DOI: 10.1175/JCLI-D-13-00746.1
Scopus记录号: 2-s2.0-84900449688
论文题名: Fast multidimensional ensemble empirical mode decomposition using a data compression technique
作者: Feng J. ; Wu Z. ; Liu G.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 10 起始页码: 3492
结束页码: 3504
语种: 英语
Scopus关键词: Algorithms
; Nanofluidics
; Principal component analysis
; Separation
; Time measurement
; Climate variability and change
; Data compression techniques
; Empirical Orthogonal Function
; Ensemble empirical mode decomposition
; Ensemble empirical mode decompositions (EEMD)
; Oscillatory components
; Principal Components
; Sea surface temperature (SST)
; Data compression
; algorithm
; climate change
; climate modeling
; climate variation
; data interpretation
; data set
; decomposition analysis
; empirical analysis
; ensemble forecasting
; principal component analysis
; sea surface temperature
; spatiotemporal analysis
; temporal evolution
; time series
英文摘要: The process of obtaining key information on climate variability and change from large climate datasets often involves large computational costs and removal of noise from the data. In this study, the authors accelerate the computation of a newly developed, multidimensional temporal-spatial analysis method, namely multidimensional ensemble empirical mode decomposition (MEEMD), for climate studies. The original MEEMD uses ensemble empirical mode decomposition (EEMD) to decompose the time series at each grid point and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated time scales, which is computationally expensive. To accelerate the algorithm, the original MEEMD is modified by 1) using principal component analysis (PCA) to transform the original temporal-spatial multidimensional climate data into principal components (PCs) and corresponding empirical orthogonal functions (EOFs); 2) retaining only a small fraction of PCs and EOFs that contain spatially and temporally coherent structures; 3) decomposing PCs into oscillatory components on naturally separated time scales; and 4) obtaining the original MEEMD components on naturally separated time scales by summing the contributions of the similar time scales from different pairs of EOFs and PCs. The study analyzes extended reconstructed sea surface temperature (ERSST) to validate the accelerated (fast) MEEMD. It is demonstrated that, for ERSST climate data, the fast MEEMD can 1) compress data with a compression rate of one to two orders and 2) increase the speed of the original MEEMD algorithm by one to two orders. © 2014 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51378
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Earth, Ocean and Atmospheric Science, Center for Ocean-Atmospheric Prediction Studies, The Florida State University, Tallahassee, FL, United States; Department of Earth, Ocean and Atmospheric Science, The Florida State University, Tallahassee, FL, United States
Recommended Citation:
Feng J.,Wu Z.,Liu G.. Fast multidimensional ensemble empirical mode decomposition using a data compression technique[J]. Journal of Climate,2014-01-01,27(10)