Abstract
The South China Sea (SCS) is one of the tropical oceans regularly affected by destructive storms. It is urgent to establish models to simulate storm formations in this area. This paper aims to simulate typhoon genesis, regarding to seasonal variation in the SCS for 1000 years using Monte Carlo simulation. We employed the seventeenth probability distributions to fit historical typhoon data which were extracted from the Best Track Data of the Japan Meteorological Agency (JMA) from 1951 to 2020. The three evaluation criteria, including mean absolute deviation (MAD), mean squared error (MSE) and (1-CC) where CC is the correlation coefficient, were applied to choose the best-fitting probability distribution for simulations of typhoon geneses. The statistical features of historical typhoons were analyzed, comprising the number of typhoons, typhoon genesis locations (latitudes and longitudes) and the seasonal effects on the formation of typhoons. The results showed that the peak typhoon season (PS) lasted from June to September and the remaining months were classified as the low typhoon seasons (LS). Typhoon genesis locations mainly distributed in latitudinal range between 10 and 25° N for PS, whereas they spread in a larger area, mostly southward of 15° N for LS. MAD is the most appropriate indicator for good-of-fit test. Tlocationscale and generalized extreme value (Gev) distributions fit well the observed typhoon genesis longitudes for LS and PS, respectively. The historical typhoon genesis latitudes for LS and PS follow Weilbull and Gev distributions, respectively. The spatial distributions of the simulated typhoon geneses for the two seasons were in good agreement with those of the historical ones.
Similar content being viewed by others
Data Availability
The Best Track data in this study were obtained from the Japanese Meteorology Agency in the period of 1951–2020. Data of South China Sea margin were collected from Marine Regions which is an integration of the VLIMAR Gazetteer and the VLIZ Maritime Boundaries Geodatabase and managed by the Flanders Marine Institute. The authors sincerely thank these organizations.
Code Availability
None.
References
Camargo SJ, Sobel AH (2005) Western North Pacific tropical cyclone intensity and ENSO. J Clim 18(15):2996–3006. https://doi.org/10.1175/JCLI3457.1
Chan JCL, Shi J, Lam C (1998) Seasonal forecasting of tropical cyclone activity over the western North Pacific and the South China Sea. Weather Forecast 13(4):997–1004. https://doi.org/10.1175/1520-0434(1998)013<0997:SFOTCA>2.0.CO;2
Chen Y, Duan Z (2018a) A statistical dynamics track model of tropical cyclones for assessing typhoon wind hazard in the coast of southeast China. J Wind Eng Ind Aerodyn 172:325–340. https://doi.org/10.1016/j.jweia.2017.11.014
Chen Y, Duan Z (2018b) Impact of ENSO on typhoon wind hazard in the coast of southeast China. Nat Hazards 92:1717–1731
Chen TC, Wang SY, Yen MC (2006) Interannual variation of the tropical cyclone activity over the western North Pacific. J Clim 19(21):5709–5720. https://doi.org/10.1175/JCLI3934.1
Cheung KKW (2004) Large-scale environmental parameters associated with tropical cyclone formations in the Western North Pacific. J Clim 17(3):466–484. https://doi.org/10.1175/1520-0442(2004)017%3c0466:LEPAWT%3e2.0.CO;2
Daniell AJ, Mühr B, Girard T, Dittrich A, Fohringer J, Lucas C, Kunz-plapp T (2013) Super typhoon Haiyan/Yolanda–report no. 2, Center for Disaster Management and Risk Reduction Technology Forensic Disaster Analysis Group (CEDIM FDA). https://www.cedim.de/download/CEDIM_FDA_Haiyan_Rep2.pdf. Accessed 20 Jan 2021
Emanuel KA (2003) Tropical cyclones. Annu Rev Earth Planet Sci 31(1):75–104
Emanuel K, Ravela S, Vivant E, Risi C (2006) A statistical deterministic approach to hurricane risk assessment. Bull Am Meteorol Soc 87(3):299–314.
