A statistical downscaling scheme to improve global precipitation forecasting
A statistical downscaling scheme to improve global precipitation forecasting
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DOI：
10.1007/s00703-012-0195-7
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发表时间：
2012-05
影响因子：
2
通讯作者：
Jianqi Sun;Huopo Chen
中科院分区：
文献类型：
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作者：
Jianqi Sun;Huopo Chen
基于“欧洲多模式Enhancement系统季节到年际预报的发展”（DEMETER）项目的后报，提出了一种适用于全球降水预报的统计降尺度（SD）方案。这个SD方案的核心思想是选择最佳的预测因子，最好的耦合大气环流模式（CGCMs）的预测，并与观测到的降水最稳定的关系。开发预测模型并使用这些预测因子进一步进行预测可以从CGCM中提取有用的信息。交叉验证和独立样本检验表明，该SD方案可以显着提高CGCM的预测能力，在北方夏季（6 - 8月），甚至在极地地区。预报降水量与实测降水量之间存在显著的相关性，与CGCM原始预报相比，SD方案预报降水量的均方根误差大大减小。模型间比较表明，多模型集成提供了最好的预测性能。研究表明，多模式集合与SD方案相结合，可以提高全球降水的预报水平，对当前业务化降水预报有一定的参考价值。
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.