Records
Author
Feng, J.
Title
Sea Surface Temperature Anomalies: A Possible Trigger for ENSO
Type
$loc['typeManuscript']
Year
2012
Publication
Abbreviated Journal
Volume
Issue
Pages
Keywords
Abstract
Address
Department of Earth, Ocean and Atmospheric Science
Corporate Author
Thesis
$loc['Master's thesis']
Publisher
Florida State University
Place of Publication
Tallahassee, FL
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
275
Permanent link to this record
Author
Feng, J. ; Wu, Z. ; Liu, G.
Title
Fast Multidimensional Ensemble Empirical Mode Decomposition Using a Data Compression Technique
Type
$loc['typeJournal Article']
Year
2014
Publication
Journal of Climate
Abbreviated Journal
J. Climate
Volume
27
Issue
10
Pages
3492-3504
Keywords
Data processing ; Data quality control ; Time series
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0894-8755
ISBN
Medium
Area
Expedition
Conference
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
126
Permanent link to this record
Author
Feng, J. ; Wu, Z. ; Zou, X.
Title
Sea Surface Temperature Anomalies off Baja California: A Possible Precursor of ENSO
Type
$loc['typeJournal Article']
Year
2014
Publication
Journal of the Atmospheric Sciences
Abbreviated Journal
J. Atmos. Sci.
Volume
71
Issue
5
Pages
1529-1537
Keywords
ENSO ; El Nino
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0022-4928
ISBN
Medium
Area
Expedition
Conference
Funding
Approved
$loc['no']
Call Number
COAPS @ mfield @
Serial
127
Permanent link to this record
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