Annales Geophysicae
Annales Geophysicae
ANGEO
Articles | Volume 40, issue 1
Articles | Volume 40, issue 1
https://doi.org/10.5194/angeo-40-11-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/angeo-40-11-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 40, issue 1
Regular paper
 | 
12 Jan 2022
Regular paper |  | 12 Jan 2022

Echo state network model for analyzing solar-wind effects on the AU and AL indices

Shin'ya Nakano and Ryuho Kataoka

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Cited articles

Akasofu, S.-I.: Energy coupling between the solar wind and the magnetosphere, Space Sci. Rev., 28, 121–190, 1981. a, b, c, d
Allen, J. H. and Kroehl, H. W.: Spatial and temporal distributions of magnetic effects of auroral electrojets as derived from AE indices, J. Geophys. Res., 80, 3667–3677, 1975. a
Amariutei, O. A. and Ganushkina, N. Y.: On the prediction of the auroral westward electrojet index, Ann. Geophys., 30, 841–847, https://doi.org/10.5194/angeo-30-841-2012, 2012. a
Arnoldy, R. L.: Signature in the interplanetary medium for substorms, J. Geophys. Res., 76, 5189–5201, 1971. a
Bala, R. and Reiff, P.: Improvements in short-term forecasting of geomagnetic activity, Space Weather, 10, S06001, https://doi.org/10.1029/2012SW000779, 2012. a
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The relationships between auroral activity and the solar-wind conditions are modeled with a machine-learning technique. The impact of various solar-wind parameters on the auroral activity is then evaluated by putting artificial inputs into the trained machine-learning model. One of the notable findings is that the solar-wind density effect on the auroral activity is emphasized under high solar-wind speed and weak solar-wind magnetic field.
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