The Joint Calibration Model in probabilistic weather forecasting: some preliminary issues
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Bologna
2013-05-01
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| Series: | Statistica |
| Online Access: | http://rivista-statistica.unibo.it/article/view/3524 |
| Summary: | 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). |
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| ISSN: | 0390-590X 1973-2201 |