2020
DOI: 10.1016/j.geog.2019.09.002
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Excitations of length-of-day seasonal variations: Analyses of harmonic and inharmonic fluctuations

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Cited by 3 publications
(3 citation statements)
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“…The mathematical methods adopted in this study include the continuous wavelet transform (CWT) and the cross wavelet transform (XWT). The CWT of a time series xn ( n = 1, 2,..., N) is defined as (Grinsted et al., 2004): Wnx(normalS)=δt/sn=1Nxnϕitalic0[(nn)δts] where Wnx(S) is the wavelet coefficient, δt is the uniform time step, s is the wavelet scale, n is the reversed time, ϕitalic0 is the mother function and we specially choose the Morlet wavelet (w0 = 6) as the month wavelet since it offers an optimal balance between frequency and time localization (Luo et al., 2019; Xu et al., 2020). The Morlet wavelet is defined as (Grinsted et al., 2004): ψ0(η)=π1/4eiw0ηeη2/2 where w0 and η are dimensionless frequency and time, respectively.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The mathematical methods adopted in this study include the continuous wavelet transform (CWT) and the cross wavelet transform (XWT). The CWT of a time series xn ( n = 1, 2,..., N) is defined as (Grinsted et al., 2004): Wnx(normalS)=δt/sn=1Nxnϕitalic0[(nn)δts] where Wnx(S) is the wavelet coefficient, δt is the uniform time step, s is the wavelet scale, n is the reversed time, ϕitalic0 is the mother function and we specially choose the Morlet wavelet (w0 = 6) as the month wavelet since it offers an optimal balance between frequency and time localization (Luo et al., 2019; Xu et al., 2020). The Morlet wavelet is defined as (Grinsted et al., 2004): ψ0(η)=π1/4eiw0ηeη2/2 where w0 and η are dimensionless frequency and time, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…is the mother function and we specially choose the Morlet wavelet ( 0 E w = 6) as the month wavelet since it offers an optimal balance between frequency and time localization (Luo et al, 2019;Xu et al, 2020). The Morlet wavelet is defined as (Grinsted et al, 2004):…”
mentioning
confidence: 99%
“…Chao et al [35] analyzed PM excitation for the period 1900-2012 using wavelet analysis, found an ~8-year quasi-periodic signal and proved the existence of the ~26-year Markowitz wobble. Besides PM excitation, Luo et al [36] also applied wavelet analysis to the excitation of length-of-day (LOD) variations and found that consideration of wavelet-based inharmonic excitation can achieve a better match with the observed LOD time series.…”
mentioning
confidence: 99%