Updating the ionospheric propagation factor, M(3000)F2, global model using the neural network technique and relevant geophysical input parameters
- Oronsaye, Samuel Iyen Jeffrey
- Authors: Oronsaye, Samuel Iyen Jeffrey
- Date: 2013
- Subjects: Neural networks (Computer science) , Ionospheric radio wave propagation , Ionosphere , Geophysics , Ionosondes
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5434 , http://hdl.handle.net/10962/d1001609 , Neural networks (Computer science) , Ionospheric radio wave propagation , Ionosphere , Geophysics , Ionosondes
- Description: This thesis presents an update to the ionospheric propagation factor, M(3000)F2, global empirical model developed by Oyeyemi et al. (2007) (NNO). An additional aim of this research was to produce the updated model in a form that could be used within the International Reference Ionosphere (IRI) global model without adding to the complexity of the IRI. M(3000)F2 is the highest frequency at which a radio signal can be received over a distance of 3000 km after reflection in the ionosphere. The study employed the artificial neural network (ANN) technique using relevant geophysical input parameters which are known to influence the M(3000)F2 parameter. Ionosonde data from 135 ionospheric stations globally, including a number of equatorial stations, were available for this work. M(3000)F2 hourly values from 1976 to 2008, spanning all periods of low and high solar activity were used for model development and verification. A preliminary investigation was first carried out using a relatively small dataset to determine the appropriate input parameters for global M(3000)F2 parameter modelling. Inputs representing diurnal variation, seasonal variation, solar variation, modified dip latitude, longitude and latitude were found to be the optimum parameters for modelling the diurnal and seasonal variations of the M(3000)F2 parameter both on a temporal and spatial basis. The outcome of the preliminary study was applied to the overall dataset to develop a comprehensive ANN M(3000)F2 model which displays a remarkable improvement over the NNO model as well as the IRI version. The model shows 7.11% and 3.85% improvement over the NNO model as well as 13.04% and 10.05% over the IRI M(3000)F2 model, around high and low solar activity periods respectively. A comparison of the diurnal structure of the ANN and the IRI predicted values reveal that the ANN model is more effective in representing the diurnal structure of the M(3000)F2 values than the IRI M(3000)F2 model. The capability of the ANN model in reproducing the seasonal variation pattern of the M(3000)F2 values at 00h00UT, 06h00UT, 12h00UT, and l8h00UT more appropriately than the IRI version is illustrated in this work. A significant result obtained in this study is the ability of the ANN model in improving the post-sunset predicted values of the M(3000)F2 parameter which is known to be problematic to the IRI M(3000)F2 model in the low-latitude and the equatorial regions. The final M(3000)F2 model provides for an improved equatorial prediction and a simplified input space that allows for easy incorporation into the IRI model.
- Full Text:
- Authors: Oronsaye, Samuel Iyen Jeffrey
- Date: 2013
- Subjects: Neural networks (Computer science) , Ionospheric radio wave propagation , Ionosphere , Geophysics , Ionosondes
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5434 , http://hdl.handle.net/10962/d1001609 , Neural networks (Computer science) , Ionospheric radio wave propagation , Ionosphere , Geophysics , Ionosondes
- Description: This thesis presents an update to the ionospheric propagation factor, M(3000)F2, global empirical model developed by Oyeyemi et al. (2007) (NNO). An additional aim of this research was to produce the updated model in a form that could be used within the International Reference Ionosphere (IRI) global model without adding to the complexity of the IRI. M(3000)F2 is the highest frequency at which a radio signal can be received over a distance of 3000 km after reflection in the ionosphere. The study employed the artificial neural network (ANN) technique using relevant geophysical input parameters which are known to influence the M(3000)F2 parameter. Ionosonde data from 135 ionospheric stations globally, including a number of equatorial stations, were available for this work. M(3000)F2 hourly values from 1976 to 2008, spanning all periods of low and high solar activity were used for model development and verification. A preliminary investigation was first carried out using a relatively small dataset to determine the appropriate input parameters for global M(3000)F2 parameter modelling. Inputs representing diurnal variation, seasonal variation, solar variation, modified dip latitude, longitude and latitude were found to be the optimum parameters for modelling the diurnal and seasonal variations of the M(3000)F2 parameter both on a temporal and spatial basis. The outcome of the preliminary study was applied to the overall dataset to develop a comprehensive ANN M(3000)F2 model which displays a remarkable improvement over the NNO model as well as the IRI version. The model shows 7.11% and 3.85% improvement over the NNO model as well as 13.04% and 10.05% over the IRI M(3000)F2 model, around high and low solar activity periods respectively. A comparison of the diurnal structure of the ANN and the IRI predicted values reveal that the ANN model is more effective in representing the diurnal structure of the M(3000)F2 values than the IRI M(3000)F2 model. The capability of the ANN model in reproducing the seasonal variation pattern of the M(3000)F2 values at 00h00UT, 06h00UT, 12h00UT, and l8h00UT more appropriately than the IRI version is illustrated in this work. A significant result obtained in this study is the ability of the ANN model in improving the post-sunset predicted values of the M(3000)F2 parameter which is known to be problematic to the IRI M(3000)F2 model in the low-latitude and the equatorial regions. The final M(3000)F2 model provides for an improved equatorial prediction and a simplified input space that allows for easy incorporation into the IRI model.
