Articles | Volume 17, issue 17
https://doi.org/10.5194/amt-17-5147-2024
© 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-17-5147-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Sep 2024
Research article |
| 05 Sep 2024
| 05 Sep 2024
A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Daniel J. Jacob
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
Nicholas Balasus
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Laura H. Yang
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Heesung Chong
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
Junsung Park
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
Hanlim Lee
Division of Earth Environmental System Science, Major of Spatial Information Engineering, Pukyong National University, Busan, South Korea
Gitaek T. Lee
School of Earth and Environmental Science, Seoul National University, Seoul, South Korea
Eunjo S. Ha
School of Earth and Environmental Science, Seoul National University, Seoul, South Korea
Rokjin J. Park
School of Earth and Environmental Science, Seoul National University, Seoul, South Korea
Hyeong-Ahn Kwon
Department of Environmental and Energy Engineering, University of Suwon, Suwon, South Korea
Jhoon Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
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Cited
9 citations as recorded by crossref.
- Air quality trends and regimes in South Korea inferred from 2015–2023 surface and satellite observations Y. Oak et al. 10.5194/acp-25-3233-2025
- Tropospheric nitrogen dioxide levels vary diurnally in Asian cities J. Park et al. 10.1038/s43247-025-02272-7
- Tropospheric NO2 Column over Tibet Plateau According to Geostationary Environment Monitoring Spectrometer: Spatial, Seasonal, and Diurnal Variations X. Zhang et al. 10.3390/rs17101690
- Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements K. Lange et al. 10.5194/amt-17-6315-2024
- Diurnal NO emission underestimation constrained using overlapping TROPOMI swaths Q. He et al. 10.1016/j.atmosenv.2025.121354
- Air quality trends and regimes in South Korea inferred from 2015–2023 surface and satellite observations Y. Oak et al. 10.5194/acp-25-3233-2025
- Interpreting Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite observations of the diurnal variation in nitrogen dioxide (NO2) over East Asia L. Yang et al. 10.5194/acp-24-7027-2024
- A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. 10.5194/amt-17-5147-2024
- A Satellite-Based Indicator for Diagnosing Particulate Nitrate Sensitivity to Precursor Emissions: Application to East Asia, Europe, and North America R. Dang et al. 10.1021/acs.est.4c08082
5 citations as recorded by crossref.
- Air quality trends and regimes in South Korea inferred from 2015–2023 surface and satellite observations Y. Oak et al. 10.5194/acp-25-3233-2025
- Tropospheric nitrogen dioxide levels vary diurnally in Asian cities J. Park et al. 10.1038/s43247-025-02272-7
- Tropospheric NO2 Column over Tibet Plateau According to Geostationary Environment Monitoring Spectrometer: Spatial, Seasonal, and Diurnal Variations X. Zhang et al. 10.3390/rs17101690
- Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements K. Lange et al. 10.5194/amt-17-6315-2024
- Diurnal NO emission underestimation constrained using overlapping TROPOMI swaths Q. He et al. 10.1016/j.atmosenv.2025.121354
4 citations as recorded by crossref.
- Air quality trends and regimes in South Korea inferred from 2015–2023 surface and satellite observations Y. Oak et al. 10.5194/acp-25-3233-2025
- Interpreting Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite observations of the diurnal variation in nitrogen dioxide (NO2) over East Asia L. Yang et al. 10.5194/acp-24-7027-2024
- A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. 10.5194/amt-17-5147-2024
- A Satellite-Based Indicator for Diagnosing Particulate Nitrate Sensitivity to Precursor Emissions: Application to East Asia, Europe, and North America R. Dang et al. 10.1021/acs.est.4c08082
Latest update: 17 Jun 2025
Short summary
We present an improved NO2 product from GEMS by calibrating it to TROPOMI using machine learning and by reprocessing both satellite products to adopt common NO2 profiles. Our corrected GEMS product combines the high data density of GEMS with the accuracy of TROPOMI, supporting the combined use for analyses of East Asia air quality including emissions and chemistry. This method can be extended to other species and geostationary satellites including TEMPO and Sentinel-4.
We present an improved NO2 product from GEMS by calibrating it to TROPOMI using machine learning...
Special issue