43 citations as recorded by crossref.

1. Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model A. Manders et al. 10.5194/gmd-10-4145-2017
2. 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
3. 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
4. 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
5. Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM A. Monteiro et al. 10.1007/s10666-012-9344-0
6. 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
7. High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation I. Kioutsioukis et al. 10.1016/j.atmosres.2015.07.015
8. Potential Use of Transport and Dispersion Model Ensembles for Forecasting Applications A. Stein et al. 10.1175/WAF-D-14-00153.1
9. Environmental data extraction from heatmaps using the AirMerge system V. Epitropou et al. 10.1007/s11042-015-2604-7
10. 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
11. Ontology-centered environmental information delivery for personalized decision support L. Wanner et al. 10.1016/j.eswa.2015.02.048
12. 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
13. 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
14. Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more? S. Galmarini et al. 10.5194/acp-18-8727-2018
15. <i>E pluribus unum</i>*: ensemble air quality predictions S. Galmarini et al. 10.5194/acp-13-7153-2013
16. 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
17. The blessing of dimensionality for the analysis of climate data B. Christiansen 10.5194/npg-28-409-2021
18. Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections G. Abramowitz & C. Bishop 10.1175/JCLI-D-14-00364.1
19. Weighting climate model ensembles for mean and variance estimates N. Haughton et al. 10.1007/s00382-015-2531-3
20. MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe M. Sofiev et al. 10.5194/acp-15-8115-2015
21. Design of a regional climate modelling projection ensemble experiment – NARCliM J. Evans et al. 10.5194/gmd-7-621-2014
22. Simulation of European air quality by WRF–CMAQ models using AQMEII-2 infrastructure D. Syrakov et al. 10.1016/j.cam.2015.01.032
23. Perspective on Mechanism Development and Structure‐Activity Relationships for Gas‐Phase Atmospheric Chemistry L. Vereecken et al. 10.1002/kin.21172
24. Getting the environmental information across: from the Web to the user L. Wanner et al. 10.1111/exsy.12100
25. Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference C. Dormann et al. 10.1002/ecm.1309
26. A science-based use of ensembles of opportunities for assessment and scenario studies E. Solazzo & S. Galmarini 10.5194/acp-15-2535-2015
27. Understanding the Distribution of Multimodel Ensembles B. Christiansen 10.1175/JCLI-D-20-0186.1
28. The Fukushima- 137 Cs deposition case study: properties of the multi-model ensemble E. Solazzo & S. Galmarini 10.1016/j.jenvrad.2014.02.017
29. 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
30. Pauci ex tanto numero: reduce redundancy in multi-model ensembles E. Solazzo et al. 10.5194/acp-13-8315-2013
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. Adjusting climate model bias for agricultural impact assessment: How to cut the mustard S. Galmarini et al. 10.1016/j.cliser.2019.01.004
38. Using STAX data to predict IMS radioxenon concentrations P. Eslinger et al. 10.1016/j.jenvrad.2022.106916
39. Improving Air Quality Predictions over the United States with an Analog Ensemble L. Delle Monache et al. 10.1175/WAF-D-19-0148.1
40. <i>De praeceptis ferendis</i>: good practice in multi-model ensembles I. Kioutsioukis & S. Galmarini 10.5194/acp-14-11791-2014
41. 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
42. Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources A. Pappa et al. 10.1016/j.renene.2023.01.030
43. 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