Implementation of the Deep Blue Aerosol Algorithm on Geostationary Satellite Data

Abstract

National Aeronautics and Space Administration's Deep Blue (DB) aerosol project aims to produce consistent, long‐term climate data records of atmospheric aerosol properties using satellite observations. The DB algorithm has been extensively applied to low Earth orbit (LEO) sensors, including the Moderate Resolution Imaging Spectroradiometer and the Visible Infrared Imaging Radiometer Suite (VIIRS), among others. Building on this foundation, this study extends the application of DB to geostationary Earth orbit (GEO) imagers to create consistent aerosol data records with vastly superior temporal coverage. The latest VIIRS Version 2 DB algorithm is adapted for use with the GOES‐16/17 Advanced Baseline Imagers and the Himawari‐8 Advanced Himawari Imager. Comparisons of retrieved aerosol optical depth (AOD) from May 2019 to April 2020 against the Aerosol Robotic Network (AERONET) show that GEO sensors provide nearly 10 times the matchup points of VIIRS, with comparable validation statistics. In addition, the GEO products effectively reproduced both the magnitude and shape of diurnal AOD variations observed by AERONET for haze events in central and eastern North America, and East Asia, as well as for biomass burning events in South America and Southeast Asia. Analyses of the diurnal cycle of AOD confirm that GEO products can serve as a useful tool to monitor continuous aerosol transport and daytime variations, providing more representative daily mean AOD than LEO sensors. Overall, the GEO DB algorithm offers a robust framework for time‐resolved monitoring of aerosol properties, complementing existing LEO observations.