Lagrangian Stochastic Modeling of Unstable Atmospheric Surface Layer

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This study evaluates the performance of a recently developed Lagrangian stochastic model (LSM), which solves the transport equations for turbulence probability density functions (PDFs), for simulating the unstable atmospheric surface layer (ASL). The simulated statistics are compared with the Monin-Obukhov similarity theory (MOST) predictions for mean gradients, standard deviations, turbulent Prandtl number, turbulence kinetic energy budgets, and turbulence PDFs. The LSM successfully captures many aspects of ASL structure and turbulence characteristics, particularly the mean gradients and standard deviations of potential temperature, specific humidity, and vertical velocity, which align closely with MOST predictions. However, the model shows limitations in reproducing the stability dependency of mean gradients of horizontal wind speed. Additionally, the model underestimates the probability of high vertical velocity fluctuations, leading to underprediction of turbulent transport and thermal convection initiation. The simulated turbulent Prandtl number shows dependency on stability but is generally weaker than MOST predictions. The study identifies that the parameterizations of dissipation, pressure redistribution, and pressure transport need to be enhanced to ensure that they have correct stability dependency, in order to improve the model’s accuracy in simulating the ASL.