Fang G, Zhao L, Cao S, Zhu L, Ge Y (2020) Estimation of tropical cyclone wind hazards in coastal regions of China. Nat Hazard. https://doi.org/10.5194/nhess-2019-375
Gray WM (1968) Global view of the origin of tropical disturbances and storms. Mon Weather Rev 96(10):669–700. https://doi.org/10.1175/1520-0493(1968)096%3c0669:GVOTOO%3e2.0.CO;2
Gray WMW (1979) Hurricanes: their formation, structure and likely role in the tropical circulation. In: Shaw DB (ed) Meteorology over the tropical oceans. Royal Meteorological Society, pp 155–218
Guo Y, Hou Y, Qi P (2019) Analysis of typhoon wind hazard in Shenzhen City by Monte-Carlo Simulation. J Oceanol Limnol 37(6):1994–2013. https://doi.org/10.1007/s00343-019-8231-9
Guo Y, Hou Y, Liu Z, Du M (2020) Risk prediction of coastal hazards induced by Typhoon: a case study in the coastal region of Shenzhen, China. Remote Sens. https://doi.org/10.3390/rs12111731
Haghroosta T, Ismail WR (2017) Typhoon activity and some important parameters in the South China Sea. Weather Clim Extremes 17:29–35. https://doi.org/10.1016/J.WACE.2017.07.002
Hall TM, Jewson S (2007) Statistical modelling of North Atlantic tropical cyclone tracks. Tellus Ser A Dyn Meteorol Oceanogr 59A(4):486–498. https://doi.org/10.1111/j.1600-0870.2007.00240.x
James MK, Mason LB (2005) Synthetic tropical cyclone database. J Waterw Port Coast Ocean Eng 131(4):181–192. https://doi.org/10.1061/(ASCE)0733-950X(2005)131:4(181)
Kossin JP, Emanuel KA, Camargo SJ (2016) Past and projected changes in western north pacific tropical cyclone exposure. J Clim 29(16):5725–5739
Lee C-S, Lin Y-L, Chengung KKW (2006) Tropical cyclone formations in the South China Sea associated with the Mei-Yu front. Mon Weather Rev 134(10):2670–2687
Li SH, Hong HP (2016) Typhoon wind hazard estimation for China using an empirical track model. Nat Hazards 82(2):1009–1029. https://doi.org/10.1007/s11069-016-2231-2
Liang J, Hodges KI, Changgui W (2017) Evaluation of tropical cyclones over the South China Sea simulated by the 12 km MetUM regional climate model Oscillation (ENSO) such as the reduced track density and accumulated cyclonic energy 1642. Q J R Meteorol Soc 143(April):1641–1656. https://doi.org/10.1002/qj.3035
Lighthill SJ (1998) Fluid mechanics of tropical cyclones. Theor Comput Fluid Dyn 10:3–21
Maue RN (2011) Recent historically low global tropical cyclone activity. Geophys Res Lett 38(14):1–6. https://doi.org/10.1029/2011GL047711
Murakami H, Wang B, Kitoh A (2011) Future change of western North Pacific typhoons : projections by a 20-km-mesh global atmospheric model *. J Clim 24(4):1154–1169. https://doi.org/10.1175/2010JCLI3723.1
Neumann CJ (1987) The national hurricane center risk analysis program (HURISK) (reprinted with corrections 1991). NOAA Tech. Memo., NWS NHC-38, p 57
Palmén EH (1948) On the formation and structure of tropical hurricanes. Geophysica 3:26–38
Pobocikova I, Sedliackova Z, Michalkova M, George F (2017) Monte Carlo comparison of the methods for estimating the Weibull distribution parameters - wind speed application. Commun Sci Lett Univ Zilina 19(2A):79–86
Ramage CS (1959) Hurricane development. J Meteorol 16(3):227–337
Riehl H (1948) On the formation of typhoons. J Meteorol 5(6):247–264
Ritchie EA, Holland GJ (1999) Large-scale patterns associated with tropical cyclogenesis in the Western Pacific. Mon Weather Rev. https://doi.org/10.1175/1520-0493(1999)127%3c2027:LSPAWT%3e2.0.CO;2
Rumpf J, Weindl H, Höppe P, Rauch E, Schmidt V (2007) Stochastic modelling of tropical cyclone tracks. Math Methods Oper Res 66(3):475–490. https://doi.org/10.1007/s00186-007-0168-7
The World Bank (2017) An integrated strategy can help Vietnam manage disaster risks: Joint World Bank – Vietnam Conference. https://www.worldbank.org/en/news/press-release/2017/10/13/integrated-strategy-can-help-vietnam-manage-disaster-risks. Accessed 20 Jan 2021
