Empirical adaptive wavelet decomposition (EAWD): an adaptive decomposition for the variability analysis of observation time series in atmospheric science
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Published:2022-07-05
Issue:3
Volume:29
Page:265-277
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ISSN:1607-7946
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Container-title:Nonlinear Processes in Geophysics
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language:en
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Short-container-title:Nonlin. Processes Geophys.
Author:
Delage Olivier,Portafaix Thierry,Bencherif Hassan,Bourdier Alain,Lagracie Emma
Abstract
Abstract. Most observational data sequences in geophysics can be interpreted
as resulting from the interaction of several physical processes at several
timescales and space scales. In consequence, measurement time series often have characteristics of non-linearity and non-stationarity and thereby exhibit
strong fluctuations at different timescales. The application of decomposition methods is an important step in the analysis of time series
variability, allowing patterns and behaviour to be extracted as components
providing insight into the mechanisms producing the time series. This study
introduces empirical adaptive wavelet decomposition (EAWD), a new adaptive method for decomposing non-linear and non-stationary time series into
multiple empirical modes with non-overlapping spectral contents. The method
takes its origin from the coupling of two widely used decomposition
techniques: empirical mode decomposition (EMD) and empirical wavelet
transformation (EWT). It thus combines the advantages of both methods and
can be interpreted as an optimization of EMD. Here, through experimental
time series applications, EAWD is shown to accurately retrieve different
physically meaningful components concealed in the original signal.
Publisher
Copernicus GmbH
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