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.

中文翻译：

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使用迭代集成卡尔曼平滑器通过数据同化改进建筑环境中的数值色散建模

局部尺度的空气污染建模需要准确的气象输入，例如来自速度场的输入。这些气象场通常使用微尺度模型（此处为 Code_Saturne ）进行模拟，这些模型受到较大尺度模型或观测提供的边界条件的影响。局部大气模拟对边界条件非常敏感，其准确估计很困难但至关重要。当在域内可以获得风速和湍流或污染物浓度的观测值时，它们通过数据同化提供补充信息，通过修改边界条件来提高模拟精度。在现有的数据同化方法中，迭代集成卡尔曼平滑器（IEnKS）适用于城市尺度模拟。已经发现这种方法可以提高风资源评估的准确性。在这里，我们通过同化城市冠层内的扰动测量来评估 IEnKS 方法改善标量色散建模（空气质量建模的重要组成部分）的能力。为了测试城市条件下的数据同化方法，我们使用模拟城市设置测试现场活动提供的观测数据，并考虑中性和稳定条件的情况，以及由水平速度分量和湍流组成的边界条件。我们证明了 IEnKS 方法同化速度和污染物浓度观测值的能力。在这两种情况下，污染物浓度估计的准确性提高了 40-60%。