Daily quantitative precipitation forecasts based on the analogue method: Improvements and application to a French large river basin
2016
Ben Daoud, A. | Sauquet, Eric | Bontron, G. | Obled, C. | Lang, M. | Compagnie Nationale du Rhône (CNR) | Hydrologie-Hydraulique (UR HHLY) ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) | Laboratoire d'étude des transferts en hydrologie et environnement (LTHE) ; Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG) ; Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU
Mostrar más [+] Menos [-]International audience
Mostrar más [+] Menos [-]Inglés. This paper presents some improvements of a probabilistic quantitative precipitation forecasting method based on analogues, formerly developed on small basins located in South-Eastern France. The scope is extended to large scale basins mainly influenced by frontal systems, considering a case study area related to the Saône river, a large basin in eastern France. For a given target situation, this method consists in searching for the most similar situations observed in a historical meteorological archive. Precipitation amounts observed during analogous situations are then collected to derive an empirical predictive distribution function, i.e. the probabilistic estimation of the precipitation amount expected for the target day. The former version of this forecasting method (Bontron, 2004) has been improved by introducing two innovative variables: temperature, that allows taking seasonal effects into account and vertical velocity, which enables a better characterization of the vertical atmospheric motion. The new algorithm is first applied in a perfect prognosis context (target situations come from a meteorological reanalysis) and then in an operational forecasting context (target situations come from weather forecasts) for a three years period. Results show that this approach yields useful forecasts, with a lower false alarm rate and improved performances from the present day D to day D+2.
Mostrar más [+] Menos [-]Palabras clave de AGROVOC
Información bibliográfica
Este registro bibliográfico ha sido proporcionado por Institut national de la recherche agronomique