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

[摘要] Most observational data sequences in geophysics can be interpretedas resulting from the interaction of several physical processes at severaltimescales and space scales. In consequence, measurement time series often have characteristics of non-linearity and non-stationarity and thereby exhibitstrong fluctuations at different timescales. The application of decomposition methods is an important step in the analysis of time seriesvariability, allowing patterns and behaviour to be extracted as componentsproviding insight into the mechanisms producing the time series. This studyintroduces empirical adaptive wavelet decomposition (EAWD), a new adaptive method for decomposing non-linear and non-stationary time series intomultiple empirical modes with non-overlapping spectral contents. The methodtakes its origin from the coupling of two widely used decompositiontechniques: empirical mode decomposition (EMD) and empirical wavelettransformation (EWT). It thus combines the advantages of both methods andcan be interpreted as an optimization of EMD. Here, through experimentaltime series applications, EAWD is shown to accurately retrieve differentphysically meaningful components concealed in the original signal.

[学科分类] 自动化工程