Post-processing methods are nowadays widely used for limiting the impact of errors in ensemble forecast of meteorological variables. Ensemble model output statistics are an easy-to-apply technique for post-processing, based on a linear regression model. In this paper we use an ensemble model output statistic for the forecast of daily maximum temperatures in Veneto. We calculate estimative and calibrated predictive distributions for a time period of three years. We then compare the different predictive distributions by means of the log-score, the continuous ranked probability score and the coverage of the corresponding predictive quantiles. We show that the calibrated approach improves on the estimative ones as regards both mean scores and coverage probabilities.

Ensemble model output statistics for temperature forecasts in Veneto

Gaetan Carlo;Giummole' Federica;Mameli Valentina;Siad Si Mokrane
2022

Abstract

Post-processing methods are nowadays widely used for limiting the impact of errors in ensemble forecast of meteorological variables. Ensemble model output statistics are an easy-to-apply technique for post-processing, based on a linear regression model. In this paper we use an ensemble model output statistic for the forecast of daily maximum temperatures in Veneto. We calculate estimative and calibrated predictive distributions for a time period of three years. We then compare the different predictive distributions by means of the log-score, the continuous ranked probability score and the coverage of the corresponding predictive quantiles. We show that the calibrated approach improves on the estimative ones as regards both mean scores and coverage probabilities.
Book of the short papers - SIS 2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5004826
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