A Bayesian hierarchical approach to ensemble weather forecasting
Authors: Di Narzo, A. F.; Cocchi, D.
Source: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 59, Number 3, May 2010 , pp. 405-422(18)
In meteorology, the traditional approach to forecasting employs deterministic models mimicking atmospheric dynamics. Forecast uncertainty due to partial knowledge of the initial conditions is tackled by ensemble predictions systems. Probabilistic forecasting is a relatively new approach which may properly account for all sources of uncertainty. We propose a hierarchical Bayesian model which develops this idea and makes it possible to deal with ensemble predictions systems with non-identifiable members by using a suitable definition of the second level of the model. An application to Italian small-scale temperature data is shown.
Document Type: Research Article
Affiliations: University of Bologna, Italy
Publication date: May 1, 2010