Volume 14 Issue 3
Jul.  2023
Turn off MathJax
Article Contents
Jiayao Wang, Tim K. T. Tse, Sunwei Li, Jimmy C. H. Fung. A Model of the Sea–Land Transition of the Mean Wind Profile in the Tropical Cyclone Boundary Layer Considering Climate Changes[J]. International Journal of Disaster Risk Science, 2023, 14(3): 413-427. doi: 10.1007/s13753-023-00488-9
Citation: Jiayao Wang, Tim K. T. Tse, Sunwei Li, Jimmy C. H. Fung. A Model of the Sea–Land Transition of the Mean Wind Profile in the Tropical Cyclone Boundary Layer Considering Climate Changes[J]. International Journal of Disaster Risk Science, 2023, 14(3): 413-427. doi: 10.1007/s13753-023-00488-9

A Model of the Sea–Land Transition of the Mean Wind Profile in the Tropical Cyclone Boundary Layer Considering Climate Changes

doi: 10.1007/s13753-023-00488-9
Funds:

The work described in this article was supported by the grants from the Research Grants Council (RGC) of the Hong Kong Special Administrative Region (HKSAR), China with GRF No. 16207118, Shenzhen Science and Technology Innovation Commission (Project No. WDZC20200819174646001), and Guangdong Basic and Applied Basic Research Foundation (Project No. 2022B1515130006). The numerical computations reported in the manuscript were partially performed at the Hefei Advanced Computing Center.

  • Accepted Date: 2023-04-30
  • Available Online: 2023-07-03
  • Publish Date: 2023-05-23
  • The tropical cyclone boundary layer (TCBL) connecting the underlying terrain and the upper atmosphere plays a crucial role in the overall dynamics of a tropical cyclone system. When tropical cyclones approach the coastline, the wind field inside the TCBL makes a sea–land transition to impact both onshore and offshore structures. So better understanding of the wind field inside the TCBL in the sea–land transition zone is of great importance. To this end, a semiempirical model that integrates the sea–land transition model from the Engineering Sciences Data Unit (ESDU), Huang’s refined TCBL wind field model, and the climate change scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is used to investigate the influence of climate changes on the sea–land transition of the TCBL wind flow in Hong Kong. More specifically, such a semiempirical method is employed in a series of Monte-Carlo simulations to predict the wind profiles inside the TCBL across the coastline of Hong Kong under the impact of future climate changes. The wind profiles calculated based on the Monte-Carlo simulation results reveal that, under the influences of the most severe climate change scenario, slightly higher and significantly lower wind speeds are found at altitudes above and below 400 m, respectively, compared to the wind speeds recommended in the Hong Kong Wind Code of Practice. Such findings imply that the wind profile model currently adopted by the Hong Kong authorities in assessing the safety of low- to high-rise buildings may be unnecessarily over-conservative under the influence of climate change. On the other hand, the coded wind loads on super-tall buildings slightly underestimate the typhoon impacts under the severe climate change conditions anticipated for coastal southern China.
  • loading
  • Architectural Institute of Japan. 2015. AIJ recommendations for loads on buildings, 6-1–6-56. Chapter 6: Wind loads, AIJ-RLB-2015.
    Barthelmie, R., J. Palutikof, and T. Davies. 1993. Estimation of sector roughness lengths and the effect on prediction of the vertical wind speed profile. Boundary-Layer Meteorology 66(1): 19–47.
    Bender, M.A., T.R. Knutson, R.E. Tuleya, J.J. Sirutis, G.A. Vecchi, S.T. Garner, and I.M. Held. 2010. Modeled impact of anthropogenic warming on the frequency of intense Atlantic Hurricanes. Science 327(5964): 454–458.
    Buildings Department. 2004. Explanatory materials to the code of practice on wind effects in Hong Kong. Hong Kong: Buildings Department.
    Buildings Department. 2004. Code of practice on wind effects in Hong Kong. Hong Kong: Buildings Department.
