Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother

Accurate wind fields simulated by CFD models are necessary for many environmental and safety micro-meteorological applications, such as wind resource assessment. Atmospheric simulations at local scale are largely determined by boundary conditions (BCs), which are generally provided by mesoscale models (e.g., WRF). In order to improve the accuracy of the BCs, especially in the lowest levels, data assimilation methods might be used to take available observations into account. Among the existing data assimilation methods, the iterative ensemble Kalman smoother (IEnKS) has been chosen and adapted to micro-meteorology by taking BCs into account. In the present study, we assess the ability of the IEnKS to improve wind simulations over a very complex topography, by assimilating a few in situ observations. The IEnKS is tested with the CFD model Code_Saturne in 2D and 3D using both twin experiments and field observations. We propose a method to determine the first estimate of the BCs and to construct the associated background error covariance matrix, from the statistical analysis of three years of WRF simulations. The IEnKS is proved to greatly reduce the error and the uncertainty of the BCs and thus of the simulated wind field. Consequently, the wind potential is more accurately estimated.