Articles | Volume 17, issue 20
https://doi.org/10.5194/amt-17-6163-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-6163-2024
© 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 Oct 2024
Research article |
| 23 Oct 2024
| 23 Oct 2024
Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany
Pieter Valks
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany
now at: EUMETSAT, Eumetsat-Allee 1, Darmstadt, Germany
Ronny Lutz
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany
Klaus-Peter Heue
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany
Pascal Hedelt
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany
Víctor Molina García
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany
Diego Loyola
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany
Hanlim Lee
Division of Earth Environmental System Science, Major of Spatial Information Engineering, Pukyong National University, Busan, Republic of Korea
Jhoon Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
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Cited
11 citations as recorded by crossref.
- Diagnosing the underperformance of satellite-based surface NO2 estimation using geostationary observations: Insights for improvement from station-type and temporal analyses in South Korea S. Park et al. https://doi.org/10.1016/j.atmosenv.2026.121775
- First Cooperative Formaldehyde Monitoring With Chinese Morning and Afternoon Satellites: Revealing Global Multitemporal Concentration Dynamics H. Hou et al. https://doi.org/10.1109/TGRS.2026.3680449
- Limitations of Polar-Orbiting Satellite Observations in Capturing the Diurnal Variability of Tropospheric NO2: A Case Study Using TROPOMI, GOME-2C, and Pandora Data Y. Li et al. https://doi.org/10.3390/rs17162846
- Hybrid transformer and physics-informed neural operator for correcting TEMPO NO2 biases over North America S. Kayastha et al. https://doi.org/10.1038/s44407-026-00056-7
- Satellite data to support air quality assessment and management T. Holloway et al. https://doi.org/10.1080/10962247.2025.2484153
- Diurnal variations of ozone formation sensitivity in southeastern China based on GEMS: Insights from gridded thresholds and machine learning N. Chen et al. https://doi.org/10.1016/j.envpol.2026.128966
- Comparison of GEMS v3.0 tropospheric NO 2 columns with ground-based DOAS instruments in Ulsan K. Bae et al. https://doi.org/10.1080/15481603.2025.2597604
- Key Challenges and Future Trends of Spectral Inversion Technology in Environmental Monitoring Applications 琳. 乔 https://doi.org/10.12677/gst.2026.141006
- Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO2 via machine learning N. Ahmad et al. https://doi.org/10.1016/j.envpol.2025.126592
- Total column water vapor retrievals from the Geostationary Environment Monitoring Spectrometer (GEMS) H. Cha et al. https://doi.org/10.1016/j.rse.2026.115401
- Do GEMS geostationary satellite observations of tropospheric NO2 always improve NOx emission estimates and related air quality modelling? F. Yao et al. https://doi.org/10.5194/acp-26-12049-2026
11 citations as recorded by crossref.
- Diagnosing the underperformance of satellite-based surface NO2 estimation using geostationary observations: Insights for improvement from station-type and temporal analyses in South Korea S. Park et al. https://doi.org/10.1016/j.atmosenv.2026.121775
- First Cooperative Formaldehyde Monitoring With Chinese Morning and Afternoon Satellites: Revealing Global Multitemporal Concentration Dynamics H. Hou et al. https://doi.org/10.1109/TGRS.2026.3680449
- Limitations of Polar-Orbiting Satellite Observations in Capturing the Diurnal Variability of Tropospheric NO2: A Case Study Using TROPOMI, GOME-2C, and Pandora Data Y. Li et al. https://doi.org/10.3390/rs17162846
- Hybrid transformer and physics-informed neural operator for correcting TEMPO NO2 biases over North America S. Kayastha et al. https://doi.org/10.1038/s44407-026-00056-7
- Satellite data to support air quality assessment and management T. Holloway et al. https://doi.org/10.1080/10962247.2025.2484153
- Diurnal variations of ozone formation sensitivity in southeastern China based on GEMS: Insights from gridded thresholds and machine learning N. Chen et al. https://doi.org/10.1016/j.envpol.2026.128966
- Comparison of GEMS v3.0 tropospheric NO 2 columns with ground-based DOAS instruments in Ulsan K. Bae et al. https://doi.org/10.1080/15481603.2025.2597604
- Key Challenges and Future Trends of Spectral Inversion Technology in Environmental Monitoring Applications 琳. 乔 https://doi.org/10.12677/gst.2026.141006
- Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO2 via machine learning N. Ahmad et al. https://doi.org/10.1016/j.envpol.2025.126592
- Total column water vapor retrievals from the Geostationary Environment Monitoring Spectrometer (GEMS) H. Cha et al. https://doi.org/10.1016/j.rse.2026.115401
- Do GEMS geostationary satellite observations of tropospheric NO2 always improve NOx emission estimates and related air quality modelling? F. Yao et al. https://doi.org/10.5194/acp-26-12049-2026
Saved (final revised paper)
Latest update: 30 Aug 2026
Short summary
In this study, we developed an advanced retrieval algorithm for tropospheric NO2 columns from geostationary satellite spectrometers and applied it to GEMS measurements. The DLR GEMS NO2 retrieval algorithm follows the heritage from previous and existing algorithms, but improved approaches are applied to reflect the specific features of geostationary satellites. The DLR GEMS NO2 retrievals demonstrate a good capability for monitoring diurnal variability with a high spatial resolution.
In this study, we developed an advanced retrieval algorithm for tropospheric NO2 columns from...
