ENSEMBLE EMPIRICAL MODE DECOMPOSITION AS AN ALTERNATIVE FOR TREE-RING CHRONOLOGY DEVELOPMENT

被引:9
作者
Guan, Biing T. [1 ]
Wright, William E. [1 ,2 ]
Cook, Edward R. [3 ]
机构
[1] Natl Taiwan Univ, Sch Forestry & Resource Conservat, Taipei 10617, Taiwan
[2] Univ Arizona, Lab Tree Ring Res, Tucson, AZ 85721 USA
[3] Columbia Univ, Lamont Doherty Earth Observ, Tree Ring Lab, Palisades, NY 10964 USA
关键词
empirical mode decomposition; ensemble empirical mode decomposition; intrinsic mode functions; nonlinear and non-stationary time series; NONSTATIONARY TIME-SERIES; HILBERT SPECTRUM; SIGNAL; EMD; VARIABILITY; PACKAGE;
D O I
10.3959/1536-1098-74.1.28
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Since its establishment, tree-ring analysis has benefitted several scientific fields. Because of its many advantages, dendrochronology is a first choice to reconstruct past environmental variability. Two major concerns about the current tree-ring reconstruction paradigm are the subjective choices of detrending functions and the lack of fidelity to data of chronology generation methods. It is difficult to recover the original tree-ring data once they have been detrended and standardized. In this study, ensemble empirical mode decomposition (EEMD) is introduced as an objective high-fidelity stand-alone approach for developing tree-ring chronologies. Basic concepts of EEMD, recommended steps in developing chronologies, and available public domain programs are discussed. To demonstrate the potentials of EEMD for chronology development, two examples are provided, one for climate and the other for streamflow reconstructions. In both examples, EEMD chronologies show higher correlations with the instrumental data and have more power in their spectra than the ones developed based on the current tree-ring reconstruction approach. General usage concerns and cautions are also addressed.
引用
收藏
页码:28 / 38
页数:11
相关论文
共 35 条
[11]   Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability [J].
Esper, J ;
Cook, ER ;
Schweingruber, FH .
SCIENCE, 2002, 295 (5563) :2250-2253
[12]   Empirical mode decomposition as a filter bank [J].
Flandrin, P ;
Rilling, G ;
Gonçalvés, P .
IEEE SIGNAL PROCESSING LETTERS, 2004, 11 (02) :112-114
[13]  
Fritts HC., 1976, TREE RINGS CLIMATE C
[14]   Ensemble empirical mode decomposition for analyzing phenological responses to warming [J].
Guan, Biing T. .
AGRICULTURAL AND FOREST METEOROLOGY, 2014, 194 :1-7
[15]   ENSO and PDO strongly influence Taiwan spruce height growth [J].
Guan, Biing T. ;
Wright, William E. ;
Chung, Chih-Hsin ;
Chang, Shang-Tzen .
FOREST ECOLOGY AND MANAGEMENT, 2012, 267 :50-57
[16]   Automatic detection of noisy channels in fNIRS signal based on correlation analysis [J].
Guerrero-Mosquera, Carlos ;
Borragan, Guillermo ;
Peigneux, Philippe .
JOURNAL OF NEUROSCIENCE METHODS, 2016, 271 :128-138
[17]   Regional curve standardization: State of the art [J].
Helama, Samuli ;
Melvin, Thomas M. ;
Briffa, Keith R. .
HOLOCENE, 2017, 27 (01) :172-177
[18]   The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J].
Huang, NE ;
Shen, Z ;
Long, SR ;
Wu, MLC ;
Shih, HH ;
Zheng, QN ;
Yen, NC ;
Tung, CC ;
Liu, HH .
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1998, 454 (1971) :903-995
[19]   A review on Hilbert-Huang transform: method and its applications to geophysical studies [J].
Huang, Norden E. ;
Wu, Zhaohua .
REVIEWS OF GEOPHYSICS, 2008, 46 (02)
[20]  
Kim D, 2009, R J, V1, P40