Summary: | The aerosol optical depth (AOD) is probably the most comprehensive and used variable for assessing the aerosol load in the atmosphere. During the last decades, satellite and ground-based remote sensing observations have become widely used to monitor aerosols' spatial and temporal distributions on a global and local scale. Satellite instruments, such as Moderate Resolution Imaging Spectroradiometer (MODIS), monitor aerosols on a regional and global scale and provide long-term and continuous coverage. Observations from ground-based stations have higher measurement precision at low spatial reach. Such ground-based optical networks for aerosols monitoring, like AErosol RObotic NETwork (AERONET), use sun photometers or sun-sky radiometers. Unfortunately, the AOD retrieval from sun-photometric measurements is sensitive to the concentration of atmospheric gases such as nitrogen dioxide (NO2), particularly in spectral regions in which light absorption is significant. Currently, either satellite climatological NO2 datasets are used to estimate NO2-related optical depth in the AOD retrievals, or NO2 is not considered at all in such calculations. However, due to inhomogeneous local emission patterns and photochemical destruction, NO2 is characterized by a relatively short lifetime and high spatial and temporal variations, especially in polluted areas, such as the area of Rome. For this reason, climatological values rarely represent the actual NO2 loading and spatial distribution in the atmosphere, introducing non-negligible errors in AOD retrievals in spectral regions with high absorption from NO2. In this report, we assess the improvement of existing AOD and Ångström exponent (AE) datasets by applying a correction using synchronous and collocated measurements of total NO2 column with respect to reference satellite AOD products. All the details about the methodology adopted for the computation of the correction are reported in [RD-1]. The inter-comparison has been performed over two close (about 13 km) sites in Rome, ...
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