Ensemble-based model predictive control  using data assimilation techniques

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

doi:10.5194/npg-32-293-2025

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

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Feb 2025	88	28	2	118
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Apr 2025	88	18	4	110
May 2025	94	22	4	120
Jun 2025	140	22	10	172
Jul 2025	70	12	2	84
Aug 2025	236	22	0	258
Sep 2025	1,422	48	27	1,497
Oct 2025	224	31	7	262
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Jan 2026	592	45	16	653
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Cumulative views and downloads (calculated since 21 Feb 2025)

Month	HTML	PDF	XML	Total
Feb 2025	88	28	2	118
Mar 2025	206	42	10	258
Apr 2025	88	18	4	110
May 2025	94	22	4	120
Jun 2025	140	22	10	172
Jul 2025	70	12	2	84
Aug 2025	236	22	0	258
Sep 2025	1,422	48	27	1,497
Oct 2025	224	31	7	262
Nov 2025	244	46	14	304
Dec 2025	307	60	21	388
Jan 2026	592	45	16	653
Feb 2026	305	52	19	376
Mar 2026	145	60	12	217
Apr 2026	101	38	9	148
May 2026	28	8	1	37

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Total article views: 5,002 (including HTML, PDF, and XML) Thereof 4,720 with geography defined and 282 with unknown origin.

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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.