Evaluating the IRI topside model for the South African region: An overview of the modelling techniques

The representation of the topside ionosphere (the region above the F2 peak) is critical because of the limited experimental data available. Over the years, a wide range of models have been developed in an effort to represent the behaviour and the shape of the electron density (Ne) profile of the topside ionosphere. Various studies have been centred around calculating the vertical scale height (VSH) and have included (a) obtaining VSH from Global Positioning System (GPS) derived total electron content (TEC), (b) calculating the VSH from ground-based ionosonde measurements, (c) using topside sounder vertical Ne profiles to obtain the VSH. One or a combination of the topside profilers (Chapman function, exponential function, sech-squared (Epstein) function, and/or parabolic function) is then used to reconstruct the topside Ne profile. The different approaches and the modelling techniques are discussed with a view to identifying the most adequate approach to apply to the South African region’s topside modelling efforts. The IRI-2001 topside model is evaluated based on how well it reproduces measured topside profiles over the South African region. This study is a first step in the process of developing a South African topside ionosphere model

Developing an ionospheric map for South Africa

The development of a map of the ionosphere over South Africa is presented in this paper. The International Reference Ionosphere (IRI) model, South African Bottomside Ionospheric Model (SABIM), and measurements from ionosondes in the South African Ionosonde Network, were combined within their own limitations to develop an accurate representation of the South African ionosphere. The map is essentially in the form of a computer program that shows spatial and temporal representations of the South African ionosphere for a given set of geophysical parameters. A validation of the map is attempted using a comparison of Total Electron Content (TEC) values derived from the map, from the IRI model, and from Global Positioning System (GPS) measurements. It is foreseen that the final South African ionospheric map will be implemented as a Space Weather product of the African Space Weather Regional Warning Centre

Neural network-based ionospheric modelling over the South African region

During the past decade, South African scientists have pioneered research in the field of ionospheric modelling using the technique of neural networks (NNs). Global ionospheric models have always been insufficient for the South African region owing to an historical paucity of available data. Within the past 10 years, however, three new ionospheric sounders have been installed locally and are operating continuously. These sounders are located at Grahamstown (33.3°S, 26.5°E), Louisvale (28.5°S, 21.2°E) and Madimbo (22.4°S, 30.9°E). The addition of a modern sounder at Grahamstown enlarged the ionospheric database for this station to 30 years, making this archive a considerable asset for ionospheric research. Quality control and online availability of the data has also added to its attraction. An important requirement for empirical modelling, but especially for employing NNs, is a large database describing the history of the relationship between the ionosphere and the geophysical parameters that define its behaviour. This review describes the path of South African ionospheric modelling over the past 10 years, the role of NNs in this development, the international collaborations that have arisen from this, and the future of ionospheric modelling in South Africa

A new empirical model for the peak ionospheric electron density using neural networks

This thesis describes the search for a temporal model for predicting the peak ionospheric electron density-(foF2). Existing models, such as the International Reference Ionosphere (IRI) and 8KYCOM, were used to predict the 12 noon foF2 value over Grahamstown (26°E, 33°8). An attempt was then made to find a model that would improve upon these results. The traditional method of linear regression was used as a first step towards a new model. It was found that this would involve a multi variable regression that is reliant on guessing the optimum variables to be used in the final equation. An extremely complicated modelling equation involving many terms would result. Neural networks (NNs) are introduced as a new technique for predicting foF2. They are also applied, for the first time, to the problem of determining the best predictors of foF2. This quantity depends upon day number, level of solar activity and level of magnetic activity. The optimum averaging lengths of the solar activity index and the magnetic activity index were determined by appling NNs, using the criterion that the best indices are those that give the lowest rms error between the measured and predicted foF2. The optimum index for solar activity was found to be a 2-month running mean value of the daily sunspot number and for magnetic activity a 2-day averaged A index was found to be optimum. In addition, it was found that the response of foF2 to magnetic activity changes is highly non-linear and seasonally dependent. Using these indices as inputs, the NN trained successfully to predict foF2 with an rms error of 0.946 MHz on the daily testing values. Comparison with the IRI showed an improvement of 40% on the rms error. It is also shown that the NN will predict the noon value of foF2 to the same level of accuracy for unseen data of the same type

An analysis of automatically scaled F1 layer data over Grahamstown, South Africa

This paper describes an analysis of automatically scaled F1 layer data over Grahamstown, South Africa (33.3°S, 26.5°E). An application for real time raytracing 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-to-real height data conversion. Experience has shown that there are times when the raytracing performance is degraded because of difficulties surrounding the real time characterisation of the F1 region by ARTIST. The purpose of this investigation is to establish the extent of the problem, the times and conditions under which it occurs, with a view to formulating remedial alternative strategies, such as predictive modelling

