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| Titel |
Probabilistic forecast of daily areal precipitation focusing on extreme events |
| VerfasserIn |
J. Bliefernicht, A. Bárdossy |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 7, no. 2 ; Nr. 7, no. 2 (2007-04-03), S.263-269 |
| Datensatznummer |
250004450
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| Publikation (Nr.) |
 copernicus.org/nhess-7-263-2007.pdf |
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| Zusammenfassung |
| A dynamical downscaling scheme is usually used to provide a short range flood
forecasting system with high-resolved precipitation fields. Unfortunately, a
single forecast of this scheme has a high uncertainty concerning intensity
and location especially during extreme events. Alternatively, statistical
downscaling techniques like the analogue method can be used which can supply
a probabilistic forecasts. However, the performance of the analogue method is
affected by the similarity criterion, which is used to identify similar
weather situations. To investigate this issue in this work, three different
similarity measures are tested: the euclidean distance (1), the Pearson
correlation (2) and a combination of both measures (3). The predictor
variables are geopotential height at 1000 and 700 hPa-level and specific
humidity fluxes at 700 hPa-level derived from the NCEP/NCAR-reanalysis
project. The study is performed for three mesoscale catchments located in the
Rhine basin in Germany. It is validated by a jackknife method for a period of
44 years (1958–2001). The ranked probability skill score, the Brier Skill
score, the Heidke skill score and the confidence interval of the
Cramer association coefficient are calculated to evaluate the system for
extreme events. The results show that the combined similarity measure yields
the best results in predicting extreme events. However, the confidence
interval of the Cramer coefficient indicates that this improvement is only
significant compared to the Pearson correlation but not for the euclidean
distance. Furthermore, the performance of the presented forecasting system
is very low during the summer and new predictors have to be tested to
overcome this problem. |
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