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:
- Date Issued: 2013
- 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:
- Date Issued: 2013
Investigation into the extended capabilities of the new DPS-4D ionosonde
- Authors: Ssessanga, Nicholas
- Date: 2011
- Subjects: Ionosondes , Ionosphere , Ionosphere -- Observations -- South Africa -- Hermanus (Cape of Good Hope) , Ionosphere -- Research -- South Africa -- Hermanus (Cape of Good Hope)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5472 , http://hdl.handle.net/10962/d1005257 , Ionosondes , Ionosphere , Ionosphere -- Observations -- South Africa -- Hermanus (Cape of Good Hope) , Ionosphere -- Research -- South Africa -- Hermanus (Cape of Good Hope)
- Description: The DPS-4D is the latest version of digital ionosonde developed by the UMLCAR (University of Massachusetts in Lowell Center for Atmospheric Research) in 2008. This new ionosonde has advances in both the hardware and software which allows for the promised advanced capabilities. The aim of this thesis was to present results from an experiment undertaken using the Hermanus DPS-4D (34.4°S 19.2°E, South Africa), the first of this version to be installed globally, to answer a science question outside of the normally expected capabilities of an ionosonde. The science question posed focused on the ability of the DPS-4D to provide information on day-time Pc3 pulsations evident in the ionosphere. Day-time Pc3 ULF waves propagating down through the ionosphere cause oscillations in the Doppler shift of High Frequency (HF) radio transmissions that are correlated with the magnetic pulsations recorded on the ground. Evidence is presented which shows that no correlation exists between the ground magnetic pulsation data and DPS-4D ionospheric data. The conclusion was reached that although the DPS-4D is more advanced in its eld of technology than its predecessors it may not be used to observe Pc3 pulsations.
- Full Text:
- Date Issued: 2011
- Authors: Ssessanga, Nicholas
- Date: 2011
- Subjects: Ionosondes , Ionosphere , Ionosphere -- Observations -- South Africa -- Hermanus (Cape of Good Hope) , Ionosphere -- Research -- South Africa -- Hermanus (Cape of Good Hope)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5472 , http://hdl.handle.net/10962/d1005257 , Ionosondes , Ionosphere , Ionosphere -- Observations -- South Africa -- Hermanus (Cape of Good Hope) , Ionosphere -- Research -- South Africa -- Hermanus (Cape of Good Hope)
- Description: The DPS-4D is the latest version of digital ionosonde developed by the UMLCAR (University of Massachusetts in Lowell Center for Atmospheric Research) in 2008. This new ionosonde has advances in both the hardware and software which allows for the promised advanced capabilities. The aim of this thesis was to present results from an experiment undertaken using the Hermanus DPS-4D (34.4°S 19.2°E, South Africa), the first of this version to be installed globally, to answer a science question outside of the normally expected capabilities of an ionosonde. The science question posed focused on the ability of the DPS-4D to provide information on day-time Pc3 pulsations evident in the ionosphere. Day-time Pc3 ULF waves propagating down through the ionosphere cause oscillations in the Doppler shift of High Frequency (HF) radio transmissions that are correlated with the magnetic pulsations recorded on the ground. Evidence is presented which shows that no correlation exists between the ground magnetic pulsation data and DPS-4D ionospheric data. The conclusion was reached that although the DPS-4D is more advanced in its eld of technology than its predecessors it may not be used to observe Pc3 pulsations.
- Full Text:
- Date Issued: 2011
Development of a neural network based model for predicting the occurrence of spread F within the Brazilian sector
- Authors: Paradza, Masimba Wellington
- Date: 2009
- Subjects: Neural networks (Computer science) , Ionosphere , F region
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5460 , http://hdl.handle.net/10962/d1005245 , Neural networks (Computer science) , Ionosphere , F region
- Description: Spread F is a phenomenon of the ionosphere in which the pulses returned from the ionosphere are of a much greater duration than the transmitted ones. The occurrence of spread F can be predicted using the technique of Neural Networks (NNs). This thesis presents the development and evaluation of NN based models (two single station models and a regional model) for predicting the occurrence of spread F over selected stations within the Brazilian sector. The input space for the NNs included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), magnetic index (measure of the magnetic activity) and magnetic position (latitude, magnetic declination and inclination). Twelve years of spread F data measured during 1978 to 1989 inclusively at the equatorial site Fortaleza and low latitude site Cachoeira Paulista are used in the development of an input space and NN architecture for the NN models. Spread F data that is believed to be related to plasma bubble developments (range spread F) were used in the development of the models while those associated with narrow spectrum irregularities that occur near the F layer (frequency spread F) were excluded. The results of the models show the dependency of the probability of spread F as a function of local time, season and latitude. The models also illustrate some characteristics of spread F such as the onset and peak occurrence of spread F as a function of distance from the equator. Results from these models are presented in this thesis and compared to measured data and to modelled data obtained with an empirical model developed for the same purpose.
