Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 31, issue 3
Articles | Volume 31, issue 3
https://doi.org/10.5194/npg-31-319-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-319-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 31, issue 3
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

Viewed

Total article views: 2,174 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,597 503 74 2,174 94 151
  • HTML: 1,597
  • PDF: 503
  • XML: 74
  • Total: 2,174
  • BibTeX: 94
  • EndNote: 151
Views and downloads (calculated since 30 Jan 2024)
Cumulative views and downloads (calculated since 30 Jan 2024)

Viewed (geographical distribution)

Total article views: 2,174 (including HTML, PDF, and XML) Thereof 2,089 with geography defined and 85 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Dec 2025
Download
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.
Read more
Share