Journal article
Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models
P Zhao, QJ Wang, W Wu, Q Yang
Journal of Hydrology | Published : 2022
Abstract
Statistical post-processing has been widely employed to correct bias and dispersion errors in raw ensemble precipitation forecasts from numerical weather prediction models. One prominent post-processing scheme is to establish a joint probability model by fitting a bivariate distribution of raw forecasts and corresponding observations. However, current joint probability models only incorporate ensemble mean as the predictor, and ensemble spread is not considered. This is a major disadvantage of joint probability models as ensemble spread can be informative for forecast uncertainty. In this paper, we propose a two-step calibration approach to combine the strengths of joint probability models a..
View full abstractGrants
Awarded by Australian Research Council
Funding Acknowledgements
This work is supported by an Australian Research Council Linkage Project (Grant No. LP170100922) and a collaborative project (Grant No. TP707466) between the University of Melbourne and Australian Bureau of Meteorology. The co-author Wenyan Wu acknowledges the support of the Australian Research Council via the Discovery Early Career Researcher Award (DE210100117). We would like to thank the Australian Bureau of Meteorology for supplying the ACCESS-GE2 and AWAP data. We also thank the National Computational Infrastructure for providing assess to computation resources to support our work. We gratefully acknowledge the two reviewers for their thorough reviews and constructive comments.