Article Type:
Research Article

Improvement of the Multimodel Superensemble Technique for Seasonal Forecasts

W. T. Yun Department of Meteorology, The Florida State University, Tallahassee, Florida, and Korea Meteorological Administration, Seoul, South Korea

Search for other papers by W. T. Yun in
Current site
Google Scholar
PubMed Close
,
L. Stefanova Department of Meteorology, The Florida State University, Tallahassee, Florida

Search for other papers by L. Stefanova in
Current site
Google Scholar
PubMed Close
, and
T. N. Krishnamurti Department of Meteorology, The Florida State University, Tallahassee, Florida

Search for other papers by T. N. Krishnamurti in
Current site
Google Scholar
PubMed Close
Print Publication:
15 Nov 2003
DOI:
https://doi.org/10.1175/1520-0442(2003)016<3834:IOTMST>2.0.CO;2
Page(s):
3834–3840
Restricted access

Abstract

The superensemble technique has previously been demonstrated to provide an improved seasonal forecast compared to the bias-removed ensemble of equally weighted models. This paper offers a further improvement to the superensemble method by modifying the regression coefficients used in the weighting of the models for the construction of the superensemble. The improvement is achieved by use of singular value decomposition of the covariance matrix, and selecting only the largest singular value, corresponding to maximal explained variance, for the calculation of the regression coefficients. The results shown here are based on calculations done with 10 yr worth of monthly forecasts from the Atmospheric Model Intercomparison Project (AMIP) dataset, using cross validation.

Corresponding author address: Dr. W. T. Yun, Department of Meteorology, The Florida State University, Tallahassee, FL 32306. Email: wtyun@io.met.fsu.edu

Abstract

The superensemble technique has previously been demonstrated to provide an improved seasonal forecast compared to the bias-removed ensemble of equally weighted models. This paper offers a further improvement to the superensemble method by modifying the regression coefficients used in the weighting of the models for the construction of the superensemble. The improvement is achieved by use of singular value decomposition of the covariance matrix, and selecting only the largest singular value, corresponding to maximal explained variance, for the calculation of the regression coefficients. The results shown here are based on calculations done with 10 yr worth of monthly forecasts from the Atmospheric Model Intercomparison Project (AMIP) dataset, using cross validation.

Corresponding author address: Dr. W. T. Yun, Department of Meteorology, The Florida State University, Tallahassee, FL 32306. Email: wtyun@io.met.fsu.edu

Save
Cover Journal of Climate
Journal of Climate

  • Brankovic, C., and T. N. Palmer, 1997: Atmospheric seasonal predictability and estimates of ensemble size. Mon. Wea. Rev., 125 , 859–874.

    • Search Google Scholar
    • Export Citation

  • Buizza, R., and T. N. Palmer, 1995: The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci., 52 , 1434–1456.

    • Search Google Scholar
    • Export Citation

  • Buizza, R., T. Petroliagis, T. Palmer, J. Barkmeijer, M. Hamrud, A. Hollingsworth, A. Simmons, and N. Wedi, 1998: Impact of model resolution and ensemble size on the performance of an ensemble prediction system. Quart. J. Roy. Meteor. Soc., 124 , 1935–1960.

    • Search Google Scholar
    • Export Citation

  • Doblas-Reyes, F. J., M. Déqué, and J-P. Piedelievre, 2000: Multi-model spread and probabilistic forecasts in PROVOST. Quart. J. Roy. Meteor. Soc., 126 , 2069–2087.

    • Search Google Scholar
    • Export Citation

  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73 , 1962–1970.

  • Hoffman, R. N., and E. Kalnay, 1983: Lagged average forecasting, an alternative to Monte Carlo forecasting. Tellus, 35A , 100–118.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124 , 1225–1242.

    • Search Google Scholar
    • Export Citation

  • Kharin, V. V., and F. W. Zwiers, 2002: Climate predictions with multimodel ensembles. J. Climate, 15 , 793–799.

  • Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D. R. Bachiochi, Z. Zhang, C. E. Williford, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285 , 1548–1550.

    • Search Google Scholar
    • Export Citation

  • Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. E. LaRow, D. R. Bachiochi, C. E. Williford, S. Gadgil, and S. Surendran, 2000: Multi-model ensemble forecasts for weather and seasonal climate. J. Climate, 13 , 4196–4216.

    • Search Google Scholar
    • Export Citation

  • Krishnamurti, T. N., and Coauthors. 2001: Real-time multianalysis–multimodel superensemble forecasts of precipitation using TRMM and SSM/I products. Mon. Wea. Rev., 129 , 2861–2883.

    • Search Google Scholar
    • Export Citation

  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122 , 73–119.

    • Search Google Scholar
    • Export Citation

  • Pavan, V., and J. Doblas-Reyes, 2000: Multimodel seasonal hindcasts over the Euro-Atlantic: Skill scores and dynamic features. Climatic Dyn., 16 , 611–625.

    • Search Google Scholar
    • Export Citation

  • Peng, P., A. Kumar, H. Van den Dool, and A. G. Barnston, 2002: An analysis of multimodel ensemble predictions for seasonal climate anomalies. J. Geophys. Res., 107 .4710, doi:10.1029/2002JD002712.

    • Search Google Scholar
    • Export Citation

  • Phillips, T. J., 1996: Documentation of the AMIP models on the World Wide Web. Bull. Amer. Meteor. Soc., 77 , 1191–1196.

  • Press, W. H., S. A. Teukolsky, W. T. Vettering, and B. P. Flannery, 1992: Numerical Recipes in Fortran. 2d ed. Cambridge University Press, 963 pp.

    • Search Google Scholar
    • Export Citation

  • Stephenson, D. B., and F. J. Doblas-Reyes, 2000: Statistical methods for interpreting Monte Carlo ensemble forecasts. Tellus, 52A , 300–322.

    • Search Google Scholar
    • Export Citation

  • Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74 , 2317–2330.

    • Search Google Scholar
    • Export Citation

  • Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125 , 3297–3319.

  • Zwiers, F. W., 1996: Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2. Climate Dyn., 12 , 825–847.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 527 132 20
PDF Downloads 273 55 4