Bayesian networks for probabilistic weather prediction

Abstract

Several standard approaches have been introduced for meteorological time series prediction (analog techniques, neural networks, etc.). However, when dealing with multivariate spatially distributed time series (e.g., a network of meteorological stations over the Iberian peninsula) the above methods do not consider all the available information (they consider special independency assumptions to simplify the model). In this work, we introduce Bayesian Networks (BNs) in this framework to model the spatial and temporal dependencies among the different stations using a directed acyclic graph.