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.
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
11 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.
- Research on horizontal distribution of NO2 around Coal-Fired power plants based on two-dimensional Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) M. Zhong et al.
- Advances and Challenges of Machine Learning in Satellite-Based Atmospheric NO2 Monitoring R. Zhang et al.
- Tropospheric nitrogen dioxide levels vary diurnally in Asian cities J. Park et al.
- Hybrid transformer and physics-informed neural operator for correcting TEMPO NO2 biases over North America S. Kayastha et al.
- Tropospheric NO2 Column over Tibet Plateau According to Geostationary Environment Monitoring Spectrometer: Spatial, Seasonal, and Diurnal Variations X. Zhang et al.
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al.
- Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements K. Lange et al.
- Long-range transport of short-lived nitrogen dioxide in East Asia S. Baek et al.
- Diurnal NO emission underestimation constrained using overlapping TROPOMI swaths Q. He et al.
- Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models J. Gödeke et al.
11 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.
- Research on horizontal distribution of NO2 around Coal-Fired power plants based on two-dimensional Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) M. Zhong et al.
- Advances and Challenges of Machine Learning in Satellite-Based Atmospheric NO2 Monitoring R. Zhang et al.
- Tropospheric nitrogen dioxide levels vary diurnally in Asian cities J. Park et al.
- Hybrid transformer and physics-informed neural operator for correcting TEMPO NO2 biases over North America S. Kayastha et al.
- Tropospheric NO2 Column over Tibet Plateau According to Geostationary Environment Monitoring Spectrometer: Spatial, Seasonal, and Diurnal Variations X. Zhang et al.
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al.
- Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements K. Lange et al.
- Long-range transport of short-lived nitrogen dioxide in East Asia S. Baek et al.
- Diurnal NO emission underestimation constrained using overlapping TROPOMI swaths Q. He et al.
- Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models J. Gödeke et al.
Saved (final revised paper)
Latest update: 25 May 2026
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