A Bayesian hierarchical approach to ensemble weather forecasting


D. Cocchi, Dipartimento di Scienze Statistiche ‘‘Paolo Fortunati’’, Università di Bologna, Via delle Belle Arti 41, 40126 Bologna, Italy.
E-mail: daniela.cocchi@unibo.it


Summary.  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.