Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several timescales and space scales. In consequence, measurement time series often have characteristics of non-linearity and non-stationarity and thereby exhibit strong fluctuations at different timescales. The application of decomposition methods is an important step in the analysis of time series variability, allowing patterns and behaviour to be extracted as components providing insight into the mechanisms producing the time series. This study introduces empirical adaptive wavelet decomposition (EAWD), a new adaptive method for decomposing non-linear and non-stationary time series into multiple empirical modes with non-overlapping spectral contents. The method takes its origin from the coupling of two widely used decomposition techniques: empirical mode decomposition (EMD) and empirical wavelet transformation (EWT). It thus combines the advantages of both methods and can be interpreted as an optimization of EMD. Here, through experimental time series applications, EAWD is shown to accurately retrieve different physically meaningful components concealed in the original signal.

Most geophysical systems are complex and the variability of the corresponding observation time series is characterized by large fluctuations at different timescales. To analyse these fluctuations and the associated multi-scale dynamics, some specific methods have been developed. Empirical mode decomposition (EMD) is part of a more general signal processing method called the Hilbert–Huang transform (Huang et al., 1998) and consists in decomposing a signal in a self-adaptive way into a sum of oscillating components named IMFs (intrinsic mode functions). Each IMF captures the repeating signal behaviour at some particular timescale. Like the wavelet transform, the EMD techniques reduce a time signal to a set of basis signals; unlike the wavelet transform, the basic functions are derived from the signal itself. The main advantages of the EMD method are that it is fully adaptive, data-driven, and, indeed, close to the observed dynamics. As the EMD acts as a bank of bandpass filters (Flandrin et al., 2004), the main limiting factor is the frequency resolution, which may give rise to the mode-mixing phenomenon where the spectral contents of some IMFs overlap each other (Gao et al., 2008).

Although several techniques exist to overcome this problem (Fosso and Molinas, 2017; Delage et al., 2019), Gilles (2013) proposed an alternative entitled empirical wavelet transform (EWT), which builds a wavelet filter bank from the segmentation of the original signal's Fourier spectrum. This approach is similar to that used in the construction of both Littlewood–Paley and Meyers wavelets (Meyer, 1997). The heart of the EWT method is the segmentation of the Fourier spectrum based on the detection of local maxima in order to obtain a set of non-overlapping segments. Because it is linked to the Fourier spectrum, the frequency resolution provided by the EWT is higher than that provided by the EMD and therefore allows the mode-mixing problem to be overcome. Although the EWT technique enables detection of the relevant frequencies involved in the original time series fluctuations, such a technique does not enable the detected frequencies to be associated with a specific mode of variability as EMD does. Because the EMD is closer to the observed dynamics than EWT, in the present work, we developed a new approach called EAWD (empirical adaptive wavelet decomposition) based on the coupling of the EMD and EWT techniques. We use the spectral content of the IMFs retrieved by EMD to optimize the segmentation of the original time series Fourier spectrum required by EWT. This document is structured around four sections. The first section is devoted to the EMD and EWT techniques. In the second one, the new adaptive decomposition EAWD is described and it is explained why such a method can be considered to be an optimization of the EMD. In the third section, two observation time series are analysed by using EMD and EAWD techniques, and the corresponding results are presented. Finally, the results obtained respectively with the EMD and EAWD techniques are compared and discussed.

Huang et al. (1998) proposed an original method called EMD, which adaptively decomposes any signal into oscillatory contributions. In a nutshell, EMD can be summarized as an iterative method where the signal is considered the superimposition of high- and low-frequency oscillations. At each iteration, high-frequency oscillation components are separated from the low-frequency oscillations and then re-injected as a new signal in the following iteration. The EMD is thus
directly controlled by the signal itself and not by some filtering
operations as in the wavelet decomposition. More precisely, the
decomposition is carried out at the scale of local oscillations, the
low-frequency modes being obtained as the mean value of an upper and a lower
envelope computed as cubic spline interpolations between maxima and minima respectively. By subtracting this component from the original signal, we
obtain what is called an IMF. The procedure is then applied to the low-frequency part as a new signal to be decomposed and
successive oscillatory components are iteratively extracted from the
original signal. The original time series

Wavelets are commonly used to analyse the variability of a signal. In the
temporal domain, a wavelet basis is defined as the mother wavelet

