DOI: 10.1002/2016GL071282
论文题名: Long temporal autocorrelations in tropical precipitation data and spike train prototypes
作者: Abbott T.H. ; Stechmann S.N. ; Neelin J.D.
刊名: Geophysical Research Letters
ISSN: 0094-8447
EISSN: 1944-8178
出版年: 2016
卷: 43, 期: 21 起始页码: 11472
结束页码: 11480
语种: 英语
英文关键词: Dry spell
; Point process
; Power law
; Rainfall
; Stochastic process
; Wet spell
Scopus关键词: Drought
; Precipitation (meteorology)
; Rain
; Random processes
; Stochastic models
; Stochastic systems
; Dry spells
; Model parameters
; Point process
; Power-law
; Simple stochastic
; Tropical precipitation
; Variable length
; Wet spell
; Autocorrelation
英文摘要: Temporal precipitation autocorrelations drop slower than exponentially at long lags, and there is a range from tens to thousands of minutes where it is relevant to ask if a scale-free process might underlie the long autocorrelations. A simple stochastic model in which precipitation appears as variable-length spikes provides a reasonable prototype for this behavior. In both observations and the model, separating the component of the autocorrelation within wet events from the interevent contribution suggests long autocorrelation behavior is primarily associated with the latter. When precipitation spikes are short compared to dry events, a true power law is obtained with analytical exponent −0.5 and precipitation autocorrelation is determined by dry-spell model parameters. In more realistic cases, wet-spell termination is also important. Although a variety of apparent power law exponents can be obtained for different parameters, the fundamental long-lag process appears to be that of the interevent correlation. ©2016. American Geophysical Union. All Rights Reserved. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84998910276&doi=10.1002%2f2016GL071282&partnerID=40&md5=5566bb917e7b5e6a5b01408c97459707
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/9410
Appears in Collections: 科学计划与规划 气候变化与战略
There are no files associated with this item.
作者单位: Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, United States
Recommended Citation:
Abbott T.H.,Stechmann S.N.,Neelin J.D.. Long temporal autocorrelations in tropical precipitation data and spike train prototypes[J]. Geophysical Research Letters,2016-01-01,43(21).