Effect of discrete time observations on synchronization in Chua model and applications to data assimilation

Abstract

Data assimilation is a tool, which incorporates observations in the model to improve the forecast, and it can be thought of as a synchronization of the model with observations. This paper discusses results of numerical identical twin experiments, with observations acting as master system coupled unidirectionally to the slave system at discrete time instances. We study the effects of varying the coupling constant, the observational frequency, and the observational noise intensity on synchronization and prediction in a low dimensional chaotic system, namely, the Chua circuit model. We observe synchrony in a finite range of coupling constant when coupling the x and y variables of the Chua model, but not when coupling the z variable. This range of coupling constant decreases with increasing levels of noise in the observations. The Chua system does not show synchrony when the time gap between observations is greater than about one-seventh of the Lyapunov time. Finally, we also note that the prediction errors are much larger when noisy observations are used than when using observations without noise.