Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks
Abstract This paper presents the development of a storm‐time total electron content (TEC) model over the African sector for the first time. The storm criterion used was |Dst| ≥ 50 nT and Kp ≥ 4. We have utilized Global Positioning System (GPS) observations from 2000 to 2018 from about 252 receivers...
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| Format: | Article |
| Language: | English |
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Wiley
2020-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2020SW002525 |
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| author | Daniel Okoh John Bosco Habarulema Babatunde Rabiu Gopi Seemala Joshua Benjamin Wisdom Joseph Olwendo Olivier Obrou Tshimangadzo Merline Matamba |
| author_facet | Daniel Okoh John Bosco Habarulema Babatunde Rabiu Gopi Seemala Joshua Benjamin Wisdom Joseph Olwendo Olivier Obrou Tshimangadzo Merline Matamba |
| author_sort | Daniel Okoh |
| collection | DOAJ |
| description | Abstract This paper presents the development of a storm‐time total electron content (TEC) model over the African sector for the first time. The storm criterion used was |Dst| ≥ 50 nT and Kp ≥ 4. We have utilized Global Positioning System (GPS) observations from 2000 to 2018 from about 252 receivers over the African continent and surroundings within spatial coverage of 40°S–40°N latitude and 25°W–60°E longitude. To increase data coverage in areas devoid of ground‐based instrumentation including oceans, we used the available radio occultation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) TEC from 2008 to 2018. The model is based on artificial neural networks which are used to learn the relationship between TEC and the corresponding physical/geophysical input parameters representing factors which influence ionospheric variability. An important result from this effort was the inclusion of the time history of the geomagnetic activity indicators dKpdtanddDstdt which improved TEC modeling by about 5% and 12% in middle and low latitudes, respectively. Overall, the model performs comparatively well with, and sometimes better than, the earlier single station modeling efforts even during quiet conditions. Given that this is a storm‐time model, this result is encouraging since it is challenging to model ionospheric parameters during geomagnetically disturbed conditions. Statistically, the average root‐mean‐square error (RMSE) between modeled and GPS TEC is 5.5 TECU (percentage error = 30.3%) and 5.0 TECU (percentage error = 30.4%) for the Southern and Northern Hemisphere midlatitudes respectively compared to 7.5 TECU (percentage error = 22.0%) in low latitudes. |
| format | Article |
| id | doaj-art-d2659de8f9e340e197e5462ef84bd1d2 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-d2659de8f9e340e197e5462ef84bd1d22025-01-14T16:30:54ZengWileySpace Weather1542-73902020-09-01189n/an/a10.1029/2020SW002525Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural NetworksDaniel Okoh0John Bosco Habarulema1Babatunde Rabiu2Gopi Seemala3Joshua Benjamin Wisdom4Joseph Olwendo5Olivier Obrou6Tshimangadzo Merline Matamba7Center for Atmospheric Research National Space Research and Development Agency Anyigba NigeriaSpace Science South African National Space Agency Hermanus South AfricaCenter for Atmospheric Research National Space Research and Development Agency Anyigba NigeriaIndian Institute of Geomagnetism Navi Mumbai IndiaDepartment of Physics Kebbi State University of Science and Technology Aliero NigeriaDepartment of Physics Pwani University Kilifi KenyaLaboratoire de Physique de l'Atmosphere Universite de Cocody Abidjan Côte d'IvoireSpace Science South African National Space Agency Hermanus South AfricaAbstract This paper presents the development of a storm‐time total electron content (TEC) model over the African sector for the first time. The storm criterion used was |Dst| ≥ 50 nT and Kp ≥ 4. We have utilized Global Positioning System (GPS) observations from 2000 to 2018 from about 252 receivers over the African continent and surroundings within spatial coverage of 40°S–40°N latitude and 25°W–60°E longitude. To increase data coverage in areas devoid of ground‐based instrumentation including oceans, we used the available radio occultation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) TEC from 2008 to 2018. The model is based on artificial neural networks which are used to learn the relationship between TEC and the corresponding physical/geophysical input parameters representing factors which influence ionospheric variability. An important result from this effort was the inclusion of the time history of the geomagnetic activity indicators dKpdtanddDstdt which improved TEC modeling by about 5% and 12% in middle and low latitudes, respectively. Overall, the model performs comparatively well with, and sometimes better than, the earlier single station modeling efforts even during quiet conditions. Given that this is a storm‐time model, this result is encouraging since it is challenging to model ionospheric parameters during geomagnetically disturbed conditions. Statistically, the average root‐mean‐square error (RMSE) between modeled and GPS TEC is 5.5 TECU (percentage error = 30.3%) and 5.0 TECU (percentage error = 30.4%) for the Southern and Northern Hemisphere midlatitudes respectively compared to 7.5 TECU (percentage error = 22.0%) in low latitudes.https://doi.org/10.1029/2020SW002525Africageomagnetic stormionosphereneural networkTEC |
| spellingShingle | Daniel Okoh John Bosco Habarulema Babatunde Rabiu Gopi Seemala Joshua Benjamin Wisdom Joseph Olwendo Olivier Obrou Tshimangadzo Merline Matamba Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks Space Weather Africa geomagnetic storm ionosphere neural network TEC |
| title | Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks |
| title_full | Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks |
| title_fullStr | Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks |
| title_full_unstemmed | Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks |
| title_short | Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks |
| title_sort | storm time modeling of the african regional ionospheric total electron content using artificial neural networks |
| topic | Africa geomagnetic storm ionosphere neural network TEC |
| url | https://doi.org/10.1029/2020SW002525 |
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