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dc.contributor.authorDemaeyer, Jonathan
dc.contributor.authorVannitsem, Stéphane
dc.date2020-05-27
dc.date.accessioned2020-10-15T13:29:40Z
dc.date.available2020-10-15T13:29:40Z
dc.identifier.citationDemaeyer, J. and Vannitsem, S.: Correcting for model changes in statistical postprocessing – an approach based on response theory, Nonlin. Processes Geophys., 27, 307–327, https://doi.org/10.5194/npg-27-307-2020, 2020en_US
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/7624
dc.descriptionFor most statistical postprocessing schemes used to correct weather forecasts, changes to the forecast model induce a considerable reforecasting effort. We present a new approach based on response theory to cope with slight model changes. In this framework, the model change is seen as a perturbation of the original forecast model. The response theory allows us then to evaluate the variation induced on the parameters involved in the statistical postprocessing, provided that the magnitude of this perturbation is not too large. This approach is studied in the context of a simple Ornstein–Uhlenbeck model and then on a more realistic, yet simple, quasi-geostrophic model. The analytical results for the former case help to pose the problem, while the application to the latter provides a proof of concept and assesses the potential performance of response theory in a chaotic system. In both cases, the parameters of the statistical postprocessing used – the Error-in-Variables Model Output Statistics (EVMOS) method – are appropriately corrected when facing a model change. The potential application in an operational environment is also discussed.en_US
dc.languageengen_US
dc.publisherCopernicusen_US
dc.titleCorrecting for model changes in statistical postprocessing – an approach based on response theoryen_US
dc.typeArticleen_US
dc.subject.frascatiEarth and related Environmental sciencesen_US
dc.audienceScientificen_US
dc.source.titleNonlinear Processes in Geophysicsen_US
dc.source.volume27en_US
dc.source.page307-327en_US
Orfeo.peerreviewedYesen_US
dc.identifier.doi10.5194/npg-27-307-2020


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