  We show how a recently introduced statistics [Patil et al, Phys. Rev. Lett.
81 5878 (2001)] provides a direct relationship between dimension and
predictability in spatiotemporal chaotic systems. Regions of low dimension are
identified as having high predictability and vice-versa. This conclusion is
reached by using methods from dynamical systems theory and Bayesian modelling.
We emphasize in this work the consequences for short time forecasting and
examine the relevance for factor analysis. Although we concentrate on coupled
map lattices and coupled nonlinear oscillators for convenience, any other
spatially distributed system could be used instead, such as turbulent fluid
flows.
