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Author (up) Baigorria, G.A.; Jones, J.W.; O'Brien, J.J.
Title Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model Type $loc['typeJournal Article']
Year 2008 Publication Agricultural and Forest Meteorology Abbreviated Journal Agricultural and Forest Meteorology
Volume 148 Issue 8-9 Pages 1353-1361
Keywords crop yield forecast; regional circulation models; crop models; bias-correction; principal components; statistical downscaling; CERES-Maize
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0168-1923 ISBN Medium
Area Expedition Conference
Funding Approved $loc['no']
Call Number COAPS @ mfield @ Serial 407
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Author (up) Bourassa, MA; Weissman, DE
Title The development and application of a sea surface stress model function for the QuikSCAT and ADEOS-II SeaWinds scatterometers Type $loc['typeConference Article']
Year 2003 Publication IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) Abbreviated Journal
Volume Issue Pages 239-241
Keywords component; surface stress; SeaWinds; scatterometer; validation
Abstract
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference 23rd International Geoscience and Remote Sensing Symposium (IGARSS 2003)
Funding Approved $loc['no']
Call Number COAPS @ mfield @ Serial 485
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Author (up) 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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