the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Ensemble Kalman filter in geoscience meets model predictive control
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.
Status: closed
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RC1: 'Comment on egusphere-2024-781', Anonymous Referee #1, 02 May 2024
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RC2: 'Reply on RC1', Anonymous Referee #2, 06 May 2024
Indeed, the Author is correct that the origins of data assimilation are in control. There are journals, textbooks, that consider control of complex nonlinear dynamics, stochastic and otherwise. The state of the art in this field is fairly advanced. Current papers consider ML methods, such as adversarial networks, Bayesian learning networks, recurrent networks, etc. Apparently none of the ML or dynamics literature is familiar to the Author. To consider the cite paper by Henderson et al pioneering is an incredible stretch.
The Author proposes an algorithmic tweak that I've not seen, but in the end the outcomes are incredibly modest and thus the work is incremental. In the analyses side of things, variance minimizing or L2 minimization is very old and mature, so at the very least I would have expected a complete analysis of the methodology, but all the paper provides is proof by figure.
I do not see a way to salvage this paper by revising it.
Citation: https://doi.org/10.5194/egusphere-2024-781-RC2
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RC2: 'Reply on RC1', Anonymous Referee #2, 06 May 2024
- AC1: 'Final response on egusphere-2024-781', Yohei Sawada, 22 Jul 2024
Status: closed
-
RC1: 'Comment on egusphere-2024-781', Anonymous Referee #1, 02 May 2024
-
RC2: 'Reply on RC1', Anonymous Referee #2, 06 May 2024
Indeed, the Author is correct that the origins of data assimilation are in control. There are journals, textbooks, that consider control of complex nonlinear dynamics, stochastic and otherwise. The state of the art in this field is fairly advanced. Current papers consider ML methods, such as adversarial networks, Bayesian learning networks, recurrent networks, etc. Apparently none of the ML or dynamics literature is familiar to the Author. To consider the cite paper by Henderson et al pioneering is an incredible stretch.
The Author proposes an algorithmic tweak that I've not seen, but in the end the outcomes are incredibly modest and thus the work is incremental. In the analyses side of things, variance minimizing or L2 minimization is very old and mature, so at the very least I would have expected a complete analysis of the methodology, but all the paper provides is proof by figure.
I do not see a way to salvage this paper by revising it.
Citation: https://doi.org/10.5194/egusphere-2024-781-RC2
-
RC2: 'Reply on RC1', Anonymous Referee #2, 06 May 2024
- AC1: 'Final response on egusphere-2024-781', Yohei Sawada, 22 Jul 2024
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