The essence of EMD is that the functions into which a signal is decomposed
are all in the time domain have the same length as the original
signal, allowing time-varying frequencies to be preserved. In this context,
Flandrin et al. (2004) described the EMD as behaving as a dyadic filter bank
like those involved in the multi-resolution analysis. This can be interpreted as the presence of several filters of overlapping frequency content that may
give rise to the mode-mixing phenomenon, which is defined as a single IMF consisting either of widely disparate scales or of similar scales residing in different IMFs. In that case, the spectral contents of some IMFs overlap
each other. To overcome this problem, Gilles (2013) proposed an alternative
named the EWT. As the EMD technique acts as a filter bank in the spectral domain, the method proposed by Gilles (2013) designs an appropriate wavelet filter bank from the segmentation of
the original signal's Fourier spectrum. The Fourier support

The filter bank (Meyer, 1997; Jaffard et al., 2001) is defined by the
empirical scaling function and the empirical wavelets on each

The heart of the empirical wavelet transform is the segmentation of the original signal's Fourier spectrum. In order to obtain a set of non-overlapping segments, the Fourier spectrum local maxima are detected. Each segment is centred around a group of one or more local maxima. The limit between two contiguous segments, each of them characterized by a group of local maxima, is determined as the local minimum closest to the midpoint between the two local maximum groups. Many of the detected local maxima are irrelevant as their contributions to the variability of the original time series are negligible. Selecting the relevant local maxima requires the setting of a threshold, which is not always possible.

The EMD enables an observation data sequence to be decomposed into multiple empirical modes of variability, each of them reflecting the observed dynamics at a specific timescale. However, in some cases, the frequency resolution provided by EMD does not allow IMFs with disjoint spectral support to be retrieved.

The adaptive wavelet transform (EWT) is one of several interesting methods pursuing the same goal as EMD, which allows the mode-mixing problem to be overcome. This method relies on robust pre-processing for peak detection, then performs original signal Fourier spectrum segmentation based on detected maxima, and then constructs a corresponding wavelet filter bank.

The main idea of the proposed EAWD method is to combine EMD and EWT techniques by setting non-overlapping groups of local maxima from the spectral contents of the IMFs returned by the EMD technique.

Each IMF local maximum group will be associated with a segment of the original signal Fourier spectrum segmentation. The boundaries of each of these segments will be set as the local minima located between local maximum groups of two consecutive IMFs. As the EMD acts like a bank of dyadic band-pass filters, the result of each of these filters, in the frequency domain, is composed of a set of local maxima relative to a specific timescale in which the resolution is divided by 2 in comparison with the timescale immediately above it. Considering that a timescale is characterized by the set of values in the range of

The proposed Fourier spectrum segmentation algorithm is based on two main
points.

The spectrum of two consecutive IMFs contains at most two different dominant frequencies. Relative to the spectral content of an IMF, the frequency with the greatest spectral density is called the dominant frequency.

The local maximum list of the spectra of two consecutive IMFs is subdivided into three groups: the local maxima belonging to the IMF

Estimation of the spectral density for each IMF
spectrum and selection of significant local maxima whose energy contribution

Calculation of the MLTAB matrix. Each row of MLTAB contains the list of significant local maxima present in the spectrum of an IMF. The rows of MLTAB have no common elements, and therefore MLTAB can be seen as a set of non-overlapping local maximum groups and represents a segmentation of the original signal Fourier spectrum.

Calculation of the BOUNDARY vector representing the boundaries between the local maximum groups contained in the MLTAB matrix. The EWT technique is run from BOUNDARY.

“Size” represents the length of list1 and “cut” is the boundary between two consecutive local maximum groups ML

The parameter k can be interpreted as the ratio between the number of scales
contained in list1 and the number of scales in the interval [fd

The EMD and EAWD techniques presented above have been applied to two
experimental time series of observations. The first time series was a series
of monthly total columns of ozone (TCO) in Dobson units (DU), recorded over 41 years, from January 1979 to December 2019, by a SAOZ (Zenithal Observation Analysis System) spectrometer on the Moufia University Campus, Saint-Denis,
Réunion (21

This TCO time series was elaborated by merging satellite data (OMI and TOMS) and ground-based observations recorded by a SAOZ spectrometer (Pommereau and Goutail, 1988) installed in Saint-Denis, Réunion, in 1993. The method used for merging satellite and ground data to obtain a homogeneous series is explained in Pastel et al. (2014).

Despite its low abundance, ozone plays an important role in the Earth's
atmosphere. It is mainly produced in the tropical stratosphere and
transported to higher latitudes by the large-scale circulation called the
Brewer–Dobson circulation. In the stratosphere, ozone acts like a filter and prevents incident solar ultraviolet (UV) radiation from reaching the ground,
thus protecting the biosphere. The significant depletion of the ozone layer
since the late 1970s has revealed the importance of ozone in the climate
system and the associated environmental and health risks. The TCO is a
parameter that measures the abundance of ozone over a given location. It is
given in DU and consists of

The second time series comprised 57 years of monthly rainfall measurements recorded at the Conakry (Guinea) meteorological station from 1961 to 2017.

