2007
DOI: 10.1175/2007jtecho511.1
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Rectification of the Bias in the Wavelet Power Spectrum

Abstract: This paper addresses a bias problem in the estimate of wavelet power spectra for atmospheric and oceanic datasets. For a time series comprised of sine waves with the same amplitude at different frequencies the conventionally adopted wavelet method does not produce a spectrum with identical peaks, in contrast to a Fourier analysis. The wavelet power spectrum in this definition, that is, the transform coefficient squared (to within a constant factor), is equivalent to the integration of energy (in physical space… Show more

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Cited by 440 publications
(375 citation statements)
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References 32 publications
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“…The well-studied transition in 1976/1977 of Pacific climate represents a relatively small change when compared to many such shifts observed throughout the Fig. 9; Liu et al, 2007;Torrence and Compo, 1998) all independently confirm the oscillatory nature of the proxy rainfall record. MTM and wavelet analyses reveal persistent concentrations of power at decadal periodicities, significant above an AR(1) red noise model (p < 0.05 for the wavelet and p < 0.01 in the MTM).…”
Section: Characterizing the Rainfall Reconstructionsupporting
confidence: 60%
See 1 more Smart Citation
“…The well-studied transition in 1976/1977 of Pacific climate represents a relatively small change when compared to many such shifts observed throughout the Fig. 9; Liu et al, 2007;Torrence and Compo, 1998) all independently confirm the oscillatory nature of the proxy rainfall record. MTM and wavelet analyses reveal persistent concentrations of power at decadal periodicities, significant above an AR(1) red noise model (p < 0.05 for the wavelet and p < 0.01 in the MTM).…”
Section: Characterizing the Rainfall Reconstructionsupporting
confidence: 60%
“…On century scales, there is no persistent or consistent relationship between stalagmite δ 18 O variability and the solar variability reconstruction of Vieira et al (2011). Periods of high solar irradiance (e.g., solar maxima centered at 1600 CE) and low solar irradiance (e.g., Maunder Minimum centered at (Liu et al, 2007;Torrence and Compo, 1998). Black contours denote power above the 95 % confidence interval when using an AR(1) red noise model; crosshatching represents the "cone of influence" (Torrence and Compo, 1998).…”
Section: Stalagmite Variability: External Forcing Vs Internal Variabmentioning
confidence: 99%
“…The first study we performed to measure the surface rotation was a time-period analysis using a wavelet decomposition (Torrence & Compo 1998), improved for the low-frequency region as in Liu et al (2007), and adapted to our asteroseismic purposes following Mathur et al (2010b). This study allows us to track the temporal evolution of any modulation in the light curve that could be related to the rotation period and hence check whether it is not caused by a sudden event in the time series (normally related to a instrumental perturbation).…”
Section: Time-frequency Analysis and Projected Power Spectrummentioning
confidence: 99%
“…To perform a time-frequency analysis, we used the wavelets tool developed by Torrence & Compo (1998), including the correction by Liu et al (2007), and adapted for asteroseismology by Mathur et al (2010b). It consists of looking for the correlation between the time series and a mother wavelet with a given period.…”
Section: Time-frequency Analysis: Magnetic Cycle Proxymentioning
confidence: 99%