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:
- Date Issued: 2021-04
- 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:
- Date Issued: 2021-04
Modelling Ionospheric vertical drifts over the African low latitude region
- Dubazane, Makhosonke Berthwell
- Authors: Dubazane, Makhosonke Berthwell
- Date: 2018
- Subjects: Ionospheric drift , Magnetometers , Functions, Orthogonal , Neural networks (Computer science) , Ionospheric electron density -- Africa , Communication and Navigation Outage Forecasting Systems (C/NOFS)
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63356 , vital:28396
- Description: Low/equatorial latitudes vertical plasma drifts and electric fields govern the formation and changes of ionospheric density structures which affect space-based systems such as communications, navigation and positioning. Dynamical and electrodynamical processes play important roles in plasma distribution at different altitudes. Because of the high variability of E × B drift in low latitude regions, coupled with various processes that sometimes originate from high latitudes especially during geomagnetic storm conditions, it is challenging to develop accurate vertical drift models. This is despite the fact that there are very few instruments dedicated to provide electric field and hence E × B drift data in low/equatorial latitude regions. To this effect, there exists no ground-based instrument for direct measurements of E×B drift data in the African sector. This study presents the first time investigation aimed at modelling the long-term variability of low latitude vertical E × B drift over the African sector using a combination of Communication and Navigation Outage Forecasting Systems (C/NOFS) and ground-based magnetometer observations/measurements during 2008-2013. Because the approach is based on the estimation of equatorial electrojet from ground-based magnetometer observations, the developed models are only valid for local daytime. Three modelling techniques have been considered. The application of Empirical Orthogonal Functions and partial least squares has been performed on vertical E × B drift modelling for the first time. The artificial neural networks that have the advantage of learning underlying changes between a set of inputs and known output were also used in vertical E × B drift modelling. Due to lack of E×B drift data over the African sector, the developed models were validated using satellite data and the climatological Scherliess-Fejer model incorporated within the International Reference Ionosphere model. Maximum correlation coefficient of ∼ 0.8 was achieved when validating the developed models with C/NOFS E × B drift observations that were not used in any model development. For most of the time, the climatological model overestimates the local daytime vertical E × B drift velocities. The methods and approach presented in this study provide a background for constructing vertical E ×B drift databases in longitude sectors that do not have radar instrumentation. This will in turn make it possible to study day-to-day variability of vertical E×B drift and hopefully lead to the development of regional and global models that will incorporate local time information in different longitude sectors.
- Full Text:
- Date Issued: 2018
- Authors: Dubazane, Makhosonke Berthwell
- Date: 2018
- Subjects: Ionospheric drift , Magnetometers , Functions, Orthogonal , Neural networks (Computer science) , Ionospheric electron density -- Africa , Communication and Navigation Outage Forecasting Systems (C/NOFS)
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63356 , vital:28396
- Description: Low/equatorial latitudes vertical plasma drifts and electric fields govern the formation and changes of ionospheric density structures which affect space-based systems such as communications, navigation and positioning. Dynamical and electrodynamical processes play important roles in plasma distribution at different altitudes. Because of the high variability of E × B drift in low latitude regions, coupled with various processes that sometimes originate from high latitudes especially during geomagnetic storm conditions, it is challenging to develop accurate vertical drift models. This is despite the fact that there are very few instruments dedicated to provide electric field and hence E × B drift data in low/equatorial latitude regions. To this effect, there exists no ground-based instrument for direct measurements of E×B drift data in the African sector. This study presents the first time investigation aimed at modelling the long-term variability of low latitude vertical E × B drift over the African sector using a combination of Communication and Navigation Outage Forecasting Systems (C/NOFS) and ground-based magnetometer observations/measurements during 2008-2013. Because the approach is based on the estimation of equatorial electrojet from ground-based magnetometer observations, the developed models are only valid for local daytime. Three modelling techniques have been considered. The application of Empirical Orthogonal Functions and partial least squares has been performed on vertical E × B drift modelling for the first time. The artificial neural networks that have the advantage of learning underlying changes between a set of inputs and known output were also used in vertical E × B drift modelling. Due to lack of E×B drift data over the African sector, the developed models were validated using satellite data and the climatological Scherliess-Fejer model incorporated within the International Reference Ionosphere model. Maximum correlation coefficient of ∼ 0.8 was achieved when validating the developed models with C/NOFS E × B drift observations that were not used in any model development. For most of the time, the climatological model overestimates the local daytime vertical E × B drift velocities. The methods and approach presented in this study provide a background for constructing vertical E ×B drift databases in longitude sectors that do not have radar instrumentation. This will in turn make it possible to study day-to-day variability of vertical E×B drift and hopefully lead to the development of regional and global models that will incorporate local time information in different longitude sectors.
- Full Text:
- Date Issued: 2018
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