Abstract
Abstract. In this work, we compare the performance of an hydrological model when driven by probabilistic rain forecast derived from two different post-processing techniques. The region of interest is Piemonte, northwestern Italy, a complex orography area close to the Mediterranean Sea where the forecast are often a challenge for weather models. The May 2008 flood is here used as a case study, and the very dense weather station network allows us for a very good description of the event and initialization of the hydrological model. The ensemble probabilistic forecasts of the rainfall fields are obtained with the Bayesian model averaging, with the classical poor man ensemble approach and with a new technique, the Multimodel SuperEnsemble Dressing. In this case study, the meteo-hydrological chain initialized with the Multimodel SuperEnsemble Dressing is able to provide more valuable discharge ranges with respect to the one initialized with Bayesian model averaging multi-model.
Highlights
High resolution spatiotemporal rainfall intensity forecasts are in model static/non ihmypdlreomsteantitcataipoHpnryo(ddacorhmo,aplionhygssiiyzcea,al prneasdroalmuteitoenri,zhatyidornos, etc.: an interesting experEimaenrtthon Shoywsctheamnges on a single the main input into rainfall-runoff models for flood forecast, debris-flow and landslide triggering
In this paper we explore three different multi-model post-processing techniques of deterministic precipitation forecasts in order to estimate the forecast rainfall probabilities: Bayesian model averaging, Multimodel SuperEnsemble Dressing and poor man ensemble: we review here some theory and set the notation for each procedure
In the training period the observed precipitation probability density function (PDF), conditioned to the forecasts of each model, is calculated: for a large set of model forecast values, we evaluate the observed precipitation that occurred in reality and we built a set of empirical PDFs from the frequency of occurrence of observed rainfall over a wide spectrum of possible values
Summary
High resolution spatiotemporal rainfall intensity forecasts are in model static/non ihmypdlreomsteantitcataipoHpnryo(ddacorhmo,aplionhygssiiyzcea,al prneasdroalmuteitoenri,zhatyidornos-, etc.: an interesting experEimaenrtthon Shoywsctheamnges on a single the main input into rainfall-runoff models for flood forecast, debris-flow and landslide triggering. Cane and Milelli (2010b) proposed a probabilistic quantitative precipitation forecasting (QPF) evaluation with the use of a new Multimodel SuperEnsemble Dressing technique This new approach, providing an estimation of the probability density function (PDF) of precipitation, widens our knowledge of the precipitation field characteristics, is a support for operational weather forecast and can be used as input for the hydrological forecast chain, propagating the QPF uncertainty to the evaluation of its effects on the territory. Once forecast rainfall amounts are obtained, they are used as input for the hydrological water-balance model FEST-WB, implemented by the environmental agency Arpa Piemonte, in order to assess flood formation and propagation in hydrographical network We applied this simulation exercise to the case study of May 2008 flood in western Piemonte, Italy.
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