## 1 INTRODUCTION

The optimal design of a wind project and the accurate prediction of its energy production depend on having an accurate and detailed understanding of the spatial distribution of the wind resource across the project area. Currently, numerical wind flow models, combined with onsite meteorological measurements, are the preferred approach for estimating this distribution. It is consequently important to continually assess potential improvements in such models.

Linear wind flow models such as WAsP,[1, 2] MS3DJH/MsMicro[3, 4] and MSFD[5] are widely used to predict the spatial variation of the average wind speed, directional frequency distribution (wind rose), wind shear and other boundary layer characteristics. Most such models are based on the theory of Jackson and Hunt.[6] They came into wide use in the 1980s when the computing resource was very limited. They run fast while performing reasonably well where the wind is not significantly affected by steep slopes, flow separations, thermally driven flows, low-level jets and other dynamic and nonlinear phenomena.

Reynolds-averaged Navier–Stokes models (referred to as RANS or CFD) are emerging as an alternative to linear models for wind energy applications. RANS models solve the conservation of mass and momentum equations, but the conservation of energy equation is not usually included. The RANS models assume steady-state flows so they tend to run relatively fast on a standard personal computer (PC). Usually, the simulation proceeds with a constant inlet wind profile until convergence is reached. For idealized cases, i.e. 2D or 3D flow over escarpments and hills, such steady-state RANS models perform well and give a high level of detail on the turbulence characteristics of the flow.[7] Several research studies at wind farm sites show that RANS models can perform better than the industry standard WAsP model, but others show little or no improvements over WAsP, and in some cases WAsP performs better.[8-11]

On the next rung up the ladder of sophistication are mesoscale numerical weather prediction (NWP) models (e.g. MASS,[12] ARPS,[13, 14] KAMM[15]). In principle, fully compressible, non-hydrostatic NWP models can simulate a broad range of meteorological phenomena from the synoptic to the microscales. However, the required computing power is substantial and increases rapidly with finer grid spacing. To circumvent this issue, NWP models are usually coupled to a diagnostic (microscale) wind flow model to achieve a higher spatial resolution. The microscale models used for this purpose include Jackson–Hunt-type models (e.g. WAsP, MsMicro) and mass-conserving models (e.g. WindMap,[16] CALMET[17]). Two leading examples of such coupled mesoscale–microscale models are the KAMM/WAsP system developed by Risø National Laboratory[18] and the SiteWind system developed by AWS Truepower.[16] AWS Truepower's approach is to run the mesoscale model (MASS) for a sample of days in nested grids from 30 km grid spacing down to 1.2 or 0.4 km. Then, the mean wind flow is downscaled to approximately a 50 m grid spacing using the microscale model (WindMap). Previous research has suggested that this approach is generally more accurate than WAsP over wind-project-scale distances in complex terrain, especially where mesoscale circulations have a significant impact on the spatial distribution of the wind resource.[19]

The next step in sophistication is coupled NWP and RANS models. Lately, research studies on the transport and dispersion of contaminants have relied on such coupled models.[20, 21] A typical approach is to initialize the RANS model from a single point or sounding profile from the mesoscale NWP model rather than from the full 3D gridded data. Since RANS models were initially developed in the engineering discipline to study flows around objects (e.g. airfoils), they do not include a complete conservation of energy equation, if any. Therefore, a challenge is to preserve the consistency of the thermodynamic (pressure, air density, etc.) and turbulent (turbulent kinetic energy, dissipation rates) quantities between the NWP and RANS model.

The next level of sophistication is NWP models coupled to LES. LES models have their origin in meteorology and weather prediction.[22-24] They solve the unsteady, nonlinear Navier–Stokes equations with the full physics parameterization schemes (radiation, microphysics, cloud convection, land surface-atmosphere interaction, turbulence, etc.). They are run at a high resolution compared with NWP models, i.e. close to the inertial sub-range of 3D turbulence, and are therefore able to explicitly resolve the energetically important eddies of the flow while parameterizing the small ones. The validity of LES depends crucially on the quality of the chosen turbulence closure scheme because of limited grid resolution and thermal stratification effects. However, LES models are mainly used as a research tool since the necessary computing power represents a major hurdle.

The present study aims to test a range of models at four sites with different topographic and surface characteristics and wind climate conditions. One of the sites is in flat terrain, one is in a coastal area and two are in mountainous terrain. Four different numerical wind flow models are compared:

- WAsP—a linear Jackson–Hunt wind flow model
- Meteodyn WT—a CFD/RANS model
- SiteWind—a coupled mesoscale NWP–mass consistent model
- ARPS—a coupled mesoscale NWP-LES