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


Interactive discussion

Status: closed

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
    • AC1: 'Reply on RC1', Shinya Nakano, 28 Oct 2021
  • RC2: 'Comment on angeo-2021-54', Anonymous Referee #2, 24 Oct 2021
    • AC2: 'Reply on RC2', Shinya Nakano, 28 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (18 Nov 2021) by Dalia Buresova
AR by Shinya Nakano on behalf of the Authors (25 Nov 2021)  Author's response
ED: Publish as is (06 Dec 2021) by Dalia Buresova
Short summary
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.