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Record 1 of 2929
Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements
External Online Source: doi:10.1175/2009JTECHA1228.1
Author and Affiliation:
Vaughan, Mark A.(NASA Langley Research Center, Hampton, VA, United States)
Powell, Kathleen A.(NASA Langley Research Center, Hampton, VA, United States)
Kuehn, Ralph E.(Science Systems and Applications, Inc., Hampton, VA, United States)
Young, Stuart A.(Commonwealth Scientific and Industrial Research Organization, Marine and Atmospheric Research, Aspendale, Australia)
Winker, David M.(NASA Langley Research Center, Hampton, VA, United States)
Hostetler, Chris A.(NASA Langley Research Center, Hampton, VA, United States)
Hunt, William H.(Science Systems and Applications, Inc., Hampton, VA, United States)
Liu, Zhaoyan(National Inst. of Aerospace, Hampton, VA, United States)
McGill, Matthew J.(NASA Goddard Space Flight Center, Greenbelt, MD, United States)
Getzewich, Brian J.(Science Systems and Applications, Inc., Hampton, VA, United States)
Abstract: Accurate knowledge of the vertical and horizontal extent of clouds and aerosols in the earth s atmosphere is critical in assessing the planet s radiation budget and for advancing human understanding of climate change issues. To retrieve this fundamental information from the elastic backscatter lidar data acquired during the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a selective, iterated boundary location (SIBYL) algorithm has been developed and deployed. SIBYL accomplishes its goals by integrating an adaptive context-sensitive profile scanner into an iterated multiresolution spatial averaging scheme. This paper provides an in-depth overview of the architecture and performance of the SIBYL algorithm. It begins with a brief review of the theory of target detection in noise-contaminated signals, and an enumeration of the practical constraints levied on the retrieval scheme by the design of the lidar hardware, the geometry of a space-based remote sensing platform, and the spatial variability of the measurement targets. Detailed descriptions are then provided for both the adaptive threshold algorithm used to detect features of interest within individual lidar profiles and the fully automated multiresolution averaging engine within which this profile scanner functions. The resulting fusion of profile scanner and averaging engine is specifically designed to optimize the trade-offs between the widely varying signal-to-noise ratio of the measurements and the disparate spatial resolutions of the detection targets. Throughout the paper, specific algorithm performance details are illustrated using examples drawn from the existing CALIPSO dataset. Overall performance is established by comparisons to existing layer height distributions obtained by other airborne and space-based lidars.
Publication Date: Jan 01, 2009
Document ID:
20100024393
(Acquired Jul 12, 2010)
Subject Category: COMMUNICATIONS AND RADAR
Document Type: Journal Article
Publication Information: Journal of Atmospheric and Oceanic Technology; Volume 26; No. 10; 2034-2050
Publisher Information: American Meteorological Society, United States
Financial Sponsor: NASA Langley Research Center; Hampton, VA, United States
Organization Source: NASA Langley Research Center; Hampton, VA, United States
Description: 17p; In English; Original contains color illustrations
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: Copyright
NASA Terms: CLOUD COVER; CLIMATE CHANGE; CALIPSO (PATHFINDER SATELLITE); ATMOSPHERIC RADIATION; TARGET RECOGNITION; OPTICAL RADAR; INFRARED ASTRONOMY; TARGET ACQUISITION; SIGNAL TO NOISE RATIOS; DEPLOYMENT; POSITION (LOCATION); RADAR MEASUREMENT; ENERGY BUDGETS
Availability Source: Other Sources
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