Probabilistic Forecasts Using Analogs in the Idealized Lorenz96 Setting

Jakob W. Messner Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, Austria

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Georg J. Mayr Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, Austria

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Abstract

Three methods to make probabilistic weather forecasts by using analogs are presented and tested. The basic idea of these methods is that finding similar NWP model forecasts to the current one in an archive of past forecasts and taking the corresponding analyses as prediction should remove all systematic errors of the model. Furthermore, this statistical postprocessing can convert NWP forecasts to forecasts for point locations and easily turn deterministic forecasts into probabilistic ones. These methods are tested in the idealized Lorenz96 system and compared to a benchmark bracket formed by ensemble relative frequencies from direct model output and logistic regression. The analog methods excel at longer lead times.

Corresponding author address: Jakob Messner, Institute of Meteorology and Geophysics, University of Innsbruck, Innrain 52, Innsbruck, A-6020, Austria. E-mail: jakob.messner@uibk.ac.at

Abstract

Three methods to make probabilistic weather forecasts by using analogs are presented and tested. The basic idea of these methods is that finding similar NWP model forecasts to the current one in an archive of past forecasts and taking the corresponding analyses as prediction should remove all systematic errors of the model. Furthermore, this statistical postprocessing can convert NWP forecasts to forecasts for point locations and easily turn deterministic forecasts into probabilistic ones. These methods are tested in the idealized Lorenz96 system and compared to a benchmark bracket formed by ensemble relative frequencies from direct model output and logistic regression. The analog methods excel at longer lead times.

Corresponding author address: Jakob Messner, Institute of Meteorology and Geophysics, University of Innsbruck, Innrain 52, Innsbruck, A-6020, Austria. E-mail: jakob.messner@uibk.ac.at
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  • Anderson, L. J., 1996: Selection of initial conditions for ensemble forecast in a simple perfect model framework. J. Atmos. Sci., 53, 2236.

    • Search Google Scholar
    • Export Citation
  • Bishop, C. H., 2008: Bayesian model averaging’s problematic treatment of extreme weather and a paradigm shift that fixes it. Mon. Wea. Rev., 136, 46414652.

    • Search Google Scholar
    • Export Citation
  • Bröcker, J., and L. A. Smith, 2007: Increasing the reliability of reliability diagrams. Wea. Forecasting, 22, 651661.

  • Fortin, V., A.-C. Favre, and M. Said, 2006: Probabilistic forecasting from ensemble prediction systems: Improving upon the best-member method by using a different weight and dressing kernel for each member. Quart. J. Roy. Meteor. Soc., 132, 13491369.

    • Search Google Scholar
    • Export Citation
  • Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 10981118.

    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., T. Hamill, and J. 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., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550560.

  • Hamill, T. M., and J. S. Whitaker, 2006: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Mon. Wea. Rev., 134, 32093229.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble reforecasting: Improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132, 14341447.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., J. S. Whitaker, and S. L. Mullen, 2006: Reforecasts: An important dataset for improving weather predictions. Bull. Amer. Meteor. Soc., 87, 3346.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., R. Hagedorn, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part II: Precipitation. Mon. Wea. Rev., 136, 26202632.

    • Search Google Scholar
    • Export Citation
  • Hastie, T., R. Tibshirani, and J. Friedman, 2001: The Elements of Statistical Learning. 1st ed. Springer, 524 pp.

  • Lorenz, E. N., 1996: Predictability—A problem partly solved. Proc. ECMWF Seminar on Predictability, Vol. 1, Reading, United Kingdom, ECMWF, 1–18.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 2005: Designing chaotic models. J. Atmos. Sci., 62, 15741587.

  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595600.

  • Orrell, D., 2003: Model error and predictability over different timescales in the Lorenz96 systems. J. Atmos. Sci., 60, 22192228.

  • 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
  • Richardson, D., 2000: Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649667.

    • Search Google Scholar
    • Export Citation
  • Roulston, M. S., and L. A. Smith, 2003: Combining dynamical and statistical ensembles. Tellus, 55A, 1630.

  • Schaake, J., T. Hamill, R. Buizza, and M. Clark, 2007: HEPEX: The Hydrological Ensemble Prediction Experiment. Bull. Amer. Meteor. Soc., 88, 15411547.

    • Search Google Scholar
    • Export Citation
  • Smith, L., 2001: Disentangling uncertainty and error: On the predictability of nonlinear systems. Nonlinear Dynamics and Statistics, A. I. Mees, Ed., Birkhauser, 31–64.

    • Search Google Scholar
    • Export Citation
  • Wang, X., and C. H. Bishop, 2005: Improvement of ensemble reliability with a new dressing kernel. Quart. J. Roy. Meteor. Soc., 131, 965986.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2005: Effects of stochastic parametrizations in the Lorenz ‘96 system. Quart. J. Roy. Meteor. Soc., 131, 389407.

  • Wilks, D. S., 2006a: Comparison of ensemble-MOS methods in the Lorenz ‘96 setting. Meteor. Appl., 13, 243256.

  • Wilks, D. S., 2006b: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 91, Academic Press, 648 pp.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2009: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteor. Appl., 16, 361368.

  • Wilks, D. S., and T. M. Hamill, 2007: Comparison of ensemble-MOS methods using GFS reforecasts. Mon. Wea. Rev., 135, 23792390.

  • Ziehmann, C., 2000: Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models. Tellus, 52A, 280299.

    • Search Google Scholar
    • Export Citation
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