The development of an ionospheric storm-time index for the South African region
- Authors: Tshisaphungo, Mpho
- Date: 2021-04
- Subjects: Ionospheric storms -- South Africa , Global Positioning System , Neural networks (Computer science) , Regression analysis , Ionosondes , Auroral electrojet , Geomagnetic indexes , Magnetic storms -- South Africa
- Language: English
- Type: thesis , text , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/178409 , vital:42937 , 10.21504/10962/178409
- Description: This thesis presents the development of a regional ionospheric storm-time model which forms the foundation of an index to provide a quick view of the ionospheric storm effects over South African mid-latitude region. The model is based on the foF2 measurements from four South African ionosonde stations. The data coverage for the model development over Grahamstown (33.3◦S, 26.5◦E), Hermanus (34.42◦S, 19.22◦E), Louisvale (28.50◦S, 21.20◦E), and Madimbo (22.39◦S, 30.88◦E) is 1996-2016, 2009-2016, 2000-2016, and 2000-2016 respectively. Data from the Global Positioning System (GPS) and radio occultation (RO) technique were used during validation. As the measure of either positive or negative storm effect, the variation of the critical frequency of the F2 layer (foF2) from the monthly median values (denoted as _foF2) is modeled. The modeling of _foF2 is based on only storm time data with the criteria of Dst 6 -50 nT and Kp > 4. The modeling methods used in the study were artificial neural network (ANN), linear regression (LR) and polynomial functions. The approach taken was to first test the modeling techniques on a single station before expanding the study to cover the regional aspect. The single station modeling was developed based on ionosonde data over Grahamstown. The inputs for the model which related to seasonal variation, diurnal variation, geomagnetic activity and solar activity were considered. For the geomagnetic activity, three indices namely; the symmetric disturbance in the horizontal component of the Earth’s magnetic field (SYM − H), the Auroral Electrojet (AE) index and local geomagnetic index A, were included as inputs. The performance of a single station model revealed that, of the three geomagnetic indices, SYM − H index has the largest contribution of 41% and 54% based on ANN and LR techniques respectively. The average correlation coefficients (R) for both ANN and LR models was 0.8, when validated during the selected storms falling within the period of model development. When validated using storms that fall outside the period of model development, the model gave R values of 0.6 and 0.5 for ANN and LR respectively. In addition, the GPS total electron content (TEC) derived measurements were used to estimate foF2 data. This is because there are more GPS receivers than ionosonde locations and the utilisation of this data increases the spatial coverage of the regional model. The estimation of foF2 from GPS TEC was done at GPS-ionosonde co-locations using polynomial functions. The average R values of 0.69 and 0.65 were obtained between actual and derived _foF2 over the co-locations and other GPS stations respectively. Validation of GPS TEC derived foF2 with RO data over regions out of ionospheric pierce points coverage with respect to ionosonde locations gave R greater than 0.9 for the selected storm period of 4-8 August 2011. The regional storm-time model was then developed based on the ANN technique using the four South African ionosonde stations. The maximum and minimum R values of 0.6 and 0.5 were obtained over ionosonde and GPS locations respectively. This model forms the basis towards the regional ionospheric storm-time index. , Thesis (PhD) -- Faculty of Science, Physics and Electronics, 2021
- Full Text:
- Authors: Tshisaphungo, Mpho
- Date: 2021-04
- Subjects: Ionospheric storms -- South Africa , Global Positioning System , Neural networks (Computer science) , Regression analysis , Ionosondes , Auroral electrojet , Geomagnetic indexes , Magnetic storms -- South Africa
- Language: English
- Type: thesis , text , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/178409 , vital:42937 , 10.21504/10962/178409
- Description: This thesis presents the development of a regional ionospheric storm-time model which forms the foundation of an index to provide a quick view of the ionospheric storm effects over South African mid-latitude region. The model is based on the foF2 measurements from four South African ionosonde stations. The data coverage for the model development over Grahamstown (33.3◦S, 26.5◦E), Hermanus (34.42◦S, 19.22◦E), Louisvale (28.50◦S, 21.20◦E), and Madimbo (22.39◦S, 30.88◦E) is 1996-2016, 2009-2016, 2000-2016, and 2000-2016 respectively. Data from the Global Positioning System (GPS) and radio occultation (RO) technique were used during validation. As the measure of either positive or negative storm effect, the variation of the critical frequency of the F2 layer (foF2) from the monthly median values (denoted as _foF2) is modeled. The modeling of _foF2 is based on only storm time data with the criteria of Dst 6 -50 nT and Kp > 4. The modeling methods used in the study were artificial neural network (ANN), linear regression (LR) and polynomial functions. The approach taken was to first test the modeling techniques on a single station before expanding the study to cover the regional aspect. The single station modeling was developed based on ionosonde data over Grahamstown. The inputs for the model which related to seasonal variation, diurnal variation, geomagnetic activity and solar activity were considered. For the geomagnetic activity, three indices namely; the symmetric disturbance in the horizontal component of the Earth’s magnetic field (SYM − H), the Auroral Electrojet (AE) index and local geomagnetic index A, were included as inputs. The performance of a single station model revealed that, of the three geomagnetic indices, SYM − H index has the largest contribution of 41% and 54% based on ANN and LR techniques respectively. The average correlation coefficients (R) for both ANN and LR models was 0.8, when validated during the selected storms falling within the period of model development. When validated using storms that fall outside the period of model development, the model gave R values of 0.6 and 0.5 for ANN and LR respectively. In addition, the GPS total electron content (TEC) derived measurements were used to estimate foF2 data. This is because there are more GPS receivers than ionosonde locations and the utilisation of this data increases the spatial coverage of the regional model. The estimation of foF2 from GPS TEC was done at GPS-ionosonde co-locations using polynomial functions. The average R values of 0.69 and 0.65 were obtained between actual and derived _foF2 over the co-locations and other GPS stations respectively. Validation of GPS TEC derived foF2 with RO data over regions out of ionospheric pierce points coverage with respect to ionosonde locations gave R greater than 0.9 for the selected storm period of 4-8 August 2011. The regional storm-time model was then developed based on the ANN technique using the four South African ionosonde stations. The maximum and minimum R values of 0.6 and 0.5 were obtained over ionosonde and GPS locations respectively. This model forms the basis towards the regional ionospheric storm-time index. , Thesis (PhD) -- Faculty of Science, Physics and Electronics, 2021
