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Reliable probabilities through statistical post-processing of ensemble forecasts

dc.contributor.authorVan Schaeybroeck, B.
dc.contributor.authorVannitsem, S.
dc.coverage.temporal21st century
dc.date2012
dc.date.accessioned2016-03-07T16:17:08Z
dc.date.accessioned2021-12-09T09:54:29Z
dc.date.available2016-03-07T16:17:08Z
dc.date.available2021-12-09T09:54:29Z
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/8915
dc.descriptionWe develop post-processing approaches based on linear regression that make ensemble forecasts more reliable. First of all we enforce climatological reliability (CR) in the sense that the total variability of the forecast is equal the variability of the observations. Second, we impose ensemble reliability (ER) such that the spread around the ensemble mean of the observation coincides with the one of the ensemble members. Since, generally, different ensembles have different sizes, standard post-processing methods tend to overcorrect ensembles with large spreads. By taking variable values of the error variances, our forecast becomes more reliable at short lead times as reflected by a flatter rank histogram. We illustrate our findings using the Lorenz 1963 model.
dc.languageeng
dc.publisherIRM
dc.publisherKMI
dc.publisherRMI
dc.relation.ispartofseriesSpringer proceedings on complexity, XVI
dc.titleReliable probabilities through statistical post-processing of ensemble forecasts
dc.typeArticle
dc.subject.frascatiEarth and related Environmental sciences
dc.audienceGeneral Public
dc.audienceScientific
dc.subject.freeClimatological reliability
dc.subject.freeLorenz 96 model
dc.subject.freeEVMOS
dc.source.issueSpringer proceedings on complexity, XVI
dc.source.page347-352
Orfeo.peerreviewedNot pertinent


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