Preprints
https://doi.org/10.5194/angeo-2021-54
https://doi.org/10.5194/angeo-2021-54

  20 Sep 2021

20 Sep 2021

Review status: this preprint is currently under review for the journal ANGEO.

Virtual sounding of solar-wind effects on the AU and AL indices based on an echo state network model

Shin'ya Nakano1,2,3 and Ryuho Kataoka4,3 Shin'ya Nakano and Ryuho Kataoka
  • 1The Institute of Statistical Mathematics, Tachikawa, 190–8562, Japan
  • 2Center for Data Assimilation Research and Applications, Joint Support Center for Data Science Research, Tachikawa, Japan
  • 3School of Multidisciplinary Science, SOKENDAI, Hayama, 240–0115, Japan
  • 4National Institute of Polar Research, Tachikawa, Japan

Abstract. The properties of the auroral electrojets are examined on the basis of a trained machine learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.

Shin'ya Nakano and Ryuho Kataoka

Status: open (until 07 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on angeo-2021-54', Anonymous Referee #1, 20 Oct 2021 reply

Shin'ya Nakano and Ryuho Kataoka

Shin'ya Nakano and Ryuho Kataoka

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Short summary
The relationships between the auroral activity and the solar-wind conditions are modeled with a machine lerning 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. In particular, nonlinear effects of the solar-wind density are suggested.