Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network
Abstract Given the potential importance of solar quiet (Sq) ionospheric current in geomagnetic field modeling, it is vital to obtain accurate parameters characterizing its variations, particularly the spatial and temporal variations. In this paper, we derived the Sq current function based on the sph...
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Wiley
2021-11-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2021SW002831 |
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| author | Charles Owolabi Haibing Ruan Yosuke Yamazaki Jinfeng Li Jiahao Zhong A. V. Eyelade Shishir Priyadarshi Akimasa Yoshikawa |
| author_facet | Charles Owolabi Haibing Ruan Yosuke Yamazaki Jinfeng Li Jiahao Zhong A. V. Eyelade Shishir Priyadarshi Akimasa Yoshikawa |
| author_sort | Charles Owolabi |
| collection | DOAJ |
| description | Abstract Given the potential importance of solar quiet (Sq) ionospheric current in geomagnetic field modeling, it is vital to obtain accurate parameters characterizing its variations, particularly the spatial and temporal variations. In this paper, we derived the Sq current function based on the spherical harmonic analysis (SHA) technique using a 14‐year (2006–2019) quiet geomagnetic field record over the American sector. The empirical orthogonal function (EOF) analysis was then applied to deduce temporal and spatial variations of the Sq current. It is observed that the first EOF mode of the Sq current function is dominated by solar activity, while the second and third EOF modes exhibit annual and semiannual variations, respectively. Also, the artificial neural network (ANN) model of Sq current function was constructed to validate the EOF model predictions. While the Sq current intensity predicted by the ANN model is underestimated by 2.83%, the EOF model underpredicted the Sq current intensity by 1.92% relative to the observation. The root mean square error (RMSE) of the EOF model is 0.64 kA. This RMSE is about 79% smaller than that of the ANN model. In addition, both the EOF and ANN models capture the variation of the total Sq current (Jtotal) intensity with respect to solar activity. In principle, the EOF model had an optimal performance at nearly 98% accuracy, with the ANN model exhibiting almost the same degree of accuracy, which appears to be a reference point for ionospheric conditions when looking for space weather applications. |
| format | Article |
| id | doaj-art-92754e738f854b19a02df97bfeeff841 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-92754e738f854b19a02df97bfeeff8412025-01-14T16:27:04ZengWileySpace Weather1542-73902021-11-011911n/an/a10.1029/2021SW002831Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural NetworkCharles Owolabi0Haibing Ruan1Yosuke Yamazaki2Jinfeng Li3Jiahao Zhong4A. V. Eyelade5Shishir Priyadarshi6Akimasa Yoshikawa7Department of Earth and Space Sciences Southern University of Science and Technology Shenzhen ChinaSchool of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing ChinaGFZ German Research Centre for Geosciences Potsdam GermanyDepartment of Earth and Space Sciences Southern University of Science and Technology Shenzhen ChinaPlanetary Environmental and Astrobiological Research Laboratory (PEARL) School of Atmospheric Sciences Sun Yat‐Sen University Zhuhai ChinaDepartment of Physics Universidad de Santiago de Chile Santiago ChileDepartment of Electronic and Electrical Engineering University of Bath Bath UKInternational Center for Space Weather Science and Education Kyushu University Fukuoka JapanAbstract Given the potential importance of solar quiet (Sq) ionospheric current in geomagnetic field modeling, it is vital to obtain accurate parameters characterizing its variations, particularly the spatial and temporal variations. In this paper, we derived the Sq current function based on the spherical harmonic analysis (SHA) technique using a 14‐year (2006–2019) quiet geomagnetic field record over the American sector. The empirical orthogonal function (EOF) analysis was then applied to deduce temporal and spatial variations of the Sq current. It is observed that the first EOF mode of the Sq current function is dominated by solar activity, while the second and third EOF modes exhibit annual and semiannual variations, respectively. Also, the artificial neural network (ANN) model of Sq current function was constructed to validate the EOF model predictions. While the Sq current intensity predicted by the ANN model is underestimated by 2.83%, the EOF model underpredicted the Sq current intensity by 1.92% relative to the observation. The root mean square error (RMSE) of the EOF model is 0.64 kA. This RMSE is about 79% smaller than that of the ANN model. In addition, both the EOF and ANN models capture the variation of the total Sq current (Jtotal) intensity with respect to solar activity. In principle, the EOF model had an optimal performance at nearly 98% accuracy, with the ANN model exhibiting almost the same degree of accuracy, which appears to be a reference point for ionospheric conditions when looking for space weather applications.https://doi.org/10.1029/2021SW002831spherical harmonic analysisempirical orthogonal function analysisartificial neural networkSq current |
| spellingShingle | Charles Owolabi Haibing Ruan Yosuke Yamazaki Jinfeng Li Jiahao Zhong A. V. Eyelade Shishir Priyadarshi Akimasa Yoshikawa Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network Space Weather spherical harmonic analysis empirical orthogonal function analysis artificial neural network Sq current |
| title | Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network |
| title_full | Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network |
| title_fullStr | Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network |
| title_full_unstemmed | Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network |
| title_short | Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network |
| title_sort | empirical modeling of ionospheric current using empirical orthogonal function analysis and artificial neural network |
| topic | spherical harmonic analysis empirical orthogonal function analysis artificial neural network Sq current |
| url | https://doi.org/10.1029/2021SW002831 |
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