  Recent studies show indication of the effectiveness of synchronization as a
data assimilation tool for small or meso-scale forecast when less number of
variables are observed frequently. Our main aim here is to understand the
effects of changing observational frequency and observational noise on
synchronization and prediction in a low dimensional chaotic system, namely the
Chua circuit model. We perform {\it identical twin experiments} in order to
study synchronization using discrete-in-time observations generated from
independent model run and coupled unidirectionally to the model through $x$,
$y$ and $z$ separately. 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 prediction errors
are much larger when noisy observations are used than when using observations
without noise.
