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Multi-sensor Cloud and Aerosol Retrieval Simulator and Its Applications

Algorithm verification is a continuing issue for atmospheric remote sensing algorithms. Field campaigns and in-situ ground sites provide means of validation, but can be very limited in location and data amount. Existing and proposed remote sensing algorithms can benefit greatly from having a controlled sandbox environment that provides synthetic data that appears equivalent to real instrument-acquired measurements. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) is such a sandbox that is able to generate MODIS-like radiances in a standard operational MODIS radiance file format using atmospheric column data provided by the GEOS-5 model coupled with the discrete-ordinates radiative transfer model using plane-parallel, homogeneous assumptions. When we examined the performance of the MODIS dark target aerosol product (MOD04DT) using synthetic granules of biomass burning in Brazil, we found the reason for the low bias in MOD04DT aerosol optical depth retrievals when compared to AERONET. We also used synthetic scenes of clouds and aerosols off the coast of Namibia to test the performance of the research-level MODIS above-cloud aerosol properties retrieval algorithm (MODACAERO). The results of executing the MODACAERO algorithm on synthetic data show that the algorithm performs well and achieves closure where the retrieval result matches well against given “truth”. We make some suggestions for quality filtering of data before using it in modeling applications and propose some additional studies of the cloud bow scattering angle range in the aerosol model used to create the lookup tables for the MODACAERO retrieval algorithm.