A three-dimensional numerical simulation approach to assess typhoon hazards in China coastal regions

  • Y. Liu
  • D. Chen
  • S. LiEmail author
  • P. W. Chan
  • Q. Zhang
Original Paper


The paper introduces a three-dimensional numerical technique to assess typhoon hazards in China coastal regions based on a series of full-set numerical meteorology simulations. The boundary and initial conditions of the simulations are provided by adding pseudorandom fluctuations, which represent the localized, short-term meteorological variations, to synoptic fields, which show the large-scale, long-term meteorological patterns. A series of bogus typhoons are inserted into the initial field to provide the “seeds” from which the artificial typhoons could grow. The initial positions and intensities of the bogus typhoons are drawn from the random variables whose statistics agree with those derived from historical typhoon track data. In the present study, 1503 full-set meteorology simulations of artificial typhoons are conducted. The extreme wind speeds versus return periods calculated from the simulation results are compared to not only the specifications in the load code, but also the results from the previous studies. It is found that the extreme wind speeds in the Pearl-River Delta are, contradicting to the common expectation, higher than at the mainland side of the Taiwan Strait, which imply that the typhoons hitting Guangdong are, on average, more intense than those influencing Fujian. Given the possibility to improve the three-dimensional meteorology model in the future, the simulation technique proposed in the present study provides a novel direction to assess the meteorological hazards, including threads posted by typhoons.


Extreme wind speeds Numerical simulation Typhoon hazards 



The authors would like to express their gratitude toward following organizations for financially supporting the work described in the present paper, which includes National Natural Science Foundation of China (Grant Nos. 51608302, 51579227).


