Articles | Volume 24, issue 1
https://doi.org/10.5194/acp-24-185-2024
© 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/acp-24-185-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jan 2024
Research article |
| 09 Jan 2024
| 09 Jan 2024
On the influence of vertical mixing, boundary layer schemes, and temporal emission profiles on tropospheric NO2 in WRF-Chem – comparisons to in situ, satellite, and MAX-DOAS observations
Institute for Environmental Physics, University of Heidelberg, Heidelberg, Germany
Max Planck Institute for Chemistry, Mainz, Germany
Steffen Beirle
Max Planck Institute for Chemistry, Mainz, Germany
Vinod Kumar
Max Planck Institute for Chemistry, Mainz, Germany
Sergey Osipov
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Max Planck Institute for Chemistry, Mainz, Germany
Andrea Pozzer
Max Planck Institute for Chemistry, Mainz, Germany
Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, Cyprus
Tim Bösch
Institute for Environmental Physics, University of Bremen, Bremen, Germany
Rajesh Kumar
National Center for Atmospheric Research, Boulder, United States of America
Thomas Wagner
Institute for Environmental Physics, University of Heidelberg, Heidelberg, Germany
Max Planck Institute for Chemistry, Mainz, Germany
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Cited
22 citations as recorded by crossref.
- WRF-Chem Modeling of Tropospheric Ozone in the Coastal Cities of the Gulf of Finland G. Nerobelov et al. https://doi.org/10.3390/atmos15070775
- Vertically resolved formation mechanisms of fine particulate nitrate in Asian megacities: integrated lidar – aircraft observations and process analysis Y. Tian et al. https://doi.org/10.5194/acp-25-17581-2025
- NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations L. Kuhn et al. https://doi.org/10.5194/amt-17-6485-2024
- Assessing the Impact of Natural and Anthropogenic Pollution on Air Quality in the Russian Far East G. Nerobelov et al. https://doi.org/10.3390/cli13120252
- Explainable Deep Learning for Research on the Synergistic Mechanisms of Multiple Pollutants: A Critical Review C. Liu et al. https://doi.org/10.3390/toxics14040335
- Street-scale traffic emission inventory derived from GeoVideo and coupled WRF/Chem–MUNICH modelling for urban air quality management: A case study in Kaifeng, China H. Song et al. https://doi.org/10.1016/j.uclim.2025.102689
- Using a chemical transport model and satellite measurements to assess ship-induced NO2 concentrations in the Barents and Kara Sea region N. Figenschau & J. Lu https://doi.org/10.1016/j.apr.2025.102745
- A concept-drift-driven hybrid modeling framework for high-resolution ambient surface NO2 retrieval in Beijing-Tianjin-Hebei Region, China J. Wang et al. https://doi.org/10.1016/j.jhazmat.2026.141309
- 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
- Advances and challenges of machine learning in satellite-based atmospheric NO2 monitoring R. Zhang et al. https://doi.org/10.1016/j.apr.2026.103066
- Large-scale evaluation of WRF-chem model and dry deposition schemes during a Saharan dust event over the Iberian Peninsula R. Silva et al. https://doi.org/10.1016/j.atmosenv.2025.121293
- Review of long-term exposure to road traffic-related air pollution and mortality: Mechanism, exposure, and meta-analysis Y. Hou et al. https://doi.org/10.1016/j.eiar.2026.108380
- Investigating ozone build-up in the east of England during the July 2015 heat wave J. Romero-Alvarez et al. https://doi.org/10.1016/j.scitotenv.2025.179464
- Validation of TROPOMI and WRF-Chem NO2 across seasons using SWING+ and surface observations over Bucharest A. Pasternak et al. https://doi.org/10.5194/acp-26-5185-2026
- Attention mechanism augmented random forest model for multiple air pollutants estimation X. Yu et al. https://doi.org/10.1016/j.jag.2025.104661
- Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques C. Zhang et al. https://doi.org/10.5194/acp-25-759-2025
- Improve OMI Observations on Ground-Level NO2 Using Multiple Observations, Simulations, and Machine Learning X. Jiang et al. https://doi.org/10.1109/TGRS.2026.3685876
- 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
- Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing Z. Yang et al. https://doi.org/10.5194/acp-25-8831-2025
- Enhancing Air Pollution Forecasts in Cities by Characterizing the Urban Heat Island Effects on Planetary Boundary Layers L. Matak & M. Momen https://doi.org/10.1016/j.atmosres.2025.107923
- Spatial-and-local-aware deep learning approach for Ground-Level NO2 estimation in England with multisource data from satellite-based observations and chemical transport models S. Wang et al. https://doi.org/10.1016/j.jag.2025.104506
- Validation of multi-model decadal simulations of present-day central European air-quality A. Prieto Perez et al. https://doi.org/10.1016/j.atmosenv.2025.121077
22 citations as recorded by crossref.
