Application of the Hilbert–Huang Transform to the Estimation of Air-Sea Turbulent Fluxes

Wang, Juanjuan; Song, Jinbao; Huang, Yongshun; Fan, Conghui

doi:10.1007/s10546-012-9784-8

Cited by 14 publications

(15 citation statements)

References 16 publications

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“…This method is a general data‐driven method without any a priori basis functions, and the signal is decomposed based on the local characteristics of the data. It has been successfully applied to the eddy‐covariance analysis of air–sea interface turbulent fluxes (Wang et al ), ADV data processing (Qiao et al ), and geophysical studies (Huang and Wu )…”

Section: Materials and Proceduresmentioning

confidence: 99%

“…This method is a general data-driven method without any a priori basis functions, and the signal is decomposed based on the local characteristics of the data. It has been successfully applied to the eddy-covariance analysis of air-sea interface turbulent fluxes (Wang et al 2013), ADV data processing (Qiao et al 2016), and geophysical studies (Huang and Wu 2008) Qiao et al (2016) showed that the EMD can effectively remove the noise in the ADV data, in which the ADV vertical velocity was decomposed into 12 IMFs, and components 1 and 2 were treated as noise because they have characteristics of white noise. In our ADV datasets, the first few components of IMFs are also associated with Doppler noise and high-frequency contamination.…”

Section: Removal Of Noisementioning

confidence: 99%

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Calculation of turbulent dissipation rate with acoustic Doppler velocimeter

Huang

Guo

et al. 2018

Limnology & Ocean Methods

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Turbulent dissipation rates are calculated from an acoustic Doppler velocimeter by fitting the measured wavenumber spectrum to a universal turbulence spectrum. A combination of the Butterworth filter and empirical model decomposition is employed to reduce Doppler noise and high‐frequency fluctuations. Different from the classical inertial subrange dissipation method fitting to the Kolmogorov − 5/3 slope, we propose the method here which can make longer available spectrum bands. We analyzed 408 bursts with turbulent dissipation rates ranging from 10−8 to 10−5 W kg−1 as measured in the coastal ocean of the South China Sea and found for all of these bursts, features of the clean spectrum can be resolved to the dissipation range of turbulence, in which about 15% bursts can be resolved to the Kolmogorov wavenumber, and 51% to 1/2 Kolmogorov wavenumber. Comparisons of this method with the inertial subrange method indicated that its estimated turbulent dissipation rates were somewhat smaller than those from the inertial subrange method in most of the bursts. Their ratios had a mean value of 0.77, which is typical for oceanic turbulence measurements.

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Section: Materials and Proceduresmentioning

confidence: 99%

Section: Removal Of Noisementioning

confidence: 99%

Calculation of turbulent dissipation rate with acoustic Doppler velocimeter

Huang

Guo

et al. 2018

Limnology & Ocean Methods

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“…17 To filter such noise the Empirical Mode Decomposition (EMD) is used. EMD was first proposed by Huang et al [22] as The intrinsic mode functions (IMFs) should present the following properties: the mean of the upper and lower envelope- 22 lines determined by the local maximum and minimum points is zero at any time; the number of zero-crossings is equal to 23 the number of local maximum/minimum points, or they differ at most by one.…”

mentioning

confidence: 99%

“…To understand if there is a relationship between the spectral spikes and a given mode of oscillation C i the power spectrum 6 of each mode is determined separately, as done by Wang et al [23] (Fig. 5).…”

mentioning

confidence: 99%

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Estimates of turbulent kinetic energy dissipation rate for a stratified flow in a wind tunnel

Puhales

Demarco

Martins

et al. 2015

Physica A: Statistical Mechanics and its Applications

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Large eddies modulating flux convergence and divergence in a disturbed unstable atmospheric surface layer

Gao

Russell

et al. 2016

JGR Atmospheres

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The effects of large eddies on turbulence structures and flux transport were studied using data collected over a flat cotton field during the Energy Balance Experiment 2000 in the San Joaquin Valley of California in August 2000. Flux convergence (FC; larger fluxes at 8.7 m than 2.7 m) and divergence (FD) in latent heat flux (LE) were observed in a disturbed, unstable atmospheric surface layer, and their magnitudes largely departed from the prediction of Monin‐Obukhov similarity theory. From our wavelet analysis, it was identified that large eddies affected turbulence structures, scalar distribution, and flux transport differently at 8.7 m and 2.7 m under the FC and FD conditions. Using the ensemble empirical mode decomposition, time series data were decomposed into large eddies and small‐scale background turbulence, the time‐domain characteristics of large eddies were examined, and the flux contribution by large eddies was also determined quantitatively. The results suggest that large eddies over the frequency range of 0.002 Hz < f < 0.02 Hz (predominantly 300–400 m) enhanced the vertical velocity spectra more significantly at 8.7 m than 2.7 m, leading to an increased magnitude of the cospectra and thus LE at 8.7 m. In the FD case, however, these large eddies were not present and even suppressed in the vertical velocity spectra at 8.7 m. Consequently, the cospectra divergence over the low‐frequency ranges primarily caused the LE divergence. This work implies that large eddies may either improve or degrade the surface energy balance closure by increasing or decreasing turbulent fluxes, respectively.

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Application of the Hilbert–Huang Transform to the Estimation of Air-Sea Turbulent Fluxes

Cited by 14 publications

References 16 publications

Calculation of turbulent dissipation rate with acoustic Doppler velocimeter

Calculation of turbulent dissipation rate with acoustic Doppler velocimeter

Estimates of turbulent kinetic energy dissipation rate for a stratified flow in a wind tunnel

Large eddies modulating flux convergence and divergence in a disturbed unstable atmospheric surface layer

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