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Sensitivity of Typhoon Simulation to Physics Parameterizations in the Global Model

전구 모델의 물리과정에 따른 태풍 모의 민감도

  • 김기병 ((재) 한국형수치예보모델개발사업단) ;
  • 이은희 ((재) 한국형수치예보모델개발사업단) ;
  • 설경희 ((재) 한국형수치예보모델개발사업단)
  • Received : 2016.09.23
  • Accepted : 2017.03.04
  • Published : 2017.03.31

Abstract

The sensitivity of the typhoon track and intensity simulation to physics schemes of the global model are examined for the typhoon Bolaven and Tembin cases by using the Global/Regional Integrated Model System-Global Model Program (GRIMs-GMP) with the physics package version 2.0 of the Korea Institute of Atmospheric Prediction Systems. Microphysics, Cloudiness, and Planetary boundary Layer (PBL) parameterizations are changed and the impact of each scheme change to typhoon simulation is compared with the control simulation and observation. It is found that change of microphysics scheme from WRF Single-Moment 5-class (WSM5) to 1-class (WSM1) affects to the typhoon simulation significantly, showing the intensified typhoon activity and increased precipitation amount, while the effect of the prognostic cloudiness and PBL enhanced mixing scheme is not noticeable. It appears that WSM1 simulates relatively unstable and drier atmospheric structure than WSM5, which is induced by the latent heat change and the associated radiative effect due to not considering ice cloud. And WSM1 results the enhanced typhoon intensity and heavy rainfall simulation. It suggests that the microphysics is important to improve the capability for typhoon simulation of a global model and to increase the predictability of medium range forecast.

