globalchange  > 气候变化事实与影响
DOI: 10.1175/JCLI-D-12-00052.1
Scopus记录号: 2-s2.0-84871885019
论文题名:
A bayes factor model for detecting artificial discontinuities via pairwise comparisons
作者: Zhang J.; Zheng W.; Menne J.M.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2012
卷: 25, 期:24
起始页码: 8462
结束页码: 8474
语种: 英语
Scopus关键词: Bayes factor ; Bayesian methods ; Bayesian model selection ; Change-point analysis ; Detection problems ; Large scale simulations ; Missing data ; Pair-wise comparison ; Posterior probability ; Statistical techniques ; Surface temperatures ; Temperature data ; Temperature series ; Atmospheric temperature ; Bayesian networks ; Computer simulation ; Climate models ; air temperature ; algorithm ; Bayesian analysis ; numerical model ; probability ; surface temperature ; trend analysis ; United States
英文摘要: In this paper, the authors present a Bayes factor model for detecting undocumented artificial discontinuities in a network of temperature series. First, they generate multiple difference series for each station with the pairwise comparison approach. Next, they treat the detection problem as a Bayesian model selection problem and use Bayes factors to calculate the posterior probabilities of the discontinuities and estimate their locations in time and space. The model can be applied to large climate networks and realistic temperature series with missing data. The effectiveness of the model is illustrated with two realistic large-scale simulations and four sensitivity analyses. Results from applying the algorithm to observed monthly temperature data from the conterminous United States are also briefly discussed in the context of what is currently known about the nature of biases in the U.S. surface temperature record. © 2012 American Meteorological Society.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52106
Appears in Collections:气候变化事实与影响

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作者单位: CICS-NC, North Carolina State University, Raleigh, NC, United States; Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, NC, United States; Sanofi-Aventis, Boston, MA, United States; NOAA/National Climatic Data Center, Asheville, NC, United States

Recommended Citation:
Zhang J.,Zheng W.,Menne J.M.. A bayes factor model for detecting artificial discontinuities via pairwise comparisons[J]. Journal of Climate,2012-01-01,25(24)
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