First Atmospheric Aerosol Monitoring Results from Geostationary Environment Monitoring Spectrometer (GEMS) over Asia

Abstract. Aerosol optical properties have been provided from the Geostationary Environment Monitoring Spectrometer (GEMS). It is the world’s first geostationary earth orbit (GEO) satellite instrument designed for atmospheric environmental monitoring. This study describes improvements to the GEMS aerosol retrieval algorithm (AERAOD). These include spectral binning, surface reflectance estimation, cloud masking, and post-processing. Furthermore, the study presents validation results. These enhancements are aimed at providing more accurate and reliable aerosol monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of the satellite measurements. In addition, we introduce a new high-resolution database for surface reflectance estimation based on the minimum reflectance method adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values are used to consistently estimate the hourly GEMS surface reflectance. Advanced cloud-removal techniques are implemented to significantly improve the effectiveness of cloud detection and enhance the quality of aerosol retrieval. An innovative post-processing correction method based on machine learning is introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. This study investigates specific aerosol events. It highlights capability of GEMS to monitor and provide insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products is validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. The GEMS AOD demonstrates a strong correlation with the AERONET AOD (R = 0.792). However, it exhibits bias patterns including underestimation of high AOD values and overestimation in low AOD conditions. Different aerosol types (highly absorbing fine, dust, and non-absorbing) exhibit distinct validation results. The GEMS single scattering albedo (SSA) retrievals agree well with the AERONET data within reasonable error ranges, with variations observed among the aerosol types. For GEMS AOD exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fall within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved the GEMS AOD and SSA performances, thereby reducing the diurnal variation in biases. The validation of the GEMS aerosol layer height (ALH) retrievals against the CALIOP data demonstrates a good agreement, with a mean bias of -0.225 km, and 55.29 % (71.70 %) of data within ±1 km (1.5 km).