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
Laboratoire de l'Atmosphère et des Cyclones, (LACy, UMR 8105
CNRS, Université de la Réunion, Météo-France),
Université de La Réunion, 97400 Saint-Denis de La Réunion,
France
Thierry Portafaix
Laboratoire de l'Atmosphère et des Cyclones, (LACy, UMR 8105
CNRS, Université de la Réunion, Météo-France),
Université de La Réunion, 97400 Saint-Denis de La Réunion,
France
Hassan Bencherif
Laboratoire de l'Atmosphère et des Cyclones, (LACy, UMR 8105
CNRS, Université de la Réunion, Météo-France),
Université de La Réunion, 97400 Saint-Denis de La Réunion,
France
School of Chemistry and Physics, University of KwaZulu-Natal,
Westville, Durban 4041, South Africa
Alain Bourdier
Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA
Emma Lagracie
Laboratoire de l'Atmosphère et des Cyclones, (LACy, UMR 8105
CNRS, Université de la Réunion, Météo-France),
Université de La Réunion, 97400 Saint-Denis de La Réunion,
France
École Nationale Supérieure des Techniques Avancées, 91120, Palaiseau, France
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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.
The complexity of geophysics systems results in time series with fluctuations at all timescales....
