Articles | Volume 29, issue 3
Nonlin. Processes Geophys., 29, 265–277, 2022
https://doi.org/10.5194/npg-29-265-2022
Nonlin. Processes Geophys., 29, 265–277, 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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-37', Anonymous Referee #1, 15 Feb 2022
    • AC1: 'Reply on RC1', Olivier Delage, 10 May 2022
  • RC2: 'Comment on npg-2021-37', Anonymous Referee #2, 05 Apr 2022
    • AC2: 'Reply on RC2', Olivier Delage, 10 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Olivier Delage on behalf of the Authors (18 May 2022)  Author's response
ED: Publish as is (23 May 2022) by Norbert Marwan
AR by Olivier Delage on behalf of the Authors (02 Jun 2022)  Author's response    Manuscript
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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.