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Titel Reliability of autoregressive error models as post-processors for probabilistic streamflow forecasts
VerfasserIn M. Morawietz, C.-Y. Xu, L. Gottschalk
Medientyp Artikel
Sprache Englisch
ISSN 1680-7340
Digitales Dokument URL
Erschienen In: Towards practical applications in ensemble hydro-meteorological forecasting ; Nr. 29 (2011-04-27), S.109-118
Datensatznummer 250016941
Publikation (Nr.) Volltext-Dokument vorhandencopernicus.org/adgeo-29-109-2011.pdf
 
Zusammenfassung
In this study, the reliability of different versions of autoregressive error models as post-processors for probabilistic streamflow forecasts is evaluated. Rank histograms and reliability indices are used as performance measures. An algorithm for the construction of confidence intervals to indicate ranges of reliable forecasts within the rank histograms is presented. To analyse differences in performance of the post-processors, scatter plots of the standardized residuals of the error models are generated to assess the homoscedacity of the residuals with respect to streamflow. A problem of distorted impressions may appear when such plots are generated with a regular x-scale. The problem is analysed with both synthetic and real data, and a rank scaled x-axis is proposed to remedy the problem. The results of the study reveal large differences in the reliability of the post-processors. Versions with empirical distribution functions are clearly superior to those with standard normal distribution, but for validations with independent data their rank histograms still lie outside of the confidence bands for reliable forecasts.
 
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