Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network

Owolabi，Charles1,2; Ruan，Haibing3; Yamazaki，Yosuke4; Li，Jinfeng1; Zhong，Jiahao5; Eyelade，A. V.6; Priyadarshi，Shishir7; Yoshikawa，Akimasa8

2021-11-01

10.1029/2021SW002831

Space Weather   影响因子和分区

1542-7390

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 (J) 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.

artificial neural network empirical orthogonal function analysis spherical harmonic analysis Sq current

SCI

英语

第一

National Natural Science Foundation (NSF) of China[41804151,42074186,41804150] ; Natural Science Foundation of Jiangsu Province[BK20211036] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515011216]

Astronomy & Astrophysics ; Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences

Astronomy & Astrophysics ; Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences

WOS:000722467500012

AMER GEOPHYSICAL UNION

SPACE SCIENCE

2-s2.0-85119691940

Scopus

1.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,China
2.Department of Physics,Federal University of Technology Akure,Akure,Nigeria
3.School of Remote Sensing and Geomatics Engineering,Nanjing University of Information Science and Technology,Nanjing,China
4.GFZ German Research Centre for Geosciences,Potsdam,Germany
5.Planetary Environmental and Astrobiological Research Laboratory (PEARL),School of Atmospheric Sciences,Sun Yat-Sen University,Zhuhai,China
6.Department of Physics,Universidad de Santiago de Chile,Santiago,Chile
7.Department of Electronic and Electrical Engineering,University of Bath,Bath,United Kingdom
8.International Center for Space Weather Science and Education,Kyushu University,Fukuoka,Japan

Owolabi，Charles,Ruan，Haibing,Yamazaki，Yosuke,et al. Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network[J]. Space Weather,2021,19(11).

Owolabi，Charles.,Ruan，Haibing.,Yamazaki，Yosuke.,Li，Jinfeng.,Zhong，Jiahao.,...&Yoshikawa，Akimasa.(2021).Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network.Space Weather,19(11).

Owolabi，Charles,et al."Empirical Modeling of Ionospheric Current Using Empirical Orthogonal Function Analysis and Artificial Neural Network".Space Weather 19.11(2021).