Preprints
https://doi.org/https://doi.org/10.48550/arXiv.2403.06371
https://doi.org/https://doi.org/10.48550/arXiv.2403.06371
03 Apr 2024
 | 03 Apr 2024
Status: this preprint is open for discussion.

Ensemble Kalman filter in geoscience meets model predictive control

Yohei Sawada

Abstract. Although data assimilation originates from control theory, the relationship between modern data assimilation methods in geoscience and model predictive control has not been extensively explored. In the present paper, I discuss that the modern data assimilation methods in geoscience and model predictive control essentially minimize the similar quadratic cost functions. Inspired by this similarity, I propose a new ensemble Kalman filter (EnKF)-based method for controlling spatio-temporally chaotic systems, which can readily be applied to high-dimensional and nonlinear Earth systems. In this method, the reference vector, which serves as the control target, is assimilated into the state space as a pseudo-observation by ensemble Kalman smoother to obtain the appropriate perturbation to be added to a system. A proof-of-concept experiment using the Lorenz 63 model is presented. The system is constrained in one wing of the butterfly attractor without tipping to the other side by reasonably small control perturbations which are comparable with previous works.

Yohei Sawada

Status: open (until 29 May 2024)

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Yohei Sawada
Yohei Sawada

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Short summary
It is generally difficult to control large-scale and complex systems, such as Earth systems, using small forces. In this paper, a new method to control such systems is proposed. The new method is inspired by the similarity between simulation-observation integration methods in geoscience and model predictive control theory in control engineering. The proposed method is particularly suitable to find the efficient strategies of weather modification.