Characterization and application of artificial light sources for nighttime aerosol optical depth retrievals using the Visible Infrared Imager Radiometer Suite Day/Night Band

• Published in: Atmospheric Measurement Techniques
• Main Authors: Zhang, Jianglong, Jaker, Shawn L., Reid, Jeffrey S., Miller, Steven D., Solbrig, Jeremy, Toth, Travis D.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2019
• Subjects: article; Verlagsveröffentlichung; Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/amt-12-3209-2019; https://noa.gwlb.de/receive/cop_mods_00001663; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00001622/amt-12-3209-2019.pdf; https://amt.copernicus.org/articles/12/3209/2019/amt-12-3209-2019.pdf

Using nighttime observations from Visible Infrared Imager Radiometer Suite (VIIRS) Day/Night band (DNB), the characteristics of artificial light sources are evaluated as functions of observation conditions, and incremental improvements are documented on nighttime aerosol retrievals using VIIRS DNB data on a regional scale. We find that the standard deviation of instantaneous radiance for a given artificial light source is strongly dependent upon the satellite viewing angle but is weakly dependent on lunar fraction and lunar angle. Retrieval of nighttime aerosol optical thickness (AOT) based on the novel use of these artificial light sources is demonstrated for three selected regions (United States, Middle East and India) during 2015. Reasonable agreement is found between nighttime AOTs from the VIIRS DNB and temporally adjacent daytime AOTs from the AErosol RObotic NETwork (AERONET) as well as from coincident nighttime AOT retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), indicating the potential of this method to begin filling critical gaps in diurnal AOT information at both regional and global scales. Issues related to cloud, snow and ice contamination during the winter season, as well as data loss due to the misclassification of thick aerosol plumes as clouds, must be addressed to make the algorithm operationally robust.