• Cheng, J., , J. Yang, , Y. Zhou, , and Y. Cui, 2006: Flexible background mixture models for foreground segmentation. Image Vis. Comput., 24, 473482.

    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., , R. Hagedorn, , and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting—II. Calibration and combination. Tellus, 57A, 234252.

    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., , F. J. Doblas-Reyes, , and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus, 57A, 219233.

    • Search Google Scholar
    • Export Citation
  • Hoeting, J. A., , D. Madigan, , A. E. Raftery, , and C. T. Volinsky, 1999: Bayesian model averaging: A tutorial. Stat. Sci., 14, 382401.

  • Hsu, W., , and A. H. Murphy, 1986: The attributes diagram: A geometrical framework for assessing the quality of probability forecasts. Int. J. Forecasting, 2, 285293.

    • Search Google Scholar
    • Export Citation
  • Jones, D. A., , W. Wang, , and R. Fawcett, 2009: High-quality spatial climate data-sets for Australia. Aust. Meteor. Oceanogr. J., 58, 233248.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B., , and A. Pirani, 2009: The state of the art of seasonal prediction: Outcomes and recommendations from the first World Climate Research Program workshop on seasonal prediction. Bull. Amer. Meteor. Soc., 90, 455458.

    • Search Google Scholar
    • Export Citation
  • 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, 15481550.

    • Search Google Scholar
    • Export Citation
  • Langford, S., , and H. H. Hendon, 2013: Improving reliability of coupled model forecasts of Australian seasonal rainfall. Mon. Wea. Rev., 141, 728741.

    • Search Google Scholar
    • Export Citation
  • Lim, E.-P., , H. H. Hendon, , D. L. T. Anderson, , A. Charles, , and O. Alves, 2011: Dynamical, statistical-dynamical, and multimodel ensemble forecasts of Australian spring season rainfall. Mon. Wea. Rev., 139, 958975.

    • Search Google Scholar
    • Export Citation
  • Luo, L., , E. F. Wood, , and M. Pan, 2007: Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. J. Geophys. Res., 112, D10102, doi:10.1029/2006JD007655.

    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., , D. Hudson, , M. C. Wheeler, , H. H. Hendon, , and O. Alves, 2011: Evaluating key drivers of Australian intra-seasonal climate variability in POAMA-2: A progress report. CAWCR Res. Lett.,7, 10–16.

  • Matheson, J. E., , and R. L. Winkler, 1976: Scoring rules for continuous probability distributions. Manage. Sci., 22, 10871096.

  • Palmer, T. N., , Č. Branković, , and D. S. Richardson, 2000: A probability and decision-model analysis of PROVOST seasonal multi-model ensemble integrations. Quart. J. Roy. Meteor. Soc., 126, 20132033.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., and Coauthors, 2004: Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER). Bull. Amer. Meteor. Soc., 85, 853872.

    • Search Google Scholar
    • Export Citation
  • Ploshay, J., , and J. Anderson, 2002: Large sensitivity to initial conditions in seasonal predictions with a coupled ocean-atmosphere general circulation model. Geophys. Res. Lett., 29, 1262, doi:10.1029/2000GL012710.

    • Search Google Scholar
    • Export Citation
  • Raftery, A. E., , T. Gneiting, , F. Balabdaoui, , and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 11551174.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 34833517.

  • Schepen, A., , Q. J. Wang, , and D. E. Robertson, 2012: Combining the strengths of statistical and dynamical modeling approaches for forecasting Australian seasonal rainfall. J. Geophys. Res., 117, D20107, doi:10.1029/2012JD018011.

    • Search Google Scholar
    • Export Citation
  • Wang, G., , D. Hudson, , Y. Ying, , O. Alves, , H. Hendon, , S. Langford, , G. Liu, , and F. Tseitkin, 2011: POAMA-2 SST skill assessment and beyond. CAWCR Res. Lett., 6, 4046.

    • Search Google Scholar
    • Export Citation
  • Wang, Q. J., , and D. E. Robertson, 2011: Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences. Water Resour. Res., 47, W02546, doi:10.1029/2010WR009333.

    • Search Google Scholar
    • Export Citation
  • Wang, Q. J., , D. E. Robertson, , and F. H. S. Chiew, 2009: A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour. Res., 45, W05407, doi:10.1029/2008WR007355.

    • Search Google Scholar
    • Export Citation
  • Wang, Q. J., , A. Schepen, , and D. E. Robertson, 2012: Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging. J. Climate, 25, 55245537.

    • Search Google Scholar
    • Export Citation
  • Weisheimer, A., and Coauthors, 2009: ENSEMBLES: A new multi-model ensemble for seasonal-to-annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs. Geophys. Res. Lett., 36, L21711, doi:10.1029/2009GL040896.

    • Search Google Scholar
    • Export Citation
  • Weisheimer, A., , T. N. Palmer, , and F. J. Doblas-Reyes, 2011: Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles. Geophys. Res. Lett., 38, L16703, doi:10.1029/2011GL048123.

    • Search Google Scholar
    • Export Citation
  • Yasuda, T., , Y. Takaya, , C. Kobayashi, , M. Kamachi, , H. Kamahori, , and T. Ose, 2007: Asian monsoon predictability in JMA/MRI seasonal forecast system. CLIVAR Exchanges, No. 43, International CLIVAR Project Office, Southampton, United Kingdom, 18–24.

  • Yeo, I. K., , and R. A. Johnson, 2000: A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954959.

  • Zivkovic, Z., , and F. van der Heijden, 2004: Recursive unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Machine Intell.,26, 651656.

    • Search Google Scholar
    • Export Citation
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Toward Accurate and Reliable Forecasts of Australian Seasonal Rainfall by Calibrating and Merging Multiple Coupled GCMs

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  • 1 Bureau of Meteorology, Brisbane, Queensland, Australia
  • | 2 CSIRO Land and Water, Melbourne, Victoria, Australia
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Abstract

The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean–Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the individual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the individually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts.

Corresponding author address: Andrew Schepen, Bureau of Meteorology, GPO Box 413, Brisbane 4001, Australia. E-mail: a.schepen@bom.gov.au

Abstract

The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean–Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the individual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the individually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts.

Corresponding author address: Andrew Schepen, Bureau of Meteorology, GPO Box 413, Brisbane 4001, Australia. E-mail: a.schepen@bom.gov.au
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