Enhanced modelling of the stratified atmospheric boundary layer over steep terrain for wind resource assessment

• Published in: Journal of physics. Conference series Vol. 1222; no. 1; pp. 12005 - 12014
• Main Authors: Flores-Maradiaga, A, Benoit, R, Masson, C
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.05.2019
• Subjects: Atmospheric boundary layer; Atmospheric models; Brunt-Vaisala frequency; Compressibility; Euler-Lagrange equation; Mathematical models; Mountains; Physics; Simulation; Surface wind; Terrain; Thermal stratification; Topography; Weather forecasting; Wind effects; Wind power
• DOI: 10.1088/1742-6596/1222/1/012005
• ISSN: 1742-6588

The Mesoscale Compressible Community (MC2) model [1], devoted for weather forecasting and used in the Wind Energy Simulation Toolkit (WEST) [2], performs well for simulations over flat, gentle and moderate terrain slopes but is subject to numerical instability and strong spurious flows in presence of steep topography. To remove its inherent computational mode and reduce the wind overestimation due to terrain-induced numerical noise, a new semi-implicit (N-SI) scheme [3] was implemented to discretize and linearize the non-hydrostatic Euler equations with respect the mean values of pressure and temperature instead of arbitrary reference state values, redefining as well the buoyancy to use it as the thermodynamic prognostic variable. Additionally, the climate-state classification of the statistical-dynamical downscaling (SDD) method [4] is upgraded by including the Brunt-Väisälä frequency that accounts for the atmospheric thermal stratification effect on wind flow over topography. The present study provides a real orographic flow validation of these numerical enhancements in MC2, assessing their individual and combined contribution for an improved initialization and calculation of the surface wind in presence of high-impact terrain. By statistically comparing the wind simulations with met-mast data, obtained within the Whitehorse area of the Canadian Rocky Mountains, it is confirmed that these numerical enhancements may reduce over 40 percent of the wind overestimation, thus, attaining more accurate results that ensure reliable wind resource assessments over complex terrain.