Open AccessMonthly‐, medium‐, and short‐range flood warning: testing the limits of predictability
Author(s) -
Thielen J.,
Bogner K.,
Pappenberger F.,
Kalas M.,
del Medico M.,
de Roo A.
Publication year - 2009
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.140
Subject(s) - predictability , flood myth , warning system , meteorology , environmental science , climatology , range (aeronautics) , numerical weather prediction , event (particle physics) , flood warning , lead time , weather prediction , computer science , geography , statistics , geology , mathematics , telecommunications , materials science , physics , archaeology , quantum mechanics , marketing , business , composite material
This paper describes a case study that explores the limits of the predictability of floods, by combining forecasts with multiple spatial and temporal resolutions. Monthly, medium‐ and short range numerical weather prediction (NWP) data are input to the European Flood Alert System for a flood event that affected rivers in Romania in October 2007. The NWP data comprise ensembles and deterministic forecasts of different spatial resolutions and lead times from different weather prediction models. Results are explored in terms of the individual NWP components as well as the ensemble. In this case study, ensembles of monthly weather forecasts contribute only marginally to the early warning, although some indication is given as early as 3 weeks before the event. The 15‐day medium‐range weather forecasts produce early flood warning information 9 to 11 days in advance. As the event draws nearer and is in range to be captured by the higher resolution ensemble forecasts, the spatial extent of the event is forecast with much more precision than with the medium‐range. A novel post‐processing method for the calculation of river discharge is applied to those stations where observations are available, and is able to correct for time‐shifts and to improve the quantitative forecast. The study illustrates how a combination of forecasts and post‐processing improves the lead time for early flood warnings by 2 to 3 days, while remaining reliable also in the short‐range. Copyright © 2009 Royal Meteorological Society