  1. Arino O, Perez JR, Kalogirou V, Defourny P, Achard F (2010) GlobCover2009Google Scholar
  2. AS/NSZ1170.2 (2002) Structural design actions—part 2: wind actions. AS/NZS 1170.2-2002Google Scholar
  3. Batts ME, Simiu E, Russell LR (1980) Hurricane wind speeds in the United States. J Struct Div 106:2001–2016Google Scholar
  4. Bouyé E, Durrleman V, Nikeghbali A, Riboulet G, Roncalli T, (2000) Copulas for finance—a reading guide and some applications. SSRN Electron J. Google Scholar
  5. Cao Y, Fovell RG, Corbosiero KL (2011) Tropical cyclone track and structure sensitivity to initialization in idealized simulations: a preliminary study. Terr Atmos Ocean Sci 22:559–578CrossRefGoogle Scholar
  6. Caribbean Community Secretariat (1985) Structural design requirements wind load, part 2, section 2Google Scholar
  7. Chang CM, Fang HM, Chen YW, Chuang SH (2015) Discussion on the maximum storm radius equations when calculating typhoon waves. J Mar Sci Technol 23:608–619Google Scholar
  8. Chavas DR, Lin N (2016) A model for the complete radial structure of the tropical cyclone wind field. Part II: wind field variability. J Atmos Sci 73:3093–3113CrossRefGoogle Scholar
  9. Chen X (2015) China city statistical yearbook. China Statistics Press, BeijingGoogle Scholar
  10. China Meteorological Administration (2014) The tropical cyclone yearbook. China Meteorological Press, BeijingGoogle Scholar
  11. Chu K, Xiao Q, Tan Z.-M., Gu J (2011) A forecast sensitivity study on the intensity change of Typhoon Sinlaku (2008). J Geophys Res Atmos 116:D22109Google Scholar
  12. Cohen E (2015) Minimum design loads for buildings and other structures. ASCE, RestonGoogle Scholar
  13. Davis MW (1987) Production of conditional simulations via the LU triangular decomposition of the covariance matrix. Math Geol 19:91–98CrossRefGoogle Scholar
  14. Demaria M, Knaff JA, Kaplan J (2006) On the decay of tropical cyclone winds crossing narrow landmasses. J Appl Meteorol Climatol 45:491–499CrossRefGoogle Scholar
  15. Dempster A (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc 39:1–38Google Scholar
  16. Dudhia J (1996) A multi-layer soil temperature model for MM5, PSU/NCAR mesoscale model users’ workshopGoogle Scholar
  17. Fisher RA, Tippett LHC (1928) Limiting forms of the frequency distribution of the largest or smallest member of a sample. Math Proc Cambridge Philos Soc 24:180–190CrossRefGoogle Scholar
  18. Georgiou PN (1985) Design wind speeds in tropical cyclone-prone regions. The University of Western Ontario, LondonGoogle Scholar
  19. Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock WC, Eder B (2005) Fully coupled “online” chemistry within the WRF model. Atmos Environ 39:6957–6975CrossRefGoogle Scholar
  20. Harper B, Kepert J, Ginger J (2010) Guidelines for converting between various wind averaging periods in tropical cyclone conditions. World Meteorological Organization, WMO/TD 1555Google Scholar
  21. Holland GJ (1980) An analytic model of the wind and pressure profiles in hurricanes. Mon Weather Rev 108:1212–1218CrossRefGoogle Scholar
  22. Holland G (2008) A revised hurricane pressure-wind model. Mon Weather Rev 136:3432–3445CrossRefGoogle Scholar
  23. Holland GJ, Belanger JI, Fritz A (2010) A revised model for radial profiles of hurricane winds. Mon Weather Rev 138:4393–4401CrossRefGoogle Scholar
  24. Hong S-Y, Pan H-L (1996) Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev 124:2322–2339CrossRefGoogle Scholar
  25. Hong S-Y, Dudhia J, Chen S-H (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132:103–120CrossRefGoogle Scholar
  26. Hong HP, Li SH, Duan ZD (2016) Typhoon wind hazard estimation and mapping for coastal region in mainland China. Nat Hazards Rev 17:04016001CrossRefGoogle Scholar
  27. Jolliffe IT (2015) Principal component analysis, vol 87. Springer, Berlin, pp 41–64Google Scholar
  28. Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181CrossRefGoogle Scholar
  29. Komaromi WA, Majumdar SJ, Rappin ED (2011) Diagnosing initial condition sensitivity of Typhoon Sinlaku (2008) and Hurricane Ike (2008). Mon Weather Rev 139:3224–3242CrossRefGoogle Scholar
  30. Laprise R (1992) The Euler equations of motion with hydrostatic pressure as an independent variable. Mon Weather Rev 120:197–207CrossRefGoogle Scholar
  31. Li SH, Hong HP (2016) Typhoon wind hazard estimation for China using an empirical track model. Nat Hazards 82:1009–1029CrossRefGoogle Scholar
  32. Li SW, Tse KT, Weerasuriya AU, Chan PW (2014) Estimation of turbulence intensities under strong wind conditions via turbulent kinetic energy dissipation rates. J Wind Eng Ind Aerodyn 131:1–11CrossRefGoogle Scholar
  33. Mckinley S, Levine M (1999) Cubic spline interpolation. Numer Math J Chin Univ 64:44–56Google Scholar
