Abstract
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.
Highlights
The fast growing wind industry requires accurate and less uncertain wind resource assessment over diverse topographic configurations for wind farm planning and development
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
With the original wind-climate classification (OC)+new semiimplicit (N-SI) combination, we achieve a reduction of the wind mean absolute error (MAE) by 0.41 ms-1, which translates into a 42.4% improvement with respect to the baseline schemes since the spurious acceleration is attenuated
Summary
The fast growing wind industry requires accurate and less uncertain wind resource assessment over diverse topographic configurations for wind farm planning and development. Pinard’s et al [13] uncertainty diagnosis (i.e., modelled versus observation data) of MC2’s performance for strongly stratified high-shear ABL flow simulations over mountainous topography in the western Canadian Rockies, confirm how the original semi-implicit (O-SI) discretization [1] in combination with the original wind-climate classification (OC) [2, 4] yield an overestimated mean velocity, directional shifting and strong spurious noise that degrade the consistency and accuracy of real case wind resource assessments The latter problem is corrected precisely with the new semi-implicit (N-SI) scheme [3], presented, which discretizes the model equations about the mean values of temperature ( T0 ) and pressure ( q0 ln p0 ) and redefines the buoyancy -prognostic thermodynamic variable for MC2-, yielding a profound restructuration of the non-linear residuals to ensure numerical stability and control the computational mode. The proposed enhancements are general enough to be applicable in any other multiscale model with similar numerical schemes
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