Publications (18)
Î -ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer
Maximilian Pierzyna, Rudolf Saathof, Sukanta Basu
Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of the…
A novel approach for deriving the stable boundary layer height and eddy viscosity profiles from the Ekman equations
Sukanta Basu, Albert A. M. Holtslag
In this study, we utilize a novel approach to solve the Ekman equations for eddy viscosity profiles in the stable boundary layer. By doing so, a well-known expression for the stabl…
On the Dissipation Rate of Temperature Fluctuations in Stably Stratified Flows
Sukanta Basu, Adam W DeMarco, Ping He
In this study, we explore several integral and outer length scales of turbulence which can be formulated by using the dissipation of temperature fluctuations () and other relev…
Parameterizing the Energy Dissipation Rate in Stably Stratified Flows
Sukanta Basu, Ping He, Adam W DeMarco
We use a database of direct numerical simulations to evaluate parametrizations for energy dissipation rate in stably stratified flows. We show that shear-based formulations are mor…
OTCliM: generating a near-surface climatology of optical turbulence strength () using gradient boosting
Maximilian Pierzyna, Sukanta Basu, Rudolf Saathof
This study introduces OTCliM (Optical Turbulence Climatology using Machine learning), a novel approach for deriving comprehensive climatologies of atmospheric optical turbulence st…
Subgrid-scale modeling of reacting scalar fluxes in large-eddy simulations of atmospheric boundary layers
Jean-François Vinuesa, Fernando Porté-Agel, Sukanta Basu +1
In large-eddy simulations of atmospheric boundary layer turbulence, the lumped coefficient in the eddy-diffusion subgrid-scale (SGS) model is known to depend on scale for the case…