An effort to study the influence of tides on the longitudinal variation of vertical E× B drift over the African sector:
- Habyarimana, Valence, Habarulema, John B, Mungufeni, Patrick, Uwamahoro, Jean C
- Authors: Habyarimana, Valence , Habarulema, John B , Mungufeni, Patrick , Uwamahoro, Jean C
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/149026 , vital:38797 , https://doi.org/10.1016/j.jastp.2020.105338
- Description: Meteorological processes such as tides influence ionospheric variability through vertical coupling. For the first time, we have used data from Communication Navigation Outage and Forecasting System (C/NOFS) satellite from 2008–2015 to develop a Neural Network (NN) vertical E×B drift model over the African region, with inclusion of a proxy of tides as one of the inputs together with other physical and geophysical inputs. Two models (with and without tidal proxy input) were developed for both East and West African sectors. To derive the tidal proxy, we first calculate the 60-day running means per year which were subtracted from the actual vertical E×B drift measurements to obtain a set of residuals. The purpose of the subtraction was to remove long-term trends in vertical E×B drift that could potentially alias into tides.
- Full Text:
- Date Issued: 2020
- Authors: Habyarimana, Valence , Habarulema, John B , Mungufeni, Patrick , Uwamahoro, Jean C
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/149026 , vital:38797 , https://doi.org/10.1016/j.jastp.2020.105338
- Description: Meteorological processes such as tides influence ionospheric variability through vertical coupling. For the first time, we have used data from Communication Navigation Outage and Forecasting System (C/NOFS) satellite from 2008–2015 to develop a Neural Network (NN) vertical E×B drift model over the African region, with inclusion of a proxy of tides as one of the inputs together with other physical and geophysical inputs. Two models (with and without tidal proxy input) were developed for both East and West African sectors. To derive the tidal proxy, we first calculate the 60-day running means per year which were subtracted from the actual vertical E×B drift measurements to obtain a set of residuals. The purpose of the subtraction was to remove long-term trends in vertical E×B drift that could potentially alias into tides.
- Full Text:
- Date Issued: 2020
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.
- Full Text:
- Date Issued: 2009
- 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.
- Full Text:
- Date Issued: 2009
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.
- Full Text:
- Date Issued: 2009
- 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.
- Full Text:
- Date Issued: 2009
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