Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-615-2021
© Author(s) 2021. 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-14-615-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jan 2021
Research article |
| 28 Jan 2021
| 28 Jan 2021
Assessment of the TROPOMI tropospheric NO2 product based on airborne APEX observations
Frederik Tack
CORRESPONDING AUTHOR
BIRA-IASB, Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
Alexis Merlaud
BIRA-IASB, Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
Marian-Daniel Iordache
VITO, Flemish Institute for Technological Research, Mol, Belgium
Gaia Pinardi
BIRA-IASB, Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
Ermioni Dimitropoulou
BIRA-IASB, Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
Henk Eskes
KNMI, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Bart Bomans
VITO, Flemish Institute for Technological Research, Mol, Belgium
Pepijn Veefkind
KNMI, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Michel Van Roozendael
CORRESPONDING AUTHOR
BIRA-IASB, Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
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- 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
- Direct estimates of biomass burning NO<sub><i>x</i></sub> emissions and lifetimes using daily observations from TROPOMI X. Jin et al. 10.5194/acp-21-15569-2021
- High-Resolution Nitrogen Dioxide Measurements from an Airborne Fiber Imaging Spectrometer over Tangshan, China X. Zhang et al. 10.3390/rs16061042
- 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
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- Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI P. Levelt et al. 10.5194/acp-22-10319-2022
- Constraining industrial ammonia emissions using hyperspectral infrared imaging L. Noppen et al. 10.1016/j.rse.2023.113559
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Latest update: 22 Nov 2024
Short summary
We assess the TROPOMI tropospheric NO2 product (OFFL v1.03.01; 3.5 km × 7 km at nadir observations) based on coinciding airborne APEX reference observations (~75 m × 120 m), acquired over polluted regions in Belgium. The TROPOMI NO2 product meets the mission requirements in terms of precision and accuracy. However, we show that TROPOMI is biased low over polluted areas, mainly due to the limited spatial resolution of a priori input for the AMF computation.
We assess the TROPOMI tropospheric NO2 product (OFFL v1.03.01; 3.5 km × 7 km at nadir...