UNDP (2004) Reducing disaster risk - a challenge for development. United Nations Development Programme
Vickery PJ, Skerlj PF, Twisdale LA (2000) Simulation of hurricane risk in the U.S. using empirical track model. J Struct Eng 126(10):1222–1237. https://doi.org/10.1061/(asce)0733-9445(2000)126:10(1222)
Wahiduzzaman M, Oliver ECJ, Wotherspoon SJ, Holbrook NJ (2017) A climatological model of North Indian Ocean tropical cyclone genesis, tracks and landfall. Clim Dyn 49(7–8):2585–2603. https://doi.org/10.1007/s00382-016-3461-4
Wang B, Chan JCL (2002) How strong ENSO events affect tropical storm activity over the western North Pacific. J Clim 15(13):1643–1658. https://doi.org/10.1175/1520-0442(2002)015%3c1643:HSEEAT%3e2.0.CO;2
Wang C, Liang J, Hodges KI (2017) Projections of tropical cyclones affecting Vietnam under climate change: downscaled HadGEM2-ES using PRECIS 2.1. Q J R Meteorol Soc 143(705):1844–1859. https://doi.org/10.1002/qj.3046
Yang J, Chen M (2019) Landfalls of tropical cyclones with rapid intensification in the Western North Pacific. Nat Hazards Earth Syst Sci Discuss. https://doi.org/10.5194/nhess-2019-279
Yasuda T, Mase H, Kunitomi S, Mori N, Hayashi Y (2010) Stochastic typhoon model and its application to future typhoon projection. In: Proceedings of 32nd international conference on coastal engineering, ASCE (in Press)
Yasui H, Cakeshi, Marukawa H, Katagiri J (2002) Study on evaluation time in typhoon simulation based on Monte Carlo method. J Wind Eng Ind Aerodyn 90(12–15):1529–1540. https://doi.org/10.1016/S0167-6105(02)00268-4
Yokoi S, Takayabu YN, Chan JCL (2009) Tropical cyclone genesis frequency over the western North Pacific simulated in medium-resolution coupled general circulation models. Clim Dyn 33:665–683. https://doi.org/10.1007/s00382-009-0593-9
Yonekura E, Hall TM (2011) A Statistical model of tropical cyclone tracks in the western North Pacific with ENSO-dependent cyclogenesis. J Appl Meteorol Climatol 50(8):1725–1739. https://doi.org/10.1175/2011JAMC2617.1
Yu J, Tim Li, Tan Z, Zhu Z (2016) Effects of tropical North Atlantic SST on tropical cyclone genesis in the western North Pacific. Clim Dyn 46:865–877. https://doi.org/10.1007/s00382-015-2618-x
Zhang S, Nishijima K (2012) Statistics-based investigation on typhoon transition modeling. In: Proc. seventh international colloquium on bluff body aerodynamics and applications, Shanghai, China, International Association for Wind Engineering, pp 364–373
Zhong R, Xu S, Huang F, Wu X (2020) Reasons for the weakening of tropical depressions in the South China Sea. Mon Weather Rev 148(8):3453–3469. https://doi.org/10.1175/MWR-D-19-0364.1
Acknowledgements
The authors sincerely thank the Japanese Meteorology Agency and the Flanders Marine Institute for data used in this study.
Funding
There is no financial support from any organization for this work.
Author information
Authors and Affiliations
Contributions
Both authors are responsible for the study conception and design. Material preparation, data collection and analysis were performed by DTBH and TQV. The manuscript was written by DTBH. All authors have read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that there is no conflict of interest.
Ethical Approval
This study abides by copyright laws.
Consent to Participate
All authors have read and approved the contents of this article.
Consent for Publication
All authors have agreed with this article.
Rights and permissions
About this article
Cite this article
Hong, D.T.B., Vinh, T.Q. Probabilistic Simulations for Seasonal Typhoon Genesis over the South China Sea. Earth Syst Environ 6, 903–916 (2022). https://doi.org/10.1007/s41748-021-00255-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41748-021-00255-0