Articles | Volume 9, issue 24
https://doi.org/10.5194/acp-9-9471-2009
© Author(s) 2009. 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-9-9471-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
16 Dec 2009
Est modus in rebus: analytical properties of multi-model ensembles
S. Potempski
European Commission – DG Joint Research Centre, Institute for Environment and Sustainability, 21020 Ispra VA, Italy
Institute of Atomic Energy, 05-400 Otwock-Swierk, Poland
S. Galmarini
European Commission – DG Joint Research Centre, Institute for Environment and Sustainability, 21020 Ispra VA, Italy
Viewed
Total article views: 3,509 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 01 Jul 2009)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,805 | 1,587 | 117 | 3,509 | 122 | 80 |
- HTML: 1,805
- PDF: 1,587
- XML: 117
- Total: 3,509
- BibTeX: 122
- EndNote: 80
Total article views: 2,876 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 16 Dec 2009)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,558 | 1,219 | 99 | 2,876 | 112 | 77 |
- HTML: 1,558
- PDF: 1,219
- XML: 99
- Total: 2,876
- BibTeX: 112
- EndNote: 77
Total article views: 633 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 01 Jul 2009)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 247 | 368 | 18 | 633 | 10 | 3 |
- HTML: 247
- PDF: 368
- XML: 18
- Total: 633
- BibTeX: 10
- EndNote: 3
Cited
42 citations as recorded by crossref.
- Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model A. Manders et al. 10.5194/gmd-10-4145-2017
- A Concept for the Analysis and Presentation of the Ensemble Simulation Results in the UDINEE Exercise S. Potempski & P. Kopka 10.1007/s10546-018-0411-1
- Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal A. Monteiro et al. 10.1007/s10666-013-9358-2
- On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes O. Montoya et al. 10.1007/s11356-020-08268-4
- Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM A. Monteiro et al. 10.1007/s10666-012-9344-0
- Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook M. Sofiev et al. 10.5194/acp-17-12341-2017
- High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation I. Kioutsioukis et al. 10.1016/j.atmosres.2015.07.015
- Environmental data extraction from heatmaps using the AirMerge system V. Epitropou et al. 10.1007/s11042-015-2604-7
- Fusion of meteorological and air quality data extracted from the web for personalized environmental information services L. Johansson et al. 10.1016/j.envsoft.2014.11.021
- Ontology-centered environmental information delivery for personalized decision support L. Wanner et al. 10.1016/j.eswa.2015.02.048
- Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework E. Solazzo et al. 10.5194/acp-17-10435-2017
- Multi-model vs. EPS-based ensemble atmospheric dispersion simulations: A quantitative assessment on the ETEX-1 tracer experiment case S. Galmarini et al. 10.1016/j.atmosenv.2010.06.003
- Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more? S. Galmarini et al. 10.5194/acp-18-8727-2018
- <i>E pluribus unum</i>*: ensemble air quality predictions S. Galmarini et al. 10.5194/acp-13-7153-2013
- Sources of uncertainty in atmospheric dispersion modeling in support of Comprehensive Nuclear–Test–Ban Treaty monitoring and verification system S. Mekhaimr & M. Abdel Wahab 10.1016/j.apr.2019.03.008
- The blessing of dimensionality for the analysis of climate data B. Christiansen 10.5194/npg-28-409-2021
- Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections G. Abramowitz & C. Bishop 10.1175/JCLI-D-14-00364.1
- Weighting climate model ensembles for mean and variance estimates N. Haughton et al. 10.1007/s00382-015-2531-3
- MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe M. Sofiev et al. 10.5194/acp-15-8115-2015
- Design of a regional climate modelling projection ensemble experiment – NARCliM J. Evans et al. 10.5194/gmd-7-621-2014
- Simulation of European air quality by WRF–CMAQ models using AQMEII-2 infrastructure D. Syrakov et al. 10.1016/j.cam.2015.01.032
- Perspective on Mechanism Development and Structure‐Activity Relationships for Gas‐Phase Atmospheric Chemistry L. Vereecken et al. 10.1002/kin.21172
- Getting the environmental information across: from the Web to the user L. Wanner et al. 10.1111/exsy.12100
- Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference C. Dormann et al. 10.1002/ecm.1309
- A science-based use of ensembles of opportunities for assessment and scenario studies E. Solazzo & S. Galmarini 10.5194/acp-15-2535-2015
- Understanding the Distribution of Multimodel Ensembles B. Christiansen 10.1175/JCLI-D-20-0186.1
- The Fukushima- 137 Cs deposition case study: properties of the multi-model ensemble E. Solazzo & S. Galmarini 10.1016/j.jenvrad.2014.02.017
- Analysis of UK and European NOx and VOC emission scenarios in the Defra model intercomparison exercise R. Derwent et al. 10.1016/j.atmosenv.2014.05.036
- Pauci ex tanto numero: reduce redundancy in multi-model ensembles E. Solazzo et al. 10.5194/acp-13-8315-2013
- Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations D. Goto et al. 10.5194/acp-20-3589-2020
- Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3 U. Im et al. 10.5194/acp-18-5967-2018
- 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. 10.1016/j.scitotenv.2018.03.317
- A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4) O. Clifton et al. 10.5194/acp-23-9911-2023
- Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev'Air platform E. Debry & V. Mallet 10.1016/j.atmosenv.2014.03.049
- Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII E. Solazzo et al. 10.1016/j.atmosenv.2012.01.003
- Adjusting climate model bias for agricultural impact assessment: How to cut the mustard S. Galmarini et al. 10.1016/j.cliser.2019.01.004
- Using STAX data to predict IMS radioxenon concentrations P. Eslinger et al. 10.1016/j.jenvrad.2022.106916
- Improving Air Quality Predictions over the United States with an Analog Ensemble L. Delle Monache et al. 10.1175/WAF-D-19-0148.1
- <i>De praeceptis ferendis</i>: good practice in multi-model ensembles I. Kioutsioukis & S. Galmarini 10.5194/acp-14-11791-2014
- Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data I. Kioutsioukis et al. 10.5194/acp-16-15629-2016
- Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources A. Pappa et al. 10.1016/j.renene.2023.01.030
- Bayesian model averaging for emergency response atmospheric dispersion multimodel ensembles: Is it really better? How many data are needed? Are the weights portable? S. Potempski et al. 10.1029/2010JD014210
41 citations as recorded by crossref.
- Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model A. Manders et al. 10.5194/gmd-10-4145-2017
- A Concept for the Analysis and Presentation of the Ensemble Simulation Results in the UDINEE Exercise S. Potempski & P. Kopka 10.1007/s10546-018-0411-1
- Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal A. Monteiro et al. 10.1007/s10666-013-9358-2
- On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes O. Montoya et al. 10.1007/s11356-020-08268-4
- Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM A. Monteiro et al. 10.1007/s10666-012-9344-0
- Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook M. Sofiev et al. 10.5194/acp-17-12341-2017
- High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation I. Kioutsioukis et al. 10.1016/j.atmosres.2015.07.015
- Environmental data extraction from heatmaps using the AirMerge system V. Epitropou et al. 10.1007/s11042-015-2604-7
- Fusion of meteorological and air quality data extracted from the web for personalized environmental information services L. Johansson et al. 10.1016/j.envsoft.2014.11.021
- Ontology-centered environmental information delivery for personalized decision support L. Wanner et al. 10.1016/j.eswa.2015.02.048
- Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework E. Solazzo et al. 10.5194/acp-17-10435-2017
- Multi-model vs. EPS-based ensemble atmospheric dispersion simulations: A quantitative assessment on the ETEX-1 tracer experiment case S. Galmarini et al. 10.1016/j.atmosenv.2010.06.003
- Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more? S. Galmarini et al. 10.5194/acp-18-8727-2018
- <i>E pluribus unum</i>*: ensemble air quality predictions S. Galmarini et al. 10.5194/acp-13-7153-2013
- Sources of uncertainty in atmospheric dispersion modeling in support of Comprehensive Nuclear–Test–Ban Treaty monitoring and verification system S. Mekhaimr & M. Abdel Wahab 10.1016/j.apr.2019.03.008
- The blessing of dimensionality for the analysis of climate data B. Christiansen 10.5194/npg-28-409-2021
- Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections G. Abramowitz & C. Bishop 10.1175/JCLI-D-14-00364.1
- Weighting climate model ensembles for mean and variance estimates N. Haughton et al. 10.1007/s00382-015-2531-3
- MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe M. Sofiev et al. 10.5194/acp-15-8115-2015
- Design of a regional climate modelling projection ensemble experiment – NARCliM J. Evans et al. 10.5194/gmd-7-621-2014
- Simulation of European air quality by WRF–CMAQ models using AQMEII-2 infrastructure D. Syrakov et al. 10.1016/j.cam.2015.01.032
- Perspective on Mechanism Development and Structure‐Activity Relationships for Gas‐Phase Atmospheric Chemistry L. Vereecken et al. 10.1002/kin.21172
- Getting the environmental information across: from the Web to the user L. Wanner et al. 10.1111/exsy.12100
- Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference C. Dormann et al. 10.1002/ecm.1309
- A science-based use of ensembles of opportunities for assessment and scenario studies E. Solazzo & S. Galmarini 10.5194/acp-15-2535-2015
- Understanding the Distribution of Multimodel Ensembles B. Christiansen 10.1175/JCLI-D-20-0186.1
- The Fukushima- 137 Cs deposition case study: properties of the multi-model ensemble E. Solazzo & S. Galmarini 10.1016/j.jenvrad.2014.02.017
- Analysis of UK and European NOx and VOC emission scenarios in the Defra model intercomparison exercise R. Derwent et al. 10.1016/j.atmosenv.2014.05.036
- Pauci ex tanto numero: reduce redundancy in multi-model ensembles E. Solazzo et al. 10.5194/acp-13-8315-2013
- Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations D. Goto et al. 10.5194/acp-20-3589-2020
- Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3 U. Im et al. 10.5194/acp-18-5967-2018
- 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. 10.1016/j.scitotenv.2018.03.317
- A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4) O. Clifton et al. 10.5194/acp-23-9911-2023
- Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev'Air platform E. Debry & V. Mallet 10.1016/j.atmosenv.2014.03.049
- Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII E. Solazzo et al. 10.1016/j.atmosenv.2012.01.003
- Adjusting climate model bias for agricultural impact assessment: How to cut the mustard S. Galmarini et al. 10.1016/j.cliser.2019.01.004
- Using STAX data to predict IMS radioxenon concentrations P. Eslinger et al. 10.1016/j.jenvrad.2022.106916
- Improving Air Quality Predictions over the United States with an Analog Ensemble L. Delle Monache et al. 10.1175/WAF-D-19-0148.1
- <i>De praeceptis ferendis</i>: good practice in multi-model ensembles I. Kioutsioukis & S. Galmarini 10.5194/acp-14-11791-2014
- Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data I. Kioutsioukis et al. 10.5194/acp-16-15629-2016
- Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources A. Pappa et al. 10.1016/j.renene.2023.01.030
Saved (final revised paper)
Saved (preprint)
Latest update: 21 Oct 2024
