Comparing Dust Optical Properties in Satellite Aerosol Retrieval Algorithms (Invited Presentation)

[09-Jan-2023] Abstract  Over the last 20+ years, there has been incredible progress in using satellite observations for deriving aerosol optical properties including dust – with reported parameters including aerosol optical depth (AOD), and under favorable retrieval conditions, single scattering albedo (SSA) and aerosol size parameters such as fine mode fraction (FMF) and Angstrom Exponent (AE). Common to many operational retrieval algorithms is the Lookup Table (LUT) approach – LUTs being forward-model simulations of typical aerosol optical properties informed by suborbital aerosol observations and theoretical modeling. Complicating factors include the diversity of dust aerosol types from different sources globally, and the limited amount of direct sampling, which is especially difficult for dust size distributions dominated by coarse-mode particles. Although nearly every algorithm includes models that are theoretically representative of 'dust', these aerosol models differ with respect to particle size distribution, particle shape, and spectral complex refractive index. For example, even for a popular sensor such as the Moderate-resolution Imaging Spectrometer (MODIS), multiple algorithms each use different dust optical models. Other dust models are used for retrieving from Ozone Monitoring Instrument (OMI) and for Multi-Angle Imaging Radiometer (MISR). Each of these dust models translates to different optical properties including spectral scattering / absorption / extinction, phase functions, depolarization and lidar ratios. As we move forward into the 2020s, our aerosol community aspires to develop algorithms that make sense across multiple types of observing platforms (imagers, spectrometers, lidars, polarimeters), while also making sense in chemical transport and climate/forcing models. A necessary first step is to organize and characterize our current inventory, ensuring readiness for future missions such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and Atmospheric Observing System (AOS). Here, we compare the dust property assumptions across a number of current remote sensing algorithms. These can be put in the context of in-situ observations and assumptions used in global aerosol models.