Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces synthetic radiance data from model output as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing an atmospheric column. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows user to insert a mixture of up to 15 different aerosol species in any of the 36 available simulated layers. The MCRS code is currently known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms; but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, thus potentially improving model skill. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of impacts of various atmospheric components can be conducted in a controlled fashion. The aerosol properties used in MCARS are directly ingested from GEOS-5 model output. They are prepared using the same model subgrid variability management methods as are used for cloud and atmospheric properties profiles, namely the Independent Column Approximation (ICA) technique. After MCRS computes sensor radiances equivalent to their observed counterparts, these radiances are substituted into an operational remote sensing algorithm. Specifically, the MCRS computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04) that would normally be produced from actual sensor output. We show direct application of this synthetic product in analysis of performance of the MOD04 operational algorithm. We use biomass burning case studies employed in a recent Working Group on Numerical Experimentation (WGNE) -sponsored study of aerosol impacts on Numerical Weather Prediction (Freitas et al. 2016). We show that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.