DOI: 10.1175/JCLI-D-15-0100.1
Scopus记录号: 2-s2.0-84947779510
论文题名: Monte Carlo singular spectrum analysis (SSA) revisited: Detecting oscillator clusters in multivariate datasets
作者: Groth A. ; Ghil M.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期: 19 起始页码: 7873
结束页码: 7893
语种: 英语
Scopus关键词: Atmospheric pressure
; Atmospheric temperature
; Clustering algorithms
; Data compression
; Frequency bands
; Functions
; Monte Carlo methods
; Ocean currents
; Oceanography
; Orthogonal functions
; Sea level
; Spectrum analysis
; Statistics
; Stochastic systems
; Submarine geophysics
; Surface properties
; Surface waters
; Time series
; Time series analysis
; Empirical Orthogonal Function
; North Atlantic oscillations
; Principal components analysis
; Sea surface temperature (SST)
; Statistical techniques
; Principal component analysis
; air-sea interaction
; annual variation
; data set
; empirical analysis
; Monte Carlo analysis
; North Atlantic Oscillation
; pressure field
; principal component analysis
; sea level pressure
; sea surface temperature
; spectral analysis
; time series analysis
; Atlantic Ocean
; Gulf Stream
英文摘要: Singular spectrum analysis (SSA) along with its multivariate extension (M-SSA) provides an efficient way to identify weak oscillatory behavior in high-dimensional data. To prevent the misinterpretation of stochastic fluctuations in short time series as oscillations, Monte Carlo (MC)-type hypothesis tests provide objective criteria for the statistical significance of the oscillatory behavior. Procrustes target rotation is introduced here as a key method for refining previously available MC tests. The proposed modification helps reduce the risk of type-I errors, and it is shown to improve the test's discriminating power. The reliability of the proposed methodology is examined in an idealized setting for a cluster of harmonic oscillators immersed in red noise. Furthermore, the common method of data compression into a few leading principal components, prior to M-SSA, is reexamined, and its possibly negative effects are discussed. Finally, the generalized Procrustes test is applied to the analysis of interannual variability in the North Atlantic's sea surface temperature and sea level pressure fields. The results of this analysis provide further evidence for shared mechanisms of variability between the Gulf Stream and the North Atlantic Oscillation in the interannual frequency band. © 2015 American Meteorological Society. 资助项目: NSF, National Science Foundation
; NSF, 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/50556
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Atmospheric and Oceanic Sciences, Institute of Geophysics and Planetary Physics, University of California, Los Angeles, Los Angeles, CA, United States; Geosciences Department, Laboratoire de Météorologie Dynamique (CNRS and IPSL), Ecole Normale Supérieure, Paris, France
Recommended Citation:
Groth A.,Ghil M.. Monte Carlo singular spectrum analysis (SSA) revisited: Detecting oscillator clusters in multivariate datasets[J]. Journal of Climate,2015-01-01,28(19)