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Statistical study of traveling ionospheric disturbances over South Africa
- Authors: Mahlangu, Daniel Fiso
- Date: 2019
- Subjects: Ionosphere -- Research , Sudden ionospheric disturbances , Gravity waves , Magnetic storms
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/76387 , vital:30556
- Description: This thesis provides a statistical analysis of traveling ionospheric disturbances (TIDs) in South Africa. The velocities of the TIDs were determined from total electron content (TEC) maps using particle image velocimetry (PIV). The periods were determined using Morlet function in wavelet analysis. The TIDs were grouped into four categories: daytime, twilight, nighttime TIDs, and those TIDs that occurred during magnetic storms. It was found that daytime medium scale TIDs (MSTIDs) propagated equatorward in all seasons (summer, autumn, winter, and spring), with velocities of about 114 to 213 m/s. Their maximum occurrence was in winter between 15:00 and 16:00 LT. The daytime large scale (TIDs) LSTIDs propagated equatorward with velocities of approximately 455 to 767 m/s. Their highest occurrence was in summer, between 12:00-13:00 LT. Most of the these TIDs (about 78%) were observed during the passing of the morning solar terminator. This implied that the morning terminator was more effective in instigating TIDs. Only a few nighttime TIDs were observed and therefore their behavior could not be statistically inferred. The TIDs that occurred during magnetically disturbed conditions propagated equatorward. This indicated that their source mechanism was atmospheric gravity waves generated at the onset of geomagnetic storms.
- Full Text:
- Authors: Mahlangu, Daniel Fiso
- Date: 2019
- Subjects: Ionosphere -- Research , Sudden ionospheric disturbances , Gravity waves , Magnetic storms
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/76387 , vital:30556
- Description: This thesis provides a statistical analysis of traveling ionospheric disturbances (TIDs) in South Africa. The velocities of the TIDs were determined from total electron content (TEC) maps using particle image velocimetry (PIV). The periods were determined using Morlet function in wavelet analysis. The TIDs were grouped into four categories: daytime, twilight, nighttime TIDs, and those TIDs that occurred during magnetic storms. It was found that daytime medium scale TIDs (MSTIDs) propagated equatorward in all seasons (summer, autumn, winter, and spring), with velocities of about 114 to 213 m/s. Their maximum occurrence was in winter between 15:00 and 16:00 LT. The daytime large scale (TIDs) LSTIDs propagated equatorward with velocities of approximately 455 to 767 m/s. Their highest occurrence was in summer, between 12:00-13:00 LT. Most of the these TIDs (about 78%) were observed during the passing of the morning solar terminator. This implied that the morning terminator was more effective in instigating TIDs. Only a few nighttime TIDs were observed and therefore their behavior could not be statistically inferred. The TIDs that occurred during magnetically disturbed conditions propagated equatorward. This indicated that their source mechanism was atmospheric gravity waves generated at the onset of geomagnetic storms.
