In this paper we examine the use of Bayesian networks (BNs) for improving weather prediction, applying them to the problem of predicting sea breezes. We compare a pre-existing Bureau of Meteorology rule-based system with an elicited BN and others learned by two data mining programs, TETRAD II [Spirtes et al., 1993] and Causal MML [Wallace and Korb, 1999]. These Bayesian nets are shown to significantly outperform the rule-based system in predictive accuracy.