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Hydrology 2016, 3(2), 21; doi:10.3390/hydrology3020021

Postprocessing of Medium Range Hydrological Ensemble Forecasts Making Use of Reforecasts

Royal Meteorological Institute, Avenue Circulaire 3, B-1180 Brussels, Belgium
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Luca Brocca
Received: 17 December 2015 / Revised: 19 May 2016 / Accepted: 23 May 2016 / Published: 31 May 2016
(This article belongs to the Special Issue Hydrological Modeling: Beyond Runoff Calibration)
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Abstract

A 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. View Full-Text
Keywords: hydrological ensemble predictions; postprocessing; reforecasts hydrological ensemble predictions; postprocessing; reforecasts
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Van den Bergh, J.; Roulin, E. Postprocessing of Medium Range Hydrological Ensemble Forecasts Making Use of Reforecasts. Hydrology 2016, 3, 21.

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