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the Creative Commons Attribution 4.0 License.
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
Emma Lagracie
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 nonlinearity and nonstationarity 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 nonlinear and nonstationary time series into multiple empirical modes with nonoverlapping 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.
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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 multiscale 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 selfadaptive 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, datadriven, 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 modemixing 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 nonoverlapping 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 modemixing 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.
2.1 EMD
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 lowfrequency oscillations. At each iteration, highfrequency oscillation components are separated from the lowfrequency oscillations and then reinjected 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 lowfrequency 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 lowfrequency part as a new signal to be decomposed and successive oscillatory components are iteratively extracted from the original signal. The original time series x(t) can finally be expressed as the sum of a finite number, N, of IMFs and a residual term, R, which cannot be assimilated to an oscillation.
The interesting fact about this algorithm is that it is highly adaptable and is able to extract the nonstationary part of the original signal. However, in practice, the EMD technique presents some limiting factors. For example, some problems appear when a certain amount of noise is present in the signal. To deal with this problem, Wu and Wang (2009) introduced an EMD optimization entitled ensemble empiricalmode decomposition (EEMD). The principle behind EEMD is to average the modes obtained by EMD after several realizations of Gaussian white noise that are added to the original signal. This approach seems to stabilize the decomposition obtained.
2.2 Wavelet approaches
Wavelets are commonly used to analyse the variability of a signal. In the temporal domain, a wavelet basis is defined as the mother wavelet ψ of zero mean, dilated with a parameter s>0 and translated by u∈R:
For the wavelet decomposition of a time series x(t_{i}), the most widely used case is the dyadic one, s=2^{j}. Then the wavelet decomposition of x is obtained by computing the inner products W_{x}(kj) as
where j represents the resolution level. The decomposition is then similar to a multiresolution analysis carrying out successive projections of x on a sequence of nested subspaces ${V}_{j}\in {L}^{\mathrm{2}}\left(R\right)j=[\mathrm{0},n]$, which leads to increasingly coarse approximations of x as j increases. The difference between two successive approximations, resulting from projections of x on V_{j−1} and V_{j}, contains the information of “details”, which existed at the scale 2^{j−1} but which is lost at the scale 2^{j}. This information is contained in the subspace W_{j} orthogonal to V_{j} such that ${V}_{j\mathrm{1}}={V}_{j}\oplus {W}_{j}$, where ⊕ denotes the direct sum of vector subspaces. The orthogonal projection of x on W_{j} gives the information of “details” at the resolution level j. Wavelets $\left\{{\mathit{\psi}}_{k,j}\left(t\right),k\in Z\right\}$ form a basis of W_{j}. According to the definition of a multiresolution analysis, there exists a function ϕ(t), called a scaling function, such that $\left\{\mathit{\phi}(tk),k\in Z\right\}$ form a basis of V_{0} corresponding to the coarsest approximation of x. The reconstruction of x is obtained from
where 〈 〉 represents the inner products. The approximation coefficients corresponding to the coarsest resolution level are given by 〈x,φ〉 and the detail coefficients corresponding to the successively decreasing resolution level $\mathrm{\Delta}{s}_{j}=\mathrm{\Delta}{s}_{j\mathrm{1}}\left./\mathrm{2}\right)$ are given by $\langle x,{\mathit{\psi}}_{k,j}\rangle $ as follows:
2.3 Empirical wavelets – EWT
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 timevarying 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 multiresolution analysis. This can be interpreted as the presence of several filters of overlapping frequency content that may give rise to the modemixing 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 [0,π] is segmented into N contiguous segments denoted ${\mathrm{\Delta}}_{n}=[{\mathit{\omega}}_{n\mathrm{1}},{\mathit{\omega}}_{n}]$.