- Full Text:
- Date Issued: 2009
- Authors: Paradza, Masimba Wellington
- Date: 2009
- Subjects: Neural networks (Computer science) , Ionosphere , F region
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5460 , http://hdl.handle.net/10962/d1005245 , Neural networks (Computer science) , Ionosphere , F region
- Description: Spread F is a phenomenon of the ionosphere in which the pulses returned from the ionosphere are of a much greater duration than the transmitted ones. The occurrence of spread F can be predicted using the technique of Neural Networks (NNs). This thesis presents the development and evaluation of NN based models (two single station models and a regional model) for predicting the occurrence of spread F over selected stations within the Brazilian sector. The input space for the NNs included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), magnetic index (measure of the magnetic activity) and magnetic position (latitude, magnetic declination and inclination). Twelve years of spread F data measured during 1978 to 1989 inclusively at the equatorial site Fortaleza and low latitude site Cachoeira Paulista are used in the development of an input space and NN architecture for the NN models. Spread F data that is believed to be related to plasma bubble developments (range spread F) were used in the development of the models while those associated with narrow spectrum irregularities that occur near the F layer (frequency spread F) were excluded. The results of the models show the dependency of the probability of spread F as a function of local time, season and latitude. The models also illustrate some characteristics of spread F such as the onset and peak occurrence of spread F as a function of distance from the equator. Results from these models are presented in this thesis and compared to measured data and to modelled data obtained with an empirical model developed for the same purpose.
- Full Text:
- Date Issued: 2009
Forecasting solar cycle 24 using neural networks
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
- Full Text:
- Date Issued: 2009
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
- Full Text:
- Date Issued: 2009
A feasibility study into total electron content prediction using neural networks
- Authors: Habarulema, John Bosco
- Date: 2008
- Subjects: Electrons , Neural networks (Computer science) , Global Positioning System , Ionosphere , Ionospheric electron density
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5466 , http://hdl.handle.net/10962/d1005251 , Electrons , Neural networks (Computer science) , Global Positioning System , Ionosphere , Ionospheric electron density
- Description: 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.
- Full Text:
- Date Issued: 2008
- Authors: Habarulema, John Bosco
- Date: 2008
- Subjects: Electrons , Neural networks (Computer science) , Global Positioning System , Ionosphere , Ionospheric electron density
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5466 , http://hdl.handle.net/10962/d1005251 , Electrons , Neural networks (Computer science) , Global Positioning System , Ionosphere , Ionospheric electron density
- Description: 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.
- Full Text:
- Date Issued: 2008
Ionospheric total electron content variability and its influence in radio astronomy
- Authors: Botai, Ondego Joel
- Date: 2006
- Subjects: Electrons , Global Positioning System , Global Positioning System -- Data processing , Ionosphere , Ionospheric radio wave propagation
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5473 , http://hdl.handle.net/10962/d1005258 , Electrons , Global Positioning System , Global Positioning System -- Data processing , Ionosphere , Ionospheric radio wave propagation
- Description: Ionospheric phase delays of radio signals from Global Positioning System (GPS) satellites have been used to compute ionospheric Total Electron Content (TEC). An extended Chapman profle model is used to estimate the electron density profles and TEC. The Chapman profle that can be used to predict TEC over the mid-latitudes only applies during day time. To model night time TEC variability, a polynomial function is fitted to the night time peak electron density profles derived from the online International Reference Ionosphere (IRI) 2001. The observed and predicted TEC and its variability have been used to study ionospheric in°uence on Radio Astronomy in South Africa region. Di®erential phase delays of the radio signals from Radio Astronomy sources have been simulated using TEC. Using the simulated phase delays, the azimuth and declination o®sets of the radio sources have been estimated. Results indicate that, pointing errors of the order of miliarcseconds (mas) are likely if the ionospheric phase delays are not corrected for. These delays are not uniform and vary over a broad spectrum of timescales. This implies that fast frequency (referencing) switching, closure phases and fringe ¯tting schemes for ionospheric correction in astrometry are not the best option as they do not capture the real state of the ionosphere especially if the switching time is greater than the ionospheric TEC variability. However, advantage can be taken of the GPS satellite data available at intervals of a second from the GPS receiver network in South Africa to derive parameters which could be used to correct for the ionospheric delays. Furthermore GPS data can also be used to monitor the occurrence of scintillations, (which might corrupt radio signals) especially for the proposed, Square Kilometer Array (SKA) stations closer to the equatorial belt during magnetic storms and sub-storms. A 10 minute snapshot of GPS data recorded with the Hermanus [34:420 S, 19:220 E ] dual frequency receiver on 2003-04-11 did not show the occurrence of scintillations. This time scale is however too short and cannot be representative. Longer time scales; hours, days, seasons are needed to monitor the occurrence of scintillations.
