Kurzbeschreibung
(Englisch)
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The project aims at contributing to the understanding, quantification, and reduction of uncertainty in meteo-hydrologic forecast systems. It will concentrate on two specific steps of the forecasting chain that are considered crucial for the transfer of uncertainty information into the hydrological application/end user involvement: In a first PhD project high-quality radar data, NWP forecasts and other observational data will be employed to establish a heuristic probabilistic forecasting tool for the nowcasting of orographic precipitation. Besides giving guidance to forecasters and hydrologists, this tool also helps to better understand the uncertainty in atmospheric and hydrologic modelling systems and hence helps to improve the latter. A second PhD project concentrates on probabilistic forecasting of potential flood events through the use of a hydrologic ensemble prediciton system. Such a system makes direct use of the uncertainty information provided by atmospheric ensemble systems and provides probabilistic runoff forecasts in real-time. A proof of concept for this advanced meteo-hydrological forecast system will be demonstrated quasi-operationally for the first time during the MAP D-PHASE project in 2007.
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Partner und Internationale Organisationen
(Englisch)
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BE, CH, CY, CZ, DE, DK, ES, FI, FR, GR, HU, IE, IT, LU, NL, NO, PL, PT, SE, UK
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Abstract
(Englisch)
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The project aims at contributing to the understanding, quantification, and reduction of uncertainty in meteo-hydrologic forecast systems. It concentrates on two specific steps of the forecasting chain that are considered crucial for the transfer of uncertainty information into the hydrological application/end user involvement: In a first PhD project high-quality radar data, NWP forecasts and other observational data are being employed to establish a heuristic probabilistic forecasting tool for the nowcasting of orographic precipitation. Besides giving guidance to forecasters and hydrologists, this tool also helps to better understand the uncertainty in atmospheric and hydrologic modelling systems and hence helps to improve the latter. A second PhD project concentrates on probabilistic forecasting of potential flood events through the use of a hydrologic ensemble prediction system. Such a system makes direct use of the uncertainty information provided by atmospheric ensemble systems and provides probabilistic runoff forecasts in real-time. A proof of concept for this advanced meteo-hydrological forecast system is demonstrated quasi-operationally for the first time during the MAP D-PHASE project in 2007.
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