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Proceedings Paper

Retrieving atmospheric properties with an optimal estimation inverse method of lidar measurements
Author(s): W. C. de Jesus; E. Landulfo
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Paper Abstract

This work suggests the use of a method to retrieve atmospheric information such as the aerosol backscatter and extinction coefficients from a elastic backscatter LIDAR observations. This approach inverts the lidar equation via an optimal estimation method. In order to get satisfactory inversion results, some boundary and measurements conditions can be estimated concomitantly instead of being assumed a priori. This method based on a Bayesian inference together with a Gaussian statistic creates an algorithm where the most probable or optimal solution corresponds to maximize probability density function as a condition to the estimate of the lidar data profile. This application to lidar data analysis presents advantages such as: 1) the possibility of incorporating multiple heterogeneous sources, such as an additional wavelength for instance or aerosol optical thickness information from AERONET (NASA Aerosol Robotic Network) as additional information; 2) the analyzed data can vary over the irradiated region or time. For example, the atmosphere during the daytime presents different characteristics from the nighttime. The algorithm can process different kinds and amounts of information; 3) the error estimation can be retrieved separately by each uncertainty source (errors), such as the model assumptions and a priori errors statements. Yet, it allows clearer and more confident measurements.

Paper Details

Date Published: 9 October 2009
PDF: 9 pages
Proc. SPIE 7479, Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing V, 747908 (9 October 2009); doi: 10.1117/12.830355
Show Author Affiliations
W. C. de Jesus, Instituto de Pesquisas Energéticas e Nucleares (Brazil)
E. Landulfo, Instituto de Pesquisas Energéticas e Nucleares (Brazil)

Published in SPIE Proceedings Vol. 7479:
Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing V
Upendra N. Singh; Gelsomina Pappalardo, Editor(s)

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