A recurrent neural network approach to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results
- Habarulema, John B, McKinnell, Lee-Anne, Opperman, Ben D L
- Authors: Habarulema, John B , McKinnell, Lee-Anne , Opperman, Ben D L
- Date: 2009
- Language: English
- Type: text , Article
- Identifier: vital:6813 , http://hdl.handle.net/10962/d1004323
- Description: 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.
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- Authors: Habarulema, John B , McKinnell, Lee-Anne , Opperman, Ben D L
- Date: 2009
- Language: English
- Type: text , Article
- Identifier: vital:6813 , http://hdl.handle.net/10962/d1004323
- Description: 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.
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Evaluating the IRI topside model for the South African region: An overview of the modelling techniques
- Sibanda, Patrick, McKinnell, Lee-Anne
- Authors: Sibanda, Patrick , McKinnell, Lee-Anne
- Date: 2009
- Language: English
- Type: text , Article
- Identifier: vital:6810 , http://hdl.handle.net/10962/d1004303
- Description: 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.
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- Authors: Sibanda, Patrick , McKinnell, Lee-Anne
- Date: 2009
- Language: English
- Type: text , Article
- Identifier: vital:6810 , http://hdl.handle.net/10962/d1004303
- Description: 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.
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Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks
- Habarulema, John B, McKinnell, Lee-Anne, Opperman, Ben D L
- Authors: Habarulema, John B , McKinnell, Lee-Anne , Opperman, Ben D L
- Date: 2009
- Language: English
- Type: text , Article
- Identifier: vital:6806 , http://hdl.handle.net/10962/d1004192
- Description: 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.
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- Authors: Habarulema, John B , McKinnell, Lee-Anne , Opperman, Ben D L
- Date: 2009
- Language: English
- Type: text , Article
- Identifier: vital:6806 , http://hdl.handle.net/10962/d1004192
- Description: 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.
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Neural network-based prediction techniques for global modeling of M(3000)F2 ionospheric parameter
- Oyeyemi, E O, McKinnell, Lee-Anne, Poole, Allon W V
- Authors: Oyeyemi, E O , McKinnell, Lee-Anne , Poole, Allon W V
- Date: 2007
- Language: English
- Type: text , Article
- Identifier: vital:6803 , http://hdl.handle.net/10962/d1004166
- Description: In recent times neural networks (NNs) have been employed to solve many problems in ionospheric predictions. This paper illustrates a new application of NNs in developing a global model of the ionospheric propagation factor M(3000)F2. NNs were trained with daily hourly values of M(3000)F2 from various ionospheric stations spanning the period 1964–1986 with the following temporal and spatial input parameters: Universal Time, geographic latitude, magnetic inclination, magnetic declination, solar zenith angle, day of the year, A16 index (a 2-day running mean of the 3-h planetary magnetic ap index), R2 index (a 2-month running mean of sunspot number), and the angle of meridian relative to the subsolar point. The performance of the NNs was verified by comparing the predicted values of M(3000)F2 with observed values from a few selected ionospheric stations and the IRI (International Reference Ionosphere) model (CCIR M(3000)F2 model) predicted values. The results obtained compared favourably with the IRI model. Based on the error differences, the result obtained justifies the potential of the NN technique for the predictions of M(3000)F2 values on a global scale.
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- Authors: Oyeyemi, E O , McKinnell, Lee-Anne , Poole, Allon W V
- Date: 2007
- Language: English
- Type: text , Article
- Identifier: vital:6803 , http://hdl.handle.net/10962/d1004166
- Description: In recent times neural networks (NNs) have been employed to solve many problems in ionospheric predictions. This paper illustrates a new application of NNs in developing a global model of the ionospheric propagation factor M(3000)F2. NNs were trained with daily hourly values of M(3000)F2 from various ionospheric stations spanning the period 1964–1986 with the following temporal and spatial input parameters: Universal Time, geographic latitude, magnetic inclination, magnetic declination, solar zenith angle, day of the year, A16 index (a 2-day running mean of the 3-h planetary magnetic ap index), R2 index (a 2-month running mean of sunspot number), and the angle of meridian relative to the subsolar point. The performance of the NNs was verified by comparing the predicted values of M(3000)F2 with observed values from a few selected ionospheric stations and the IRI (International Reference Ionosphere) model (CCIR M(3000)F2 model) predicted values. The results obtained compared favourably with the IRI model. Based on the error differences, the result obtained justifies the potential of the NN technique for the predictions of M(3000)F2 values on a global scale.
