Performance of the ARPA-SMR limited-area ensemble prediction system: two flood cases

نویسنده

  • A. Montani
چکیده

The performance of the ARPA-SMR Limited-area Ensemble Prediction System (LEPS), generated by nesting a limited-area model on selected members of the ECMWF targeted ensemble, is evaluated for two flood events that occurred during September 1992. The predictability of the events is studied for forecast times ranging from 2 to 4 days. The extent to which floods localised in time and space can be forecast at high resolution in probabilistic terms was investigated. Rainfall probability maps generated by both LEPS and ECMWF targeted ensembles are compared for different precipitation thresholds in order to assess the impact of enhanced resolution. At all considered forecast ranges, LEPS performs better, providing a more accurate description of the event with respect to the spatio-temporal location, as well as its intensity. In both flood cases, LEPS probability maps turn out to be a very valuable tool to assist forecasters to issue flood alerts at different forecast ranges. It is also shown that at the shortest forecast range, the deterministic prediction provided by the limited area model, when run in a higherresolution configuration, provides a very accurate rainfall pattern and a good quantitative estimate of the total rainfall deployed in the flooded regions.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

The Soverato flood in Southern Italy: performance of global and limited-area ensemble forecasts

The predictability of the flood event affecting Soverato (Southern Italy) in September 2000 is investigated by considering three different configurations of ECMWF ensemble: the operational Ensemble Prediction System (EPS), the targeted EPS and a high-resolution version of EPS. For each configuration, three successive runs of ECMWF ensemble with the same verification time are grouped together so...

متن کامل

zoning of flood hazard in Nowshahr city using machine learning models

  The aim of this study is to predict and model flood hazard in the city of Nowshahr, Mazandaran province using machine learning models. The criteria and indicators affecting flood hazard were identified based on the review of resources, and then the indicators were converted into rasters in ArcGIS environment, and finally standardized by fuzzy method for use in the models. K-nearest neighbor ...

متن کامل

A probabilistic view on the August 2005 floods in the upper Rhine catchment

Appropriate precautions in the case of flood occurrence often require long lead times (several days) in hydrological forecasting. This in turn implies large uncertainties that are mainly inherited from the meteorological precipitation forecast. Here we present a case study of the extreme flood event of August 2005 in the Swiss part of the Rhine catchment (total area 34 550 km2). This event caus...

متن کامل

Probabilistic high-resolution forecast of heavy precipitation over Central Europe

Abstract. The limited-area ensemble prediction system COSMO-LEPS has been running operationally at ECMWF since November 2002. Five runs of the non-hydrostatic limited-area model Lokal Modell (LM) are available every day, nested on five selected members of three consecutive 12-h lagged ECMWF global ensembles. The limited-area ensemble forecasts range up to 120 h and LM-based probabilistic produc...

متن کامل

A probabilistic view on the August 2005 floods in the upper Rhine catchment Jaun

Appropriate precautions in the case of flood occurrence often require long lead times (several days) in hydrological forecasting. This in turn implies large uncertainties that are mainly inherited from the meteorological precipitation forecast. Here we present a case study of the extreme flood event of August 2005 in the Swiss part of the Rhine catchment (total area 34 550 km2). This event caus...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000