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Author (up) Wu, Z.; Feng, J.; Qiao, F.; Tan, Z.-M. url  doi
  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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