A Bayesian hierarchical model for local precipitation by downscaling large-scale atmospheric circulation patterns

Precipitation over the Western part of Iberian Peninsula is known to be related to the large-scale sea level pressure field and thus to advection of humidity into this area. The major problem is to downscale this synoptic atmospheric information to local daily precipitation patterns. One way to handle this problem is by weather-state models, where, based on the pressure field, each day is classified into a weather state and precipitation is then modeled within each weather state via multivariate distributions. In this paper, we propose a spatiotemporal Bayesian hierarchical model for precipitation. Basic objective and novelty of the paper is to capture and model the essential spatiotemporal relationships that exist between large-scale sea level pressure field and local daily precipitation. A specific local spatial ordering that mimics the essential large-scale patterns is used in the likelihood. The model is then applied to a network of rain gauge stations in the river Tagus valley. The inference is then carried out using appropriate MCMC methods.