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A Bayesian Hierarchical Approach to Ensemble Weather Forecasting

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  • Daniela Cocchi
  • Antonio Fabio Di Narzo

Abstract

In meteorology, the traditional approach to forecasting employs deterministic models mimicking atmospheric dynamics. Forecast uncertainty due to the partial knowledge of initial conditions is tackled by Ensemble Predictions Systems (EPS). Probabilistic forecasting is a relatively new approach which may properly account for all sources of uncertainty. In this work we propose a hierarchical Bayesian model which develops this idea and makes it possible to deal with an EPS with non-identifiable members using a suitable definition of the second level of the model. An application to Italian small-scale temperature data is shown.

Suggested Citation

  • Daniela Cocchi & Antonio Fabio Di Narzo, 2008. "A Bayesian Hierarchical Approach to Ensemble Weather Forecasting," Quaderni di Dipartimento 5, Department of Statistics, University of Bologna.
    • Handle: RePEc:bot:quadip:wpaper:95
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    References listed on IDEAS

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    1. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
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