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).
P.Agati, D. G. Calò, L. Stracqualursi (2008). The joint calibration model in probabilistic weather forecasting: some preliminary issues. STATISTICA, LXVIII, 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.