The resulting original time series of total ozone columns and rainfall measurements are displayed in Figs. 4 and 5 respectively.

Monthly time series of total ozone columns in Dobson units (DU) over Réunion from 1979 to 2019. The time axis is expressed in years.

Monthly rainfall record time series recorded in Conakry (Guinea) from 1960 to 2019. The time axis is expressed in years.

Very often, noise is present in the original time series. To deal with this
problem, the authors suggest computing an EEMD (Wu and Wang, 2009). This approach seems to reduce
the noise present in the original signal. Generally, some of the IMFs
returned by the EEMD technique present poor correlations with the original
signal and therefore make a weak contribution to the variability of the original series. Such IMFs are qualified as irrelevant. Relevant IMFs are
discriminated from the irrelevant ones by means of two criteria. The first
of these criteria uses Pearson's correlation to estimate the degree of
correlation of each IMF with the original signal by setting a threshold. A
threshold commonly used in the literature (Ayenu-Prah and Attoh-Okine, 2010) can be
expressed as:

The second criterion determines the energy contribution of each IMF compared
with the energy contained in the original signal. The energy contribution of each IMF is calculated as a percentage as follows:

The threshold set for this second criterion is 1 %.

IMFs with a degree of correlation less than

When EEMD was applied to the Réunion TCO time series presented in Fig. 4, seven IMFs were found in addition to the residual mode. Taking account of the selection procedure mentioned above, the first five of the seven IMFs initially identified were relevant, and IMFs 6 and 7 were added to the residual mode. Results are displayed in Fig. 6 below.

Relevant IMFs and trend from the EEMD applied to time series of Réunion total columns of ozone from 1979 to 2019.

TCO IMF

For each IMF, the cycle defined as the period associated with the dominant
frequency is specified. Mode mixing occurs in IMF

To estimate the accuracy of the residual returned by the EEMD, it is compared with the trend of the original signal obtained from a moving window with a size set at the maximum of the relevant IMF cycles, i.e. 125 months (10.4 years).

Trend obtained from the EEMD technique (black curve). The trend of the original signal obtained from a moving window (blue curve) with a 125-month size (i.e. 10.4 years).

The accuracy of the residual,

To overcome the mixing mode occurring in IMFs 2 and 3 returned by the EEMD technique, we applied the EAWD technique to the Réunion TCO time series. The results obtained with EAWD are displayed in Fig. 9 below.

Réunion total ozone time series EAWD decomposition results.

As shown in Fig. 10 below, the 6-month and 1-year cycles relating respectively to EAWD1 and EAWD2 are correctly separated, and the mode mixing occurring in the EEMD results has been removed.

TCO EAWD

Similarly, the accuracy of the residual returned by the EAWD technique was estimated and compared with that returned by the EEMD.

The residual Trend

Likewise, the accuracy of the residual returned by the EAWD technique was
estimated using Eq. (16):

The EAWD trend expressed in DU per decade is shown in Fig. 12.

The DU per decade trend returned by the EAWD technique

Results in Fig. 12 show that ozone levels in Réunion decreased from 1981, stopped decreasing in 1992, and started rising again from 2002.

The accuracy of the DU per decade EAWD trend in Fig. 12 was calculated as

EEMD was applied to the rainfall time series measurements, and relevant IMFs were selected. Results are represented in Fig. 13 below.

Relevant IMFs and trend from the EEMD applied to the Conakry rainfall time series from 1960 to 2019.

Mode mixing occurs in IMF

Rainfall IMF

The accuracy of the trend calculated from EEMD results is compared below with the trend of the original signal obtained from a moving window with its size set at the maximum of the relevant IMF cycles, i.e. 144 months (12 years).

Trend obtained from the EEMD technique (black curve) with the trend of the original signal obtained from a moving window (blue curve) with a size of 144 months (i.e. 12 years).

The accuracy of the residual returned by the EEMD technique was estimated
using Eq. (16):

Results obtained with EAWD are displayed in Fig. 16 below.

Rainfall time series EAWD decomposition results.

The 6-month and 1-year cycles relating respectively to EAWD

Rainfall EAWD

The accuracy of the residual returned by the EAWD technique was estimated at

The residual Trend

The EAWD trend represented by an annual average of rainfall (in millimetres) is shown in Fig. 19 below.

The EAWD trend as a function of the average rainfall per year.

The results reported in Fig. 19 show that the rainfall average amount
decreased by 2.3 mm yr

The method commonly employed in atmospheric physics to determine the variability and trend of observation time series is to use a multi-linear regression model. This type of approach has been used very often (Randel and Thompson, 2011; Bourassa et al., 2014; Toihir et al., 2018).

