Paper
9 October 2009 Retrieving atmospheric properties with an optimal estimation inverse method of lidar measurements
W. C. de Jesus, E. Landulfo
Author Affiliations +
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
W. C. de Jesus and E. Landulfo "Retrieving atmospheric properties with an optimal estimation inverse method of lidar measurements", Proc. SPIE 7479, Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing V, 747908 (9 October 2009); https://doi.org/10.1117/12.830355
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Cited by 1 scholarly publication.
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KEYWORDS
LIDAR

Aerosols

Backscatter

Atmospheric modeling

Atmospheric particles

Data modeling

Atmospheric optics

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