ABSTRACT

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.

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