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Feng, J. | ||||
| Title | Sea Surface Temperature Anomalies: A Possible Trigger for ENSO | Type | $loc['typeManuscript'] | ||
| Year | 2012 | Publication | Abbreviated Journal | ||
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| 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 | |
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| Funding | Approved | $loc['no'] | |||
| Call Number | COAPS @ mfield @ | Serial | 275 | ||
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| 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 | ||||
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| ISSN | 0894-8755 | ISBN | Medium | ||
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| Funding | Approved | $loc['no'] | |||
| Call Number | COAPS @ mfield @ | Serial | 126 | ||
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| 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 | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0022-4928 | ISBN | Medium | ||
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| 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 | ||||
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| Language | English | Summary Language | Original Title | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1364-503X | ISBN | Medium | ||
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| Funding | PMID:26953173; PMCID:PMC4792406 | Approved | $loc['no'] | ||
| Call Number | COAPS @ mfield @ | Serial | 57 | ||
| Permanent link to this record | |||||
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