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Title: Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.
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Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0739-0572
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Atmospheric and Oceanic Technology; Journal Volume: 33; Related Information: Journal of Atmospheric and Oceanic Technology
Research Org:
NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))
Sponsoring Org:
National Center for Atmospheric Research (NCAR)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING observational techniques and algorithms; algorithms; data processing; lidars/lidar observations; mathematical and statistical techniques; classification