Annales Geophysicae
Annales Geophysicae
ANGEO
Articles | Volume 41, issue 2
Articles | Volume 41, issue 2
https://doi.org/10.5194/angeo-41-529-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/angeo-41-529-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 41, issue 2
Regular paper
 | 
21 Nov 2023
Regular paper |  | 21 Nov 2023

Probabilistic modelling of substorm occurrences with an echo state network

Shin'ya Nakano, Ryuho Kataoka, Masahito Nosé, and Jesper W. Gjerloev

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Cited articles

Amariutei, O. A. and Ganushkina, N. Y.: On the prediction of the auroral westward electojet index, Ann. Geophys., 30, 841–847, https://doi.org/10.5194/angeo-30-841-2012, 2012. a
Baker, D. N., Pulkkinen, T. I., Angelopoulos, V., Baumjohann, W., and McPherron, R. L.: Neutral line model of substorms: Past results and present view, J. Geophys. Res., 101, 12975–13010, https://doi.org/10.1029/95JA03753, 1996. a
Bishop, C. M.: Pattern recognition and machine learning, Springer, New York, ISBN: 978-0387310732, 2006. a
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Cliver, E. W., Kamide, Y., and Ling, A. G.: Mountain and valleys: Semiannual variation of geomagnetic activity, J. Geophys. Res., 105, 2413–2424, 2000. a
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Substorms are a phenomenon in the magnetosphere–ionosphere system, which are characterised by brightening of an auroral arc and enhancement of electric currents in the polar ionosphere. Since substorms are difficult to predict, this study treats a substorm occurrence as a stochastic phenomenon and represents the substorm occurrence rate with a machine learning model. We then analyse the response of substorm activity to solar wind conditions by feeding synthetic solar wind data into the model.
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