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A Bayesian Hierarchical Approach to Ensemble Weather Forecasting

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  • Daniela Cocchi

    ()

  • Antonio Fabio Di Narzo

    ()

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    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.

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    File URL: http://amsacta.cib.unibo.it/2462/1/Quaderni_2008_dc_BayesianHierarchical.pdf
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    Bibliographic Info

    Paper provided by Department of Statistics, University of Bologna in its series Quaderni di Dipartimento with number 5.

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    Length: 26
    Date of creation: 2008
    Date of revision:
    Handle: RePEc:bot:quadip:95

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    Related research

    Keywords: Ensemble Prediction System; hierarchical Bayesian model; predictive distribution; probabilistic forecast; verification rank histogram.;

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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.
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