Impact of aerosol layering, complex aerosol mixing, and cloud coverage on high-resolution MAIAC aerosol optical depth measurements: Fusion of lidar, AERONET, satellite, and ground-based measurements

摘要: Abstract Knowledge of the vertical distribution and layering aerosols identification corresponding aerosol sources are needed to improve our understanding spatial temporal variability pollution. To achieve this goal, we combined both passive active remote-sensing techniques provide a 3D view local levels regional long-range pollution transport. We studied optical depth (AOD) data from Multi-Angle Implementation Atmospheric Correction (MAIAC) algorithm at 1-km resolution along with multiwavelength polarization lidar observations profiles in Haifa, Israel, Aerosol Robotic Network (AERONET) sun photometer site, local-network concentrations (PM2.5). This comprehensive dataset enabled analyzing performance MAIAC AOD retrieval cases complex mixing states which typical Eastern Mediterranean. While satellite-derived ground-based measurements generally showed good agreement, 35 out 100 low correspondence. Analysis those revealed that overestimation was mostly related cloud-contaminated pixels water-uptake effects moist, cloud-free air cloud level. Furthermore, over- underestimations were presence mixture conditions, especially when dust mixed aged anthropogenic marine lofted In these 50–70% outside expected error limit. Perhaps conditions not considered retrieval. Finally, investigated link between bias, performed cluster analysis corroborating strong impact contamination on quality. Our observation-based results raise importance carefully uncertainties satellite used as an important input variable numerous health-related exposure studies climate models.