Khaled Ghannam | Searcharxiv

arXiv2026

Data-Driven Flux Parameterization for the Atmospheric Boundary Layer

Turbulent fluxes in the atmospheric boundary layer (ABL) govern exchanges of momentum, heat, and mass between the surface and atmosphere, shaping boundary layer structure and influencing weather, climate, and engineering applications. Yet their representation in coarse resolution models remains challenging, particularly under unstable conditions with strongly nonlocal transport and stable conditions with intermittent turbulence. Here, we develop a data driven turbulent flux parameterization in which nondimensional fluxes are represented by a linearized convolution operator acting on nondimensional mean state profiles. We train and evaluate the closure using high resolution large eddy simulations (LES) of idealized flow over homogeneous surfaces spanning multiple stability regimes. Several first order closure variants are constructed from different combinations of mean temperature and velocity profiles to predict heat and momentum fluxes, and the best model is selected by minimizing mean squared error across training and unseen test cases. The resulting parameterization improves predictive skill relative to a standard K-profile closure while retaining an interpretable operator form. Its learned kernels expose the locality and nonlocality of turbulent transport across stability regimes, linking empirical performance to physically inspectable flux--profile relationships. In a posteriori single column simulations, the closure remains stable and produces state profiles that closely match LES, demonstrating its potential as an accurate and transparent ABL flux parameterization.

physics.flu-dyn↗

arXiv2021

A scale-wise analysis of intermittent momentum transport in dense canopy flows

We investigate the intermittent dynamics of momentum transport and its underlying time scales in the near-wall region of the neutrally stratified atmospheric boundary layer in the presence of a vegetation canopy. This is achieved through an empirical analysis of the persistence time scales (periods between successive zero-crossings) of momentum flux events, and their connection to the ejection-sweep cycle. Using high-frequency measurements from the GoAmazon campaign, spanning multiple heights within and above a dense canopy, the analysis suggests that when the persistence time scales ($t_p$) of momentum flux events from four different quadrants are separately normalized by $\Gamma_{w}$ (integral time scale of the vertical velocity), their distributions ($P(t_p/\Gamma_{w})$) remain height-invariant. This result points to a persistent memory imposed by canopy-induced coherent structures, and to their role as an efficient momentum transport mechanism between the canopy airspace and the region immediately above. Moreover, $P(t_p/\Gamma_{w})$ exhibits a power-law scaling at times $t_{p}<\Gamma_{w}$ with an exponential tail appearing for $t_{p} \geq \Gamma_{w}$. By separating the flux events based on $t_p$, we discover that around 80\% of the momentum is transported through the long-lived events ($t_{p} \geq \Gamma_{w}$) at heights immediately above the canopy while the short-lived ones ($t_{p} < \Gamma_{w}$) only contribute marginally ($\approx$ 20\%). To explain the role of instantaneous flux amplitudes towards momentum transport, we compare the measurements with a newly-developed surrogate data and establish that the range of time scales involved with amplitude variations in the fluxes tend to increase as one transitions from within to above the canopy.

physics.flu-dyn↗