THE JOINT CALIBRATION MODEL IN PROBILISTIC WEATHER FORECASTING: SOME PRELIMINARY ISSUES

Patrizia Agati, Daniela Giovanna Calò () and Luisa Stracqualursi ()

Statistica, 2008, vol. 68, issue 1, 117-127

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).

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:68:y:2008:i:1:p:117-127

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