Unveiling the turbulence scale couplings in fluctuating time series during EUREC4A

Inside the Marine Atmospheric Boundary Layer (MABL) turbulence drives a large amount of physical processes through a dynamical coupling. Understanding this coupling is a key issue in weather and climate modeling, but an adapted statistical representation is still lacking. A strong limitation comes from the non-Gaussianities existing inside the MABL. We seek an approach to describe statistically the couplings across scales, which is poorly measured by the power spectrum. Recent developments in data science provide new tools as the Wavelet Scattering Transform (WST), which gives a low-variance statistical description of non-Gaussian processes and offers to go beyond the power spectrum representation.

We proposes to apply WST technique on turbulent moments recorded during the EUREC4A campaign in trade-wind cloud regimes. Using WST analysis on both fluctuating time series and realistic Large-Eddy Simulations (LES), we study how the different scales in turbulent flows interact between each other in the MABL. Are the turbulent flows organised or purely random in the MABL ? Do the couplings change for different cloud organisations ? Do the models show evidence of such scale couplings ?

In this presentation, we first look at the expected WST coefficients law for turbulent flows in the inertial subrange. Second, we present what are the WST coefficients for the measured turbulent moments and how they evolve for different cloud organizations.