Empirical adaptive wavelet decomposition (EAWD): an adaptive decomposition for the variability analysis of observation time series in atmospheric science

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 stro...

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Bibliographic Details
Published in:Nonlinear processes in geophysics 2022-07, Vol.29 (3), p.265-277
Main Authors: Delage, Olivier, Portafaix, Thierry, Bencherif, Hassan, Bourdier, Alain, Lagracie, Emma
Format: Article
Language:English
Subjects:
Approximation
Atmospheric sciences
Components
Decomposition
Empirical analysis
Geophysics
Methods
Optimization
Sciences of the Universe
Sequences
Signal processing
Time measurement
Time series
Variability
Wavelet transforms
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Summary: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.
ISSN:1607-7946
1023-5809
1607-7946
DOI:10.5194/npg-29-265-2022