Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-483-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-483-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
28 Jan 2025
Development and technical paper |
| 28 Jan 2025
| 28 Jan 2025
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Satellite Observations department, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Department of Earth Sciences, Vrije Universiteit, Amsterdam, the Netherlands
Henk Eskes
Satellite Observations department, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Arlene Dingemans
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
currently at: Koninklijke Luchmacht, Breda, the Netherlands
K. Folkert Boersma
Satellite Observations department, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Meteorology and Air Quality group, Wageningen University, Wageningen, the Netherlands
Takashi Sekiya
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
Kazuyuki Miyazaki
Jet Propulsion Laboratory/California Institute for Technology, Pasadena, California, USA
Sander Houweling
Department of Earth Sciences, Vrije Universiteit, Amsterdam, the Netherlands
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
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Cited
13 citations as recorded by crossref.
- A detailed comparison of the Dutch emission inventory with satellite-derived NOx emissions H. Witt et al. https://doi.org/10.5194/acp-26-5237-2026
- Impacts of multi-source data assimilation and model resolution on anthropogenic NOₓ emission inversions C. Wu et al. https://doi.org/10.1016/j.atmosres.2026.109105
- Retrieval of satellite-derived black carbon concentration and mitigation approaches for Karnataka, India N. Kayet et al. https://doi.org/10.1016/j.atmosenv.2025.121591
- An improved Bayesian inversion to estimate daily NOx emissions of Paris from TROPOMI NO2 observations between 2018–2023 A. Mols et al. https://doi.org/10.5194/acp-26-1497-2026
- Clear-sky and cloudy-sky differences in NO2 concentrations over the United States: implications for satellite measurement applications D. Goldberg et al. https://doi.org/10.5194/acp-25-16287-2025
- TROPOMI Level 3 tropospheric NO2 dataset with advanced uncertainty analysis from the ESA CCI+ ECV precursor project I. Glissenaar et al. https://doi.org/10.5194/essd-17-4627-2025
- Machine Learning for Urban Air Quality Prediction Using Google AlphaEarth Foundations Satellite Embeddings: A Case Study of Quito, Ecuador C. Alvarez et al. https://doi.org/10.3390/rs17203472
- How does perceived industrial pollution shape community evaluation and migration intentions in rural Ghana? integrating household surveys with satellite-based air-quality context I. Abu et al. https://doi.org/10.1088/2515-7620/ae56d8
- Uncertainty assessment of TROPOMI NO2 over Europe using ground-based remote sensing observations F. Cifuentes et al. https://doi.org/10.5194/amt-19-2437-2026
- On the capability of the Changing Atmosphere Infra-Red Tomography explorer (CAIRT) candidate mission to constrain O3 and H2O in the upper troposphere and lower stratosphere Q. Errera et al. https://doi.org/10.5194/amt-19-2601-2026
- Investigating the influence of remote working conditions on tropospheric NO2 vertical column density over northern Italy observed by Aura/OMI R. Engert et al. https://doi.org/10.1016/j.atmosenv.2025.121649
- Assessing the ability to quantify the decrease in NOx anthropogenic emissions in 2019 compared to 2005 using OMI and TROPOMI satellite observations A. Fortems-Cheiney et al. https://doi.org/10.5194/acp-25-6047-2025
- 2019–2024 trends in African livestock and wetland emissions as contributors to the global methane rise N. Balasus et al. https://doi.org/10.5194/acp-26-4601-2026
13 citations as recorded by crossref.
- A detailed comparison of the Dutch emission inventory with satellite-derived NOx emissions H. Witt et al. https://doi.org/10.5194/acp-26-5237-2026
- Impacts of multi-source data assimilation and model resolution on anthropogenic NOₓ emission inversions C. Wu et al. https://doi.org/10.1016/j.atmosres.2026.109105
- Retrieval of satellite-derived black carbon concentration and mitigation approaches for Karnataka, India N. Kayet et al. https://doi.org/10.1016/j.atmosenv.2025.121591
- An improved Bayesian inversion to estimate daily NOx emissions of Paris from TROPOMI NO2 observations between 2018–2023 A. Mols et al. https://doi.org/10.5194/acp-26-1497-2026
- Clear-sky and cloudy-sky differences in NO2 concentrations over the United States: implications for satellite measurement applications D. Goldberg et al. https://doi.org/10.5194/acp-25-16287-2025
- TROPOMI Level 3 tropospheric NO2 dataset with advanced uncertainty analysis from the ESA CCI+ ECV precursor project I. Glissenaar et al. https://doi.org/10.5194/essd-17-4627-2025
- Machine Learning for Urban Air Quality Prediction Using Google AlphaEarth Foundations Satellite Embeddings: A Case Study of Quito, Ecuador C. Alvarez et al. https://doi.org/10.3390/rs17203472
- How does perceived industrial pollution shape community evaluation and migration intentions in rural Ghana? integrating household surveys with satellite-based air-quality context I. Abu et al. https://doi.org/10.1088/2515-7620/ae56d8
- Uncertainty assessment of TROPOMI NO2 over Europe using ground-based remote sensing observations F. Cifuentes et al. https://doi.org/10.5194/amt-19-2437-2026
- On the capability of the Changing Atmosphere Infra-Red Tomography explorer (CAIRT) candidate mission to constrain O3 and H2O in the upper troposphere and lower stratosphere Q. Errera et al. https://doi.org/10.5194/amt-19-2601-2026
- Investigating the influence of remote working conditions on tropospheric NO2 vertical column density over northern Italy observed by Aura/OMI R. Engert et al. https://doi.org/10.1016/j.atmosenv.2025.121649
- Assessing the ability to quantify the decrease in NOx anthropogenic emissions in 2019 compared to 2005 using OMI and TROPOMI satellite observations A. Fortems-Cheiney et al. https://doi.org/10.5194/acp-25-6047-2025
- 2019–2024 trends in African livestock and wetland emissions as contributors to the global methane rise N. Balasus et al. https://doi.org/10.5194/acp-26-4601-2026
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Clustering high-resolution satellite observations into superobservations improves model...