The filter bank (Meyer, 1997; Jaffard et al., 2001) is defined by the empirical scaling function and the empirical wavelets on each Δ_{n} through Eqs. (6) and (7) respectively:
The function β(x) is an arbitrary C^{k}([0,1]) function defined as
Many functions satisfy this property, and the one most used in the literature (Daubechies, 1992) is
The parameter γ is chosen to satisfy the following criterion:
The details and approximation coefficients are calculated by using Eqs. (6) and (7) and are respectively given by inner products with the empirical wavelets ψ_{n} and the scaling function ϕ_{1}
where X is the Fourier transform of the original signal x, ${}^{\stackrel{\mathrm{\u203e}}{\phantom{\rule{0.25em}{0ex}}}}$ represents the complex conjugate, IFFT represents the inverse Fourier transform, and ψ_{n} and ϕ_{1} are the results of the inverse Fourier transforms of $\widehat{{\mathit{\psi}}_{n}}$ and $\widehat{{\mathit{\varphi}}_{\mathrm{1}}}$ respectively.
The heart of the empirical wavelet transform is the segmentation of the original signal's Fourier spectrum. In order to obtain a set of nonoverlapping 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 modemixing problem to be overcome. This method relies on robust preprocessing 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 nonoverlapping 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 bandpass 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 $[{\mathrm{2}}^{n},{\mathrm{2}}^{n+\mathrm{1}}]$, to carry out a segmentation of the Fourier spectrum, it is necessary to distribute the local maximum groups relative to a grid $[{\mathrm{2}}^{i},{\mathrm{2}}^{i+\mathrm{1}}]$, $i\in \left[\mathrm{2},J\right]$ with $J=\text{int}\left(\frac{\mathrm{log}\left(N\right)}{\mathrm{log}\left(\mathrm{2}\right)}\right)\mathrm{1}$, where N is the size of the original time series.
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_{i} spectrum, ML1, the local maxima belonging to the intersection of IMF_{i} and IMF_{i+1} spectra, MLINTER, and the local maxima belonging to the IMF_{i+1} spectrum, ML2.
The proposed EAWD algorithm is composed of three steps as described in the diagrams below (see Figs. 1–3).
“Size” represents the length of list1 and “cut” is the boundary between two consecutive local maximum groups ML_{i} and ML_{i+1}.
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_{i}, fd_{i+1}] of two consecutive dominant frequencies.
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, SaintDenis, Réunion (21^{∘} S, 55.5^{∘} E).
This TCO time series was elaborated by merging satellite data (OMI and TOMS) and groundbased observations recorded by a SAOZ spectrometer (Pommereau and Goutail, 1988) installed in SaintDenis, 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 largescale 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 ∼90 % stratospheric ozone and ∼10 % tropospheric ozone.
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.
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 (AyenuPrah and AttohOkine, 2010) can be expressed as:
In Eq. (13), Cor(IMF_{i},x) stands for the Pearson correlation coefficient between the ith IMF and the original signal x(t), i.e.
where SD represents the standard deviation and Cov the covariance.
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:
where ${E}_{i}={\sum}_{\mathrm{1}}^{N}{\text{IMF}}_{i}^{\mathrm{2}}\left(t\right)=\text{var}\left({\text{IMF}}_{i}\right)$ because each IMF is a zeromean function by construction. ${E}_{x}={\sum}_{\mathrm{1}}^{N}{x}^{\mathrm{2}}\left(t\right)=\text{var}(x\stackrel{\mathrm{\u203e}}{x})$.
The threshold set for this second criterion is 1 %.
IMFs with a degree of correlation less than τ and for which the energy contribution is less than 1 % are said to be irrelevant. The irrelevant IMFs are added to the residual mode R returned by the EEMD to form the trend of the original signal. For such IMFs to be part of the trend of the observed dynamics, they must not be too oscillating and are therefore contiguous to the residue.
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.
For each IMF, the cycle defined as the period associated with the dominant frequency is specified. Mode mixing occurs in IMF_{2} and IMF_{3} as they have the same 1year oscillation cycle. As shown in Fig. 7, the IMF_{2} spectrum contains ${F}_{\mathrm{1}}=\mathrm{1}/\mathrm{6}$ and ${F}_{\mathrm{2}}=\mathrm{1}/\mathrm{12}$ frequencies, and F_{2} is contained in both IMF_{2} and IMF_{3}.
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).
The accuracy of the residual, R, returned by the EEMD and compared with the trend of the original signal by using a moving window, Tmb, was estimated using the following expression:
where N is the original time series length and $\stackrel{\mathrm{\u203e}}{.}$ represents the mean operator.
