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dc.contributor.authorSivasubramaniam, Kuganesan
dc.contributor.authorSharma, Ashish
dc.contributor.authorAlfredsen, Knut
dc.date.accessioned2019-06-11T05:51:15Z
dc.date.available2019-06-11T05:51:15Z
dc.date.created2019-05-28T09:55:02Z
dc.date.issued2019
dc.identifier.citationEnvironmental Modelling & Software. 2019, 119 99-110.nb_NO
dc.identifier.issn1364-8152
dc.identifier.urihttp://hdl.handle.net/11250/2600394
dc.description.abstractThis study presents a dynamic forecast combination approach adapted to incorporate multiple sources of precipitation. Dynamic combination serves to utilise the varying merit each data source exhibits with time. The dynamic model combination framework presented merges a nonparametric k-nearest neighbour (k-nn) estimation of radar precipitation with Thin Plate Spline (TPS) interpolated gauge precipitation. Since air temperature is an essential variable to discriminate the phase of the precipitation in cold climates, this study uses radar precipitation and air temperature as the two variables in the dynamic combination algorithm. The merging of k-nn and TPS estimates is shown to reduce the RMSE by 25% compared to the original radar precipitation rates. The usefulness of air temperature is found not to be as significant in the combination as it is in the formulation of the nonparametric radar precipitation fields for cold incident temperatures.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1364815218311678
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleMerging radar and gauge information within a dynamical model combination framework for precipitation estimation in cold climatesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber99-110nb_NO
dc.source.volume119nb_NO
dc.source.journalEnvironmental Modelling & Softwarenb_NO
dc.identifier.doi10.1016/j.envsoft.2019.05.013
dc.identifier.cristin1700739
dc.relation.projectNorges forskningsråd: 255852/O30nb_NO
dc.description.localcodeThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)nb_NO
cristin.unitcode194,64,91,0
cristin.unitnameInstitutt for bygg- og miljøteknikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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