Articles | Volume 31, issue 3
https://doi.org/10.5194/npg-31-319-2024
https://doi.org/10.5194/npg-31-319-2024
Research article
 | 
10 Jul 2024
Research article |  | 10 Jul 2024

Leading the Lorenz 63 system toward the prescribed regime by model predictive control coupled with data assimilation

Fumitoshi Kawasaki and Shunji Kotsuki

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2024-4', Anonymous Referee #1, 04 Mar 2024
    • AC1: 'Reply on RC1', Fumitoshi Kawasaki, 02 Apr 2024
  • RC2: 'Comment on npg-2024-4', Anonymous Referee #2, 14 Mar 2024
    • AC2: 'Reply on RC2', Fumitoshi Kawasaki, 02 Apr 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Fumitoshi Kawasaki on behalf of the Authors (03 Apr 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Apr 2024) by Pierre Tandeo
RR by Anonymous Referee #1 (05 Apr 2024)
ED: Publish as is (19 Apr 2024) by Pierre Tandeo
AR by Fumitoshi Kawasaki on behalf of the Authors (26 Apr 2024)  Manuscript 
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Short summary
Recently, scientists have been looking into ways to control the weather to lead to a desirable direction for mitigating weather-induced disasters caused by torrential rainfall and typhoons. This study proposes using the model predictive control (MPC), an advanced control method, to control a chaotic system. Through numerical experiments using a low-dimensional chaotic system, we demonstrate that the system can be successfully controlled with shorter forecasts compared to previous studies.