- Full Text:
- Date Issued: 2006
- Authors: Botai, Ondego Joel
- Date: 2006
- Subjects: Electrons , Global Positioning System , Global Positioning System -- Data processing , Ionosphere , Ionospheric radio wave propagation
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5473 , http://hdl.handle.net/10962/d1005258 , Electrons , Global Positioning System , Global Positioning System -- Data processing , Ionosphere , Ionospheric radio wave propagation
- Description: Ionospheric phase delays of radio signals from Global Positioning System (GPS) satellites have been used to compute ionospheric Total Electron Content (TEC). An extended Chapman profle model is used to estimate the electron density profles and TEC. The Chapman profle that can be used to predict TEC over the mid-latitudes only applies during day time. To model night time TEC variability, a polynomial function is fitted to the night time peak electron density profles derived from the online International Reference Ionosphere (IRI) 2001. The observed and predicted TEC and its variability have been used to study ionospheric in°uence on Radio Astronomy in South Africa region. Di®erential phase delays of the radio signals from Radio Astronomy sources have been simulated using TEC. Using the simulated phase delays, the azimuth and declination o®sets of the radio sources have been estimated. Results indicate that, pointing errors of the order of miliarcseconds (mas) are likely if the ionospheric phase delays are not corrected for. These delays are not uniform and vary over a broad spectrum of timescales. This implies that fast frequency (referencing) switching, closure phases and fringe ¯tting schemes for ionospheric correction in astrometry are not the best option as they do not capture the real state of the ionosphere especially if the switching time is greater than the ionospheric TEC variability. However, advantage can be taken of the GPS satellite data available at intervals of a second from the GPS receiver network in South Africa to derive parameters which could be used to correct for the ionospheric delays. Furthermore GPS data can also be used to monitor the occurrence of scintillations, (which might corrupt radio signals) especially for the proposed, Square Kilometer Array (SKA) stations closer to the equatorial belt during magnetic storms and sub-storms. A 10 minute snapshot of GPS data recorded with the Hermanus [34:420 S, 19:220 E ] dual frequency receiver on 2003-04-11 did not show the occurrence of scintillations. This time scale is however too short and cannot be representative. Longer time scales; hours, days, seasons are needed to monitor the occurrence of scintillations.
- Full Text:
- Date Issued: 2006
An investigation into improved ionospheric F1 layer predictions over Grahamstown, South Africa
- Authors: Jacobs, Linda
- Date: 2005
- Subjects: Ionosphere , Ionospheric electron density -- South Africa -- Grahamstown , Neural networks (Computer Science)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5511 , http://hdl.handle.net/10962/d1008094 , Ionosphere , Ionospheric electron density -- South Africa -- Grahamstown , Neural networks (Computer Science)
- Description: 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.
- Full Text:
- Date Issued: 2005
- Authors: Jacobs, Linda
- Date: 2005
- Subjects: Ionosphere , Ionospheric electron density -- South Africa -- Grahamstown , Neural networks (Computer Science)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5511 , http://hdl.handle.net/10962/d1008094 , Ionosphere , Ionospheric electron density -- South Africa -- Grahamstown , Neural networks (Computer Science)
- Description: 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.
- Full Text:
- Date Issued: 2005
First H F Doppler soundings of the ionosphere at SANAE
- Authors: De Kock, Errol James
- Date: 1980
- Subjects: Ionosphere
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5500 , http://hdl.handle.net/10962/d1006869 , Ionosphere
- Full Text:
- Date Issued: 1980
- Authors: De Kock, Errol James
- Date: 1980
- Subjects: Ionosphere
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5500 , http://hdl.handle.net/10962/d1006869 , Ionosphere
- Full Text:
- Date Issued: 1980
Some properties of a model F1 layer of the ionosphere
- Authors: De Jager, Gerhard
- Date: 1963
- Subjects: Ionosphere , Ionospheric electron density
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5515 , http://hdl.handle.net/10962/d1011046 , Ionosphere , Ionospheric electron density
- Description: The present work was initially aimed at providing an explanation for some of the phenomena that occur in the ionosphere at sunrise. The approach that was taken was to determine the changes that take place on a theoretical model of the ionosphere and then to compare these with observations. A prerequisite for this approach was a theoretical model that would show, among other things, a bifurcation of the F layer during daytime without making unjustified arbitrary assumptions. The absence of such a model, particularly as far as non-equilibrium conditions are concerned, resulted in the present attempt to provide such a model for the F1 region.
- Full Text:
- Date Issued: 1963
- Authors: De Jager, Gerhard
- Date: 1963
- Subjects: Ionosphere , Ionospheric electron density
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5515 , http://hdl.handle.net/10962/d1011046 , Ionosphere , Ionospheric electron density
- Description: The present work was initially aimed at providing an explanation for some of the phenomena that occur in the ionosphere at sunrise. The approach that was taken was to determine the changes that take place on a theoretical model of the ionosphere and then to compare these with observations. A prerequisite for this approach was a theoretical model that would show, among other things, a bifurcation of the F layer during daytime without making unjustified arbitrary assumptions. The absence of such a model, particularly as far as non-equilibrium conditions are concerned, resulted in the present attempt to provide such a model for the F1 region.
- Full Text:
- Date Issued: 1963
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