1,2,3,7,- 1Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea (minseok.kim@unist.ac.kr)
- 2AI-Transformed Aerospace InnoCORE Research Center, Korea Advanced Institute of Science and Technology, (KAIST), Daejeon, Korea, Republic of Korea
- 3Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
- 4Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD, USA
- 5NASA Goddard Space Flight Center (GSFC), Greenbelt, MD, USA
- 6Goddard Earth Sciences Technology and Research (GESTAR) II, Morgan State University, Baltimore, MD, USA
- 7Atomic and Molecular Physics Division, Center for Astrophysics (CfA), Harvard & Smithsonian, Cambridge, MA, USA
Aerosol absorption (scattering) property is a key parameter for assessing aerosol radiative effects and identifying aerosol composition. However, current geostationary Earth orbit (GEO) satellite aerosol retrieval algorithms lack accuracy in estimating aerosol absorption. The Geostationary Environment Monitoring Spectrometer (GEMS) provides hyperspectral observations of Earth-reflected solar radiation from 300 nm to 500 nm, which is sensitive to aerosol absorption. However, the current aerosol retrieval algorithm for GEMS struggles to quantify aerosol loading and aerosol absorption simultaneously. Meanwhile, the Advanced Meteorological Imager (AMI) conducts band observations of Earth-reflected solar radiation from 470 nm to 1,330 nm. The longer visible wavelength bands of AMI are less sensitive to assumption errors related to aerosol absorption properties. As a result, aerosol optical depth (AOD) products retrieved from AMI are generally more stable than those from GEMS. Therefore, synergistic use of the GEMS and the AMI can be more effective than using a single instrument to obtain both aerosol loading and absorption data. Furthermore, a wide range of wavelength from UV to visible is covered by using both GEMS and AMI. This study presents sensitivity analyses and preliminary results of a synergistic retrieval algorithm for aerosol spectral absorption properties using synergistic observations from GEMS and AMI. The algorithm incorporates a Transformer-based deep learning model for radiative transfer (RT) calculations. By replacing the traditional line-by-line RT code with a deep learning model, the algorithm enables real-time RT calculations embedded within the retrieval process. This online RT calculation approach enhances the flexibility of the aerosol retrieval algorithm and reduces errors that arise from look-up table interpolation. The developed algorithm can work on other GEO-ring missions such as GeoXO, TEMPO, ABI, Sentinel-4, and FCI.
How to cite: Kim, M., Kim, J., Go, S., Cho, Y., Kim, M., Chong, H., Cha, H., Chai, Y., and Park, S. S.: A Synergistic GEO Satellite Algorithm for UV–VIS Spectral Aerosol Absorption Retrieval, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8558, https://doi.org/10.5194/egusphere-egu26-8558, 2026.
