• Buizza, R., , P. L. Houtekamer, , Z. Toth, , G. Pellerin, , M. Wei, , and Y. Zhu, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133 , 10761097.

    • Search Google Scholar
    • Export Citation
  • Candille, G., 2009: The multiensemble approach: The NAEFS example. Mon. Wea. Rev., 137 , 16551665.

  • Candille, G., , C. Côté, , P. L. Houtekamer, , and G. Pellerin, 2007: Verification of an ensemble prediction system against observations. Mon. Wea. Rev., 135 , 26882699.

    • Search Google Scholar
    • Export Citation
  • Charron, M., , G. Pellerin, , L. Spacek, , P. L. Houtekamer, , N. Gagnon, , H. L. Mitchell, , and L. Michelin, 2010: Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev., 138 , 18771901.

    • Search Google Scholar
    • Export Citation
  • Cui, B., , Z. Toth, , Y. Zhu, , and D. Hou, 2008: Statistical downscaling approach and its application. Preprints, 19th Conf. on Probability and Statistics, New Orleans, LA, Amer. Meteor. Soc., 11.2.

    • Search Google Scholar
    • Export Citation
  • Efron, B., , and R. Tibshirani, 1993: An Introduction to the Bootstrap. Chapman & Hall, 436 pp.

  • Elms, J., 2003: WMO catalogue of radiosondes and upper-air wind systems in use by members in 2002 and compatibility of radiosonde geopotential measurements for period from 1998 to 2001. Instruments and Observing Methods, Rep. 80, TD 1197, World Meteorological Organization, 12–21.

    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., , T. M. Hamill, , and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I: Two-meter temperatures. Mon. Wea. Rev., 136 , 26082619.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., , and J. S. Whitaker, 2007: Ensemble calibration of 500-hPa geopotential height and 850-hPa and 2-m temperatures using reforecasts. Mon. Wea. Rev., 135 , 32733280.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15 , 559570.

    • Search Google Scholar
    • Export Citation
  • Johnson, C., , and R. Swinbank, 2009: Medium-range multimodel ensemble combination and calibration. Quart. J. Roy. Meteor. Soc., 135 , 777794.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., and Coauthors, 2004: Development of a European multimodel ensemble for seasonal-to-interannual prediction (DEMETER). Bull. Amer. Meteor. Soc., 85 , 853872.

    • Search Google Scholar
    • Export Citation
  • Park, Y-Y., , R. Buizza, , and M. Leutbecher, 2008: TIGGE: Preliminary results on comparing and combining ensembles. Quart. J. Roy. Meteor. Soc., 134 , 20292050.

    • Search Google Scholar
    • Export Citation
  • Stanski, H. R., , L. J. Wilson, , and W. R. Burrows, 1989: Survey of common verification in meteorology. World Weather Watch Rep. 8, TD 358, World Meteorological Organization, Geneva, Switzerland, 114 pp.

    • Search Google Scholar
    • Export Citation
  • Talagrand, O., , R. Vautard, , and B. Strauss, 1997: Evaluation of probabilistic prediction systems. Proc. Workshop on Predictability, Reading, Berkshire, United Kingdom, ECMWF, 1–26.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., , and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125 , 32973319.

  • Toth, Z., , O. Talagrand, , G. Candille, , and Y. Zhu, 2003: Probability and ensemble forecasts. Forecast Verification: A Practitioner’s Guide in Atmospheric Science, I. T. Jolliffe and D. B. Stephenson, Eds., John Wiley & Sons, 137–163.

    • Search Google Scholar
    • Export Citation
  • Wei, M., , Z. Toth, , R. Wobus, , and Y. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus, 60A , 6279.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 30 30 4
PDF Downloads 13 13 4

Bias Correction and Multiensemble in the NAEFS Context or How to Get a “Free Calibration” through a Multiensemble Approach

View More View Less
  • 1 Université de Québec à Montréal, Montréal, Québec, Canada
  • 2 CMC, Dorval, Québec, Canada
© Get Permissions
Restricted access

Abstract

Previous studies have shown that the raw combination (i.e., the combination of the direct output model without any postprocessing procedure) of the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble prediction systems (EPS) improves the probabilistic forecast both in terms of reliability and resolution. This combination palliates the lack of reliability of the NCEP EPS because of the too small dispersion of the predicted ensemble and the lack of probabilistic resolution of the MSC EPS. Such a multiensemble, called the North American Ensemble Forecast System (NAEFS), especially shows bias reductions and dispersion improvements that could only come from the combination of different forecast errors. It is then legitimate to wonder whether these improvements in terms of biases and dispersions, and by extension the skill improvements, are only due to the balancing between opposite model errors.

In the NAEFS framework, bias corrections “on the fly,” where the bias is updated over time, are applied to the operational EPSs. Each model of the EPS components (NCEP/MSC) is individually bias corrected against its own analysis with the same process. The bias correction improves the reliability of each EPS component. It also slightly improves the accuracy of the predicted ensembles and thus the probabilistic resolution of the forecasts. Once the EPSs are combined, the improvements due to the bias correction are not so obvious, tending to show that the success of the multiensemble method does not only come from the cancellation of different biases. This study also shows that the combination of the raw EPS components (NAEFS) is generally better than either the bias corrected NCEP or MSC ensembles.

Corresponding author address: Guillem Candille, Department of Earth and Atmospheric Sciences, Université de Québec à Montréal (UQAM), C.P. 8888, Succ. Centre-ville, Montréal, QC H3C 1P8, Canada. Email: candille.guillem@uqam.ca

Abstract

Previous studies have shown that the raw combination (i.e., the combination of the direct output model without any postprocessing procedure) of the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble prediction systems (EPS) improves the probabilistic forecast both in terms of reliability and resolution. This combination palliates the lack of reliability of the NCEP EPS because of the too small dispersion of the predicted ensemble and the lack of probabilistic resolution of the MSC EPS. Such a multiensemble, called the North American Ensemble Forecast System (NAEFS), especially shows bias reductions and dispersion improvements that could only come from the combination of different forecast errors. It is then legitimate to wonder whether these improvements in terms of biases and dispersions, and by extension the skill improvements, are only due to the balancing between opposite model errors.

In the NAEFS framework, bias corrections “on the fly,” where the bias is updated over time, are applied to the operational EPSs. Each model of the EPS components (NCEP/MSC) is individually bias corrected against its own analysis with the same process. The bias correction improves the reliability of each EPS component. It also slightly improves the accuracy of the predicted ensembles and thus the probabilistic resolution of the forecasts. Once the EPSs are combined, the improvements due to the bias correction are not so obvious, tending to show that the success of the multiensemble method does not only come from the cancellation of different biases. This study also shows that the combination of the raw EPS components (NAEFS) is generally better than either the bias corrected NCEP or MSC ensembles.

Corresponding author address: Guillem Candille, Department of Earth and Atmospheric Sciences, Université de Québec à Montréal (UQAM), C.P. 8888, Succ. Centre-ville, Montréal, QC H3C 1P8, Canada. Email: candille.guillem@uqam.ca

Save