Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-509-2023
© Author(s) 2023. 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-16-509-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
25 Jan 2023
Model evaluation paper |
| 25 Jan 2023
| 25 Jan 2023
Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS regional air quality ensemble
Royal Netherlands Meteorological Institute, 3730 AE De Bilt, the Netherlands
Henk Eskes
Royal Netherlands Meteorological Institute, 3730 AE De Bilt, the Netherlands
Jos van Geffen
Royal Netherlands Meteorological Institute, 3730 AE De Bilt, the Netherlands
K. Folkert Boersma
Royal Netherlands Meteorological Institute, 3730 AE De Bilt, the Netherlands
Meteorology and Air Quality group, Wageningen University, 6708 PB Wageningen, the Netherlands
Steven Compernolle
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Ringlaan 3, 1180 Uccle, Belgium
Gaia Pinardi
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Ringlaan 3, 1180 Uccle, Belgium
Anne-Marlene Blechschmidt
Institute of Environmental Physics, University of Bremen, IUP-UB, Otto-Hahn-Allee 1, 28359 Bremen, Germany
Vincent-Henri Peuch
European Centre for Medium-Range Weather Forecast (ECMWF), Sinfield Park, Reading, UK
Augustin Colette
National Institute for Industrial Environment and Risks (INERIS), Verneuil-en-Halatte, France
Pepijn Veefkind
Royal Netherlands Meteorological Institute, 3730 AE De Bilt, the Netherlands
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24 citations as recorded by crossref.
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- BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer A. Verma et al. 10.1007/s10661-024-12455-y
- Monitoring European anthropogenic NOx emissions from space R. van der A et al. 10.5194/acp-24-7523-2024
- Monitoring Trends of SO2 level Using Time-Series Sentinel-5 Images Based on Google Earth Engine B. Muslimbekov et al. 10.1051/e3sconf/202456303068
- 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
- Informing Near-Airport Satellite NO2 Retrievals Using Pandora Sky-Scanning Observations A. Mouat et al. 10.1021/acsestair.4c00158
- Assessing Nitrogen Dioxide in the Highveld Troposphere: Pandora Insights and TROPOMI Sentinel-5P Evaluation R. Kai-Sikhakhane et al. 10.3390/atmos15101187
- Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)? E. Dammers et al. 10.5194/gmd-17-4983-2024
- Anomalous NO2 emitting ship detection with TROPOMI satellite data and machine learning S. Kurchaba et al. 10.1016/j.rse.2023.113761
- Characterization of Aerosol and CO2 Co-Emissions around Power Plants through Satellite-Based Synergistic Observations L. Sun et al. 10.3390/rs16091609
- Applications of Sentinel-5P TROPOMI Satellite Sensor: A Review A. Reshi et al. 10.1109/JSEN.2024.3355714
- A lightweight NO2-to-NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations S. Meier et al. 10.5194/acp-24-7667-2024
- Validation of Sentinel-5P TROPOMI tropospheric NO2 products by comparison with NO2 measurements from airborne imaging DOAS, ground-based stationary DOAS, and mobile car DOAS measurements during the S5P-VAL-DE-Ruhr campaign K. Lange et al. 10.5194/amt-16-1357-2023
- To new heights by flying low: comparison of aircraft vertical NO2 profiles to model simulations and implications for TROPOMI NO2 retrievals T. Riess et al. 10.5194/amt-16-5287-2023
- NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations L. Kuhn et al. 10.5194/amt-17-6485-2024
- Maritime sector contributions on NO2 surface concentrations in major ports of the Mediterranean Basin A. Pseftogkas et al. 10.1016/j.apr.2024.102228
- Evaluating the spatial patterns of U.S. urban NOx emissions using TROPOMI NO2 D. Goldberg et al. 10.1016/j.rse.2023.113917
- Horizontal distribution of tropospheric NO2 and aerosols derived by dual-scan multi-wavelength multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in Uccle, Belgium E. Dimitropoulou et al. 10.5194/amt-15-4503-2022
- Sentinel-5P TROPOMI NO<sub>2</sub> retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data J. van Geffen et al. 10.5194/amt-15-2037-2022
- Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula H. Petetin et al. 10.5194/acp-23-3905-2023
- Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO D. Goldberg et al. 10.5194/acp-22-10875-2022
- Estimating surface-level nitrogen dioxide concentrations from Sentinel-5P/TROPOMI observations in Finland H. Virta et al. 10.1016/j.atmosenv.2023.119989
- Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. 10.5194/gmd-16-479-2023
- Estimation of biomass burning emission of NO2 and CO from 2019–2020 Australia fires based on satellite observations N. Wan et al. 10.5194/acp-23-711-2023
17 citations as recorded by crossref.
