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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Sep 2025
Research article |
| 08 Sep 2025
| 08 Sep 2025
Ensemble-based model predictive control using data assimilation techniques
Kenta Kurosawa
CORRESPONDING AUTHOR
Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Atsushi Okazaki
Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Institute for Advanced Academic Research, Chiba University, Chiba, Japan
Fumitoshi Kawasaki
Graduate School of Science and Engineering, Chiba University, Chiba, Japan
Shunji Kotsuki
Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Institute for Advanced Academic Research, Chiba University, Chiba, Japan
Research Institute of Disaster Medicine, Chiba University, Chiba, Japan
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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.
We propose ensemble-based model predictive control (EnMPC), a novel method that improves the...