    Buildings Department. 2019. Code of practice on wind effects in Hong Kong. Hong Kong: Buildings Department.
    Buildings Department. 2019. Explanatory notes to the code of practice on wind effects in Hong Kong. Hong Kong: Buildings Department.
    Chan, J.C.L., and K.S. Liu. 2004. Global warming and western North Pacific typhoon activity from an observational perspective. Journal of Climate 17(23): 4590–4602.
    Charki, A., N. Aghbalou, S. Elazzouzi, and K. Reklaoui. 2018. A probabilistic assessment approach for wind turbine-site matching. International Journal of Electrical Power & Energy Systems 103: 497–510.
    Chen, J., Z. Wang, C.-Y. Tam, N.-C. Lau, D.S.D. Lau, and H.-Y. Mok. 2020. Impacts of climate change on tropical cyclones and induced storm surges in the Pearl River Delta region using pseudo-global-warming method. Scientific Reports 10(1): 1–10.
    Deaves, D.M. 1981. Computations of wind flow over changes in surface roughness. Journal of Wind Engineering and Industrial Aerodynamics 7(1): 65–94.
    DeMarrais, G.A. 1959. Wind-speed profiles at Brookhaven National Laboratory. Journal of Atmospheric Sciences 16(2): 181–190.
    ESDU (Engineering Sciences Data Unit). 1982a. Strong winds in the atmospheric boundary layer. Part 1: Hourly-mean wind speeds. No. 82026. Arlington, VA: ESDU.
    ESDU (Engineering Sciences Data Unit). 1982b. Mean wind speeds over hills and other topography. No. 91043. Arlington, VA: ESDU.
    Eyring, V., S. Bony, G.A. Meehl, C.A. Senior, B. Stevens, R.J. Stouffer, and K.E. Taylor. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9(5): 1937–1958.
    Fernández-Cabán, P.L., and F.J. Masters. 2017. Near surface wind longitudinal velocity positively skews with increasing aerodynamic roughness length. Journal of Wind Engineering and Industrial Aerodynamics 169: 94–105.
    Gao, F., K. Bergant, A. Filipčič, B. Forte, D. Hua, X.Q. Song, S. Stanič, D. Veberič, and M. Zavrtanik. 2011. Observations of the atmospheric boundary layer across the land–sea transition zone using a scanning Mie lidar. Journal of Quantitative Spectroscopy and Radiative Transfer 112(2): 182–188.
    Georgiou, P.N. 1986. Design wind speeds in tropical cyclone-prone regions. Ph.D. thesis. Digitized theses no. 1523. Ontario, CA: University of Western Ontario.
    Giammanco, I.M., J.L. Schroeder, and M.D. Powell. 2012. Observed characteristics of tropical cyclone vertical wind profiles. Wind and Structures 15(1): Article 65.
    Grimmond, C.S.B., and T.R. Oke. 1999. Aerodynamic properties of urban areas derived from analysis of surface form. Journal of Applied Meteorology 38(9): 1262–1292.
    Heier, S. 2014. Grid integration of wind energy: Onshore and offshore conversion systems. Hoboken, NJ: John Wiley & Sons.
    Huang, W.F., and Y.L. Xu. 2012. A refined model for typhoon wind field simulation in boundary layer. Advances in Structural Engineering 15(1): 77–89.
    Huang, W.F., Y.L. Xu, C.W. Li, and H.J. Liu. 2011. Prediction of design typhoon wind speeds and profiles using refined typhoon wind field model. Advanced Steel Construction 7(4): 387–402.
    IPCC (Intergovernmental Panel on Climate Change). 2021. Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. ed. V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, et al. Cambridge: Cambridge University Press.
    Kloetzke, T. 2019. Simulation and analysis of surface wind fields during landfalling tropical cyclones. Ph.D thesis. School of Civil Engineering, The University of Queensland, St Lucia, Queensland.
