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dc.contributor.authorSivasubramaniam, Kuganesan
dc.contributor.authorSharma, Ashish
dc.contributor.authorAlfredsen, Knut
dc.date.accessioned2019-01-04T14:32:47Z
dc.date.available2019-01-04T14:32:47Z
dc.date.created2018-11-28T22:45:35Z
dc.date.issued2017
dc.identifier.issn1812-2108
dc.identifier.urihttp://hdl.handle.net/11250/2579275
dc.description.abstractAbstract. In cold climates, the form of precipitation (snow or rain or mixture of snow and rain) results in uncertainty in radar precipitation estimation. Estimation often proceeds without distinguishing the state of precipitation which can be reliably specified as a function of associated air temperature. In the present study, we hypothesise that incident air temperature is related to the phase of the precipitation and ensuing reflectivity measurement, and therefore could be used in prediction models to improve radar precipitation estimates in cold climates. This is the first study to our knowledge that assesses the dependence of radar precipitation on incident air temperature and presents a procedure that can be used for taking it into consideration. We use a data based nonparametric statistical approach for this assessment. A nonparametric predictive model is constructed with radar rain rate and air temperature as predictor variables and gauge precipitation as observed response using a k-nearest neighbour (k-nn) regression estimator. A partial information theoretic technique is used to ascertain the relative importance of the two predictors. Six years (2011–2017) of hourly radar rain rate from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 88 raingauges and gridded observational air temperature were used to formulate the predictive model and hence evaluate our hypothesis. The predictive model with temperature as an additional covariate reduces root mean squared error (RMSE) up to 15% compared to the predictive model with radar rain rate as the sole predictor. More than 80% of the raingauge locations in the study area showed improvement with the new method. Further, the estimated partial weight for air temperature assumed a zero value for more than 85% of gauge locations when temperature was above 10°C, which indicates that the partial dependence of precipitation on air temperature is most important for colder climates.nb_NO
dc.language.isoengnb_NO
dc.publisherCopernicus Publicationsnb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleShould radar precipitation depend on incident air temperature? A new estimation algorithm for cold climates.nb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.journalHydrology and Earth System Sciences Discussionsnb_NO
dc.identifier.doihttps://doi.org/10.5194/hess-2017-662
dc.identifier.cristin1636630
dc.description.localcode© Author(s) 2017. This work is distributed under the Creative Commons Attribution 4.0 License.nb_NO
cristin.unitcode194,64,91,0
cristin.unitnameInstitutt for bygg- og miljøteknikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode0


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