  34. Mclachlan GJ, Peel D (2000) Finite mixture models. Encycl Mach Learn 39:521–541Google Scholar
  35. Michalakes J (1998) Design of a next-generation regional weather research and forecast model. In: Proceedings of the eighth ECMWF workshop on the use of parallel processors in meteorology European centre for medium range weather forecasts, vol 1, pp 269–276Google Scholar
  36. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos 1984–2012(102):16663–16682CrossRefGoogle Scholar
  37. Nguyen HV, Chen Y-L (2014) Improvements to a tropical cyclone initialization scheme and impacts on forecasts. Mon Weather Rev 142:4340–4356CrossRefGoogle Scholar
  38. Nikolić-Despotović D (1983) Probabilistic metric spaces. North Holland, AmsterdamGoogle Scholar
  39. Ou JP, Duan ZD, Chang L (2002) Typhoon risk analysis for key coastal cities in southeast China. J Nat Disasters 11:9–17Google Scholar
  40. Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteorol 9:857–861CrossRefGoogle Scholar
  41. Pollard RT, Rhines PB, Thompson RORY (1972) The deepening of the wind-mixed layer. Geophys Fluid Dyn 4:381–404CrossRefGoogle Scholar
  42. Russell LB (1969) Probability distributions for Texas gulf coast hurricane effects of engineering interest. Stanford University, StanfordGoogle Scholar
  43. Russell LR (1971) Probability distributions for hurricane effects. J Waterw Harb Coast Eng Div 97:139–154Google Scholar
  44. Shapiro LJ (2009) Hurricane climatic fluctuations. Part II: relation to large-scale circulation. Mon Weather Rev 110:1014–1023CrossRefGoogle Scholar
  45. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2008) A description of the advanced research WRF version 3, pp 7–25Google Scholar
  46. Thompson EF, Cardone VJ (1996) Practical modeling of hurricane surface wind fields. J Waterw Port Coast Ocean Eng 122:195–205CrossRefGoogle Scholar
  47. Tse KT, Li SW, Fung JCH (2014) A comparative study of typhoon wind profiles derived from field measurements, meso-scale numerical simulations, and wind tunnel physical modeling. J Wind Eng Ind Aerodyn 131:46–58CrossRefGoogle Scholar
  48. Vickery PJ, Twisdale LA (1995) Wind-field and filling models for hurricane wind-speed predictions. J Struct Eng 121:1700–1709CrossRefGoogle Scholar
  49. Vickery PJ, Skerlj P, Steckley A, Twisdale L (2000a) Hurricane wind field model for use in hurricane simulations. J Struct Eng 126:1203–1221CrossRefGoogle Scholar
  50. Vickery PJ, Skerlj PF, Twisdale LA (2000b) Simulation of hurricane risk in the U.S. using empirical track model. J Struct Eng 126:1222–1237CrossRefGoogle Scholar
  51. Vickery PJ, Masters FJ, Powell MD, Wadhera D (2009) Hurricane hazard modeling: the past, present, and future. J Wind Eng Ind Aerodyn 97:392–405CrossRefGoogle Scholar
  52. Wang N (2013) Study of typhoon hazard analysis methods based on CE wind field numerical simulation. Harbin Institute of Technology, Harbin (in Chinese) Google Scholar
  53. Wang W, Bruyere C, Duda M, Dudhia J, Gill D, Lin H, Michalakes J, Rizvi S, Zhang X, Beezley J (2010) ARW version 3 modeling system user’s guide. Mesoscale & Microscale Meteorology Division. National Center for Atmospheric ResearchGoogle Scholar
  54. Willoughby HE, Rahn ME (2004) Parametric representation of the primary hurricane vortex. Part I: observations and evaluation of the Holland (1980) model. Mon Weather Rev 132:3033–3048CrossRefGoogle Scholar
  55. Willoughby HE, Darling RWR, Rahn ME (2006) Parametric representation of the primary hurricane vortex. Part II: a new family of sectionally continuous profiles. Mon Weather Rev 134:1102–1120CrossRefGoogle Scholar
  56. Xiao Y (2011) Typhoon wind hazard analysis based on numerical simulation and fragility of light-gauge steel structure in southeast China coastal regions. Harbin Institute of Technology, Harbin (in Chinese) Google Scholar
  57. Xie R (2008) Numerical simulation and typhoon wind hazard analysis based on CE wind-field model and Yan Meng wind-field model. Harbin Institute of Technology, Harbin (in Chinese) Google Scholar
  58. Yang Y, Lei X (2004) Statistics of strong wind distribution caused by landfall typhoon in China. J Trop Meteorol 20:633–642Google Scholar
  59. Yuan J, Wang D, Wan Q, Liu C (2007) A 28-year climatological analysis of size parameters for northwestern Pacific tropical cyclones. Adv Atmos Sci 24:24–34CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Y. Liu
    • 1
    • 2
  • D. Chen
    • 1
    • 2
  • S. Li
    • 1
    Email author
  • P. W. Chan
    • 3
  • Q. Zhang
    • 1
    • 4
  1. 1.Division of Ocean Science and Technology, Graduate School at ShenzhenTsinghua UniversityShenzhenChina
  2. 2.School of EnvironmentTsinghua UniversityBeijingChina
  3. 3.Hong Kong ObservatoryKowloonHong Kong
  4. 4.Department of Civil EngineeringTsinghua UniversityBeijingChina

Personalised recommendations