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
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:
- Date Issued: 2015
- 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:
- Date Issued: 2015
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