Preprints
https://doi.org/10.5194/npg-2024-4
https://doi.org/10.5194/npg-2024-4
30 Jan 2024
 | 30 Jan 2024
Status: this preprint is currently under review for the journal NPG.

Leading the Lorenz-63 system toward the prescribed regime by model predictive control coupled with data assimilation

Fumitoshi Kawasaki and Shunji Kotsuki

Abstract. In recent years, concerns have been raised regarding the intensification and increase of extreme weather events such as torrential rainfall and typhoons. To mitigate the damage caused by weather-induced disasters, recent studies have started developing weather control technologies to lead the weather to a desirable direction with feasible manipulations. This study proposes introducing the model predictive control (MPC), an advanced control method explored in control engineering, into the framework of the control simulation experiment (CSE). In contrast to previous CSE studies, the proposed method explicitly considers physical constraints such as the maximum allowable manipulations within the cost function of the MPC. As the first step toward applying the MPC to real weather control, this study performed a series of MPC experiments with the Lorenz-63 model. Our results showed that the Lorenz-63 system can be led to the positive regime with control inputs determined by the MPC. Furthermore, the MPC significantly reduced necessary forecast length compared to earlier CSE studies. It was beneficial to select a member showing a larger regime shift for the initial state when dealing with uncertainty in initial states.

Fumitoshi Kawasaki and Shunji Kotsuki

Status: open (until 26 Mar 2024)

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Fumitoshi Kawasaki and Shunji Kotsuki
Fumitoshi Kawasaki and Shunji Kotsuki

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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 proposed using model predictive control, known as an advanced control method, to control a chaotic system. Through numerical experiments using a low-dimensional chaotic system, we demonstrated that the system can be controlled successfully with shorter forecasts compared to previous studies.