Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 32, issue 3
Articles | Volume 32, issue 3
https://doi.org/10.5194/npg-32-293-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-32-293-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 32, issue 3
Research article
 | 
08 Sep 2025
Research article |  | 08 Sep 2025

Ensemble-based model predictive control using data assimilation techniques

Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki

Viewed

Total article views: 1,647 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,523 100 24 1,647 54 64
  • HTML: 1,523
  • PDF: 100
  • XML: 24
  • Total: 1,647
  • BibTeX: 54
  • EndNote: 64
Views and downloads (calculated since 21 Feb 2025)
Cumulative views and downloads (calculated since 21 Feb 2025)

Viewed (geographical distribution)

Total article views: 1,647 (including HTML, PDF, and XML) Thereof 1,582 with geography defined and 65 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Oct 2025
Download
Short summary
We propose ensemble-based model predictive control (EnMPC), a novel method that improves the control of complex systems like the atmosphere by integrating control theory with data assimilation. Unlike traditional methods, which are computationally expensive, EnMPC uses ensemble simulations to efficiently handle uncertainties and optimize solutions. This approach reduces computational cost while maintaining accuracy, making it a promising step toward real-world applications in dynamic system control.
Read more
Share