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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
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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
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