Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4369-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-17-4369-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jul 2024
Research article |
| 23 Jul 2024
| 23 Jul 2024
First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia
Yeseul Cho
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Sujung Go
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Mijin Kim
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Goddard Earth Sciences Technology and Research (GESTAR) II, Morgan State University, Baltimore, MD 21251, USA
Seoyoung Lee
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Minseok Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Heesung Chong
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA 02138, USA
Won-Jin Lee
National Institute of Environmental Research, Incheon, Republic of Korea
Dong-Won Lee
National Institute of Environmental Research, Incheon, Republic of Korea
Omar Torres
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Sang Seo Park
Department of Civil, Urban, Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
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Cited
9 citations as recorded by crossref.
- Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS) S. Sim et al. 10.5194/amt-17-5601-2024
- Pioneering Air Quality Monitoring over East and Southeast Asia with the Geostationary Environment Monitoring Spectrometer (GEMS) K. Lee et al. 10.7780/kjrs.2024.40.5.2.5
- Estimating hourly ground-level aerosols using Geostationary Environment Monitoring Spectrometer aerosol optical depth: a machine learning approach S. O et al. 10.5194/amt-18-1471-2025
- Utilisation of WRF-HYSPLIT modelling approach and GEMS to identify PM2.5 sources in Central Kalimantan – study case: 2023 forest fire A. Nurlatifah et al. 10.1071/ES24006
- Improved mean field estimates from the Geostationary Environment Monitoring Spectrometer (GEMS) Level-3 aerosol optical depth (L3 AOD) product: using spatiotemporal variability S. Kim et al. 10.5194/amt-17-5221-2024
- Aerosol layer height (ALH) retrievals from oxygen absorption bands: intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI H. Kim et al. 10.5194/amt-18-327-2025
- GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model J. Yu et al. 10.22761/GD.2024.0060
- First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns J. Park et al. 10.1038/s41598-024-76223-1
- Dataset for Deep Learning-based GEMS Asian Dust Detection J. Yu et al. 10.22761/GD.2023.0049
6 citations as recorded by crossref.
- Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS) S. Sim et al. 10.5194/amt-17-5601-2024
- Pioneering Air Quality Monitoring over East and Southeast Asia with the Geostationary Environment Monitoring Spectrometer (GEMS) K. Lee et al. 10.7780/kjrs.2024.40.5.2.5
- Estimating hourly ground-level aerosols using Geostationary Environment Monitoring Spectrometer aerosol optical depth: a machine learning approach S. O et al. 10.5194/amt-18-1471-2025
- Utilisation of WRF-HYSPLIT modelling approach and GEMS to identify PM2.5 sources in Central Kalimantan – study case: 2023 forest fire A. Nurlatifah et al. 10.1071/ES24006
- Improved mean field estimates from the Geostationary Environment Monitoring Spectrometer (GEMS) Level-3 aerosol optical depth (L3 AOD) product: using spatiotemporal variability S. Kim et al. 10.5194/amt-17-5221-2024
- Aerosol layer height (ALH) retrievals from oxygen absorption bands: intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI H. Kim et al. 10.5194/amt-18-327-2025
3 citations as recorded by crossref.
- GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model J. Yu et al. 10.22761/GD.2024.0060
- First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns J. Park et al. 10.1038/s41598-024-76223-1
- Dataset for Deep Learning-based GEMS Asian Dust Detection J. Yu et al. 10.22761/GD.2023.0049
Latest update: 17 Apr 2025
Short summary
Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world’s first geostationary-Earth-orbit (GEO) satellite instrument designed for atmospheric environmental monitoring. This study describes improvements made to the GEMS aerosol retrieval algorithm (AERAOD) and presents its validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia.
Aerosol optical properties have been provided by the Geostationary Environment Monitoring...
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