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Near-real time foF2 predictions using neural networks
- Oyeyemi, E O, McKinnell, Lee-Anne, Poole, Allon W V
- Authors: Oyeyemi, E O , McKinnell, Lee-Anne , Poole, Allon W V
- Date: 2006
- Language: English
- Type: text , Article
- Identifier: vital:6804 , http://hdl.handle.net/10962/d1004167
- Description: This paper describes the use of the neural network (NN) technique for the development of a near-real time global foF2 (NRTNN) empirical model. The data used are hourly daily values of foF2 from 26 worldwide ionospheric stations (based on availability) during the period 1976–1986 for training the NN and between 1977 and 1989 for verifying the prediction accuracy. The training data set includes all periods of quiet and disturbed geomagnetic conditions. Two categories of input parameters were used as inputs to the NN. The first category consists of geophysical parameters that are temporally or spatially related to the training stations. The second category, which is related to the foF2 itself, consists of three recent past observations of foF2 (i.e. real-time foF2 (F0), 2 h (F−2) and 1 h (F−1) prior to F0) from four control stations (i.e. Boulder (40.0°N, 254.7°E), Grahamstown (33.3°S, 26.5°E), Dourbes (50.1°N, 4.6°E) and Port Stanley (51.7°S, 302.2°E). The performance of the NRTNN was verified under both geomagnetically quiet and disturbed conditions with observed data from a few verification stations. A comparison of the root mean square error (RMSE) differences between measured values and the NRTNN predictions with our earlier standard foF2 NN empirical model is also illustrated. The results reveal that NRTNN will predict foF2 in near-real time with about 1 MHz RMSE difference anywhere on the globe, provided real time data is available at the four control stations. From the results it is also evident that in addition to the geophysical information from any geographical location, recent past observations of foF2 from these control stations could be used as inputs to a NN for near-real time foF2 predictions. Results also reveal that there is a temporal correlation between measured foF2 values at different locations.
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- Authors: Oyeyemi, E O , McKinnell, Lee-Anne , Poole, Allon W V
- Date: 2006
- Language: English
- Type: text , Article
- Identifier: vital:6804 , http://hdl.handle.net/10962/d1004167
- Description: This paper describes the use of the neural network (NN) technique for the development of a near-real time global foF2 (NRTNN) empirical model. The data used are hourly daily values of foF2 from 26 worldwide ionospheric stations (based on availability) during the period 1976–1986 for training the NN and between 1977 and 1989 for verifying the prediction accuracy. The training data set includes all periods of quiet and disturbed geomagnetic conditions. Two categories of input parameters were used as inputs to the NN. The first category consists of geophysical parameters that are temporally or spatially related to the training stations. The second category, which is related to the foF2 itself, consists of three recent past observations of foF2 (i.e. real-time foF2 (F0), 2 h (F−2) and 1 h (F−1) prior to F0) from four control stations (i.e. Boulder (40.0°N, 254.7°E), Grahamstown (33.3°S, 26.5°E), Dourbes (50.1°N, 4.6°E) and Port Stanley (51.7°S, 302.2°E). The performance of the NRTNN was verified under both geomagnetically quiet and disturbed conditions with observed data from a few verification stations. A comparison of the root mean square error (RMSE) differences between measured values and the NRTNN predictions with our earlier standard foF2 NN empirical model is also illustrated. The results reveal that NRTNN will predict foF2 in near-real time with about 1 MHz RMSE difference anywhere on the globe, provided real time data is available at the four control stations. From the results it is also evident that in addition to the geophysical information from any geographical location, recent past observations of foF2 from these control stations could be used as inputs to a NN for near-real time foF2 predictions. Results also reveal that there is a temporal correlation between measured foF2 values at different locations.
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An analysis of automatically scaled F1 layer data over Grahamstown, South Africa
- Jacobs, Linda, Poole, Allon W V, McKinnell, Lee-Anne
- Authors: Jacobs, Linda , Poole, Allon W V , McKinnell, Lee-Anne
- Date: 2004
- Language: English
- Type: text , Article
- Identifier: vital:6808 , http://hdl.handle.net/10962/d1004194
- Description: 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.
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- Authors: Jacobs, Linda , Poole, Allon W V , McKinnell, Lee-Anne
- Date: 2004
- Language: English
- Type: text , Article
- Identifier: vital:6808 , http://hdl.handle.net/10962/d1004194
- Description: 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.
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