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| Titel |
Heteroscedastic Extended Logistic Regression for Post-Processing of Ensemble Guidance |
| VerfasserIn |
Jakob W. Messner, Georg J. Mayr, Daniel S. Wilks, Achim Zeileis |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250092308
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| Publikation (Nr.) |
 EGU/EGU2014-6639.pdf |
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| Zusammenfassung |
| To achieve well-calibrated probabilistic weather forecasts, numerical ensemble forecasts are
often statistically post-processed. One recent ensemble-calibration method is extended
logistic regression which extends the popular logistic regression to yield full probability
distribution forecasts. Although the purpose of this method is to post-process ensemble
forecasts, usually only the ensemble mean is used as predictor variable, whereas the ensemble
spread is neglected because it does not improve the forecasts. In this study we show that when
simply used as ordinary predictor variable in extended logistic regression, the ensemble
spread only affects the location but not the variance of the predictive distribution. Uncertainty
information contained in the ensemble spread is therefore not utilized appropriately.
To solve this drawback we propose a new approach where the ensemble spread is
directly used to predict the dispersion of the predictive distribution. With wind
speed data and ensemble forecasts from the European Centre for Medium-Range
Weather Forecasts (ECMWF) we show that using this approach, the ensemble spread
can be used effectively to improve forecasts from extended logistic regression. |
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