NPG Nonlinear Processes in Geophysics NPG Nonlin. Processes Geophys. 1607-7946 Copernicus Publications Göttingen, Germany 10.5194/npg-15-1013-2008 An assessment of Bayesian bias estimator for numerical weather prediction Son J. 1 Hou D. 2 Toth Z. 3 Numerical Prediction Center KMA, Seoul, Korea Environmental Modeling Center/NCEP/NWS/NOAA and SAIC, Washington DC, USA Environmental Modeling Center/NCEP/NWS/NOAA, Washington DC, USA 16 12 2008 15 6 1013 1022 Copyright: © 2008 J. Son et al. 2008 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://npg.copernicus.org/articles/15/1013/2008/npg-15-1013-2008.html The full text article is available as a PDF file from https://npg.copernicus.org/articles/15/1013/2008/npg-15-1013-2008.pdf

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. <br><br> 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. <br><br> 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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