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Statistical analysis of the ionospheric response during storm conditions over South Africa using ionosonde and GPS data
- Matamba, Tshimangadzo Merline
- Authors: Matamba, Tshimangadzo Merline
- Date: 2015
- Subjects: Ionospheric storms -- South Africa -- Grahamstown , Ionospheric storms -- South Africa -- Madimbo , Magnetic storms -- South Africa -- Grahamstown , Magnetic storms -- South Africa -- Madimbo , Ionosondes , Global Positioning System
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5555 , http://hdl.handle.net/10962/d1017899
- Description: Ionospheric storms are an extreme form of space weather phenomena which affect space- and ground-based technological systems. Extreme solar activity may give rise to Coronal Mass Ejections (CME) and solar flares that may result in ionospheric storms. This thesis reports on a statistical analysis of the ionospheric response over the ionosonde stations Grahamstown (33.3◦S, 26.5◦E) and Madimbo (22.4◦S,30.9◦E), South Africa, during geomagnetic storm conditions which occurred during the period 1996 - 2011. Total Electron Content (TEC) derived from Global Positioning System (GPS) data by a dual Frequency receiver and an ionosonde at Grahamstown, was analysed for the storms that occurred during the period 2006 - 2011. A comprehensive analysis of the critical frequency of the F2 layer (foF2) and TEC was done. To identify the geomagnetically disturbed conditions the Disturbance storm time (Dst) index with a storm criteria of Dst ≤ −50 nT was used. The ionospheric disturbances were categorized into three responses, namely single disturbance, double disturbance and not significant (NS) ionospheric storms. Single disturbance ionospheric storms refer to positive (P) and negative (N) ionospheric storms observed separately, while double disturbance storms refer to negative and positive ionospheric storms observed during the same storm period. The statistics show the impact of geomagnetic storms on the ionosphere and indicate that negative ionospheric effects follow the solar cycle. In general, only a few ionospheric storms (0.11%) were observed during solar minimum. Positive ionospheric storms occurred most frequently (47.54%) during the declining phase of solar cycle 23. Seasonally, negative ionospheric storms occurred mostly during the summer (63.24%), while positive ionospheric storms occurred frequently during the winter (53.62%). An important finding is that only negative ionospheric storms were observed during great geomagnetic storm activity (Dst ≤ −350 nT). For periods when both ionosonde and GPS was available, the two data sets indicated similar ionospheric responses. Hence, GPS data can be used to effectively identify the ionospheric response in the absence of ionosonde data.
- Full Text:
- Authors: Matamba, Tshimangadzo Merline
- Date: 2015
- Subjects: Ionospheric storms -- South Africa -- Grahamstown , Ionospheric storms -- South Africa -- Madimbo , Magnetic storms -- South Africa -- Grahamstown , Magnetic storms -- South Africa -- Madimbo , Ionosondes , Global Positioning System
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5555 , http://hdl.handle.net/10962/d1017899
- Description: Ionospheric storms are an extreme form of space weather phenomena which affect space- and ground-based technological systems. Extreme solar activity may give rise to Coronal Mass Ejections (CME) and solar flares that may result in ionospheric storms. This thesis reports on a statistical analysis of the ionospheric response over the ionosonde stations Grahamstown (33.3◦S, 26.5◦E) and Madimbo (22.4◦S,30.9◦E), South Africa, during geomagnetic storm conditions which occurred during the period 1996 - 2011. Total Electron Content (TEC) derived from Global Positioning System (GPS) data by a dual Frequency receiver and an ionosonde at Grahamstown, was analysed for the storms that occurred during the period 2006 - 2011. A comprehensive analysis of the critical frequency of the F2 layer (foF2) and TEC was done. To identify the geomagnetically disturbed conditions the Disturbance storm time (Dst) index with a storm criteria of Dst ≤ −50 nT was used. The ionospheric disturbances were categorized into three responses, namely single disturbance, double disturbance and not significant (NS) ionospheric storms. Single disturbance ionospheric storms refer to positive (P) and negative (N) ionospheric storms observed separately, while double disturbance storms refer to negative and positive ionospheric storms observed during the same storm period. The statistics show the impact of geomagnetic storms on the ionosphere and indicate that negative ionospheric effects follow the solar cycle. In general, only a few ionospheric storms (0.11%) were observed during solar minimum. Positive ionospheric storms occurred most frequently (47.54%) during the declining phase of solar cycle 23. Seasonally, negative ionospheric storms occurred mostly during the summer (63.24%), while positive ionospheric storms occurred frequently during the winter (53.62%). An important finding is that only negative ionospheric storms were observed during great geomagnetic storm activity (Dst ≤ −350 nT). For periods when both ionosonde and GPS was available, the two data sets indicated similar ionospheric responses. Hence, GPS data can be used to effectively identify the ionospheric response in the absence of ionosonde data.