GPS TEC and ionosonde TEC over Grahamstown, South Africa: first comparisons

The Grahamstown, South Africa (33.3°S, 26.5°E) ionospheric field station operates a UMass Lowell digital pulse ionospheric sounder (Digisonde) and an Ashtech geodetic grade dual frequency GPS receiver. The GPS receiver is owned by Chief Directorate Surveys and Mapping (CDSM) in Cape Town, forms part of the national TrigNet network and was installed in February 2005. The sampling rates of the GPS receiver and Digisonde were set to 1 s and 15 min, respectively. Data from four continuous months, March–June 2005 inclusive, were considered in this initial investigation. Data available from the Grahamstown GPS receiver was limited, and, therefore, only these 4 months have been considered. Total Electron Content (TEC) values were determined from GPS measurements obtained from satellites passing near vertical (within an 80° elevation) to the station. TEC values were obtained from ionograms recorded at times within 5 min of the near vertical GPS measurement. The GPS derived TEC values are referred to as GTEC and the ionogram derived TEC values as ITEC. Comparisons of GTEC and ITEC values are presented in this paper. The differential clock biases of the GPS satellites and receivers are taken into account. The plasmaspheric contribution to the TEC can be inferred from the results, and confirm findings obtained by other groups. This paper describes the groundwork for a procedure that will allow the validation of GPS derived ionospheric information with ionosonde data. This work will be of interest to the International Reference Ionosphere (IRI) community since GPS receivers are becoming recognised as another source for ionospheric information

Validation of University of New Brunswick Ionospheric Modeling Technique with ionosonde TEC estimation over South Africa

For more than a decade, ionospheric research over South Africa has been carried out using data from ionosondes geographically located at Madimbo (28.38°S, 30.88°E), Grahamstown (33.32°S, 26.50°E), and Louisvale (28.51°S, 21.24°E). The objective has been modelling the bottomside ionospheric characteristics using neural networks. The use of Global Navigation Satellite System (GNSS) data is described as a new technique to monitor the dynamics and variations of the ionosphere over South Africa, with possible future application in high frequency radio communication. For this task, the University of New Brunswick Ionospheric Modelling Technique (UNB-IMT) was applied to compute midday (10:00 UT) GNSS-derived total electron content (GTEC). GTEC values were computed using GNSS data for stations located near ionosondes for the years 2002 and 2005 near solar maximum and minimum, respectively. The GTEC was compared with the midday ionosonde-derived TEC (ITEC) measurements to validate the UNB-IMT results. It was found that the variation trends of GTEC and ITEC over all stations are in good agreement and show a pronounced seasonal variation for the period near solar maximum, with maximum values ( 80 TECU) around autumn and spring equinoxes, and minimum values ( 22 TECU) around winter and summer. Furthermore, the residual ΔTEC = GTEC − ITEC was computed. It was evident that ΔTEC, which is believed to correspond to plasmaspheric electron content, showed a pronounced seasonal variation with maximum values ( 20 TECU) around equinoxes and minimum ( 5 TECU) around winter near solar maximum. The equivalent ionospheric and total slab thicknesses were also computed and comprehensively discussed. The results verified the use of UNB-IMT as one of the tools for future ionospheric TEC research over South Africa

A neural network based ionospheric model for the bottomside electron density profile over Grahamstown, South Africa

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

Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks

In this paper, first results from a national Global Positioning System (GPS) based total electron content (TEC) prediction model over South Africa are presented. Data for 10 GPS receiver stations distributed through out the country were used to train a feed forward neural network (NN) over an interval of at most five years. In the NN training, validating and testing processes, five factors which are well known to influence TEC variability namely diurnal variation, seasonal variation, magnetic activity, solar activity and the geographic position of the GPS receivers were included in the NN model. The database consisted of 1-min data and therefore the NN model developed can be used to forecast TEC values 1 min in advance. Results from the NN national model (NM) were compared with hourly TEC values generated by the earlier developed NN single station models (SSMs) at Sutherland (32.38°S, 20.81°E) and Springbok (29.67°S, 17.88°E), to predict TEC variations over the Cape Town (33.95°S, 18.47°E) and Upington (28.41°S, 21.26°E) stations, respectively, during equinoxes and solstices. This revealed that, on average, the NM led to an improvement in TEC prediction accuracy compared to the SSMs for the considered testing periods

A recurrent neural network approach to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results

This paper attempts to describe the search for the parameter(s) to represent solar wind effects in Global Positioning System total electron content (GPS TEC) modelling using the technique of neural networks (NNs). A study is carried out by including solar wind velocity (Vsw), proton number density (Np) and the Bz component of the interplanetary magnetic field (IMF Bz) obtained from the Advanced Composition Explorer (ACE) satellite as separate inputs to the NN each along with day number of the year (DN), hour (HR), a 4-month running mean of the daily sunspot number (R4) and the running mean of the previous eight 3-hourly magnetic A index values (A8). Hourly GPS TEC values derived from a dual frequency receiver located at Sutherland (32.38° S, 20.81° E), South Africa for 8 years (2000–2007) have been used to train the Elman neural network (ENN) and the result has been used to predict TEC variations for a GPS station located at Cape Town (33.95° S, 18.47° E). Quantitative results indicate that each of the parameters considered may have some degree of influence on GPS TEC at certain periods although a decrease in prediction accuracy is also observed for some parameters for different days and seasons. It is also evident that there is still a difficulty in predicting TEC values during disturbed conditions. The improvements and degradation in prediction accuracies are both close to the benchmark values which lends weight to the belief that diurnal, seasonal, solar and magnetic variabilities may be the major determinants of TEC variability