    Knutson, T.R., J.J. Sirutis, S.T. Garner, G.A. Vecchi, and I.M. Held. 2008. Simulated reduction in Atlantic hurricane frequency under twenty-first-century warming conditions. Nature Geoscience 1(6): 359–364.
    Li, L., A. Kareem, Y. Xiao, L. Song, and C. Zhou. 2015. A comparative study of field measurements of the turbulence characteristics of typhoon and hurricane winds. Journal of Wind Engineering and Industrial Aerodynamics 140: 49–66.
    Li, Q.S., J.Y. He, K. Zhou, X. Li, P.W. Chan, and L. Li. 2022. City-scale typhoon hazard analysis and field monitoring of wind effects on skyscrapers during super Typhoon Mangkhut. Journal of Structural Engineering 148(4): Article 04022008.
    Liu, Y., D. Chen, Q. Yi, and S. Li. 2017. Wind profiles and wave spectra for potential wind farms in South China Sea. Part I: Wind speed profile model. Energies 10(1): Article 125.
    Liu, Y., X. Fang, and Q. Luan. 2016. Estimation of roughness length of Beijing area based on satellite data and GIS technique. Plateau Meteorology 35(6): 1625–1638.
    Meng, Y., M. Matsui, and K. Hibi. 1995. An analytical model for simulation of the wind-field in a typhoon boundary-layer. Journal of Wind Engineering and Industrial Aerodynamics 56(2–3): 291–310.
    Mudd, L., D. Rosowsky, and C. Letchford. 2015. Implications of hurricane-sea surface temperature relationship. In Proceedings of the 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12, 12–15 July 2015, Vancouver, Canada.
    O’Neill, B.C., E. Kriegler, K.L. Ebi, E. Kemp-Benedict, K. Riahi, D.S. Rothman, B.J. van Ruijven, and D.P. van Vuuren et al. 2017. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change 42: 169–180.
    Okorie, M.E., F. Inambao, and Z. Chiguvare. 2017. Evaluation of wind shear coefficients, surface roughness and energy yields over inland locations in Namibia. Procedia Manufacturing 7: 630–638.
    Powell, M. 1982. The transition of the Hurricane Frederic boundary-layer wind-field from the open Gulf of Mexico to landfall. Monthly Weather Review 110(12): 1912–1932.
    Powell, M., G. Soukup, S. Cocke, S. Gulati, N. Morisseau-Leroy, S. Hamid, N. Dorst, and L. Axe. 2005. State of Florida hurricane loss projection model: Atmospheric science component. Journal of Wind Engineering and Industrial Aerodynamics 93(8): 651–674.
    Raupach, M.R. 1994. Simplified expressions for vegetation roughness length and zero-plane displacement as functions of canopy height and area index. Boundary-Layer Meteorology 71(1): 211–216.
    Ren, F.M., J. Liang, G.X. Wu, W.J. Dong, and X.Q. Yang. 2011. Reliability analysis of climate change of tropical cyclone activity over the Western North Pacific. Journal of Climate 24(22): 5887–5898.
    Sarkar, A., S. Singh, and D. Mitra. 2011. Wind climate modeling using Weibull and extreme value distribution. International Journal of Engineering, Science and Technology 3(5): 100–106.
    Seibert, S.L., J. Degenhardt, J. Ahrens, A. Reckhardt, K. Schwalfenberg, and H. Waska. 2020. Investigating the land-sea transition zone. In YOUMARES 9The oceans: Our research, our future: Proceedings of the 2018 conference for YOUng MArine RESearcher in Oldenburg, Germany, ed. S. Jungblut, V. Liebich, and M. Bode-Dalby, 225–242. Cham: Springer International Publishing.
    Shen, C., A. Shen, C. Tian, S. Zhou, L. Zhu, P. Chan, Q. Fan, S. Fan, and W. Li. 2020. Evaluating the impacts of updated aerodynamic roughness length in the WRF/Chem model over Pearl River Delta. Meteorology and Atmospheric Physics 132(3): 427–440.
