Articles | Volume 17, issue 14
https://doi.org/10.5194/gmd-17-5431-2024
© 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/gmd-17-5431-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Jul 2024
Model description paper |
| 17 Jul 2024
| 17 Jul 2024
The CHIMERE chemistry-transport model v2023r1
Laboratoire de Météorologie Dynamique (LMD), École Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Université Paris 06, CNRS, Route de Saclay, 91128 Palaiseau, France
Arineh Cholakian
Laboratoire de Météorologie Dynamique (LMD), École Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Université Paris 06, CNRS, Route de Saclay, 91128 Palaiseau, France
Romain Pennel
Laboratoire de Météorologie Dynamique (LMD), École Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Université Paris 06, CNRS, Route de Saclay, 91128 Palaiseau, France
Guillaume Siour
Laboratoire Inter-Universitaire des Systèmes Atmosphériques (LISA), Université Paris-Est Créteil and Université Paris Cité, CNRS, 94010 Créteil, France
Sylvain Mailler
Laboratoire de Météorologie Dynamique (LMD), École Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Université Paris 06, CNRS, Route de Saclay, 91128 Palaiseau, France
Myrto Valari
Laboratoire de Météorologie Dynamique (LMD), École Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Université Paris 06, CNRS, Route de Saclay, 91128 Palaiseau, France
Lya Lugon
CEREA, Ecole des Ponts ParisTech, EdF R&D, 77455 Marne-la-Vallée, France
Yann Meurdesoif
Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA/CNRS/UVSQ, 91190 Gif-sur-Yvette, France
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Cited
13 citations as recorded by crossref.
- High-Resolution Spatial Forecasting of Hourly Air Quality: A Fast Method for a Better Representation of Industrial Plumes and Traffic Emissions Contributions A. Rorat et al. https://doi.org/10.3390/atmos16040439
- Climate-Driven Changes in Air Quality: Trends Across Emission and Socioeconomic Pathways A. Monteiro et al. https://doi.org/10.3390/su172310857
- Status and Perspectives of the Validation and Evaluation of Wind-Blown Dust Models Using Three-Dimensional Air Quality Models H. Lee & S. Park https://doi.org/10.5572/KOSAE.2025.41.2.199
- Characterization of the Regional Carbon Balance Based on Satellite Observations of Carbon Dioxide I. Konovalov et al. https://doi.org/10.1134/S1028334X25606893
- Technical note: sensitivity of the CAMS regional air quality modelling system to anthropogenic emission temporal variability M. Guevara et al. https://doi.org/10.5194/acp-25-13245-2025
- Data fusion in air pollution exposure assessment: Methods, applications, and future directions Y. Liu et al. https://doi.org/10.1080/10962247.2026.2660605
- Representing improved tropospheric ozone distribution over the Northern Hemisphere by including lightning NOx emissions in CHIMERE S. Ghosh et al. https://doi.org/10.5194/acp-25-6273-2025
- The atmospheric composition component of the ICON modeling framework: ICON-ART version 2025.10 G. Hoshyaripour et al. https://doi.org/10.5194/gmd-19-1645-2026
- Modelling of atmospheric copper, iron, and manganese over Europe N. Dubois et al. https://doi.org/10.1016/j.atmosenv.2026.121884
- Spatially Continuous PM10 Exposure Mapping in the Campania Region Using a Land Use Random Forest Model: Integration of Monitoring Data, Geographic Predictors, ERA5 Reanalysis, and CHIMERE Model Output E. Chianese & A. Riccio https://doi.org/10.3390/atmos17050507
- Assessment of fine particle variability and sources in Nairobi, Kenya with WRF-CHIMERE simulations, surface measurements, and satellite observations E. Nyaga et al. https://doi.org/10.1016/j.atmosenv.2026.121986
- High resolution air quality simulation in the Himalayan valleys, a case study in Bhutan B. Bessagnet et al. https://doi.org/10.5194/acp-25-18675-2025
- Advanced modeling of gas chemistry and aerosol dynamics with SSH-aerosol v2.0 K. Sartelet et al. https://doi.org/10.5194/gmd-19-389-2026
13 citations as recorded by crossref.
- High-Resolution Spatial Forecasting of Hourly Air Quality: A Fast Method for a Better Representation of Industrial Plumes and Traffic Emissions Contributions A. Rorat et al. https://doi.org/10.3390/atmos16040439
- Climate-Driven Changes in Air Quality: Trends Across Emission and Socioeconomic Pathways A. Monteiro et al. https://doi.org/10.3390/su172310857
- Status and Perspectives of the Validation and Evaluation of Wind-Blown Dust Models Using Three-Dimensional Air Quality Models H. Lee & S. Park https://doi.org/10.5572/KOSAE.2025.41.2.199
- Characterization of the Regional Carbon Balance Based on Satellite Observations of Carbon Dioxide I. Konovalov et al. https://doi.org/10.1134/S1028334X25606893
- Technical note: sensitivity of the CAMS regional air quality modelling system to anthropogenic emission temporal variability M. Guevara et al. https://doi.org/10.5194/acp-25-13245-2025
- Data fusion in air pollution exposure assessment: Methods, applications, and future directions Y. Liu et al. https://doi.org/10.1080/10962247.2026.2660605
- Representing improved tropospheric ozone distribution over the Northern Hemisphere by including lightning NOx emissions in CHIMERE S. Ghosh et al. https://doi.org/10.5194/acp-25-6273-2025
- The atmospheric composition component of the ICON modeling framework: ICON-ART version 2025.10 G. Hoshyaripour et al. https://doi.org/10.5194/gmd-19-1645-2026
- Modelling of atmospheric copper, iron, and manganese over Europe N. Dubois et al. https://doi.org/10.1016/j.atmosenv.2026.121884
- Spatially Continuous PM10 Exposure Mapping in the Campania Region Using a Land Use Random Forest Model: Integration of Monitoring Data, Geographic Predictors, ERA5 Reanalysis, and CHIMERE Model Output E. Chianese & A. Riccio https://doi.org/10.3390/atmos17050507
- Assessment of fine particle variability and sources in Nairobi, Kenya with WRF-CHIMERE simulations, surface measurements, and satellite observations E. Nyaga et al. https://doi.org/10.1016/j.atmosenv.2026.121986
- High resolution air quality simulation in the Himalayan valleys, a case study in Bhutan B. Bessagnet et al. https://doi.org/10.5194/acp-25-18675-2025
- Advanced modeling of gas chemistry and aerosol dynamics with SSH-aerosol v2.0 K. Sartelet et al. https://doi.org/10.5194/gmd-19-389-2026
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
A new version of the CHIMERE model is presented. This version contains both computational and...
