globalchange  > 过去全球变化的重建
DOI: 10.1007/s00382-015-2569-2
Scopus记录号: 2-s2.0-84955698161
论文题名:
Warm season heavy rainfall events over the Huaihe River Valley and their linkage with wintertime thermal condition of the tropical oceans
作者: Li L.; Li W.; Tang Q.; Zhang P.; Liu Y.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2016
卷: 46, 期:2017-01-02
起始页码: 71
结束页码: 82
语种: 英语
英文关键词: Bayesian inference on precipitation ; Heavy rainfall events ; Huaihe River Valley ; Normal mixture model ; Seasonal climate prediction ; Support vector machine
英文摘要: Warm season heavy rainfall events over the Huaihe River Valley (HRV) of China are amongst the top causes of agriculture and economic loss in this region. Thus, there is a pressing need for accurate seasonal prediction of HRV heavy rainfall events. This study improves the seasonal prediction of HRV heavy rainfall by implementing a novel rainfall framework, which overcomes the limitation of traditional probability models and advances the statistical inference on HRV heavy rainfall events. The framework is built on a three-cluster Normal mixture model, whose distribution parameters are sampled using Bayesian inference and Markov Chain Monte Carlo algorithm. The three rainfall clusters reflect probability behaviors of light, moderate, and heavy rainfall, respectively. Our analysis indicates that heavy rainfall events make the largest contribution to the total amount of seasonal precipitation. Furthermore, the interannual variation of summer precipitation is attributable to the variation of heavy rainfall frequency over the HRV. The heavy rainfall frequency, in turn, is influenced by sea surface temperature anomalies (SSTAs) over the north Indian Ocean, equatorial western Pacific, and the tropical Atlantic. The tropical SSTAs modulate the HRV heavy rainfall events by influencing atmospheric circulation favorable for the onset and maintenance of heavy rainfall events. Occurring 5 months prior to the summer season, these tropical SSTAs provide potential sources of prediction skill for heavy rainfall events over the HRV. Using these preceding SSTA signals, we show that the support vector machine algorithm can predict HRV heavy rainfall satisfactorily. The improved prediction skill has important implication for the nation’s disaster early warning system. © 2015, Springer-Verlag Berlin Heidelberg.
资助项目: NSF, National Science Foundation ; NSF, National Science Foundation ; NSF, National Science Foundation ; WHOI, National Science Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53891
Appears in Collections:过去全球变化的重建

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作者单位: Earth and Ocean Sciences, Nicholas School of the Environment and Earth Sciences, Duke University, 321C Old Chem. Bldg, P.O. Box 90227, Durham, NC, United States; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, United States

Recommended Citation:
Li L.,Li W.,Tang Q.,et al. Warm season heavy rainfall events over the Huaihe River Valley and their linkage with wintertime thermal condition of the tropical oceans[J]. Climate Dynamics,2016-01-01,46(2017-01-02)
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