23 citations as recorded by crossref.

1. Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation E. Solazzo & S. Galmarini 10.5194/acp-16-6263-2016
2. Third international challenge to model the medium- to long-range transport of radioxenon to four Comprehensive Nuclear-Test-Ban Treaty monitoring stations C. Maurer et al. 10.1016/j.jenvrad.2022.106968
3. The Fukushima- 137 Cs deposition case study: properties of the multi-model ensemble E. Solazzo & S. Galmarini 10.1016/j.jenvrad.2014.02.017
4. A science-based use of ensembles of opportunities for assessment and scenario studies E. Solazzo & S. Galmarini 10.5194/acp-15-2535-2015
5. NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System A. Stein et al. 10.1175/BAMS-D-14-00110.1
6. Effect of Meteorological Variability on Fine Particulate Matter Simulations Over the Contiguous United States R. Kumar et al. 10.1029/2018JD029637
7. A multimodel evaluation of the potential impact of shipping on particle species in the Mediterranean Sea L. Fink et al. 10.5194/acp-23-10163-2023
8. 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
9. Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project J. Struzewska et al. 10.5194/acp-15-3971-2015
10. 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
11. Comparison and Combination of Regional and Global Ensemble Prediction Systems for Probabilistic Predictions of Hub-Height Wind Speed C. Junk et al. 10.1175/WAF-D-15-0021.1
12. 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. 10.5194/acp-18-8929-2018
13. De praeceptis ferendis: good practice in multi-model ensembles I. Kioutsioukis & S. Galmarini 10.5194/acp-14-11791-2014
14. Uncertainty quantification of atmospheric transport and dispersion modelling using ensembles for CTBT verification applications P. De Meutter & A. Delcloo 10.1016/j.jenvrad.2022.106918
15. Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation E. Solazzo & S. Galmarini 10.1016/j.atmosenv.2015.04.037
16. Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron E. Chianese et al. 10.1016/j.ecoinf.2018.12.001
17. Online coupled regional meteorology chemistry models in Europe: current status and prospects A. Baklanov et al. 10.5194/acp-14-317-2014
18. Using STAX data to predict IMS radioxenon concentrations P. Eslinger et al. 10.1016/j.jenvrad.2022.106916
19. Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations E. Chianese et al. 10.1016/j.ecolmodel.2018.07.019
20. Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more? S. Galmarini et al. 10.5194/acp-18-8727-2018
21. 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
22. 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
23. Evaluation of operational on-line-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part I: Ozone U. Im et al. 10.1016/j.atmosenv.2014.09.042