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UBC Theses and Dissertations

Aerosol type analysis with single wavelength, dual polarization elastic LIDAR Cottle, Paul Wesley


Aerosols play an important role in many atmospheric processes but their highly heterogeneous nature makes them difficult to study. Thus, new advancements in the field frequently focus on finding ways to more accurately determine more information about aerosols as they occur. LIght Detection And Ranging (LIDAR) systems have become an important tool in the study of aerosols because they can provide high resolution vertical profiles of quantities of interest (including aerosol concentrations, temperature, and wind speed, among others) over several kilometres of altitude. But on the other hand, the data can be ambiguous or difficult to correctly interpret and LIDAR systems can be costly and usually require a great deal of technical expertise to maintain and operate. In recent years, technological developments in lasers and detectors have led to the development of relatively inexpensive LIDAR systems that are robust and simple to operate, but to date these single-wavelength elastic LIDARs have provided only basic analysis products, such as determining the heights of clouds or qualitative monitoring of aerosol layers. There is a need for more extensive analyses using these simpler LIDARs. To this end, an algorithm has been developed that employs ground-based, single-wavelength elastic LIDAR to create high resolution maps of aerosol and cloud types as well as backscatter and extinction coefficients. Applications for maps such as these include studies of long-range transport of aerosols, air quality within the planetary boundary layer, cloud-aerosol interactions, and visibility. Algorithms similar to this have been developed in the past, but they have required either multi-wavelength LIDAR systems or have stopped short of differentiating between aerosol and cloud types. This algorithm also includes a novel utilization of depolarization ratio profiles for sub-layer discrimination. Thus far, the algorithm has been applied to limited number of cases, resulting in a high degree of uncertainty compared to some more complex systems. The algorithm is thus merely a first step, and further refinements are suggested as a way to continue to improve its performance.

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