Records
Author
Wu, Z. ; Feng, J. ; Qiao, F. ; Tan, Z.-M.
Title
Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets
Type
$loc['typeJournal Article']
Year
2016
Publication
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
Abbreviated Journal
Philos Trans A Math Phys Eng Sci
Volume
374
Issue
2065
Pages
20150197
Keywords
adaptive and local data analysis ; data compression ; empirical orthogonal function ; fast algorithm ; multidimensional ensemble empirical mode decomposition ; principal component analysis
Abstract
In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.
Address
School of Atmospheric Sciences, Nanjing University, Nanjing, Jiangsu Province, People's Republic of China
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
English
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
1364-503X
ISBN
Medium
Area
Expedition
Conference
Funding
PMID:26953173; PMCID:PMC4792406
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
57
Permanent link to this record
Author
Zhang, M. ; Zhang, Y. ; Shu, Q. ; Zhao, C. ; Wang, G. ; Wu, Z. ; Qiao, F.
Title
Spatiotemporal evolution of the chlorophyll a trend in the North Atlantic Ocean
Type
$loc['typeJournal Article']
Year
2018
Publication
The Science of the Total Environment
Abbreviated Journal
Sci Total Environ
Volume
612
Issue
Pages
1141-1148
Keywords
Chlorophyll a ; Dipole pattern ; Multidimensional ensemble empirical mode decomposition ; Propagation ; Spatiotemporal evolution ; The variable trend
Abstract
Analyses of the chlorophyll a concentration (chla) from satellite ocean color products have suggested the decadal-scale variability of chla linked to the climate change. The decadal-scale variability in chla is both spatially and temporally non-uniform. We need to understand the spatiotemporal evolution of chla in decadal or multi-decadal timescales to better evaluate its linkage to climate variability. Here, the spatiotemporal evolution of the chla trend in the North Atlantic Ocean for the period 1997-2016 is analyzed using the multidimensional ensemble empirical mode decomposition method. We find that this variable trend signal of chla shows a dipole pattern between the subpolar gyre and along the Gulf Stream path, and propagation along the opposite direction of the North Atlantic Current. This propagation signal has an overlapping variability of approximately twenty years. Our findings suggest that the spatiotemporal evolution of chla during the two most recent decades is part of the multidecadal variations and possibly regulated by the changes of Atlantic Meridional Overturning Circulation, whereas the mechanisms of such evolution patterns still need to be explored.
Address
First Institute of Oceanography, State Oceanic Administration, Qingdao, China; Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China; Key Laboratory of Data Analysis and Applications, State Oceanic Administration, Qingdao, China. Electronic address: qiaofl@fio.org.cn
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
English
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0048-9697
ISBN
Medium
Area
Expedition
Conference
Funding
PMID:28892858
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
363
Permanent link to this record