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| Titel |
Multivariate postprocessing techniques for probabilistic hydrological forecasting |
| VerfasserIn |
Stephan Hemri, Dmytro Lisniak, Bastian Klein |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250122398
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| Publikation (Nr.) |
 EGU/EGU2016-1422.pdf |
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| Zusammenfassung |
| Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need
statistical postprocessing in order to account for systematic errors in terms of both mean and
spread. Runoff is an inherently multivariate process with typical events lasting from hours in
case of floods to weeks or even months in case of droughts. This calls for multivariate
postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a
realistic temporal dependence structure at the same time. To this end, the univariate ensemble
model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is
combined with two different copula approaches that ensure multivariate calibration
throughout the entire forecast horizon. These approaches comprise ensemble copula
coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure
of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard,
2012), which estimates the temporal correlations from training observations. Both
methods are tested in a case study covering three subcatchments of the river Rhine
that represent different sizes and hydrological regimes: the Upper Rhine up to the
gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to
the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for
modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al.,
2015).
References
Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated
probabilistic forecasting using ensemble model output statistics and minimum CRPS
estimation, Monthly Weather Review, 133(5), 1098–1118, DOI: 10.1175/MWR2904.1.
Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for
probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436–7451, DOI:
10.1002/2014WR016473.
Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind
power generation, Applied Energy, 96, 12–20, DOI: 10.1016/j.apenergy.2011.11.004.
Schefzik, R., T. L. Thorarinsdottir, and T. Gneiting (2013), Uncertainty quantification in
complex simulation models using ensemble copula coupling, Statistical Science, 28,
616–640, DOI: 10.1214/13-STS443. |
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