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| Titel |
An assessment of Bayesian bias estimator for numerical weather prediction |
| VerfasserIn |
J. Son, D. Hou, Z. Toth |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 15, no. 6 ; Nr. 15, no. 6 (2008-12-16), S.1013-1022 |
| Datensatznummer |
250012816
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| Publikation (Nr.) |
 copernicus.org/npg-15-1013-2008.pdf |
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| Zusammenfassung |
Various statistical methods are used to process operational Numerical
Weather Prediction (NWP) products with the aim of reducing forecast errors
and they often require sufficiently large training data sets. Generating
such a hindcast data set for this purpose can be costly and a well designed
algorithm should be able to reduce the required size of these data sets.
This issue is investigated with the relatively simple case of bias
correction, by comparing a Bayesian algorithm of bias estimation with the
conventionally used empirical method. As available forecast data sets are
not large enough for a comprehensive test, synthetically generated time
series representing the analysis (truth) and forecast are used to increase
the sample size. Since these synthetic time series retained the statistical
characteristics of the observations and operational NWP model output, the
results of this study can be extended to real observation and forecasts and
this is confirmed by a preliminary test with real data.
By using the climatological mean and standard deviation of the
meteorological variable in consideration and the statistical relationship
between the forecast and the analysis, the Bayesian bias estimator
outperforms the empirical approach in terms of the accuracy of the estimated
bias, and it can reduce the required size of the training sample by a factor
of 3. This advantage of the Bayesian approach is due to the fact that it is
less liable to the sampling error in consecutive sampling. These results
suggest that a carefully designed statistical procedure may reduce the need
for the costly generation of large hindcast datasets. |
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