Articles | Volume 13, issue 14
https://doi.org/10.5194/acp-13-7153-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-13-7153-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
29 Jul 2013
Research article |
| 29 Jul 2013
E pluribus unum*: ensemble air quality predictions
S. Galmarini
European Commission, Joint Research Center, Institute for Environment and Sustainability, Ispra (VA), Italy
I. Kioutsioukis
European Commission, Joint Research Center, Institute for Environment and Sustainability, Ispra (VA), Italy
Region of Central Macedonia, Thessaloniki, Greece
E. Solazzo
European Commission, Joint Research Center, Institute for Environment and Sustainability, Ispra (VA), Italy
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Cited
27 citations as recorded by crossref.
- Copernicus Atmosphere Monitoring Service – Regional Air Quality Production System v1.0 A. Colette et al. https://doi.org/10.5194/gmd-18-6835-2025
- On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes O. Montoya et al. https://doi.org/10.1007/s11356-020-08268-4
- Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources A. Pappa et al. https://doi.org/10.1016/j.renene.2023.01.030
- Addressing the quantification of meteorological uncertainties in the atmospheric transport simulations of the 133Xe industrial background S. Generoso et al. https://doi.org/10.1016/j.jenvrad.2023.107263
- Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 A. Petersen et al. https://doi.org/10.5194/gmd-12-1241-2019
- De praeceptis ferendis: good practice in multi-model ensembles I. Kioutsioukis & S. Galmarini https://doi.org/10.5194/acp-14-11791-2014
- Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework E. Solazzo et al. https://doi.org/10.5194/acp-17-10435-2017
- Operational, diagnostic, and probabilistic evaluation of AQMEII-4 regional-scale ozone dry deposition: time to harmonize our LULC masks I. Kioutsioukis et al. https://doi.org/10.5194/acp-25-12923-2025
- Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments P. Porter et al. https://doi.org/10.1016/j.atmosenv.2015.04.039
- Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3 U. Im et al. https://doi.org/10.5194/acp-18-8929-2018
- Meteorological Conditions During an Ozone Episode in Dallas‐Fort Worth, Texas, and Impact of Their Modeling Uncertainties on Air Quality Prediction X. Hu et al. https://doi.org/10.1029/2018JD029791
- The Fukushima- 137 Cs deposition case study: properties of the multi-model ensemble E. Solazzo & S. Galmarini https://doi.org/10.1016/j.jenvrad.2014.02.017
- Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation E. Solazzo & S. Galmarini https://doi.org/10.5194/acp-16-6263-2016
- Impacts of regional climate change on air quality projections and associated uncertainties G. Lacressonnière et al. https://doi.org/10.1007/s10584-016-1619-z
- A new method for assessing the efficacy of emission control strategies H. Luo et al. https://doi.org/10.1016/j.atmosenv.2018.11.010
- Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data I. Kioutsioukis et al. https://doi.org/10.5194/acp-16-15629-2016
- 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
- Impact of inherent meteorology uncertainty on air quality model predictions R. Gilliam et al. https://doi.org/10.1002/2015JD023674
- Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more? S. Galmarini et al. https://doi.org/10.5194/acp-18-8727-2018
- Environmental data extraction from heatmaps using the AirMerge system V. Epitropou et al. https://doi.org/10.1007/s11042-015-2604-7
- Simulating the integrated summertime Δ14CO2 signature from anthropogenic emissions over Western Europe D. Bozhinova et al. https://doi.org/10.5194/acp-14-7273-2014
- Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation E. Solazzo & S. Galmarini https://doi.org/10.1016/j.atmosenv.2015.04.037
- Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown E. Solazzo et al. https://doi.org/10.5194/acp-17-3001-2017
- Pauci ex tanto numero: reduce redundancy in multi-model ensembles E. Solazzo et al. https://doi.org/10.5194/acp-13-8315-2013
- Evaluation and uncertainty estimation of the impact of air quality modelling on crop yields and premature deaths using a multi-model ensemble E. Solazzo et al. https://doi.org/10.1016/j.scitotenv.2018.03.317
- Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1 G. Brasseur et al. https://doi.org/10.5194/gmd-12-33-2019
- A regional air quality forecasting system over Europe: the MACC-II daily ensemble production V. Marécal et al. https://doi.org/10.5194/gmd-8-2777-2015
27 citations as recorded by crossref.