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Tomographic imaging of East African equatorial ionosphere and study of equatorial plasma bubbles
- Authors: Giday, Nigussie Mezgebe
- Date: 2018
- Subjects: Ionosphere -- Africa, Central , Tomography -- Africa, Central , Global Positioning System , Neural networks (Computer science) , Space environment , Multi-Instrument Data Analysis System (MIDAS) , Equatorial plasma bubbles
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63980 , vital:28516
- Description: In spite of the fact that the African ionospheric equatorial region has the largest ground footprint along the geomagnetic equator, it has not been well studied due to the absence of adequate ground-based instruments. This thesis presents research on both tomographic imaging of the African equatorial ionosphere and the study of the ionospheric irregularities/equatorial plasma bubbles (EPBs) under varying geomagnetic conditions. The Multi-Instrument Data Analysis System (MIDAS), an inversion algorithm, was investigated for its validity and ability as a tool to reconstruct multi-scaled ionospheric structures for different geomagnetic conditions. This was done for the narrow East African longitude sector with data from the available ground Global Positioning Sys-tem (GPS) receivers. The MIDAS results were compared to the results of two models, namely the IRI and GIM. MIDAS results compared more favourably with the observation vertical total electron content (VTEC), with a computed maximum correlation coefficient (r) of 0.99 and minimum root-mean-square error (RMSE) of 2.91 TECU, than did the results of the IRI-2012 and GIM models with maximum r of 0.93 and 0.99, and minimum RMSE of 13.03 TECU and 6.52 TECU, respectively, over all the test stations and validation days. The ability of MIDAS to reconstruct storm-time TEC was also compared with the results produced by the use of a Artificial Neural Net-work (ANN) for the African low- and mid-latitude regions. In terms of latitude, on average,MIDAS performed 13.44 % better than ANN in the African mid-latitudes, while MIDAS under performed in low-latitudes. This thesis also reports on the effects of moderate geomagnetic conditions on the evolution of EPBs and/or ionospheric irregularities during their season of occurrence using data from (or measurements by) space- and ground-based instruments for the east African equatorial sector. The study showed that the strength of daytime equatorial electrojet (EEJ), the steepness of the TEC peak-to-trough gradient and/or the meridional/transequatorial thermospheric winds sometimes have collective/interwoven effects, while at other times one mechanism dominates. In summary, this research offered tomographic results that outperform the results of the commonly used (“standard”) global models (i.e. IRI and GIM) for a longitude sector of importance to space weather, which has not been adequately studied due to a lack of sufficient instrumentation.
- Full Text:
- Authors: Giday, Nigussie Mezgebe
- Date: 2018
- Subjects: Ionosphere -- Africa, Central , Tomography -- Africa, Central , Global Positioning System , Neural networks (Computer science) , Space environment , Multi-Instrument Data Analysis System (MIDAS) , Equatorial plasma bubbles
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63980 , vital:28516
- Description: In spite of the fact that the African ionospheric equatorial region has the largest ground footprint along the geomagnetic equator, it has not been well studied due to the absence of adequate ground-based instruments. This thesis presents research on both tomographic imaging of the African equatorial ionosphere and the study of the ionospheric irregularities/equatorial plasma bubbles (EPBs) under varying geomagnetic conditions. The Multi-Instrument Data Analysis System (MIDAS), an inversion algorithm, was investigated for its validity and ability as a tool to reconstruct multi-scaled ionospheric structures for different geomagnetic conditions. This was done for the narrow East African longitude sector with data from the available ground Global Positioning Sys-tem (GPS) receivers. The MIDAS results were compared to the results of two models, namely the IRI and GIM. MIDAS results compared more favourably with the observation vertical total electron content (VTEC), with a computed maximum correlation coefficient (r) of 0.99 and minimum root-mean-square error (RMSE) of 2.91 TECU, than did the results of the IRI-2012 and GIM models with maximum r of 0.93 and 0.99, and minimum RMSE of 13.03 TECU and 6.52 TECU, respectively, over all the test stations and validation days. The ability of MIDAS to reconstruct storm-time TEC was also compared with the results produced by the use of a Artificial Neural Net-work (ANN) for the African low- and mid-latitude regions. In terms of latitude, on average,MIDAS performed 13.44 % better than ANN in the African mid-latitudes, while MIDAS under performed in low-latitudes. This thesis also reports on the effects of moderate geomagnetic conditions on the evolution of EPBs and/or ionospheric irregularities during their season of occurrence using data from (or measurements by) space- and ground-based instruments for the east African equatorial sector. The study showed that the strength of daytime equatorial electrojet (EEJ), the steepness of the TEC peak-to-trough gradient and/or the meridional/transequatorial thermospheric winds sometimes have collective/interwoven effects, while at other times one mechanism dominates. In summary, this research offered tomographic results that outperform the results of the commonly used (“standard”) global models (i.e. IRI and GIM) for a longitude sector of importance to space weather, which has not been adequately studied due to a lack of sufficient instrumentation.
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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:
- 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.
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