Footprint model development and validation for homogeneous and inhomogeneous terrain using high resolution large eddy simulation

该项目的重点是开发和验证计算热量和空气动力学异质表面上方大气成分足迹的方法。足迹函数通过定义成分的源或汇与传感器位置之间的传递函数来确定测量的空间背景。由此产生的源区域是一个重要的质量评估工具，例如评估可变地形要素对数据的影响。虽然微气象研究越来越多地关注复杂的地形，足迹模型发展到目前为止，仅限于水平均匀流条件下的理想情况下，因此不能考虑异质性的影响。在该项目的框架内，考虑非均匀性影响的一种手段是高分辨率的大涡模拟（LES）模式，该模式允许湍流输送过程的显式分辨率。作为第二，计算成本较低的方法，前向和后向拉格朗日随机（LS）算法进行了修改，以考虑水平非均匀流条件。LES数据集将用于导出运行LS模型所需的流量统计数据，并评估其产生的源区域。最后，将通过现场试验数据评估LES模型和LS算法的足迹估计值的适用性。

The project focuses on developing and validating methods to compute footprints of atmospheric constituents above thermally and aerodynamically heterogeneous surfaces. A footprint function determines the spatial context of a measurement by defining a transfer function between sources or sinks of the constituent and the sensor position. The resulting source area is an important quality assessment tool, e.g. to assess the influence of variable terrain elements on the data. Although micrometeorological research focuses increasingly on complex terrain, footprint models developed to date are restricted to idealised cases of horizontally homogeneous flow conditions, and thus cannot consider the impact of heterogeneity. In the framework of this project, one means to consider influence of heterogeneity is a highresolution large eddy simulation (LES) model that allows the explicit resolution of the turbulent transport processes. As a second, computationally less costly method, both forward and backward Lagrangian stochastic (LS) algorithms are modified to account for horizontally heterogeneous flow conditions. The LES datasets will be used both to derive the flow statistics required to run the LS models, and to evaluate their produced source areas. The applicability of the footprint estimates from both LES model and LS algorithms will finally be assessed with data from field experiments.