Articles | Volume 12, issue 2
https://doi.org/10.5194/amt-12-1029-2019
© Author(s) 2019. 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-12-1029-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2019
Research article |
| 18 Feb 2019
| 18 Feb 2019
An improved total and tropospheric NO2 column retrieval for GOME-2
Song Liu
CORRESPONDING AUTHOR
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Methodik der Fernerkundung (IMF), Oberpfaffenhofen, Germany
Pieter Valks
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Methodik der Fernerkundung (IMF), Oberpfaffenhofen, Germany
Gaia Pinardi
Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Isabelle De Smedt
Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Steffen Beirle
Max Planck Institute for Chemistry, Mainz, Germany
Andreas Richter
Institute of Environmental Physics (IUP-UB), University of Bremen, Bremen, Germany
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Cited
22 citations as recorded by crossref.
- 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
- An improved air mass factor calculation for nitrogen dioxide measurements from the Global Ozone Monitoring Experiment-2 (GOME-2) S. Liu et al. https://doi.org/10.5194/amt-13-755-2020
- Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 2: Impact on NO2 retrieval and mitigation strategies H. Yu et al. https://doi.org/10.5194/amt-15-5743-2022
- Improving machine-learned surface NO2 concentration mapping models with domain knowledge from data science perspective M. Hu et al. https://doi.org/10.1016/j.atmosenv.2024.120372
- Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to TROPOMI L. Cheng et al. https://doi.org/10.3390/rs16214087
- Spatiotemporal inhomogeneity of total column NO2 in a polluted urban area inferred from TROPOMI and Pandora intercomparisons J. Park et al. https://doi.org/10.1080/15481603.2022.2026640
- An improved TROPOMI tropospheric NO2 research product over Europe S. Liu et al. https://doi.org/10.5194/amt-14-7297-2021
- Total column water vapor retrieval for Global Ozone Monitoring Experience-2 (GOME-2) visible blue observations K. Chan et al. https://doi.org/10.5194/amt-13-4169-2020
- Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns K. Chan et al. https://doi.org/10.5194/essd-15-1831-2023
- Discussion of “Satellite data to support air quality assessment and management” S. Wierman et al. https://doi.org/10.1080/10962247.2025.2541561
- Validation of Aura-OMI QA4ECV NO2 climate data records with ground-based DOAS networks: the role of measurement and comparison uncertainties S. Compernolle et al. https://doi.org/10.5194/acp-20-8017-2020
- Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks T. Verhoelst et al. https://doi.org/10.5194/amt-14-481-2021
- NO2 Retrieval from the Environmental Trace Gases Monitoring Instrument (EMI): Preliminary Results and Intercomparison with OMI and TROPOMI L. Cheng et al. https://doi.org/10.3390/rs11243017
- Validation of tropospheric NO2 column measurements of GOME-2A and OMI using MAX-DOAS and direct sun network observations G. Pinardi et al. https://doi.org/10.5194/amt-13-6141-2020
- Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic S. Liu et al. https://doi.org/10.1007/s11869-021-01046-2
- Observing network effect of shipping emissions from space: A natural experiment in the world’s busiest port S. Liu et al. https://doi.org/10.1093/pnasnexus/pgad391
- Inversion Models for the Retrieval of Total and Tropospheric NO2 Columns S. Liu https://doi.org/10.3390/atmos10100607
- Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001–2018) S. Liu et al. https://doi.org/10.1021/acs.est.3c02111
- The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 K. Qin et al. https://doi.org/10.5194/essd-16-5287-2024
- Global Observations of Tropospheric Bromine Monoxide (BrO) Columns From TROPOMI Y. Chen et al. https://doi.org/10.1029/2023JD039091
- Developing a Machine Learning Approach for Estimating Urban NO2 Concentrations Using Multi-Source Remote Sensing Data M. Kukavica et al. https://doi.org/10.3390/smartcities9100166
- 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
22 citations as recorded by crossref.
- 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
- An improved air mass factor calculation for nitrogen dioxide measurements from the Global Ozone Monitoring Experiment-2 (GOME-2) S. Liu et al. https://doi.org/10.5194/amt-13-755-2020
- Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 2: Impact on NO2 retrieval and mitigation strategies H. Yu et al. https://doi.org/10.5194/amt-15-5743-2022
- Improving machine-learned surface NO2 concentration mapping models with domain knowledge from data science perspective M. Hu et al. https://doi.org/10.1016/j.atmosenv.2024.120372
- Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to TROPOMI L. Cheng et al. https://doi.org/10.3390/rs16214087
- Spatiotemporal inhomogeneity of total column NO2 in a polluted urban area inferred from TROPOMI and Pandora intercomparisons J. Park et al. https://doi.org/10.1080/15481603.2022.2026640
- An improved TROPOMI tropospheric NO2 research product over Europe S. Liu et al. https://doi.org/10.5194/amt-14-7297-2021
- Total column water vapor retrieval for Global Ozone Monitoring Experience-2 (GOME-2) visible blue observations K. Chan et al. https://doi.org/10.5194/amt-13-4169-2020
- Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns K. Chan et al. https://doi.org/10.5194/essd-15-1831-2023
- Discussion of “Satellite data to support air quality assessment and management” S. Wierman et al. https://doi.org/10.1080/10962247.2025.2541561
- Validation of Aura-OMI QA4ECV NO2 climate data records with ground-based DOAS networks: the role of measurement and comparison uncertainties S. Compernolle et al. https://doi.org/10.5194/acp-20-8017-2020
- Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks T. Verhoelst et al. https://doi.org/10.5194/amt-14-481-2021
- NO2 Retrieval from the Environmental Trace Gases Monitoring Instrument (EMI): Preliminary Results and Intercomparison with OMI and TROPOMI L. Cheng et al. https://doi.org/10.3390/rs11243017
- Validation of tropospheric NO2 column measurements of GOME-2A and OMI using MAX-DOAS and direct sun network observations G. Pinardi et al. https://doi.org/10.5194/amt-13-6141-2020
- Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic S. Liu et al. https://doi.org/10.1007/s11869-021-01046-2
- Observing network effect of shipping emissions from space: A natural experiment in the world’s busiest port S. Liu et al. https://doi.org/10.1093/pnasnexus/pgad391
- Inversion Models for the Retrieval of Total and Tropospheric NO2 Columns S. Liu https://doi.org/10.3390/atmos10100607
- Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001–2018) S. Liu et al. https://doi.org/10.1021/acs.est.3c02111
- The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 K. Qin et al. https://doi.org/10.5194/essd-16-5287-2024
- Global Observations of Tropospheric Bromine Monoxide (BrO) Columns From TROPOMI Y. Chen et al. https://doi.org/10.1029/2023JD039091
- Developing a Machine Learning Approach for Estimating Urban NO2 Concentrations Using Multi-Source Remote Sensing Data M. Kukavica et al. https://doi.org/10.3390/smartcities9100166
- 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
Saved (final revised paper)
Latest update: 04 Oct 2026
Short summary
Nitrogen dioxide (NO2) plays significant roles in both stratospheric and tropospheric chemistry, and the observations from satellites enable reliable monitoring of NO2 columns on a global scale and on long time scales. This work presents a refined algorithm for the retrieval of NO2 columns from the satellite instrument Global Ozone Monitoring Experiment-2 (GOME-2), which shows a clear improvement comparing to the previous algorithm.
Nitrogen dioxide (NO2) plays significant roles in both stratospheric and tropospheric chemistry,...
