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dc.contributor.authorGALMARINI Stefanoen_GB
dc.contributor.authorKIOUTSIOUKIS IOANNISen_GB
dc.contributor.authorSOLAZZO EFISIOen_GB
dc.date.accessioned2015-06-24T00:01:27Z-
dc.date.available2015-06-23en_GB
dc.date.available2015-06-24T00:01:27Z-
dc.date.created2015-06-19en_GB
dc.date.issued2013en_GB
dc.date.submitted2012-11-07en_GB
dc.identifier.citationATMOSPHERIC CHEMISTRY AND PHYSICS vol. 13 no. 14 p. 7153–7182en_GB
dc.identifier.issn1680-7316en_GB
dc.identifier.uriwww.atmos-chem-phys.net/13/7153/2013/en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC76377-
dc.description.abstractIn this study we present a novel approach for improving the air quality predictions using an ensemble of air quality models generated in the context of AQMEII (Air Quality Model Evaluation International Initiative). . The development of the forecasting method makes use of modeled and observed time series (either spatially aggregated or relative to single monitoring stations) of ozone concentrations over different areas of Europe and North America. The technique considers the underlying forcing mechanisms on ozone by means of the spectrally decomposed previsions. By means of diverse applications we demonstrate how the approach screens the ensemble members, extracts the best components and generates bias-free forecasts with improved accuracy over the candidate models.en_GB
dc.description.sponsorshipJRC.H.2-Air and Climateen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherCOPERNICUS GESELLSCHAFT MBHen_GB
dc.relation.ispartofseriesJRC76377en_GB
dc.titleE pluribus unum: Ensemble Air Quality Predictionsen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.5194/acp-13-7153-2013en_GB
JRC Directorate:Sustainable Resources

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