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www.hydrol-earth-syst-sci-discuss.net/6/1843/2009/
doi:10.5194/hessd-6-1843-2009
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Evaluation of a probabilistic hydrometeorological forecast system
1Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
2Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
3Institute for Atmosphere and Environment, Goethe-University Frankfurt a. M., Germany
Abstract. Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m.
Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with an appropriate spread-skill relationship. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.
Discussion Paper (PDF, 1986 KB) Interactive Discussion (Closed, 7 Comments) Final Revised Paper (HESS)
Citation: Jaun, S. and Ahrens, B.: Evaluation of a probabilistic hydrometeorological forecast system, Hydrol. Earth Syst. Sci. Discuss., 6, 1843-1877, doi:10.5194/hessd-6-1843-2009, 2009. Bibtex EndNote Reference Manager XML
