Accurate wind fields simulated by CFD models are necessary for many environmental and safety micrometeorological 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 micrometeorology 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.
Air-pollution modelling at the local scale requires accurate meteorological inputs such as from the velocity field. These meteorological fields are generally simulated with microscale models (here Code_Saturne), which are forced with boundary conditions provided by larger scale models or observations. Local atmospheric simulations are very sensitive to the boundary conditions, whose accurate estimation is difficult but crucial. When observations of the wind speed and turbulence or pollutant concentration are available inside the domain, they provide supplementary information via data assimilation, to enhance the simulation accuracy by modifying the boundary conditions. Among the existing data assimilation methods, the iterative ensemble Kalman smoother (IEnKS) is adapted to urban-scale simulations. This method has already been found to increase the accuracy of wind-resource assessment. Here we assess the ability of the IEnKS method to improve scalar-dispersion modelling—an important component of air-quality modelling—by assimilating perturbed measurements inside the urban canopy. To test the data assimilation method in urban conditions, we use the observations provided by the Mock Urban Setting Test field campaign and consider cases with neutral and stable conditions, and the boundary conditions consisting of the horizontal velocity components and turbulence. We prove the capacity of the IEnKS method to assimilate observations of velocity as well as pollutant concentration. In both cases, the accuracy of pollutant concentration estimates is enhanced by 40–60%. We also show that assimilating both types of observations allows further improvements of turbulence predictions by the model.
Atmospheric dispersion modelling requires meteorological inputs over local domains with possibly complex topographies. These local wind fields may be difficult to simulate with CFD models, in particular because of their sensitivity to geometrical features and to model inputs, especially the boundary conditions which are generally provided by larger-scale models or measurements. Using data assimilation, a few measurements inside the domain could add information to the imprecise boundary conditions and thus greatly enhance the precision of the dispersion simulations. Three data assimilation techniques (3DVar, the back and forth nudging algorithm, and the iterative ensemble Kalman smoother) have been adapted to local scale simulations by taking boundary conditions into account instead of initial conditions for which they are usually applied. Their performances have been evaluated at small scales, with a simple representation of the atmosphere into two layers, using 1D solution of the shallow water equations.
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