Asia-Pacific Journal of Atmospheric Sciences

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Tropical Cyclone Track and Intensity Prediction with a Structure Adjustable Balanced Vortex

  • Cheong, Hyeong-Bin (Department of Environmental Atmospheric Sciences, Pukyong National University) ;
  • Kwon, In-Hyuk (NOAA/NCEP/EMC) ;
  • Kang, Hyun-Gyu (Department of Environmental Atmospheric Sciences, Pukyong National University) ;
  • Park, Ja-Rin (Department of Environmental Atmospheric Sciences, Pukyong National University) ;
  • Han, Hyun-Jun (Department of Environmental Atmospheric Sciences, Pukyong National University) ;
  • Kim, Jae-Jin (Department of Environmental Atmospheric Sciences, Pukyong National University)
  • Published : 2011.05.01
⟨ Previous Next ⟩

Abstract

A new Tropical Cyclone (TC) initialization method with the structure adjustable bogus vortex was applied to the forecasts of track, central pressure, and wind intensity for the 417 TCs observed in the Western North Pacific during the 3-year period of 2005-2007. In the simulations the Final Analyses (FNL) with $1^{\circ}{\times}1^{\circ}$ resolution of National Center for Environmental Prediction (NCEP) were incorporated as initial conditions. The present method was shown to produce improved forecasts over those without the TC initialization and those made by Regional Specialized Meteorological Center Tokyo. The average track (central pressure, wind intensity) errors were as small as 78.0 km (11.4 hPa, $4.9ms^{-1}$) and 139.9 km (12.4 hPa, $5.5ms^{-1}$) for 24-h and 48-h forecasts, respectively. It was found that the forecast errors are almost independent on the size and intensity of the observed TCs because the size and intensity of the bogus vortex can be adjusted to fit the best track data. The results of this study indicate that a bogus method is useful in predicting simultaneously the track, central pressure, and intensity with accuracy using a dynamical forecast model.

