Nonlinear Processes in Geophysics www.nonlin-processes-geophys.net 1023-5809 1607-7946 15 6 2008 10.5194/npg-15-1013-2008 http://www.nonlin-processes-geophys.net/15/1013/2008/ http://www.nonlin-processes-geophys.net/15/1013/2008/npg-15-1013-2008.html http://www.nonlin-processes-geophys.net/15/1013/2008/npg-15-1013-2008.pdf 1013 1022 2008-12-16 An assessment of Bayesian bias estimator for numerical weather prediction J. Son jhson@kma.go.kr D. Hou Z. Toth 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 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. 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