Spectral estimation-What is new? What is next?

Tary, Jean Baptiste; Herrera, Roberto; Han, Jianxun; Baan, Mirko van der

doi:10.1002/2014rg000461

Cited by 199 publications

(98 citation statements)

References 107 publications

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“…This effect is similar to the "cone of influence" in the popular wavelet transform (Torrence and Compo, 1998). With the EMD, edge effects are usually contained within a half-period of a component at data boundaries .…”

Section: Hilbert Spectral Analysismentioning

confidence: 73%

“…A general class of useful signal processing techniques can thus be identified in the so-called time-frequency distributions that depict the intensity (or energy) of a signal in the time and the frequency domains simultaneously (Cohen, 1989). Such methods are commonly employed for geophysical signal processing (Tary et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…A time-frequency-energy representation of the data is then constructed from these basis functions. The HHT has seen extensive use in geophysical signal analysis and spectral estimation (Solé et al, 2007;Huang and Wu, 2008;Vecchio et al, 2010;Lee and Ouarda, 2011;Alberti et al, 2014;Huang and Shen, 2014;Tary et al, 2014). The HHT has also been previously employed on SSI datasets (Duffy, 2004;Calif et al, 2013;Bengulescu et al, 2016a, b).…”

Section: Introductionmentioning

confidence: 99%

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On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

Bengulescu

Blanc

Wald

2018

Nonlin. Processes Geophys.

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Abstract. This study is concerned with the intrinsic temporal scales of the variability in the surface solar irradiance (SSI). The data consist of decennial time series of daily means of the SSI obtained from high-quality measurements of the broadband solar radiation impinging on a horizontal plane at ground level, issued from different Baseline Surface Radiation Network (BSRN) ground stations around the world. First, embedded oscillations sorted in terms of increasing timescales of the data are extracted by empirical mode decomposition (EMD). Next, Hilbert spectral analysis is applied to obtain an amplitude-modulation-frequencymodulation (AM-FM) representation of the data. The timevarying nature of the characteristic timescales of variability, along with the variations in the signal intensity, are thus revealed. A novel, adaptive null hypothesis based on the general statistical characteristics of noise is employed in order to discriminate between the different features of the data, those that have a deterministic origin and those being realizations of various stochastic processes. The data have a significant spectral peak corresponding to the yearly variability cycle and feature quasi-stochastic high-frequency variability components, irrespective of the geographical location or of the local climate. Moreover, the amplitude of this latter feature is shown to be modulated by variations in the yearly cycle, which is indicative of nonlinear multiplicative cross-scale couplings. The study has possible implications on the modeling and the forecast of the surface solar radiation, by clearly discriminating the deterministic from the quasistochastic character of the data, at different local timescales.

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Section: Hilbert Spectral Analysismentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

Bengulescu

Blanc

Wald

2018

Nonlin. Processes Geophys.

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“…Most notably, analysis techniques that reveal the temporal evolution of the scrutinized data have been privileged. Two such techniques, popular within the geophysics community (Tary et al, 2014), are the Continuous Wavelet Transform (CWT) an the HilbertHuang Transform (HHT). The methods are of interest mostly due to their innate ability to offer insight into the processes underlying the data by painting so called time-frequencyenergy representations, i.e.…”

Section: Time-frequency-energy Representationsmentioning

confidence: 99%

On the temporal variability of the surface solar radiation by means of spectral representations

2016

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Abstract. This work deals with the temporal variability of daily means of the global broadband surface solar irradiance (SSI) impinging on a horizontal plane by studying a decennial time-series of high-quality measurements recorded at a BSRN ground station. Since the data have a non-linear and non-stationary character, two time-frequency-energy representations of signal processing are compared in their ability to resolve the temporal variability of the pyranometric signal. First, the continuous wavelet transform is used to construct the wavelet power spectrum of the data. Second, the adaptive, noise-assisted empirical mode decomposition is employed to extract the intrinsic mode functions of the signal, followed by Hilbert spectral analysis. In both spectral representations, the temporal variability of the SSI is portrayed having clearly distinguishable features: a plateau between scales of two days and two-three months that has decreasing power with increasing scale, a large spectral peak corresponding to the annual variability cycle, and a low power regime in between the previous two. It is shown that the data-driven, noise-assisted method yields a somewhat more sparse representation and that it is a suitable tool for inspecting the temporal variability of SSI measurements.

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“…2d-f); hence, in this study, a nonlinear and non-stationary time series analysis method, the ensemble empirical mode decomposition (EEMD) (Huang and Wu, 2008;Wu and Huang, 2009), is applied to a PM time series as a filter bank to detect the 531 dW signal. (To know the differences and relations between different spectral analysis methods, we suggest the readers to refer some other literatures, such as Tary et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Search for the 531-day-period wobble signal in the polar motion based on EEMD

Ding

Shen

2015

Nonlin. Processes Geophys.

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Abstract. In this study, we use a nonlinear and nonstationary time series analysis method, the ensemble empirical mode decomposition method (EEMD), to analyze the polar motion (PM) time series (EOP C04 series from 1962 to 2013) to find a 531-day-period wobble (531 dW) signal. The 531 dW signal has been found in the early PM series (1962)(1963)(1964)(1965)(1966)(1967)(1968)(1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977), but cannot be found in the recent PM series (1978-2013) using conventional analysis approaches. By virtue of the demodulation feature of EEMD, the 531 dW can be confirmed to be present in PM based on the differences of the amplitudes and phases between different intrinsic mode functions. Results from three sub-series divided from the EOP C04 series show that the period of the 531 dW is subject to variations, in the range of 530.9-524 days, and its amplitude is also time-dependent (about 2-11 mas). Synthetic tests are carried out to explain why the 531 dW can only be observed in recent 30-year PM time series after using EEMD. The 531 dW is also detected in the two longest available superconducting gravimeter (SG) records, which further confirms the presence of the 531 dW. The confirmation of the 531 dW existence could be significant in establishing a more reasonable Earth rotation model and may effectively contribute to the prediction of the PM and its mechanism interpretation.

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Spectral estimation-What is new? What is next?

Cited by 199 publications

References 107 publications

On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

On the temporal variability of the surface solar radiation by means of spectral representations

Search for the 531-day-period wobble signal in the polar motion based on EEMD

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