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Single station TEC modelling during storm conditions
- Authors: Uwamahoro, Jean Claude
- Date: 2016
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/3812 , vital:20545
- Description: It has been shown in ionospheric research that modelling total electron content (TEC) during storm conditions is a big challenge. In this study, mathematical equations were developed to estimate TEC over Sutherland (32.38oS, 20.81oE), during storm conditions, using the Empirical Orthogonal Function (EOF) analysis, combined with regression analysis. TEC was derived from GPS observations and a geomagnetic storm was defined for Dst ≤ -50 nT. The inputs for the model were chosen based on the factors that influence TEC variation, such as diurnal, seasonal, solar and geomagnetic activity variation, and these were represented by hour of the day, day number of the year, F10.7 and A index respectively. The EOF model was developed using GPS TEC data from 1999 to 2013 and tested on different storms. For the model validation (interpolation), three storms were chosen in 2000 (solar maximum period) and three others in 2006 (solar minimum period), while for extrapolation six storms including three in 2014 and three in 2015 were chosen. Before building the model, TEC values for the selected 2000 and 2006 storms were removed from the dataset used to construct the model in order to make the model validation independent on data. A comparison of the observed and modelled TEC showed that the EOF model works well for storms with non-significant ionospheric TEC response and storms that occurred during periods of low solar activity. High correlation coefficients between the observed and modelled TEC were obtained showing that the model covers most of the information contained in the observed TEC. Furthermore, it has been shown that the EOF model developed for a specific station may be used to estimate TEC over other locations within a latitudinal and longitudinal coverage of 8.7o and 10.6o respectively. This is an important result as it reduces the data dimensionality problem for computational purposes. It may therefore not be necessary for regional storm-time TEC modelling to compute TEC data for all the closest GPS receiver stations since most of the needed information can be extracted from measurements at one location.
- Full Text:
- Authors: Uwamahoro, Jean Claude
- Date: 2016
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/3812 , vital:20545
- Description: It has been shown in ionospheric research that modelling total electron content (TEC) during storm conditions is a big challenge. In this study, mathematical equations were developed to estimate TEC over Sutherland (32.38oS, 20.81oE), during storm conditions, using the Empirical Orthogonal Function (EOF) analysis, combined with regression analysis. TEC was derived from GPS observations and a geomagnetic storm was defined for Dst ≤ -50 nT. The inputs for the model were chosen based on the factors that influence TEC variation, such as diurnal, seasonal, solar and geomagnetic activity variation, and these were represented by hour of the day, day number of the year, F10.7 and A index respectively. The EOF model was developed using GPS TEC data from 1999 to 2013 and tested on different storms. For the model validation (interpolation), three storms were chosen in 2000 (solar maximum period) and three others in 2006 (solar minimum period), while for extrapolation six storms including three in 2014 and three in 2015 were chosen. Before building the model, TEC values for the selected 2000 and 2006 storms were removed from the dataset used to construct the model in order to make the model validation independent on data. A comparison of the observed and modelled TEC showed that the EOF model works well for storms with non-significant ionospheric TEC response and storms that occurred during periods of low solar activity. High correlation coefficients between the observed and modelled TEC were obtained showing that the model covers most of the information contained in the observed TEC. Furthermore, it has been shown that the EOF model developed for a specific station may be used to estimate TEC over other locations within a latitudinal and longitudinal coverage of 8.7o and 10.6o respectively. This is an important result as it reduces the data dimensionality problem for computational purposes. It may therefore not be necessary for regional storm-time TEC modelling to compute TEC data for all the closest GPS receiver stations since most of the needed information can be extracted from measurements at one location.
- Full Text:
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