final_program_abstracts[1]

11 IMSC Session Program
Ensemble model output statistics using heteroskedastic
censored regression
Wednesday - Parallel Session 11
Thordis L. Thorarinsdottir and Tilmann Gneiting
University of Heidelberg, Germany
We propose a novel way of statistically post-processing ensemble forecasts by using
heteroskedastic regression which allows for cencoring and/or asymmetry in the
resulting predictive distribution in correspondence with the weather quantity of
interest. The location and the spread of the probabilistic forecast derive from the
ensemble while the method can substantially improve the calibration of the ensemble
system and correct for possible biases. We discuss both how to obtain the
probabilistic forecasts as well as the verification process and show that these two are
linked in that using a scoring rule such as the continuous rank probability score for
parameter estimation may lead to significant improvement of the predictive
performance. We apply the ensemble model output statistics method to 48-h-ahead
forecasts of maximum wind speed and maximum gust speed over the North American
Pacific Northwest by using the University of Washington mesoscale ensemble. The
statistically post-processed density forecasts turn out to be calibrated and sharp, and
result in a substantial improvement over the unprocessed ensemble or climatological
reference forecasts.
Abstracts 191