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).
The joint calibration model in probabilistic weather forecasting: some preliminary issues / P.Agati; D. G. Calò; L. Stracqualursi. - In: STATISTICA. - ISSN 0390-590X. - STAMPA. - LXVIII:(2008), pp. 117-127.
The joint calibration model in probabilistic weather forecasting: some preliminary issues
AGATI, PATRIZIA;CALO', DANIELA GIOVANNA;STRACQUALURSI, LUISA
2008
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).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.