    Simiu, E., and D. Yeo. 2019. Wind effects on structures: Modern structural design for wind. Hoboken, NJ: John Wiley & Sons.
    Simiu, E., N.A. Heckert, J.J. Filliben, and S.K. Johnson. 2001. Extreme wind load estimates based on the Gumbel distribution of dynamic pressures: An assessment. Structural Safety 23(3): 221–229.
    Slangen, A.B.A., B. Meyssignac, C. Agosta, N. Champollion, J.A. Church, X. Fettweis, S.R.M. Ligtenberg, B. Marzeion, et al. 2017. Evaluating model simulations of twentieth-century sea level rise. Part I: Global mean sea level change. Journal of Climate 30(21): 8539–8563.
    Standards Australia / Standards New Zealand. 2021. Structural design actionsPart 2: Wind actions. AS/NZS 1170.2:2021. Wellington, New Zealand: Standards Australia / Standards New Zealand A. N. Z.
    Sung, H.M., J. Kim, J.-H. Lee, S. Shim, K.-O. Boo, J.-C. Ha, and Y.-H. Kim. 2021. Future changes in the global and regional sea level rise and sea surface temperature based on CMIP6 models. Atmosphere 12(1): Article 90.
    Tse, K.T., S.W. Li, P.W. Chan, H.Y. Mok, and A.U. Weerasuriya. 2013. Wind profile observations in tropical cyclone events using wind-profilers and doppler SODARs. Journal of Wind Engineering and Industrial Aerodynamics 115: 93–103.
    Verkaik, J.W., and A.A.M. Holtslag. 2007. Wind profiles, momentum fluxes and roughness lengths at Cabauw revisited. Boundary-Layer Meteorology 122(3): 701–719.
    Vickery, P.J., F.J. Masters, M.D. Powell, and D. Wadhera. 2009. Hurricane hazard modeling: The past, present, and future. Journal of Wind Engineering and Industrial Aerodynamics 97(7–8): 392–405.
    Vickery, P.J., P.F. Skerlj, and L.A. Twisdale. 2000. Simulation of hurricane risk in the US using empirical track model. Journal of Structural Engineering 126(10): 1222–1237.
    Vickery, P.J., D. Wadhera, L.A. Twisdale, and F.M. Lavelle. 2009. US hurricane wind speed risk and uncertainty. Journal of Structural Engineering 135(3): 301–320.
    Wang, J.Y., K.T. Tse, and S.W. Li. 2022a. Integrating the effects of climate change using representative concentration pathways into typhoon wind field in Hong Kong. In Proceedings of the 8th European African Conference on Wind Engineering, 20–23 September 2022a, Bucharest, Romania.
    Wang, J.Y., K.T. Tse, S.W. Li, and C.H. Fung. 2022. Prediction of the typhoon wind field in Hong Kong: Integrating the effects of climate change using the Shared Socioeconomic Pathways. Climate Dynamics 59(7–8): 2311–2329.
    Xie, J., Z. Liao, X. Fang, X. Xu, Y. Wang, Y. Zhang, J. Liu, S. Fan, and B. Wang. 2019. The characteristics of hourly wind field and its impacts on air quality in the Pearl River Delta region during 2013–2017. Atmospheric Research 227: 112–124.
    Xu, J., H. Jia, H. Zhou, Y. Kang, and K. Zhong. 2021. Influences of offshore background wind on the formation of sea-land breeze and the characteristics of pollutant diffusion. Environmental Science and Pollution Research 28(48): 68318–68329.
    Yeung, P.S., C.H. Fung, M.F. Wong, and C. Ren. 2018. Refinement of roughness length value for the Weather Research and Forecast (WRF) Model based on the understanding of local climate zone. In Proceedings of the 10th International Conference on Urban Climate (ICUC10) and 14th Symposium on Urban Environment, 6–10 August 2018, New York, USA.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (145) PDF downloads(3) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return