- WRF-Chem Modeling of Tropospheric Ozone in the Coastal Cities of the Gulf of Finland G. Nerobelov et al. https://doi.org/10.3390/atmos15070775
- Vertically resolved formation mechanisms of fine particulate nitrate in Asian megacities: integrated lidar – aircraft observations and process analysis Y. Tian et al. https://doi.org/10.5194/acp-25-17581-2025
- NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations L. Kuhn et al. https://doi.org/10.5194/amt-17-6485-2024
- Assessing the Impact of Natural and Anthropogenic Pollution on Air Quality in the Russian Far East G. Nerobelov et al. https://doi.org/10.3390/cli13120252
- Explainable Deep Learning for Research on the Synergistic Mechanisms of Multiple Pollutants: A Critical Review C. Liu et al. https://doi.org/10.3390/toxics14040335
- Street-scale traffic emission inventory derived from GeoVideo and coupled WRF/Chem–MUNICH modelling for urban air quality management: A case study in Kaifeng, China H. Song et al. https://doi.org/10.1016/j.uclim.2025.102689
- Using a chemical transport model and satellite measurements to assess ship-induced NO2 concentrations in the Barents and Kara Sea region N. Figenschau & J. Lu https://doi.org/10.1016/j.apr.2025.102745
- A concept-drift-driven hybrid modeling framework for high-resolution ambient surface NO2 retrieval in Beijing-Tianjin-Hebei Region, China J. Wang et al. https://doi.org/10.1016/j.jhazmat.2026.141309
- 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
- Advances and challenges of machine learning in satellite-based atmospheric NO2 monitoring R. Zhang et al. https://doi.org/10.1016/j.apr.2026.103066
- Large-scale evaluation of WRF-chem model and dry deposition schemes during a Saharan dust event over the Iberian Peninsula R. Silva et al. https://doi.org/10.1016/j.atmosenv.2025.121293
- Review of long-term exposure to road traffic-related air pollution and mortality: Mechanism, exposure, and meta-analysis Y. Hou et al. https://doi.org/10.1016/j.eiar.2026.108380
- Investigating ozone build-up in the east of England during the July 2015 heat wave J. Romero-Alvarez et al. https://doi.org/10.1016/j.scitotenv.2025.179464
- Validation of TROPOMI and WRF-Chem NO2 across seasons using SWING+ and surface observations over Bucharest A. Pasternak et al. https://doi.org/10.5194/acp-26-5185-2026
- Attention mechanism augmented random forest model for multiple air pollutants estimation X. Yu et al. https://doi.org/10.1016/j.jag.2025.104661
- Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques C. Zhang et al. https://doi.org/10.5194/acp-25-759-2025
- Improve OMI Observations on Ground-Level NO2 Using Multiple Observations, Simulations, and Machine Learning X. Jiang et al. https://doi.org/10.1109/TGRS.2026.3685876
- 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
- Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing Z. Yang et al. https://doi.org/10.5194/acp-25-8831-2025
- Enhancing Air Pollution Forecasts in Cities by Characterizing the Urban Heat Island Effects on Planetary Boundary Layers L. Matak & M. Momen https://doi.org/10.1016/j.atmosres.2025.107923
- Spatial-and-local-aware deep learning approach for Ground-Level NO2 estimation in England with multisource data from satellite-based observations and chemical transport models S. Wang et al. https://doi.org/10.1016/j.jag.2025.104506
- Validation of multi-model decadal simulations of present-day central European air-quality A. Prieto Perez et al. https://doi.org/10.1016/j.atmosenv.2025.121077
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
NO₂ is an important air pollutant. It was observed that the WRF-Chem model shows significant deviations in NO₂ abundance when compared to measurements. We use a 1-month simulation over central Europe to show that these deviations can be mostly resolved by reparameterization of the vertical mixing routine. In order to validate our results, they are compared to in situ, satellite, and MAX-DOAS measurements.
NO₂ is an important air pollutant. It was observed that the WRF-Chem model shows significant...
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