Journal article

Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts

Wentao Li, Qingyun Duan, Quan J Wang

Weather and Forecasting | American Meteorological Society | Published : 2019

Abstract

Statistical postprocessing models can be used to correct bias and dispersion errors in raw precipitation forecasts from numerical weather prediction models. In this study, we conducted experiments to investigate four factors that influence the performance of regression-based postprocessing models with normalization transformations for short-term precipitation forecasts. The factors are 1) normalization transformations, 2) incorporation of ensemble spread as a predictor in the model, 3) objective function for parameter inference, and 4) two postprocessing schemes, including distributional regression and joint probability models. The experiments on the first three factors are based on variants..

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University of Melbourne Researchers

Grants

Awarded by National Basic Research Program of China


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


Awarded by National Key Research and Development Program of China


Funding Acknowledgements

We are grateful to the valuable comments from the editor and anonymous reviewers. The study is supported by the National Basic Research Program of China (2015CB953703), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA2006040104), and the National Key Research and Development Program of China (2018YFE0196000). The first author is supported by China Scholarship Council.