Journal article

A Variable-Correlation Model to Characterize Asymmetric Dependence for Postprocessing Short-Term Precipitation Forecasts

Wentao Li, Quan J Wang, Qingyun Duan

Monthly Weather Review | American Meteorological Society | Published : 2020

Abstract

Statistical postprocessing methods can be used to correct bias and dispersion error in raw ensemble forecasts from numerical weather prediction models. Existing postprocessing models generally perform well when they are assessed on all events, but their performance for extreme events still needs to be investigated. Commonly used joint probability postprocessing models are based on the correlation between forecasts and observations. Because the correlation may be lower for extreme events as a result of larger forecast uncertainty, the dependence between forecasts and observations can be asymmetric with respect to the magnitude of the precipitation. However, the constant correlation coefficien..

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University of Melbourne Researchers

Grants

Awarded by National Basic Research Programof China


Awarded by Strategic Priority Research Program of the Chinese Academy of Sciences


Awarded by National Key Research and Development Program of China


Awarded by Special Fund for Meteorological Scientific Research in Public Interest


Funding Acknowledgements

We are grateful to the valuable comments from the editor and anonymous reviewers. We are also thankful for Dr. Yating Tang for providing useful comments on the early version of the paper. The study is supported by the National Basic Research Programof China (2015CB953703), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA2006040104), the National Key Research and Development Program of China (2018YFE0196000), and the Special Fund for Meteorological Scientific Research in Public Interest (GYHY201506002; CRA-40: The 40-Year CMA Global Atmospheric Reanalysis). The first author is supported by China Scholarship Council.