) of the observed trough being two main identifiers of the mid-latitude electron density trough. A number of case studies were performed on ARIEL 4 satellite data, composed of different geomagnetic, annual and diurnal characteristics. The results obtained from fuzzy modeling show that the model is able to detect the appropriate conditions for trough occurrence and the trough shape was effectively identified for each selected case by using the predefined descriptions of mid-latitude electron density trough. The overall results are observed to be promising.

Thesis (M.Sc. (Physics & Electronics)) - Rhodes University, 2008. / A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science.

Thesis (Ph.D. (Physics & Electronics)) - Rhodes University, 2009 / A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science

This thesis describes an analysis of the F1 layer data obtained from the Grahamstown (33.32°S, 26.500 E), South Africa ionospheric station and the use of this data in improving a Neural Network (NN) based model of the F1 layer of the ionosphere. An application for real-time ray tracing through the South African ionosphere was identified, and for this application real-time evaluation of the electron density profile is essential. Raw real-time virtual height data are provided by a Lowell Digisonde (DPS), which employs the automatic scaling software, ARTIST whose output includes the virtual-toreal height data conversion. Experience has shown that there are times when the ray tracing performance is degraded because of difficulties surrounding the real-time characterization of the F1 region by ARTIST. Therefore available DPS data from the archives of the Grahamstown station were re-scaled manually in order to establish the extent of the problem and the times and conditions under which most inaccuracies occur. The re-scaled data were used to update the F1 contribution of an existing NN based ionospheric model, the LAM model, which predicts the values of the parameters required to produce an electron density profile. This thesis describes the development of three separate NNs required to predict the ionospheric characteristics and coefficients that are required to describe the F1 layer profile. Inputs to the NNs include day number, hour and measures of solar and magnetic activity. Outputs include the value of the critical frequency of the F1 layer, foF1, the real height of reflection at the peak, hmFl, as well as information on the state of the F1 layer. All data from the Grahamstown station from 1973 to 2003 was used to train these NNs. Tests show that the predictive ability of the LAM model has been improved by incorporating the re-scaled data.

This thesis presents research pertaining to the development of an African Ionospheric Map (AIM). An ionospheric map is a computer program that is able to display spatial and temporal representations of ionospheric parameters such as, electron density and critical plasma frequencies, for every geographical location on the map. The purpose of this development was to make the most optimum use of all available data sources, namely ionosondes, satellites and models, and to implement error minimisation techniques in order to obtain the best result at any given location on the African continent. The focus was placed on the accurate estimation of three upper atmosphere parameters which are important for radio communications: critical frequency of the F2 layer (foF2), Total Electron Content (TEC) and the maximum usable frequency over a distance of 3000 km (M3000F2). The results show that AIM provided a more accurate estimation of the three parameters than the internationally recognised and recommended ionosphere model (IRI-2012) when used on its own. Therefore, the AIM is a more accurate solution than single independent data sources for applications requiring ionospheric mapping over the African continent.

Global Positioning System (GPS) networks provide an opportunity to study the dynamics and continuous changes in the ionosphere by supplementing ionospheric measurements which are usually obtained by various techniques such as ionosondes, incoherent scatter radars and satellites. Total electron content (TEC) is one of the physical quantities that can be derived from GPS data, and provides an indication of ionospheric variability. This thesis presents 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. Three South African locations were identified and used in the development of an input space and NN architecture for the model. The input space includes the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), and magnetic index(measure of the magnetic activity). An attempt to study the effects of solar wind on TEC variability was carried out using the Advanced Composition Explorer (ACE) data and it is recommended that more study be done using low altitude satellite data. An analysis was done by comparing predicted NN TEC with TEC values from the IRI2001 version of the International Reference Ionosphere (IRI), validating GPS TEC with ionosonde TEC (ITEC) and assessing the performance of the NN model during equinoxes and solstices. Results show that NNs predict GPS TEC more accurately than the IRI at South African GPS locations, but that more good quality GPS data is required before a truly representative empirical GPS TEC model can be released.

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.