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.
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
10 citations as recorded by crossref.
- Attention mechanism augmented random forest model for multiple air pollutants estimation X. Yu et al. 10.1016/j.jag.2025.104661
- WRF-Chem Modeling of Tropospheric Ozone in the Coastal Cities of the Gulf of Finland G. Nerobelov et al. 10.3390/atmos15070775
- Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques C. Zhang et al. 10.5194/acp-25-759-2025
- 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
- Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing Z. Yang et al. 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 10.1016/j.atmosres.2025.107923
- Large-scale evaluation of WRF-chem model and dry deposition schemes during a Saharan dust event over the Iberian Peninsula R. Silva et al. 10.1016/j.atmosenv.2025.121293
- 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. 10.1016/j.jag.2025.104506
- Investigating ozone build-up in the east of England during the July 2015 heat wave J. Romero-Alvarez et al. 10.1016/j.scitotenv.2025.179464
- Validation of multi-model decadal simulations of present-day central European air-quality A. Prieto Perez et al. 10.1016/j.atmosenv.2025.121077
10 citations as recorded by crossref.
- Attention mechanism augmented random forest model for multiple air pollutants estimation X. Yu et al. 10.1016/j.jag.2025.104661
- WRF-Chem Modeling of Tropospheric Ozone in the Coastal Cities of the Gulf of Finland G. Nerobelov et al. 10.3390/atmos15070775
- Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques C. Zhang et al. 10.5194/acp-25-759-2025
- 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
- Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing Z. Yang et al. 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 10.1016/j.atmosres.2025.107923
- Large-scale evaluation of WRF-chem model and dry deposition schemes during a Saharan dust event over the Iberian Peninsula R. Silva et al. 10.1016/j.atmosenv.2025.121293
- 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. 10.1016/j.jag.2025.104506
- Investigating ozone build-up in the east of England during the July 2015 heat wave J. Romero-Alvarez et al. 10.1016/j.scitotenv.2025.179464
- Validation of multi-model decadal simulations of present-day central European air-quality A. Prieto Perez et al. 10.1016/j.atmosenv.2025.121077
Latest update: 28 Aug 2025
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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