Articles | Volume 29, issue 3
https://doi.org/10.5194/npg-29-265-2022
https://doi.org/10.5194/npg-29-265-2022
Research article
 | 
05 Jul 2022
Research article |  | 05 Jul 2022

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

Olivier Delage, Thierry Portafaix, Hassan Bencherif, Alain Bourdier, and Emma Lagracie

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Latest update: 21 Nov 2024
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Short summary
The complexity of geophysics systems results in time series with fluctuations at all timescales. The analysis of their variability then consists in decomposing them into a set of basis signals. We developed here a new adaptive filtering method called empirical adaptive wavelet decomposition that optimizes the empirical-mode decomposition existing technique, overcoming its drawbacks using the rigour of wavelets as defined in the recently published empirical wavelet transform method.