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Abstract. We have developed a Bayesian aerosol retrieval (BAR) algorithm for theretrieval of aerosol optical depth (AOD) over land from the Moderate ResolutionImaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneouslyretrieve all dark land pixels in a granule, utilize spatial correlationmodels for the unknown aerosol parameters, use a statistical prior model forthe surface reflectance, and take into account the uncertainties due to fixedaerosol models. The retrieved parameters are total AOD at 0.55  µ m,fine-mode fraction (FMF), and surface reflectances at four differentwavelengths (0.47, 0.55, 0.64, and 2.1  µ m). The accuracy of the newalgorithm is evaluated by comparing the AOD retrievals to Aerosol RoboticNetwork (AERONET) AOD. The results show that the BAR significantly improvesthe accuracy of AOD retrievals over the operational Dark Target (DT)algorithm. A reduction of about 29 % in the AOD root mean square error anddecrease of about 80 % in the median bias of AOD were found globally whenthe BAR was used instead of the DT algorithm. Furthermore, the fraction ofAOD retrievals inside the ± ( 0.05 + 15 % ) expected error envelopeincreased from 55 to 76 %. In addition to retrieving the values of AOD,FMF, and surface reflectance, the BAR also gives pixel-level posterioruncertainty estimates for the retrieved parameters. The BAR algorithm alwaysresults in physical, non-negative AOD values, and the average computationtime for a single granule was less than a minute on a modern personalcomputer.

Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land