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.
As shown in Fig. 10 below, the 6month and 1year cycles relating respectively to EAWD1 and EAWD2 are correctly separated, and the mode mixing occurring in the EEMD results has been removed.
Similarly, the accuracy of the residual returned by the EAWD technique was estimated and compared with that returned by the EEMD.
Likewise, the accuracy of the residual returned by the EAWD technique was estimated using Eq. (16): Pr_{EAWD}=0.3 %.
The EAWD trend expressed in DU per decade is shown in Fig. 12.
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 2×Pr_{EAWD}, i.e. 0.6 %.
EEMD was applied to the rainfall time series measurements, and relevant IMFs were selected. Results are represented in Fig. 13 below.
Mode mixing occurs in IMF_{2} and IMF_{3} as they have the same 1year oscillation cycle. As shown in Fig. 14 below, frequency ${F}_{\mathrm{1}}=\mathrm{1}/\mathrm{6}$ is contained in both IMF_{1} and IMF_{2}, while ${F}_{\mathrm{2}}=\mathrm{1}/\mathrm{12}$ is contained in IMF_{1}, IMF_{2}, and IMF_{3}.
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).
The accuracy of the residual returned by the EEMD technique was estimated using Eq. (16): Pr_{EEMD}=15 %.
Results obtained with EAWD are displayed in Fig. 16 below.
The 6month and 1year cycles relating respectively to EAWD_{1} and EAWD_{2} are correctly separated, and the mode mixing occurring in the EEMD results has been removed (see Fig. 17).
The accuracy of the residual returned by the EAWD technique was estimated at Pr_{EAWD}=0.9 %.
The EAWD trend represented by an annual average of rainfall (in millimetres) is shown in Fig. 19 below.
The results reported in Fig. 19 show that the rainfall average amount decreased by 2.3 mm yr^{−1} in the period [1960, 1984], then increased by 1 mm yr^{−1} in the period [1984, 2003], and decreased again, by 0.7 mm yr^{−1}, in the period [2003, 2020].
The method commonly employed in atmospheric physics to determine the variability and trend of observation time series is to use a multilinear 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 (QuasiBiennial 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 TRENDRUN multilinear 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 TRENDRUN model were QBO, ENSO, and solar flux. A linear function was used to estimate the longterm 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 semiannual oscillations (AO and SAO), these two analytic oscillations were also used as input to the model. The TRENDRUN 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 TRENDRUN 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 TRENDRUN model over the whole monthly mean ozone series from 1979 to 2019 is $\mathrm{1.11}\pm \mathrm{0.19}$ % per decade. The error is estimated from the SD of the residual found by the model.
The variability components returned by the EAWD method are in agreement with the climatic forcings used as input to the TRENDRUN model, and the energy contributions respectively estimated by the TRENDRUN model and the EAWD technique are reported in Table 1.
The main advantage of the EAWD technique for analysing the ozone variability from an observational time series is that it autoadaptively 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 TRENDRUN 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 TRENDRUN method (Table 1).
In contrast to the trend returned by the TRENDRUN 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.
TRENDRUN has not been applied to the Conakry rainfall time series as it contains intermittent processes, which makes use of multilinear 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 nonlinear and nonstationary. 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 multilinear regression model. If we look at the results obtained with the TRENDRUN model on Réunion TCO time series, the variability modes returned by the EAWD are in good agreement with those obtained with the TRENDRUN multilinear regression model.
From a comparison between the results obtained with the EMD and EAWD techniques, it emerges that the EAWD technique enables the modemixing 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 coauthors 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.
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 Abstract
 Introduction
 The existing approaches
 The empirical adaptive wavelet decomposition (EAWD)
 Time series analysis and results
 Consistency of the EAWD method
 Conclusion
 Code availability
 Data availability
 Author contributions
 Competing interests
 Disclaimer
 Financial support
 Review statement
 References
 Abstract
 Introduction
 The existing approaches
 The empirical adaptive wavelet decomposition (EAWD)
 Time series analysis and results
 Consistency of the EAWD method
 Conclusion
 Code availability
 Data availability
 Author contributions
 Competing interests
 Disclaimer
 Financial support
 Review statement
 References