Intermittent particle dynamics in marine coastal waters

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Abstract

Marine coastal processes are highly variable over different space scales and timescales. In this paper we analyse the intermittency properties of particle size distribution (PSD) recorded every second using a LISST instrument (Laser In-Situ Scattering and Transmissometry). The particle concentrations have been recorded over 32 size classes from 2.5 to 500 1/4m, at 1 Hz resolution. Such information is used to estimate at each time step the hyperbolic slope of the particle size distribution, and to consider its dynamics. Shannon entropy, as an indicator of the randomness, is estimated at each time step and its dynamics is analysed. Furthermore, particles are separated into four classes according to their size, and the intermittent properties of these classes are considered. The empirical mode decomposition (EMD) is used, associated with arbitrary-order Hilbert spectral analysis (AHSA), in order to retrieve scaling multifractal moment functions, for scales from 10 s to 8 min. The intermittent properties of two other indicators of particle concentration are also considered in the same range of scales: the total volume concentration Cvol-total and the particulate beam attenuation coefficient cp(670). Both show quite similar intermittent dynamics and are characterised by the same exponents. Globally we find here negative Hurst exponents (meaning the small scales show larger fluctuation than large scales) for each time series considered, and nonlinear moment functions.

Figures

  • Figure 1. Location (black triangle) of the sampling station in the eastern English Channel together with the isobaths.
  • Figure 2. The first 3000 samples of the time series of volume concentrations of different size classes of PSD. (a) Silt/clay, (b) fine particles, (c) coarse/micro particles and (d) macro particles/flocs.
  • Figure 3. The first 3000 samples of the time series of PSD slope (ξ ) (a) and PSD slope of the entire data set with a power-law fit with a slope value of ξ = 2.9± 0.16 (b).
  • Figure 4. Turbulent power spectra of U and V components of velocity fields showing different scaling regimes as calculated by both FFT and HSA (a). The scaling exponents estimated using the HSA method (b). The vertical line in (a) shows the memory time of 36.26 min found in Eq. (6).
  • Figure 5. The first 3000 samples of the time series of Shannon entropy in (a), PDF of Shannon entropy along with a Gaussian fit in semi-log plot (inset) in (b) and the autocorrelation of Shannon entropy in (c).
  • Figure 6. Power spectra for different size classes of PSD estimated for Fourier and Hilbert transforms. Silt/clay (a), fine (b), coarse/micro (c) and macro particles/flocs (d). The red lines shows the scaling range and the slope of the best fit in this range.
  • Figure 7. Scaling exponents ζ(q) estimated for different particle sizes using the HSA method.
  • Figure 8. The first 3000 samples of the time series of cp(670) in (a), the first 3000 samples of the time series of Cvol-total in (b), the turbulent power spectrum of cp(670) and the turbulent power spectrum of Cvol-total showing different scaling regimes (the scaling regime indicated as red is used for the scaling exponent computation) in (c, d) and the scaling moment function of cp(670) and Cvol-total in (e).

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The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis

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APA

Renosh, P. R., Schmitt, F. G., & Loisel, H. (2015). Intermittent particle dynamics in marine coastal waters. Nonlinear Processes in Geophysics, 22(5), 633–643. https://doi.org/10.5194/npg-22-633-2015

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