The main requirement of this method is the a priori knowledge of the atmospheric climate forcings that control the variability of the time series studied. The ozone–QBO (Quasi-Biennial Oscillation) relationship has been discussed in several papers, as has the influence of ENSO (El Niño–Southern Oscillation) on ozone variability (Butchart et al., 2003; Brunner et al., 2006; Randel and Thompson, 2011). Other papers have shown the role played by solar flux in the temporal variability of ozone (Randel and Wu, 2007).

To validate results obtained with the EAWD method on the TCO time series for Réunion, we used the TREND-RUN multi-linear regression model developed at the University of Réunion and already applied in many studies (Bencherif et al., 2006; Bègue et al., 2010; Toihir et al., 2018).

Climate forcings used as input to the TREND-RUN model were QBO, ENSO, and solar flux. A linear function was used to estimate the long-term linear trend of the series. The trend was then estimated by calculating the slope of the normalized linear function. As the variability of ozone is also affected by the annual and semi-annual oscillations (AO and SAO), these two analytic oscillations were also used as input to the model. The TREND-RUN model and the climate forcings used (such as ENSO, QBO, and solar flux at 10.7 cm) are described in detail in Toihir et al. (2018).

The results obtained here with TREND-RUN on the 1979–2019 Réunion TCO time series are consistent with those obtained by Toihir et al. (2018) on a shorter total ozone series at Réunion (1998–2013).

The trend obtained from the TREND-RUN model over the whole monthly mean
ozone series from 1979 to 2019 is

The variability components returned by the EAWD method are in agreement with the climatic forcings used as input to the TREND-RUN model, and the energy contributions respectively estimated by the TREND-RUN model and the EAWD technique are reported in Table 1.

Comparison of total ozone variability in Réunion (from 1979 to 2019) obtained with the TREND-RUN model and the EAWD method.

The main advantage of the EAWD technique for analysing the ozone variability from an observational time series is that it auto-adaptively extracts the modes of variability implicitly contained in the original time series without requiring any a priori knowledge of the atmospheric forcings controlling the ozone variability. It can be seen that the results returned by the EAWD method are in complete agreement with those returned by the TREND-RUN model. On the other hand, the energy contributions of the various modes of variability obtained with the EAWD method are compatible with those obtained with the TREND-RUN method (Table 1).

In contrast to the trend returned by the TREND-RUN model, which is linear over the whole duration of the series, the EAWD technique enables the evolution of the trend to be visualized over the whole period studied.

TREND-RUN has not been applied to the Conakry rainfall time series as it contains intermittent processes, which makes use of multi-linear methods inappropriate.

EMD is a method for breaking down a signal without leaving the time domain. It can be compared with other analysis methods, such as Fourier transforms and wavelet decomposition, and the process is very useful for analysing raw signals, which are most often non-linear and non-stationary. Even though the EMD adaptability seems useful for many applications, the major drawback with EMD lies in the mode mixing, where the spectral contents of IMFs overlap each other. To overcome this problem, the EWT technique proposes a new approach, introducing a way of building adaptive wavelets and thus of extracting the different oscillatory modes from a signal by designing an appropriate filter bank. At the heart of the EWT technique lies the segmentation of the Fourier spectrum of the original signal. The main idea of the proposed EAWD technique is to use the results provided by EMD to segment the Fourier spectrum of the original signal. The algorithm implemented uses the spectral contents of the IMFs and ensures that their supports do not overlap, so that they can constitute a segmentation of the Fourier spectrum of the original signal. In another way, the method most commonly used to analyse the variability of observation time series is based on a multi-linear regression model. If we look at the results obtained with the TREND-RUN model on Réunion TCO time series, the variability modes returned by the EAWD are in good agreement with those obtained with the TREND-RUN multi-linear regression model.

From a comparison between the results obtained with the EMD and EAWD techniques, it emerges that the EAWD technique enables the mode-mixing problem to be overcome while providing a trend with better accuracy than that obtained with the EMD technique. Thanks to the rigour of the EWT, the EAWD keeps the behaviour of the signal in the timescales returned by the EMD. In this context, the EAWD technique can be seen as an optimization of the EMD.

The first version of the EAWD method was written in R and is currently a beta version of the code and still requires some validation so that it can run on any time series before being included in an R package.

The data are available from Thierry Portafaix and Hassan Bencherif at LACy UMR 8105 University of Reunion.

OD and AB developed and implemented the EAWD method. EL conducted the test and numerical validation. TP and HB conducted physical validation and comparison between the EAWD results with those obtained with a multilinear regression model.

The contact author has declared that neither they nor their co-authors have any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This study has been funded by the LEFE French National programme.

This paper was edited by Norbert Marwan and reviewed by two anonymous referees.