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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 6
Nonlin. Processes Geophys., 15, 1013–1022, 2008
https://doi.org/10.5194/npg-15-1013-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 15, 1013–1022, 2008
https://doi.org/10.5194/npg-15-1013-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 Dec 2008

16 Dec 2008

An assessment of Bayesian bias estimator for numerical weather prediction

J. Son1, D. Hou2, and Z. Toth3 J. Son et al.
  • 1Numerical Prediction Center KMA, Seoul, Korea
  • 2Environmental Modeling Center/NCEP/NWS/NOAA and SAIC, Washington DC, USA
  • 3Environmental Modeling Center/NCEP/NWS/NOAA, Washington DC, USA

Abstract. 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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