  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.
