DOI: 10.1175/JCLI-D-13-00489.1
Scopus记录号: 2-s2.0-84901843540
论文题名: Comparison of seasonal potential predictability of precipitation
作者: Feng X. ; Delsole T. ; Houser P.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 11 起始页码: 4094
结束页码: 4110
语种: 英语
Scopus关键词: Estimation
; Regression analysis
; Spurious signal noise
; Analysis of covariances
; Atmospheric general circulation models
; Auto regressive models
; Bootstrap method
; Estimation methods
; Markov chain models
; Monte Carlo experiments
; Seasonal variation
; Monte Carlo methods
; ensemble forecasting
; Markov chain
; Monte Carlo analysis
; precipitation (climatology)
; precipitation assessment
; prediction
; seasonal variation
英文摘要: Three methods for estimating potential seasonal predictability of precipitation from a single realization of daily data are assessed. The estimation methods include a first-order Markov chain model proposed by Katz (KZ), and an analysis of covariance (ANOCOVA) method and a bootstrap method proposed by the authors. The assessment is based on Monte Carlo experiments, ensemble atmospheric general circulation model (AGCM) simulations, and observation-based data. For AGCM time series, ANOCOVA produces the most accurate estimates of weather noise variance, despite the fact that it makes the most unrealistic assumptions about precipitation (in particular, it assumes precipitation is generated by a Gaussian autoregressive model). The KZ method significantly underestimates noise variance unless the autocorrelation of precipitation amounts on consecutive wet days is taken into account. Both AGCM and observation-based data reveal that the fraction of potentially predictable variance is greatest in the tropics, smallest in the extratropics, and undergoes a strong seasonal variation. The three methods give consistent estimates of potential predictability for 67% of the globe. © 2014 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51101
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geography and Geoinformation Science, George Mason University, 4400 University Dr., MS 6C3, Fairfax, VA 22030, United States; Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA, United States
Recommended Citation:
Feng X.,Delsole T.,Houser P.. Comparison of seasonal potential predictability of precipitation[J]. Journal of Climate,2014-01-01,27(11)