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

Viewed

Total article views: 2,094 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,625 422 47 2,094 35 29
  • HTML: 1,625
  • PDF: 422
  • XML: 47
  • Total: 2,094
  • BibTeX: 35
  • EndNote: 29
Views and downloads (calculated since 07 Jan 2022)
Cumulative views and downloads (calculated since 07 Jan 2022)

Viewed (geographical distribution)

Total article views: 2,094 (including HTML, PDF, and XML) Thereof 1,964 with geography defined and 130 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 27 Mar 2024
Download
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.