Keywords

References

  1. Anderson, S. D., 2008: An alternative tropical cyclone intensity forecast verification technique. Wea. Forecasting, 23, 1304-1310. https://doi.org/10.1175/2008WAF2222123.1
  2. Bender, M. A., I. Ginis, R. Tuleya, B. Thomas, and T. Marchok, 2007: The operational GFDL coupled hurricane-ocean prediction system and a summary of its performance. Mon. Wea. Rev., 135, 3965-3989. https://doi.org/10.1175/2007MWR2032.1
  3. Cheong, H.-B., I.-H. Kwon, and T.-Y. Goo, 2004: Further study on the high-order double Fourier series spectral filtering on a sphere. J. Comput. Phys., 193, 180-197. https://doi.org/10.1016/j.jcp.2003.07.029
  4. Cooper, G. A., and R. J. Falvey, 2008: Annual Tropical Cyclone Report. U.S. Naval Maritime Forecast Center/Joint Typhoon Warning Center Pearl Harbor, Hawaii, 177 pp.
  5. Davis, C. A., and S. Low-Nam, 2001: The NCAR-AFWA tropical cyclone bogussing scheme. A report prepared for the Air Force Weather Agency (AFWA), National Center for Atmospheric Research, Boulder, Colorado, USA, 13 pp.
  6. DeMaria, M., M. Mainelli, L. K. Shay, J. A. Knaff, and J. Kaplan, 2005: Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531-543. https://doi.org/10.1175/WAF862.1
  7. DeMaria, M., J. A. Knaff, and C. Sampson, 2007: Evaluation of long-term trends in tropical cyclone intensity forecasts. Meteor. Atmos. Phys., 97, 19-28. https://doi.org/10.1007/s00703-006-0241-4
  8. Elsberry, R. L., 2005: Achievement of USWRP hurricane landfall research goal. Bull. Amer. Meteor. Soc., 86, 643-645. https://doi.org/10.1175/BAMS-86-5-643
  9. Elsberry, R. L., M. A. Boothe, G. A. Ulses, and P. A. Harr, 1999: Statistical postprocessing of NOGAPS tropical cyclone track forecasts. Mon. Wea. Rev., 127, 1912-1919. https://doi.org/10.1175/1520-0493(1999)127<1912:SPONTC>2.0.CO;2
  10. Frank, W. M., 1977: The structure and energetics of the tropical cyclone. Part I. Storm structure. Mon. Wea. Rev., 105, 1119-1135. https://doi.org/10.1175/1520-0493(1977)105<1119:TSAEOT>2.0.CO;2
  11. Franklin, J. L., 2008: National Hurricane Center Forecast Verification Report 2007. National Hurricane Center, NOAA/NWS/NCEP/Tropical Prediction Center, 71 pp.
  12. Goerss, J. S., 2009: Impact of satellite observations on the tropical cyclone track forecasts of the Navy operational global atmospheric prediction system. Mon. Wea. Rev., 137, 41-50. https://doi.org/10.1175/2008MWR2601.1
  13. Harper, B. A., and G. J. Holland, 1999: An updated parametric model of the tropical cyclone. Proc 23rd conference of hurricane and tropical meteorology, Dallas, Texas, 893-896.
  14. Holland, G. J., 1980: An analytic model of the wind and pressure profiles in hurricane. Mon. Wea. Rev., 108, 1212-1218. https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2
  15. Knaff, J. A., C. R. Sampson, and M. DeMaria, 2005: An operational statistical typhoon intensity prediction scheme for the Western North Pacific. Wea. Forecasting, 20, 688-699. https://doi.org/10.1175/WAF863.1
  16. Kwon, I.-H., and H.-B. Cheong, 2010: Tropical cyclone initialization with a spherical high-order filter and an idealized three-dimensional bogus vortex. Mon. Wea. Rev., 138, 1344-1367. https://doi.org/10.1175/2009MWR2943.1
  17. Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1995: Improvements in the GFDL hurricane prediction system. Mon. Wea. Rev., 123, 2791-2801. https://doi.org/10.1175/1520-0493(1995)123<2791:IITGHP>2.0.CO;2
  18. Leslie, L. M., and G. J. Holland, 1995: On the bogussing of tropical cyclones in numerical models: A comparison of vortex profiles. Meteor. Atmos. Phys., 56, 101-110. https://doi.org/10.1007/BF01022523
  19. Moskaitis, J. R., 2008: A case study of deterministic forecast verification: Tropical cyclone intensity. Wea. Forecasting, 23, 1195-1220. https://doi.org/10.1175/2008WAF2222133.1
  20. Nagata, M., and Coauthors, 2001: Meeting summary of third COMPARE workshop: A model intercomparison experiment of tropical cyclone intensity and track prediction. Bull. Amer. Meteor. Soc., 82, 2007-2020. https://doi.org/10.1175/1520-0477(2001)082<2007:MSTCWA>2.3.CO;2
  21. Nuissier, O., R. F. Rogers, and F. Roux, 2005: A numerical simulation of hurricane Bret on 22-23 August 1999 initialized with airborne Doppler radar and dropsonde data. Quart. J. Roy. Meteor. Soc., 131, 155-194. https://doi.org/10.1256/qj.02.233
  22. Park, K., and X. Zou, 2004: Toward developing an objective 4DVAR BDA scheme for hurricane initialization based on TPC observed parameters. Mon. Wea. Rev., 132, 2054-2069. https://doi.org/10.1175/1520-0493(2004)132<2054:TDAODB>2.0.CO;2
  23. Petty, K. R., and J. S. Hobgood, 2000: Improving tropical cyclone intensity guidance in the Eastern North Pacific. Wea. Forecasting, 15, 233-244. https://doi.org/10.1175/1520-0434(2000)015<0233:ITCIGI>2.0.CO;2
  24. Phadke, A. C., C. D. Martino, K. F. Cheung, and S. H. Houston, 2003: Modeling of tropical cyclone winds and waves for emergency management. Ocean Engineering, 30, 553-578. https://doi.org/10.1016/S0029-8018(02)00033-1
  25. Pu, Z. X., and S. A. Braun, 2001: Evaluation of bogus vortex techniques with four-dimensional variational data assimilation. Mon. Wea. Rev., 129, 2023-2039. https://doi.org/10.1175/1520-0493(2001)129<2023:EOBVTW>2.0.CO;2
  26. Pu, Z. X., X. Li, and J. Sun, 2009: Impact of airborne Doppler radar data assimilation on the numerical simulation of intensity changes of Hurricane Dennis near landfall. J. Atmos. Sci., 66, 3351-3365. https://doi.org/10.1175/2009JAS3121.1
  27. Rogers, R., and Coauthors, 2006: The intensity forecasting experiment: A NOAA multilayer field program for improving tropical cyclone intensity forecasts. Bull. Amer. Meteor. Soc., 87, 1523-1537. https://doi.org/10.1175/BAMS-87-11-1523
  28. Wang, D., X. Liang, Y. Zhao, and B. Wang, 2008: A comparison of two tropical cyclone bogussing scheme. Wea. Forecasting, 23, 194-204. https://doi.org/10.1175/2007WAF2006094.1
  29. Wu, L., B. Wang, and S. A. Braun, 2005: Impacts of air-sea interaction on tropical cyclone track and intensity. Mon. Wea. Rev., 133, 3299-3314. https://doi.org/10.1175/MWR3030.1
  30. Xiao, Q., L. Chen, and X. Zhang, 2009: Evaluation of BDA scheme using Advanced Research WRF (ARW) model. J. Appl. Meteor. Climatol., 48, 680-689. https://doi.org/10.1175/2008JAMC1994.1
  31. Zhang, X., Q. Xiao, and P. J. Fitzpatrick, 2007: The impact of multisatellite data on the initialization and simulation of Hurricane Lili's (2002) rapid weakening phase. Mon. Wea. Rev., 135, 526-548. https://doi.org/10.1175/MWR3287.1
  32. Zou, X., and Q. Xiao, 2000: Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57, 836-860. https://doi.org/10.1175/1520-0469(2000)057<0836:SOTIAS>2.0.CO;2

Cited by

  1. Fourier Finite-Element Method with Linear Basis Functions on a Sphere: Application to Elliptic and Transport Equations vol.143, pp.4, 2011, https://doi.org/10.1175/mwr-d-14-00093.1
  2. Construction of the Spherical High-Order Filter for Applications to Global Meteorological Data vol.36, pp.5, 2015, https://doi.org/10.5467/jkess.2015.36.5.476
  3. A Finite Element Galerkin High Order Filter for the Spherical Limited Area Model vol.38, pp.2, 2011, https://doi.org/10.5467/jkess.2017.38.2.105
  4. Application of Vertical Grid-nesting to the Tropical Cyclone Track and Intensity Forecast vol.40, pp.4, 2011, https://doi.org/10.5467/jkess.2019.40.4.382