A statistical downscaling scheme to improve global precipitation forecasting

被引:20
作者
Jianqi Sun
Huopo Chen
机构
[1] Nansen-Zhu International Research Centre (NZC),Climate Change Research Center
[2] Institute of Atmospheric Physics,undefined
[3] Chinese Academy of Sciences,undefined
[4] Chinese Academy of Sciences,undefined
[5] Graduate University of Chinese Academy of Sciences,undefined
来源
Meteorology and Atmospheric Physics | 2012年 / 117卷
关键词
Statistical Downscaling; Prediction Skill; Climatic Research Unit; Precipitation Forecast; Couple General Circulation Model;
D O I
暂无
中图分类号
学科分类号
摘要
Based on hindcasts obtained from the “Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction” (DEMETER) project, this study proposes a statistical downscaling (SD) scheme suitable for global precipitation forecasting. The key idea of this SD scheme is to select the optimal predictors that are best forecast by coupled general circulation models (CGCMs) and that have the most stable relationships with observed precipitation. Developing the prediction model and further making predictions using these predictors can extract useful information from the CGCMs. Cross-validation and independent sample tests indicate that this SD scheme can significantly improve the prediction capability of CGCMs during the boreal summer (June–August), even over polar regions. The predicted and observed precipitations are significantly correlated, and the root-mean-square-error of the SD scheme-predicted precipitation is largely decreased compared with the raw CGCM predictions. An inter-model comparison shows that the multi-model ensemble provides the best prediction performance. This study suggests that combining a multi-model ensemble with the SD scheme can improve the prediction skill for precipitation globally, which is valuable for current operational precipitation prediction.
引用
收藏
页码:87 / 102
页数:15
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