Articles | Volume 15, issue 13
https://doi.org/10.5194/gmd-15-5195-2022
https://doi.org/10.5194/gmd-15-5195-2022
Model evaluation paper
 | 
07 Jul 2022
Model evaluation paper |  | 07 Jul 2022

Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain. A case study using WRF-LES V4.0.1

Julian Quimbayo-Duarte, Johannes Wagner, Norman Wildmann, Thomas Gerz, and Juerg Schmidli

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Cited articles

Aumond, P., Masson, V., Lac, C., Gauvreau, B., Dupont, S., and Berengier, M.: Including the drag effects of canopies: real case large-eddy simulation studies, Bound.-Lay. Meteorol., 146, 65–80, 2013. a
Beljaars, A. C. M.: The parametrization of surface fluxes in large-scale models under free convection, Q. J. Roy. Meteor. Soc., 121, 255–270, https://doi.org/10.1002/qj.49712152203, 1995. a
Chow, F. K., Weigel, A. P., Street, R. L., Rotach, M. W., and Xue, M.: High-resolution large-eddy simulations of flow in a steep Alpine valley. Part I: Methodology, verification, and sensitivity experiments, J. Appl. Meteorol. Clim., 45, 63–86, 2006. a
Cuxart, J.: When can a high-resolution simulation over complex terrain be called LES?, Front. Earth Sci., 3, 87, https://doi.org/10.3389/feart.2015.00087, 2015. a
Dupont, S. and Brunet, Y.: Impact of forest edge shape on tree stability: a large-eddy simulation study, Forestry, 81, 299–315, 2008. a
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Short summary
The ultimate objective of this model evaluation is to improve boundary layer flow representation over complex terrain. The numerical model is tested against observations retrieved during the Perdigão 2017 field campaign (moderate complex terrain). We observed that the inclusion of a forest parameterization in the numerical model significantly improves the representation of the wind field in the atmospheric boundary layer.