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A Bayesian hierarchical approach to ensemble weather forecasting

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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.

Keywords: Ensemble prediction system; Hierarchical Bayesian model; Predictive distribution; Probabilistic forecast; Verification rank histogram

Document Type: Research Article


Affiliations: University of Bologna, Italy

Publication date: May 1, 2010

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