Learning Atmospheric Boundary Layer Turbulence
This preprint , by Sara Shamekh and Pierre Gentine, proposes a novel data-driven parameterization of vertical turbulent fluxes in convective boundary layer, that models the fluxes of various scalars across turbulent regimes. By incorporating a physics-based constraint this approach allows us to decompose the total vertical flux into two main modes of variability based on large scale forces that control the turbulence in the atmosphere.