A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China
Relationships between tropical cyclone (TC) precipitation, wind, and storm damage are analyzed for China based on TCs over the period from 1984 to 2013. The analysis shows that the maximum daily areal precipitation from stations with daily precipitation of ⩾ 50 mm and the sum of wind gusts of ⩾ 13.9 m/s can be used to estimate the main damage caused by TCs, and an index combining the precipitation and wind gust of a TC (IPWT) is defined to assess the severity of the combined impact of precipitation and wind. The correlation coefficient between IPWT and the damage index for affecting TCs is 0.80, which is higher than that for only precipitation or wind. All TCs with precipitation and wind affecting China are divided into five categories, Category 0 to Category 4, based on IPWT, where higher categories refer to higher combined impacts of precipitation and wind. The combined impact category is closely related to damage category and it can be used to estimate the potential damage category in operational work. There are 87.7%, 72.9%, 69.8%, and 73.4% of cases that have the same or one category difference between damage category and combined impact category for Categories 1, 2, 3, and 4, respectively. IPWT and its classification can be used to assess the severity of the TC impact and of combined precipitation and wind conveniently and accurately, and the potential damage caused by TCs. The result will be a good supplementary data for TC intensity, precipitation, wind, and damage. In addition, IPWT can be used as an index to judge the reliability of damage data. Further analysis of the annual frequency of combined precipitation-wind impact categories reveals no significant increasing or decreasing trend in impact over China over the past 30 years.
Keywordstropical cyclone impact precipitation wind
Unable to display preview. Download preview PDF.
This study was sponsored by the National Basic Research Program of China (Grant No. 2015CB452806), the National Natural Science Foundations of China (Grant Nos. 41475082 and 41875114), Shanghai Science & Technology Research Program (Grant No. 19dz1200101), and the Fundamental Research Funds of the STI/CMA (Grant No. 2019JB06).
- Chen H Y, Yu H, Ye G J, Xu M, Yang Q Z (2019). Return period and the trend of extreme disastrous rainstorm events in Zhejiang Province. J Trop Meteorol, 25: 192–200Google Scholar
- Chen P Y, Lei X T, Ying M (2013). Introduction and application of a new comprehensive assessment index for damage caused by tropical cyclones. Trop Cyclone Res Rev, 2: 176–183Google Scholar
- Chen P Y, Yang Y H, Lei X T, Qian Y Z (2009). Cause analysis and preliminary hazard estimate of typhoon disaster in China. J Nat Disast, 18: 64–73 (in Chinese)Google Scholar
- GB/T 19201-2006 (2006). Grade of tropical cyclones. Beijing: Standards Press of ChinaGoogle Scholar
- Lei X T, Chen P Y, Yang Y H, Qian Y Z (2009). Characters and objective assessment of disasters caused by typhoons in China. Acta Meteorol Sin, 67: 875–883 (in Chinese)Google Scholar
- Liang B Q, Fan Q, Yang J, Wang T M (1999). A fuzzy mathematic evaluation of the disaster by tropical cyclones. J Trop Meteorol, 15: 305–311 (in Chinese)Google Scholar
- Lin Q W, Lu C J (1987). An analysis on satellite cloud imageries and radar echoes from the tropical depression “8.27”. J Guangxi Meteor: 13–16 (in Chinese)Google Scholar
- Lu W F (1995). Assessment and prediction of disastrous losses due to tropical cyclones in Shanghai. J Nat Disast, 4: 40–45 (in Chinese)Google Scholar
- Lu X Q, Yu H, Ying M, Qi L B (2018a). The effects of station network density on statistical analyses oftropical cyclone precipitation. J Trop Meteorol, 24(4): 421–431Google Scholar
- Ma Q Y, Li G Y, Wang X R, Wang W G, Gao L Y (2008). A fuzzy synthetic evaluation model for typhoon disaster. Meteor Mon, 34: 20–25 (in Chinese)Google Scholar
- Powell M D, Reinhold T A (2007). Tropical cyclone destructive potential by integrated kinetic energy. Bull Am Meteorol Soc, 879(2): 219–221Google Scholar
- Ren F M, Gleason B, Easterling D (2001). A numerical technique for partitioning cyclone troical precipitation. J Trop Meteorol, 17(3): 308–313 (in Chinese)Google Scholar
- STI/CMA (2006). Climatological Atlas for Tropical Cyclones Affecting China (1951–2000). Beijing: Science Press (in Chinese)Google Scholar
- STI/CMA (2017). Climatological Atlas of Tropical Cyclones over the Western North Pacific (1981–2010). Beijing: Science PressGoogle Scholar
- Wu H Y, Kang L H, Chen L L, Ma X M (2007). Effect of meteorological observation environment variability on homogeneity of temperature series in Zhiejiang Province. Mater Sci Technol, 35(1): 152–156 (in Chinese)Google Scholar
- Xiao F J, Yin Y Z, Luo Y, Song L C, Ye D X (2011). Tropical cyclone hazards analysis based on tropical cyclone potential impact index. J Geosci (Prague), 21: 791–800Google Scholar
- Xu H S, Lian J J, Bin L L, Xu K (2018). Joint distribution of muliple typhoon hazard factors. Scientia Geographica Sinica, 38(12): 2118–2124 (in Chinese)Google Scholar
- Yang Q Z, Xu M (2010). Preliminary study of the assessment of methods for disaster-inducing risks by TCs using sample events of TCs that affected Shanghai. J Trop Meteorol, 16: 299–304Google Scholar
- Zhao S S, Ren F M, Gao G, Huang D P (2015). Characteristics of Chinese tropical cyclone disaster in the past 10 years. J Trop Meteorol, 31(03): 424–432 (in Chinese)Google Scholar