- NOx emissions in France in 2019–2021 as estimated by the high-spatial-resolution assimilation of TROPOMI NO2 observations R. Plauchu et al. 10.5194/acp-24-8139-2024
- BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer A. Verma et al. 10.1007/s10661-024-12455-y
- Monitoring European anthropogenic NOx emissions from space R. van der A et al. 10.5194/acp-24-7523-2024
- Monitoring Trends of SO2 level Using Time-Series Sentinel-5 Images Based on Google Earth Engine B. Muslimbekov et al. 10.1051/e3sconf/202456303068
- 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
- Informing Near-Airport Satellite NO2 Retrievals Using Pandora Sky-Scanning Observations A. Mouat et al. 10.1021/acsestair.4c00158
- Assessing Nitrogen Dioxide in the Highveld Troposphere: Pandora Insights and TROPOMI Sentinel-5P Evaluation R. Kai-Sikhakhane et al. 10.3390/atmos15101187
- Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)? E. Dammers et al. 10.5194/gmd-17-4983-2024
- Anomalous NO2 emitting ship detection with TROPOMI satellite data and machine learning S. Kurchaba et al. 10.1016/j.rse.2023.113761
- Characterization of Aerosol and CO2 Co-Emissions around Power Plants through Satellite-Based Synergistic Observations L. Sun et al. 10.3390/rs16091609
- Applications of Sentinel-5P TROPOMI Satellite Sensor: A Review A. Reshi et al. 10.1109/JSEN.2024.3355714
- A lightweight NO2-to-NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations S. Meier et al. 10.5194/acp-24-7667-2024
- Validation of Sentinel-5P TROPOMI tropospheric NO2 products by comparison with NO2 measurements from airborne imaging DOAS, ground-based stationary DOAS, and mobile car DOAS measurements during the S5P-VAL-DE-Ruhr campaign K. Lange et al. 10.5194/amt-16-1357-2023
- To new heights by flying low: comparison of aircraft vertical NO2 profiles to model simulations and implications for TROPOMI NO2 retrievals T. Riess et al. 10.5194/amt-16-5287-2023
- NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations L. Kuhn et al. 10.5194/amt-17-6485-2024
- Maritime sector contributions on NO2 surface concentrations in major ports of the Mediterranean Basin A. Pseftogkas et al. 10.1016/j.apr.2024.102228
- Evaluating the spatial patterns of U.S. urban NOx emissions using TROPOMI NO2 D. Goldberg et al. 10.1016/j.rse.2023.113917
7 citations as recorded by crossref.
- Horizontal distribution of tropospheric NO2 and aerosols derived by dual-scan multi-wavelength multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in Uccle, Belgium E. Dimitropoulou et al. 10.5194/amt-15-4503-2022
- Sentinel-5P TROPOMI NO<sub>2</sub> retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data J. van Geffen et al. 10.5194/amt-15-2037-2022
- Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula H. Petetin et al. 10.5194/acp-23-3905-2023
- Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO D. Goldberg et al. 10.5194/acp-22-10875-2022
- Estimating surface-level nitrogen dioxide concentrations from Sentinel-5P/TROPOMI observations in Finland H. Virta et al. 10.1016/j.atmosenv.2023.119989
- Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. 10.5194/gmd-16-479-2023
- Estimation of biomass burning emission of NO2 and CO from 2019–2020 Australia fires based on satellite observations N. Wan et al. 10.5194/acp-23-711-2023
Latest update: 20 Nov 2024
Short summary
We focus on the challenges associated with comparing atmospheric composition models with satellite products such as tropospheric NO2 columns. The aim is to highlight the methodological difficulties and propose sound ways of doing such comparisons. Building on the comparisons, a new satellite product is proposed and made available, which takes advantage of higher-resolution, regional atmospheric modelling to improve estimates of troposheric NO2 columns over Europe.
We focus on the challenges associated with comparing atmospheric composition models with...
