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dc.contributor.author박선기*
dc.date.accessioned2016-08-27T04:08:33Z-
dc.date.available2016-08-27T04:08:33Z-
dc.date.issued2015*
dc.identifier.issn1991-959X*
dc.identifier.issn1991-9603*
dc.identifier.otherOAK-14990*
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/217239-
dc.description.abstractIn this study, we examined the structure of an ensemble-based coupled atmosphere-chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere-chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert an impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate that the ensemble-based coupled atmosphere-chemistry data assimilation will respond similarly to assimilation of real observations.*
dc.languageEnglish*
dc.publisherCOPERNICUS GESELLSCHAFT MBH*
dc.titleStructure of forecast error covariance in coupled atmosphere-chemistry data assimilation*
dc.typeArticle*
dc.relation.issue5*
dc.relation.volume8*
dc.relation.indexSCIE*
dc.relation.indexSCOPUS*
dc.relation.startpage1315*
dc.relation.lastpage1320*
dc.relation.journaltitleGEOSCIENTIFIC MODEL DEVELOPMENT*
dc.identifier.doi10.5194/gmd-8-1315-2015*
dc.identifier.wosidWOS:000355289000004*
dc.identifier.scopusid2-s2.0-84929149162*
dc.author.googlePark, S. K.*
dc.author.googleLim, S.*
dc.author.googleZupanski, M.*
dc.contributor.scopusid박선기(7501828935)*
dc.date.modifydate20220621104206*


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