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Representation of Model Uncertainty in the Short-Range Ensemble Prediction for Typhoon Rusa (2002)

단기 앙상블 예보에서 모형의 불확실성 표현: 태풍 루사

  • Kim, Sena (School of Earth and Environmental Sciences, Seoul National University) ;
  • Lim, Gyu-Ho (School of Earth and Environmental Sciences, Seoul National University)
  • 김세나 (서울대학교 지구환경과학부) ;
  • 임규호 (서울대학교 지구환경과학부)
  • Received : 2014.07.11
  • Accepted : 2015.01.09
  • Published : 2015.03.31

Abstract

The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.

Keywords

References

  1. Berner, J., G. J. Shutts, M. Leutbecher, and T. N. Palmer, 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603-626. https://doi.org/10.1175/2008JAS2677.1
  2. Berner, J., S. Y. Ha, J. P. Hacker, A. Fournier, and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 1972-1995. https://doi.org/10.1175/2010MWR3595.1
  3. Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 2887-2908. https://doi.org/10.1002/qj.49712556006
  4. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J.Roy. Meteor. Soc., 137, 553-597. https://doi.org/10.1002/qj.828
  5. Doblas-Reyes, F. J., and Coauthors, 2009: Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts. Quart. J. Roy. Meteor. Soc., 135, 1538-1559. https://doi.org/10.1002/qj.464
  6. Epstein, E. S., 1969: Stochastic dynamic prediction. Tellus, 21, 739-759. https://doi.org/10.1111/j.2153-3490.1969.tb00483.x
  7. Hacker, J. P., and Coauthors, 2011: The U.S. Air Force Weather Agency's mesoscale ensemble: Scientific description and performance results. Tellus A., 63, 625-641. https://doi.org/10.1111/j.1600-0870.2010.00497.x
  8. 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 A., 57, 219-233. https://doi.org/10.1111/j.1600-0870.2005.00103.x
  9. Hill, K. A., and G. M. Lackmann, 2009: Influence of environmental humidity on tropical cyclone size. Mon. Wea. Rev., 137, 3294-3315. https://doi.org/10.1175/2009MWR2679.1
  10. 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. https://doi.org/10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2
  11. Krishnamurti, T. N., and Coauthors, 2000: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13, 4196-4216. https://doi.org/10.1175/1520-0442(2000)013<4196:MEFFWA>2.0.CO;2
  12. Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130-141. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
  13. Palmer, T. N., 2001: A nonlinear dynamical perspective on model error: A proposal for non-local stochastic dynamic parametrization in weather and climate prediction models. Quart. J. Roy. Meteor. Soc., 127, 279-304.
  14. Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079-3102. https://doi.org/10.1256/qj.04.106
  15. Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019-3032. https://doi.org/10.1175/MWR2830.1
  16. Shutts, G., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 3465-3485. https://doi.org/10.1016/j.jcp.2007.01.037
  17. Stainforth, D. A., and Coauthors, 2005: Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433, 403-406. https://doi.org/10.1038/nature03301