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-32-281-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-32-281-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 32, issue 3
Research article
 | 
15 Aug 2025
Research article |  | 15 Aug 2025

Evaluation of the effectiveness of an intervention strategy in a control simulation experiment through comparison with model predictive control

Rikuto Nagai, Yang Bai, Masaki Ogura, Shunji Kotsuki, and Naoki Wakamiya

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Aizawa, J., Ogura, M., Shimono, M., and Wakamiya, N.: Firing pattern manipulation of neuronal networks by deep unfolding-based model predictive control, IET Control Theory A., 18, 2003–2013, 2024 a, b
Atlas, R.: Atmospheric observations and experiments to assess their usefulness in data assimilation, J. Meteorol. Soc. Jpn., 75, 111–130, 1997. a
Bae, K., Ryu, H., and Shin, H.: Does Adam optimizer keep close to the optimal point?, arXiv [preprint], https://doi.org/10.48550/arXiv.1911.00289, 1 November 2019. a
Ban-Weiss, G. and Caldeira, K.: Geoengineering as an optimization problem, Environ. Res. Lett., 5, 034009, https://doi.org/10.1088/1748-9326/5/3/034009, 2010. a, b
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, 2015. 
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Controlling chaotic systems is a key step toward weather control. The control simulation experiment (CSE) modifies weather systems using small perturbations, as shown in studies with the Lorenz-63 model. However, the effectiveness of CSE compared to other methods is unclear. This study evaluates CSE against model predictive control (MPC). Simulations reveal that MPC achieves higher success rates with less effort under certain conditions, linking control theory and atmospheric science.
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