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

International audience; 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 outputs of 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. Data assimilation methods have generally been developed for larger scale meteorology and deal with initial conditions. Among the existing 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 adapted IEnKS to improve wind simulations over a very complex topography in a context of wind resource assessment, 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 real 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 over the small-scale domain. As a consequence, the wind resource estimate is also much more accurate. Highlights • This article provides a framework to perform ensemble variational data assimilation of in situ observations to improve local scale simulations with a CFD model. • The iterative ensemble Kalman smoother is adapted to correct boundary conditions and is tested with twin experiments and real data experiments in 2D and 3D with a CFD model over very complex topography. • The adapted IEnKS is proved to enhance the accuracy of boundary conditions and local scale simulations in operationally affordable conditions.