Keywords

References

  1. Braun, S. A., and W. K. Tao, 2000: Sensitivity of high-resolution simulations of Hurricane Bob (1991) to planetary boundary layer parameterizations. Mon. Wea. Rev., 128, 3941-3961, doi:10.1175/1520-0493(2000)129<3941:SOHRSO>2.0.CO;2.
  2. Choi, S. J., and S. Y. Hong, 2016: A global non-hydrostatic dynamical core using the spectral element method on a cubed-sphere grid. Asia-Pac. J. Atmos. Sci., 52, 291-307, doi:10.1007/s13143-016-0005-0.
  3. Chun, H. Y., and J. J. Baik, 1998: Momentum flux by thermally induced internal gravity waves and its approximation for large-scale models. J. Atmos. Sci., 55, 3299-3310. https://doi.org/10.1175/1520-0469(1998)055<3299:MFBTII>2.0.CO;2
  4. Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA/TM-1999-104606, 15, 40 pp.
  5. Chou, M. D., K.-T. Lee, S.-C. Tsay, and Q. Fu, 1999: Parameterization for cloud longwave scattering for use in atmospheric models. J. Climate, 12, 159-169. https://doi.org/10.1175/1520-0442-12.1.159
  6. DeMaria, M., and J. Kaplan, 1997: An operational evaluation of a statistical hurricane intensity prediction scheme (SHIPS). Preprint, 22nd Conf. on Hurricanes and Tropical Meteorology, 280-281.
  7. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.
  8. Elsberry, R. L., 2014: Advances in research and forecasting of tropical cyclones from 1963-2013. Asia-Pac. J. Atmos. Sci., 50, 3-16, doi:10.1007/s13143-014-0001-1.
  9. Emanuel, K. A., 1987: An air-sea interaction model of intraseasonal oscillations in the tropics. J. Atmos. Sci., 44, 2324-2340, doi:10.1175/1520-0469(1987)044<2324:AASIMO>2.0.CO;2.
  10. Emanuel, K., C. DesAutels, C. Holloway, and R. Korty, 2004: Environmental control of tropical cyclone intensity. J. Atmos. Sci., 61, 843-858, doi:10.1175/1520-0469(2004)061<0843:ECOTCI>2.0.CO;2.
  11. Emanuel, K., S. Solomon, D. Folini, S. Davis, and C. Cagnazzo, 2013: Influence of tropical tropopause layer cooling on Atlantic hurricane activity. J. Climate, 26, 2288-2301, doi:10.1175/JCLI-D-12-00242.1.
  12. Gao, S., L. Ran, and X. Li, 2006: Impacts of ice microphysics on rainfall and thermodynamic processes in the tropical deep convective regime: A 2D cloudresolving modeling study. Mon. Wea. Rev., 134, 3015-3024, doi:10.1175/MWR3220.1.
  13. Ham, S., S. Y. Hong, Y. H. Byun, and J. Kim, 2009: Effects of precipitation physics algorithms on a simulated climate in a general circulation model. J. Atmos. Sol. Terr. Phys., 71, 1924-1934, doi:10.1016/j.jastp.2009.08.001.
  14. Han, J., and H. L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Wea. Forecasting, 26, 520-533, doi:10.1175/WAF-D-10-05038.1.
  15. Hong, S. Y., H. M. H. Juang, and Q. Zhao, 1998: Implementation of prognostic cloud scheme for a regional spectral model. Mon. Wea. Rev., 126, 2621-2639, doi:10.1175/1520-0493(1998)126<2621:IOPCSF>2.0.CO;2.
  16. Hong, S. Y., J. Dudhia, and S. H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103-120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.
  17. Hong, S. Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341, doi:10.1175/MWR3199.1.
  18. Hong, S. Y., J. Choi, E. C. Chang, H. Park, and Y. J. Kim, 2008: Lower-tropospheric enhancement of gravity wave drag in a global spectral atmospheric forecast model. Wea. Forecasting, 23, 523-531, doi:10.1175/2007WAF2007030.1.
  19. Hong, S. Y., S. Ham, Y. H. Byun, and J. Kim, 2009: Investigation of ice-cloud radiation interaction in a General Circulation Model. Asia-Pac. J. Atmos. Sci., 45, 391-409.
  20. Hong, S. Y., and Coauthors, 2013: The global/regional integrated model system (GRIMs). Asia-Pac. J. Atmos. Sci., 49, 219-243, doi:10.1007/s13143-013-0023-0.
  21. Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.
  22. Islam, T., P. K. Srivastava, M. A. Rico-Ramirez, Q. Dai, M. Gupta, and S. K. Singh, 2015: Tracking a tropical cyclone through WRF-ARW simulation and sensitivity of model physics. Nat. Hazards, 76, 1473-1495, doi:10.1007/s11069-014-1494-8.
  23. Kim, Y. J., and A. Arakawa, 1995, Improvement of orographic gravity wave parameterization using a mesoscale gravity wave model. J. Atmos. Sci., 52, 1875-1902. https://doi.org/10.1175/1520-0469(1995)052<1875:IOOGWP>2.0.CO;2
  24. Lee, E. H., S. Y. Hong, and J. Dudhia, 2015: Evaluation of the parameterization for cloud top-down mixing in the boundary layer. Abstracts, EGU General Assembly Conference, 17, 8384.
  25. Li, X., and Z. Pu, 2008: Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations. Mon. Wea. Rev., 136, 4819-4838, doi:10.1175/2008MWR2366.1.
  26. Lim, K. S., S. Y. Hong, J. H. Yoon, and J. Han, 2014: Simulation of the summer monsoon rainfall over East Asia using the NCEP GFS cumulus parameterization at different horizontal resolutions. Wea. Forecasting, 29, 1143-1154, doi:10.1175/WAF-D-13-00143.1.
  27. Ministry of Public Safety and Security, 2014: Statistical Yearbook of Natural Disaster 2014, 608 pp.
  28. National Typhoon Center, 2012: Typhoon Analysis Report 2012, 324 pp.
  29. Park, R. S., J. H. Chae, and S. Y. Hong, 2016: A Revised Prognostic Cloud Fraction Scheme in a Global Forecasting System. Mon. Wea. Rev., 144, 1219-1229, doi:10.1175/MWR-D-15-0273.1.
  30. Slingo, J. M., 1987: The development and verification of a cloud prediction scheme for the ECMWF model. Quart. J. Roy. Meteor. Soc., 113, 899-927. https://doi.org/10.1002/qj.49711347710
  31. Smith, R. K., and G. L. Thomsen, 2010: Dependence of tropical-cyclone intensification on the boundary-layer representation in a numerical model. Quart. J. Roy. Meteor. Soc., 136, 1671-1685, doi:10.1002/qj.687.
  32. Tao, W. K., J. J. Shi, S. S. Chen, S. Lang, P. L. Lin, S. Y. Hong, C. Peters-Lidard, and A. Hou, 2011: The impact of microphysical schemes on hurricane intensity and track. Asia-Pac. J. Atmos. Sci., 47, 1-16, doi:10.1007/s13143-011-1001-z.
  33. Wang, Y., 2002: An explicit simulation of tropical cyclones with a triply nested movable mesh primitive equation model: TCM3. Part II: Model refinements and sensitivity to cloud microphysics parameterization*. Mon. Wea. Rev., 130, 3022-3036, doi:10.1175/1520-0493(2002)130<3022:AESOTC>2.0.CO;2.
  34. Wang, S., S. J. Camargo, A. H. Sobel, and L. M. Polvani, 2014: Impact of the tropopause temperature on the intensity of tropical cyclones: An idealized study using a mesoscale model. J. Atmos. Sci., 71, 4333-4348, doi:10.1175/JAS-D-14-0029.1.
  35. Willoughby, H. E., H.-L. Jin, S. J. Lord, and J. M. Piotrowicz, 1984: Hurricane structure and evolution as simulated by an axisymmetric nonhydrostatic numerical model. J. Atmos. Sci., 41, 1169-1186. https://doi.org/10.1175/1520-0469(1984)041<1169:HSAEAS>2.0.CO;2
  36. Xu, K. M., and D. A. Randall, 1996: A semiempirical cloudiness parameterization for use in climate models. J. Atmos. Sci., 53, 3084-3102. https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2
  37. Yang, M. J., and L. Ching, 2005: A modeling study of Typhoon Toraji (2001): Physical parameterization sensitivity and topographic effect. Terr. Atmos. Ocean. Sci., 16, 177-213. https://doi.org/10.3319/TAO.2005.16.1.177(A)
  38. Zhang, F., and D. Tao, 2013: Effects of vertical wind shear on the predictability of tropical cyclones. J. Atmos. Sci., 70, 975-983, doi:10.1175/JAS-D-12-0133.1.