Editorial Type:
Article
Article Type:
Research Article

MOS, Perfect Prog, and Reanalysis

Caren Marzban Department of Statistics, University of Washington, Seattle, Washington, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Scott Sandgathe Applied Physics Laboratory, University of Washington, Seattle, Washington

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Eugenia Kalnay Department of Meteorology, University of Maryland, College Park, College Park, Maryland

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Print Publication:
01 Feb 2006
DOI:
https://doi.org/10.1175/MWR3088.1
Page(s):
657–663
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Abstract

Statistical postprocessing methods have been successful in correcting many defects inherent in numerical weather prediction model forecasts. Among them, model output statistics (MOS) and perfect prog have been most common, each with its own strengths and weaknesses. Here, an alternative method (called RAN) is examined that combines the two, while at the same time utilizes the information in reanalysis data. The three methods are examined from a purely formal/mathematical point of view. The results suggest that whereas MOS is expected to outperform perfect prog and RAN in terms of mean squared error, bias, and error variance, the RAN approach is expected to yield more certain and bias-free forecasts. It is suggested therefore that a real-time RAN-based postprocessor be developed for further testing.

Corresponding author address: Caren Marzban, Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73019. Email: marzban@caps.ou.edu

Abstract

Statistical postprocessing methods have been successful in correcting many defects inherent in numerical weather prediction model forecasts. Among them, model output statistics (MOS) and perfect prog have been most common, each with its own strengths and weaknesses. Here, an alternative method (called RAN) is examined that combines the two, while at the same time utilizes the information in reanalysis data. The three methods are examined from a purely formal/mathematical point of view. The results suggest that whereas MOS is expected to outperform perfect prog and RAN in terms of mean squared error, bias, and error variance, the RAN approach is expected to yield more certain and bias-free forecasts. It is suggested therefore that a real-time RAN-based postprocessor be developed for further testing.

Corresponding author address: Caren Marzban, Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73019. Email: marzban@caps.ou.edu

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  • Brunet, N., R. Verret, and N. Yacowar, 1988: An objective comparison of model output statistics and perfect prog systems in producing numerical weather element forecasts. Wea. Forecasting, 3 , 273–283.

    • Search Google Scholar
    • Export Citation

  • Denis, B., and R. Verret, 2004: Toward a new Canadian medium-range perfect-prog temperature forecast system. Preprints, 17th Conf. on Probability and Statistics in Atmospheric Sciences, Seattle, WA, Amer. Meteor. Soc., CD-ROM, 1.6.

  • Draper, N. R., and H. Smith, 1998: Applied Regression Analysis. John Wiley & Sons, 706 pp.

  • Glahn, H. R., and D. A. Lowry, 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor, 11 , 1203–1211.

    • Search Google Scholar
    • Export Citation

  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc, 77 , 437–471.

  • Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-year reanalysis: Monthly mean CD-ROM and documentation. Bull. Amer. Meteor. Soc, 82 , 247–267.

    • Search Google Scholar
    • Export Citation

  • Klein, W. H., B. M. Lewis, and I. Enger, 1959: Objective prediction of five-day mean temperature during winter. J. Meteor, 16 , 672–682.

    • Search Google Scholar
    • Export Citation

  • Marzban, C., 2003: A neural network for post-processing model output: ARPS. Mon. Wea. Rev, 131 , 1103–1111.

  • Stephenson, D. B., C. A. S. Coelho, F. J. Doblas-Reyes, and M. Balmaseda, 2004: Forecast assimilation: A unified framework for the combination of multi-model weather and climate predictions. Tellus A, 57 , 253–264.

    • Search Google Scholar
    • Export Citation

  • Vislocky, R. L., and G. S. Young, 1989: The use of perfect prog forecasts to improve model output statistics forecasts of precipitation probability. Wea. Forecasting, 4 , 202–209.

    • Search Google Scholar
    • Export Citation

  • Vislocky, R. L., and J. M. Fritsch, 1997: An automated, observations-based system for short-term prediction of ceiling and visibility. Wea. Forecasting, 12 , 31–43.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

  • Wilson, L. J., and M. Vallée, 2002: The Canadian updateable model output statistics (UMOS) system: Design and development tests. Wea. Forecasting, 17 , 206–222.

    • Search Google Scholar
    • Export Citation

  • Wilson, L. J., and M. Vallée, 2003: The Canadian updateable model output statistics (UMOS) system: Validation against perfect prog. Wea. Forecasting, 18 , 288–302.

    • Search Google Scholar
    • Export Citation
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