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The Joint Calibration Model In Probilistic Weather Forecasting: Some Preliminary Issues

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  • Patrizia Agati
  • Daniela Giovanna Calò
  • Luisa Stracqualursi

Abstract

Ensemble Prediction Systems play today a fundamental role in weather forecasting. They can represent and measure uncertainty, thereby allowing distributional forecasting as well as deterministic-style forecasts. In this context, we show how the Joint Calibration Model (Agati et al., 2007) – based on a modelization of the Probability Integral Transform distribution – can provide a solution to the problem of information combining in probabilistic forecasting of continuous variables. A case study is presented, where the potentialities of the method are explored and the accuracy of deterministic-style forecasts from JCM is compared with that from Bayesian Model Averaging (Raftery et al., 2005).

Suggested Citation

  • Patrizia Agati & Daniela Giovanna Calò & Luisa Stracqualursi, 2008. "The Joint Calibration Model In Probilistic Weather Forecasting: Some Preliminary Issues," Statistica, Department of Statistics, University of Bologna, vol. 68(1), pages 117-127.
    • Handle: RePEc:bot:rivsta:v:68:y:2008:i:1:p:117-127
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