孙丞虎^1,2,,
崔童^3,,,
李维京^3,2,
左金清^3,2
1. 中国气象科学研究院灾害天气国家重点实验室及气候与气候变化研究所, 北京 100081
2. 南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京 210044
3. 国家气候中心气候研究开放实验室, 北京 100081

基金项目: 十三五"国家重点研发计划项目（2016YFA0601501），公益性行业专项（GYHY201406018），Basic Scientific Research and Operation Foundation of CAMS（2018Z006，2018KJ029，2018KJ030），国家重点基础研究发展计划973项目（2013CB430203）共同资助


详细信息

作者简介: 孙丞虎, 主要从事气候与气候变化研究.E-mail:sunch@cma.gov.cn

通讯作者: 崔童, 主要从事气候监测预测研究.E-mail:cuitong@cma.gov.cn

中图分类号: P456

收稿日期:2018-11-16
修回日期:2019-01-26
上线日期:2019-11-05

A merging method for dynamic model and statistical predictions and evaluation of summer precipitation prediction skill in China

SUN ChengHu^1,2,,
CUI Tong^3,,,
LI WeiJing^3,2,
ZUO JinQing^3,2
1. State Key Laboratory of Severe Weather and Institute of Climate System, Chinese Academy of Meteorological Sciences, Beijing 100081, China
2. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. Laboratory for Climate Studies, National Climate Center, Beijing 100081, China


More Information

Corresponding author: CUI Tong,E-mail:cuitong@cma.gov.cn

MSC: P456

--> Received Date: 16 November 2018
Revised Date: 26 January 2019
Available Online: 05 November 2019


摘要
摘要:短期气候预测中如何将气候模式和统计方法的预测结果科学、客观的集成起来，一直是非常重要的问题.本文针对动力模式和统计方法预测结果相结合的问题，引入资料同化中信息融合的思想，采用最优内插同化方法，实现了动力模式和统计季节降水预测结果的融合.检验表明，对1982-2015年我国夏季降水百分率的回报，融合预测结果与观测的平均空间相关系数可达0.44，分别较统计预测和CFSv2模式统计降尺度订正的技巧提高了0.1左右，而均方根误差较两者可以降低5%~20%.可见，该方法可以进一步提升对我国夏季降水的预测技巧，具有显著的业务应用价值.
关键词: 中国夏季降水/
统计预测/
动力模式预测/
融合

Abstract:How to make an objective combination for the predictions from climate dynamic model and statistical method is an important issue in the short-term climate prediction area. To resolve this problem, we use the optimum interpolation assimilation method to merge the predictions of summer precipitation percentage anomalies in China from CFSv2 model and statistical method. The verification of prediction skill indicates that during 1982-2015 periods, the spatial correlation coefficient of merging predictions with the observations is as highly as 0.44, which is higher than the skill of statistical prediction method and the downscaling of CFSv2 model about 0.1. Moreover, the root mean square error of merging predictions is also reduced by 5%~20% than that of statistical method and CFSv2 model downscaling. Our results indicate that this new prediction method can further improve the prediction skill of summer precipitation in China and has the potential application values.
Key words:China summer precipitation/
Statistic prediction/
Dynamic mode prediction/
Merging



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