Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 29, issue 3
Articles | Volume 29, issue 3
https://doi.org/10.5194/npg-29-265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 3
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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Ayenu-Prah, A. Y. and Attoh-Okine, N.: A Criterion for Selecting Relevant Intrinsic Mode Functions in Empirical Mode Decomposition, Advances in Adaptive Data Analysis, 2, 1–24, https://doi.org/10.1142/S1793536910000367, 2010. 
Bègue, N., Bencherif, H., Sivakumar, V., Kirgis, G., Mze, N., and Leclair de Bellevue, J.: Temperature variability and trends in the UT-LS over a subtropical site: Reunion (20.8∘ S, 55.5∘ E), Atmos. Chem. Phys., 10, 8563–8574, https://doi.org/10.5194/acp-10-8563-2010, 2010. 
Bencherif, H., Diab, R. D., Portafaix, T., Morel, B., Keckhut, P., and Moorgawa, A.: Temperature climatology and trend estimates in the UTLS region as observed over a southern subtropical site, Durban, South Africa, Atmos. Chem. Phys., 6, 5121–5128, https://doi.org/10.5194/acp-6-5121-2006, 2006. 
Bourassa, A. E., Degenstein, D. A., Randel, W. J., Zawodny, J. M., Kyrölä, E., McLinden, C. A., Sioris, C. E., and Roth, C. Z.: Trends in stratospheric ozone derived from merged SAGE II and Odin-OSIRIS satellite observations, Atmos. Chem. Phys., 14, 6983–6994, https://doi.org/10.5194/acp-14-6983-2014, 2014. 
Brunner, D., Staehelin, J., Maeder, J. A., Wohltmann, I., and Bodeker, G. E.: Variability and trends in total and vertically resolved stratospheric ozone based on the CATO ozone data set, Atmos. Chem. Phys., 6, 4985–5008, https://doi.org/10.5194/acp-6-4985-2006, 2006. 
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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.
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