10 Jan 2023
10 Jan 2023
Status: this preprint is currently under review for the journal NPG.

Reducing manipulations in control simulation experiment based on instability vectors with Lorenz-63 model

Mao Ouyang1, Keita Tokuda2, and Shunji Kotsuki1,3,4 Mao Ouyang et al.
  • 1Center for Environmental Remote Sensing (CEReS), Chiba University, 1–33 Yayoi, Inage, Chiba 263–8522, Japan
  • 2Department of Computer Science, University of Tsukuba, 1–1–1 Tennodai, Tsukuba, Ibaraki 305–8577, Japan
  • 3Institute for Advanced Academic Research (IAAR), Chiba University, 1–33 Yayoi, Inage, Chiba 263–8522, Japan
  • 4PRESTO, Japan Science and Technology Agency, 4–1–8 Honcho, Kawaguchi, Saitama 332–0012, Japan

Abstract. Controlling weather is an outstanding and pioneering challenge for researchers around the world, due to the chaotic features of the complex atmosphere. A control simulation experiment (CSE) on the Lorenz-63 model, which consists of positive and negative regimes represented by the states of variable x, demonstrated that the variables can be controlled to stay in the target regime by adding perturbations with a constant magnitude to an independent model run (Miyoshi and Sun, 2022). The current study tries to reduce the input manipulation of CSE, including the total control times and magnitudes of perturbations, by investigating how controls affect the instability of systems. For that purpose, we first explore the bred vector (BV) and singular vector (SV), which represent the instability properties of chaotic models without and under control in the Lorenz-63 model. The maximum growth rate of SV shows significant reductions when the variable x was controlled into the target regime. Subsequently, this research proposes to update the magnitude of perturbations adaptively based on the maximum growth rate of SV; consequently, the times to control will also change. The proposed method successfully reduces around 40 % of total control times, and around 20 % of total magnitudes of perturbations, compared to the case with constant magnitude. Results of this research suggest that investigating the impacts of control on instability would be beneficial for designing methods to control the complex atmosphere with feasible manipulations.

Mao Ouyang et al.

Status: open (until 07 Mar 2023)

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Mao Ouyang et al.

Mao Ouyang et al.


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Short summary
This research found that control the weather would change the chaotic behavior of atmospheric model. We proposed to introduce chaos theory in the weather control. Experimental results demonstrated that the proposed approach reduced the manipulations, including the control times and magnitudes, which throw light on the weather control in a real atmospheric model.