An Improved Aerosol Retrieval Algorithm for FY-4A/AGRI Data Based on the GRASP Framework

Abstract

Accurate satellite-derived aerosol optical depth (AOD) with high temporal resolution is crucial for monitoring diurnal aerosol variations and understanding their impacts on atmospheric processes and air quality. The Advanced Geostationary Radiation Imager (AGRI) aboard the Fengyun 4A (FY-4A) satellite offers high spatiotemporal resolution, making it suitable for continuous atmospheric aerosol monitoring. In this study, an improved AOD retrieval algorithm is proposed for FY-4A/AGRI based on the generalized retrieval of atmosphere and surface properties (GRASP) framework. The algorithm incorporates multitemporal and multispectral FY-4A/AGRI observations within a 30-min window to enhance observational constraints for AOD retrieval. Reasonable prior information from Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) products and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) aerosol components is introduced, enabling hourly AOD retrievals with high accuracy and robustness. Compared with AOD derived from the single-temporal strategy with fixed BRDF and aerosol models, results of validation against aerosol robotic network (AERONET) AOD measurements over Beijing-Tianjin–Hebei (BTH) region indicate that our improved FY-4A/AGRI AOD retrievals increase the R from 0.543 to 0.864, and reduce root-mean-square error (RMSE) from 0.149 to 0.09, with the percentage of data falling within the expected error (EE) range rising from 46.1% to 69.9%. In Asia, such advancements led to significant improvements in AOD retrieval performance in 2021, with validation results demonstrating a strong correlation (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R =0.826$ </tex-math></inline-formula> for hourly retrievals and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R =0.891$ </tex-math></inline-formula> for daily means) and high accuracy (RMSE =0.118 for hourly retrievals and RMSE =0.09 for daily means) against ground-based AOD measurements from 32 AERONET sites. Comparative analyses reveal that FY-4A/AGRI AOD retrievals outperform Himawari-8/AHI products and are comparable to MODIS multiangle implementation of atmospheric correction (MAIAC) data, particularly in capturing diurnal variations and spatial distributions of aerosols. The algorithm also demonstrates robustness across diverse land cover types and vegetation densities. Our AOD retrieval strategy provides a scalable approach for geostationary satellite aerosol retrieval, with implications for regional air quality monitoring and climate studies.