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https://doi.org/10.5194/amt-2017-359
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
01 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).
Bayesian Dark Target Algorithm for MODIS AOD retrieval over land
Antti Lipponen1, Tero Mielonen1, Mikko R. A. Pitkänen1,3, Robert C. Levy2, Virginia R. Sawyer2, Sami Romakkaniemi1, Ville Kolehmainen3, and Antti Arola1 1Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
2Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
3University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
Abstract. We have developed a Bayesian Dark Target (BDT) algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval over land. In the BDT algorithm, we simultaneously retrieve all pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 550 nm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (466, 550, 644, and 2100 nm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BDT significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BDT was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05 + 15 %) expected error envelope increased from 55 % to 76 %. In addition to retrieving the values of AOD, FMF and surface reflectance, the BDT also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BDT algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

Citation: Lipponen, A., Mielonen, T., Pitkänen, M. R. A., Levy, R. C., Sawyer, V. R., Romakkaniemi, S., Kolehmainen, V., and Arola, A.: Bayesian Dark Target Algorithm for MODIS AOD retrieval over land, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-359, in review, 2017.
Antti Lipponen et al.
Antti Lipponen et al.
Antti Lipponen et al.

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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 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.
Atmospheric aerosols are small solid or liquid particles suspended in the atmosphere and they...
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