Paper
14 October 2004 Aerosol optical depth approximation using an optimization-subdivision method
Nicolas Pfister, Karyne Charbonneau, Martin Ducharme, Mathieu Houle, Carina Poulin, Marie-Eve Randlett, Christine Rioux-Perreault
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Abstract
Two methods to estimate aerosol optical depth (AOD) at a relatively low computational cost, using the data of the sun photometers from the Aerosol Robotic Network (AERONET) are presented and compared. One interpolates the data and the other approximates the data. The technique is based on a geometric approach. Assuming that AOD can be represented as a highly continuous surface function of time and position, a height field approximating AOD using the data from the sun photometers is obtained. Both methods use an optimization-subdivision iterative algorithm to create a function that interpolates or approximates the AOD values measured by the sun photometers. The methods begin by constructing a Delaunay triangulation of the location of the sun photometer sites over the region were the AOD value is to be approximated. The algorithm then alternatively optimizes and subdivides the triangular mesh. At each iteration, the optimization step first creates a data dependent triangulation which is then subdivided. The results obtained by the two methods are compared with those obtained from piecewise linear interpolation.
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Nicolas Pfister, Karyne Charbonneau, Martin Ducharme, Mathieu Houle, Carina Poulin, Marie-Eve Randlett, and Christine Rioux-Perreault "Aerosol optical depth approximation using an optimization-subdivision method", Proc. SPIE 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective, (14 October 2004); https://doi.org/10.1117/12.559993
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KEYWORDS
Photometry

Aerosols

Sun

Atmospheric modeling

Data modeling

Clouds

Algorithm development

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