Collaborative Research: Data Assimilation of Dual Doppler Lidar Observations of the Urban Boundary Layer

多普勒激光雷达可以探测到晴朗空气中的空气运动，它主要看到气溶胶粒子随空气移动。因此，扫描多普勒激光雷达系统可以表征部分大气边界层(接近地球表面)中的空气运动，但它只看到运动的一个分量，即激光雷达波束方向的径向分量。发展了一种称为数据同化的技术，通过使用测量数据来约束流动的数值模型，从单台多普勒激光雷达确定三维风场。当使用随时间重复测量时，同化方案除了直接测量的信息外，还可以产生额外的信息，包括速度的交叉波束分量。然而，推导出的风场在一定程度上依赖于模型中的假设，因此还没有证明这种方法能够提供真实的风场重建。在这个项目中，研究人员将使用两个扫描多普勒激光雷达从不同的方向观测相同部分的风场，从而直接测量风矢量的两个分量。这些测量将以两种方式使用：一次只使用一个激光雷达检验数据同化所获得的结果，然后由另一个激光雷达对其进行验证；将数据同化技术扩展到使用两个激光雷达，从而获得更好的风场重建。然后，重建的风场将用于研究湍流相干结构及其在城市边界层中的作用，特别是当它们可能影响污染物的传输和扩散时。由于激光雷达同时提供风和后向散射信息，数据集可以约束边界层中气溶胶传输和扩散的模型预测。因此，这些反演技术将为研究湍流的结构和统计提供一个重要的工具。这项研究的预期成果包括：验证(和可能改进)应用于单激光雷达观测的数据同化技术，单激光雷达观测比双激光雷达观测更常见，成本也更低；关于大气边界层湍流结构的性质及其对扩散的贡献的新信息；以及改进预测扩散的能力。其结果应该会导致更好的监测和预测城市环境中的扩散情况的能力，并可能对公共安全产生重大好处。

Air motions in the clear air can be detected by Doppler lidar, which sees primarily aerosol particles moving with the air. A scanning Doppler lidar system can therefore characterize the air motions in a portion of the atmospheric boundary layer (near the surface of the earth), but it only sees one component of the motion, the radial component in the direction of the lidar beam. A technique called data assimilation has been developed to determine the three-dimensional wind field from a single Doppler lidar by using the measurements to constrain a numerical model of the flow. When repeated measurements over time are used, assimilation schemes can produce additional information in addition to that measured directly, including cross-beam components of the velocity. However, the deduced wind fields are to some extent dependent on assumptions in the model, so it has not been demonstrated that this approach provides realistic reconstruction of the wind fields. In this project, the investigators will use two scanning Doppler lidars to observe the same parts of the wind fields from different directions and so will have direct measurements of two components of the wind vector. These measurements will be used in two ways: to test the results obtained by data assimilation using only one lidar at a time, for which the other lidar then provides verification, and to extend the data-assimilation technique to use two lidars and thus obtain a still better reconstruction of the wind field. The reconstructed wind fields will then be used to study turbulent coherent structures and their roles in the urban boundary layer, especially as they may influence the transport and dispersion of pollutants. Because the lidars provide both wind and backscatter information, the data set can constrain the model predictions of transport and dispersion of aerosols in the boundary layer. These retrieval techniques will thus provide an important tool for studying the structures and statistics of turbulence. The expected outcomes of this research include: verification (and possible improvement) of the data-assimilation technique as applied to single-lidar observations, which are far more common and less expensive to obtain than dual-lidar observations; new information on the nature of turbulent structures in the atmospheric boundary layer and their contributions to dispersion; and improved ability to predict dispersion. The results should lead to better capabilities for monitoring and predicting dispersion in an urban setting, with possible important benefits to public safety.