Articles | Volume 11, issue 3
https://doi.org/10.5194/amt-11-1529-2018
https://doi.org/10.5194/amt-11-1529-2018
Research article
 | 
19 Mar 2018
Research article |  | 19 Mar 2018

Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

Antti Lipponen, Tero Mielonen, Mikko R. A. Pitkänen, Robert C. Levy, Virginia R. Sawyer, Sami Romakkaniemi, Ville Kolehmainen, and Antti Arola

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Cited articles

Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and Holmén, K.: Mesoscale variations of tropospheric aerosols, J. Atmos. Sci., 60, 119–136, 2003. a, b
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A limited memory algorithm for bound constrained optimization, SIAM J. Sci. Comput., 16, 1190–1208, 1995. a
Calvetti, D. and Somersalo, E.: An Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing, vol. 2, Springer Science & Business Media, 2007. a
Chandrasekhar, S.: Radiative transfer, Dover, New York, 1960. a
Chatterjee, A., Michalak, A., Kahn, R., Paradise, S., Braverman, A., and Miller, C.: A geostatistical data fusion technique for merging remote sensing and ground-based observations of aerosol optical thickness, J. Geophys. Res.-Atmos., 115, D20207, https://doi.org/10.1029/2009JD013765, 2010. a
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Short summary
Atmospheric aerosols are small solid or liquid particles suspended in the atmosphere and they have a significant effect on the climate. Satellite data are used to get global estimates of atmospheric aerosols. In this work, a statistics-based Bayesian aerosol retrieval algorithm was developed to improve the accuracy and quantify the uncertainties related to the aerosol estimates. The algorithm is tested with NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data.
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