Handout (106.1 kB)
Stability correction functions are integral parts of every present-day NWP model and from a scientific perspective, specifying them in an ad-hoc manner is not satisfactory. It has to be emphasized that the shapes of the field-observations-based correction functions in the very stable regime are quite uncertain and a subject of ongoing debate. This ambiguity arises from the fact that stable boundary layer (SBL) measurements are rarely free from nonstationarities (e.g., bursting, mesoscale disturbances, wave activities) and the observations become increasingly uncertain with increasing stability. This inevitable limitation highlights the need for high-resolution spatio-temporal simulated information about these flows to supplement the observations. With the recent developments in computing resources, three dimensional numerical simulations (particularly large-eddy simulation LES, at present the most efficient technique available for high Reynolds number flow simulations, in which the larger scales of motion are resolved explicitly and the smaller ones are modeled) of turbulent flow in the atmospheric boundary layer can provide this kind of information. Unlike NWP models, a few state-of-the-art LES models for example, the Scale Dependent Dynamic models (Porté-Agel et al., J. Fluid Mech., 2000; Porté-Agel, Boundary-Layer Meteorology, 2004) are even capable of dynamically adjusting (i.e., without tuning) model coefficients to account for atmospheric stability. In other words, these specific LES models do not require any ad-hoc prescription of stability correction functions for turbulent flux parameterizations inside the BL. Therefore, these LES models have the potential to evaluate existing stability correction functions and also come up with revised and improved yet physically-based stability formulations for SBL parameterization.
In this work, we propose a new form of SBL parameterization for large-scale models based on a suite of idealized large-eddy simulations of varying atmospheric stabilities utilizing the scale dependent dynamic approach.
Supplementary URL: http://efd.safl.umn.edu
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