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| Titel |
Discrete post-processing of total cloud cover ensemble forecasts |
| VerfasserIn |
Stephan Hemri, Thomas Haiden, Florian Pappenberger |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250139532
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| Publikation (Nr.) |
 EGU/EGU2017-2792.pdf |
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| Zusammenfassung |
| This contribution presents an approach to post-process ensemble forecasts for the discrete and
bounded weather variable of total cloud cover. Two methods for discrete statistical
post-processing of ensemble predictions are tested. The first approach is based on
multinomial logistic regression, the second involves a proportional odds logistic regression
model. Applying them to total cloud cover raw ensemble forecasts from the European Centre
for Medium-Range Weather Forecasts improves forecast skill significantly. Based on
station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of
3330 stations over the period from 2007 to early 2014, the more parsimonious proportional
odds logistic regression model proved to slightly outperform the multinomial logistic
regression model.
Reference
Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud
cover ensemble forecasts. Monthly Weather Review 144, 2565–2577. |
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