- Copernicus Atmosphere Monitoring Service – Regional Air Quality Production System v1.0 A. Colette et al. https://doi.org/10.5194/gmd-18-6835-2025
- On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes O. Montoya et al. https://doi.org/10.1007/s11356-020-08268-4
- Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources A. Pappa et al. https://doi.org/10.1016/j.renene.2023.01.030
- Addressing the quantification of meteorological uncertainties in the atmospheric transport simulations of the 133Xe industrial background S. Generoso et al. https://doi.org/10.1016/j.jenvrad.2023.107263
- Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 A. Petersen et al. https://doi.org/10.5194/gmd-12-1241-2019
- De praeceptis ferendis: good practice in multi-model ensembles I. Kioutsioukis & S. Galmarini https://doi.org/10.5194/acp-14-11791-2014
- Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework E. Solazzo et al. https://doi.org/10.5194/acp-17-10435-2017
- Operational, diagnostic, and probabilistic evaluation of AQMEII-4 regional-scale ozone dry deposition: time to harmonize our LULC masks I. Kioutsioukis et al. https://doi.org/10.5194/acp-25-12923-2025
- Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments P. Porter et al. https://doi.org/10.1016/j.atmosenv.2015.04.039
- Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3 U. Im et al. https://doi.org/10.5194/acp-18-8929-2018
- Meteorological Conditions During an Ozone Episode in Dallas‐Fort Worth, Texas, and Impact of Their Modeling Uncertainties on Air Quality Prediction X. Hu et al. https://doi.org/10.1029/2018JD029791
- The Fukushima- 137 Cs deposition case study: properties of the multi-model ensemble E. Solazzo & S. Galmarini https://doi.org/10.1016/j.jenvrad.2014.02.017
- Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation E. Solazzo & S. Galmarini https://doi.org/10.5194/acp-16-6263-2016
- Impacts of regional climate change on air quality projections and associated uncertainties G. Lacressonnière et al. https://doi.org/10.1007/s10584-016-1619-z
- A new method for assessing the efficacy of emission control strategies H. Luo et al. https://doi.org/10.1016/j.atmosenv.2018.11.010
- Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data I. Kioutsioukis et al. https://doi.org/10.5194/acp-16-15629-2016
- 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
- Impact of inherent meteorology uncertainty on air quality model predictions R. Gilliam et al. https://doi.org/10.1002/2015JD023674
- Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more? S. Galmarini et al. https://doi.org/10.5194/acp-18-8727-2018
- Environmental data extraction from heatmaps using the AirMerge system V. Epitropou et al. https://doi.org/10.1007/s11042-015-2604-7
- Simulating the integrated summertime Δ14CO2 signature from anthropogenic emissions over Western Europe D. Bozhinova et al. https://doi.org/10.5194/acp-14-7273-2014
- Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation E. Solazzo & S. Galmarini https://doi.org/10.1016/j.atmosenv.2015.04.037
- Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown E. Solazzo et al. https://doi.org/10.5194/acp-17-3001-2017
- Pauci ex tanto numero: reduce redundancy in multi-model ensembles E. Solazzo et al. https://doi.org/10.5194/acp-13-8315-2013
- Evaluation and uncertainty estimation of the impact of air quality modelling on crop yields and premature deaths using a multi-model ensemble E. Solazzo et al. https://doi.org/10.1016/j.scitotenv.2018.03.317
- Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1 G. Brasseur et al. https://doi.org/10.5194/gmd-12-33-2019
- A regional air quality forecasting system over Europe: the MACC-II daily ensemble production V. Marécal et al. https://doi.org/10.5194/gmd-8-2777-2015
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