Paper
12 January 2004 Algorithm for retrieving lidar ratios at 1064 nm from space-based lidar backscatter data
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Abstract
Accurate estimation of cloud and aerosol optical depths using backscatter lidar data requires knowledge of the particulate lidar ratio (i.e., the extinction-to-backscatter ratio). In those cases for which a measurement of molecular backscatter can be made on the far side of a layer, knowledge of the lidar ratio can be derived directly from the data. However, obtaining a reliable clear air constraint is a function of layer optical depth, system sensitivity and overall signal-to-noise ratio (SNR). To date, the design constraints imposed on space-based lidars such as LITE and CALIPSO have rendered the use of this retrieval technique virtually impossible for measurements made at 1064 nm. Layers to which the constraint method can be successfully applied are assumed to be homogeneous with respect to particle composition and size distribution, and therefore are characterized by lidar ratios that are range-invariant throughout the layer. By extending this assumption of homogeneity to include the layer backscatter color ratio, this work derives a new technique that simultaneously retrieves both the color ratio and the 1064 nm lidar ratio from two wavelength elastic backscatter lidar measurements of transmissive clouds and/or lofted aerosol layers. Retrieval examples are illustrated using data obtained from LITE. Initial error estimates derived from numerical experiments using simulated data show the retrieval of the backscatter color ratio to be stable, even in the presence of considerable noise in the data.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark Vaughan "Algorithm for retrieving lidar ratios at 1064 nm from space-based lidar backscatter data", Proc. SPIE 5240, Laser Radar Technology for Remote Sensing, (12 January 2004); https://doi.org/10.1117/12.510770
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Cited by 20 scholarly publications.
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KEYWORDS
LIDAR

Backscatter

Clouds

Signal to noise ratio

Aerosols

Error analysis

Scattering

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