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dc.contributor.authorVan den Bergh, Joris
dc.contributor.authorRoulin, Emmanuel
dc.contributor.editorBrocca, Luca
dc.coverage.spatialOurthe basin, Belgiumen_US
dc.coverage.temporal2012-2014en_US
dc.date2016-05-31
dc.date.accessioned2018-09-13T13:31:44Z
dc.date.available2018-09-13T13:31:44Z
dc.identifier.citationVan den Bergh, J.; Roulin, E. Postprocessing of Medium Range Hydrological Ensemble Forecasts Making Use of Reforecasts. Hydrology 2016, 3, 21.en_US
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/7072
dc.descriptionA hydrological ensemble prediction system is running operationally at the Royal Meteorological Institute of Belgium (RMI) for ten catchments in the Meuse basin. It makes use of the conceptual semi-distributed hydrological model SCHEME and the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system (ENS). An ensemble of 51 discharge forecasts is generated daily. We investigate the improvements attained through postprocessing the discharge forecasts, using the archived ECMWF reforecasts for precipitation and other necessary meteorological variables. We use the 5-member reforecasts that have been produced since 2012, when the horizontal resolution of ENS was increased to the N320 resolution (≈30 km over Belgium). The reforecasts were issued weekly, going back 20 years, and we use a calibration window of five weeks. We use these as input to create a set of hydrological reforecasts. The implemented calibration method is an adaption of the variance inflation method. The parameters of the calibration are estimated based on the hydrological reforecasts and the observed discharge. The postprocessed forecasts are verified based on a two-and-a-half year period of data, using archived 51 member ENS forecasts. The skill is evaluated using summary scores of the ensemble mean and probabilistic scores: the Brier Score and the Continuous Ranked Probability Score (CRPS). We find that the variance inflation method gives a significant improvement in probabilistic discharge forecasts. The Brier score, which measures probabilistic skill for forecasts of discharge threshold exceedance, is improved for the entire forecast range during the hydrological summer period, and the first three days during hydrological winter. The CRPS is also significantly improved during summer, but not during winter. We conclude that it is valuable to apply the postprocessing method during hydrological summer. During winter, the method is also useful for forecasting exceedance probabilities of higher thresholds, but not for lead times beyond five days. Finally, we also note the presence of some large outliers in the postprocessed discharge forecasts, arising from the fact that the postprocessing is performed on the logarithmically transformed discharges. We suggest some ways to deal with this in the future for our operational setting.en_US
dc.languageengen_US
dc.publisherMDPIen_US
dc.titlePostprocessing of Medium Range Hydrological Ensemble Forecasts Making Use of Reforecastsen_US
dc.typeArticleen_US
dc.subject.frascatiNatural sciencesen_US
dc.subject.frascatiEarth and related Environmental sciencesen_US
dc.audienceScientificen_US
dc.subject.freehydrological ensemble predictions; postprocessing; reforecastsen_US
dc.source.titleHydrologyen_US
Orfeo.peerreviewedYesen_US
dc.identifier.doihttps://doi.org/10.3390/hydrology3020021


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