A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China

doi:10.1007/s11707-019-0793-5

Front. Earth Sci.

2019, Vol. 13

Issue (4) : 672-681 https://doi.org/10.1007/s11707-019-0793-5

RESEARCH ARTICLE

A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China

Peiyan CHEN^1,²(

), Hui YU^1,³, Ming XU¹, Xiaotu LEI⁴, Feng ZENG^2,⁵

¹. Shanghai Typhoon Institute, China Meteotological Administration, Shanghai 200030, China
². The Joint Laboratory for Typhoon Forecasting Technique Applications between Shanghai Typhoon Institute and Wenzhou Meteorological Bureau, Wenzhou 325000, China
³. Key Laboratory of Numerical Modeling for Tropical Cyclone, China Meteotological Administration, Shanghai 200030, China
⁴. Shanghai Meteorological Service, Shanghai 200030, China
⁵. Wenzhou Meteorological Bureau, Wenzhou 325000, China

Download: PDF(637 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

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.

Keywords tropical cyclone impact precipitation wind

Corresponding Author(s): Peiyan CHEN

Just Accepted Date: 16 October 2019 Online First Date: 22 November 2019 Issue Date: 30 December 2019

Cite this article:

Peiyan CHEN,Hui YU,Ming XU, et al. A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China[J]. Front. Earth Sci., 2019, 13(4): 672-681.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0793-5
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I4/672

Fig.1 Proportion of ATCs according to selected conditions (C1 to C3).

Fig.2 Distribution of the annual frequency of ATCs and those that meet conditions 1 and 2 (C1&2). The upper and lower limits of the boxes represent the third (75%) and first (25%) quartiles of the annual frequency. The median of the annual frequency is denoted by a short bar in the box. The upper and lower ends of the whiskers represent the maximum and minimum of the annual frequency.

Tab.1 Classification standards for TC damage based on TDPr (^{Chen et al. 2013})

Tab.2 Variable names, abbreviations, and their correlation coefficients when correlated with TDPr. The coefficients are calculated using all TCs with damage records from 1984 to 2013

Fig.3 Distribution of number of precipitation days over China during a TC influence period (

N d p

) based on DTCs from 1984 to 2013. Bars denote frequency and the numbers on the bars are the sample sizes. The black solid line is the cumulative percentage of

N d p

for all cases. The dotted line is the percentage of

N d p

from all cases.

Fig.4 The distributions of IPT and IWT based on DTCs (a) and ATCs (b) from 1984 to 2013. The solid (hollow) bar labeled Pro_IPT (Pro_IWT) is the percentage of TC cases with IPT (IWT) within the range labeled on the abscissa. Numbers on the abscissa (except for 0) are ranges of IPT or IWT values. For example, “0.1” in (a) means 0.05<IPT (or IWT)≤0.1. The “0” means that IPT or IWT is 0.

Fig.5 Sample size distributions of ATCs and DTCs as a function of IPWT from 1984 to 2013. Note that the IPWT scale changes at 0.05. The numbers on the abscissa (except for 0) indicate ranges (not including the lower value). For example, 0.01–0.02 means 0.01<IPWT≤0.02. The 0 on the abscissa means that IPT or IWT is 0. “ATC” indicates the number of ATCs, “DTC” indicates the number of DTCs, and “Per” indicates the cumulative percentage of DTCs from all ATCs.

Fig.6 Percentage (a) and sample size (b) distributions of damage categories for all ATCs from 1984 to 2013 as a function of IPWT condition (except for the tropical depression in August 1985). The numbers on the abscissa (except for 0) indicate statistical ranges. For example, 0.01–0.02 means 0.01< IPWT≤0.02. The 0 on the abscissa means that IPT or IWT is 0. C*_DAM is damage severity category based on TDPr. C0_DAM means no damage record, whereas C4_DAM indicates the highest level of damage. More details about damage categories and TDPr can be found in ^{Chen et al. (2013)}.

Tab.3 IPWT category definitions and sample sizes for ATCs

Fig.7 Damage category distribution as a function of IPWT category. C*_DAM is the category of damage. The numbers on the bars are the percentages of cases in that impact category with the same IPWT and damage category.

Fig.8 Annual frequencies of ATCs during 1984–2013 in (a) and (b). The “all” means all ATCs. “Trend is the” linear trend. “T=5” (“T=10”) means the period is 5 years (10 years). “1+2” is the total number of TCs with combined precipitation and wind impact Category 1 or Category 2. The “4” is the number with combined impact Category 4.

1	H Y Chen, H Yu, G J Ye, M Xu, Q Z Yang (2019). Return period and the trend of extreme disastrous rainstorm events in Zhejiang Province. J Trop Meteorol, 25: 192–200
2	P Y Chen, X T Lei, M Ying (2013). Introduction and application of a new comprehensive assessment index for damage caused by tropical cyclones. Trop Cyclone Res Rev, 2: 176–183
3	P Y Chen, Y H Yang, X T Lei, Y Z Qian (2009). Cause analysis and preliminary hazard estimate of typhoon disaster in China. J Nat Disast, 18: 64–73 (in Chinese)
4	D R Easterling, T C Peterson, T R Karl (1996). On the development and use of homogenized climate datasets. J Clim, 9(6): 1429–1434 https://doi.org/10.1175/1520-0442(1996)009<1429:OTDAUO>2.0.CO;2
5	K Emanuel (2005). Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436(7051): 686–688 https://doi.org/10.1038/nature03906 pmid: 16056221
6	GB/T 19201–2006 (2006). Grade of tropical cyclones. Beijing: Standards Press of China
7	L Kantha (2006). Time to replace the Saffir-Simpson hurricane scale? Eos (Wash DC), 87(1): 3–6 https://doi.org/10.1029/2006EO010003
8	L Kantha (2013). Classification of hurricanes: lessons from Katrina, Ike, Irene, Isaac and Sandy. Ocean Eng, 70: 124–128 https://doi.org/10.1016/j.oceaneng.2013.06.007
9	X T Lei, P Y Chen, Y H Yang, Y Z Qian (2009). Characters and objective assessment of disasters caused by typhoons in China. Acta Meteorol Sin, 67: 875–883 (in Chinese)
10	B Q Liang, Q Fan, J Yang, T M Wang (1999). A fuzzy mathematic evaluation of the disaster by tropical cyclones. J Trop Meteorol, 15: 305–311 (in Chinese)
11	Q W Lin, C J Lu (1987). An analysis on satellite cloud imageries and radar echoes from the tropical depression “8.27”. J Guangxi Meteor: 13–16 (in Chinese)
12	W P Lou, H Y Chen, X F Qiu, Q Y Tang, F Zheng (2012). Assessment of economic losses from tropical cyclone disasters based on PCA-BP. Nat Hazards, 60(3): 819–829 https://doi.org/10.1007/s11069-011-9881-x
13	W F Lu (1995). Assessment and prediction of disastrous losses due to tropical cyclones in Shanghai. J Nat Disast, 4: 40–45 (in Chinese)
14	X Q Lu, H Yu, M Ying, L B Qi (2018a). The effects of station network density on statistical analyses of tropical cyclone precipitation. J Trop Meteorol, 24(4): 421–431
15	Y Lu, F M Ren, W J Zhu (2018b). Risk zoning of typhoon disasters in Zhejiang Province, China. Nat Hazards Earth Syst Sci, 18(11): 2921–2932 https://doi.org/10.5194/nhess-18-2921-2018
16	Q Y Ma, G Y Li, X R Wang, W G Wang, L Y Gao (2008). A fuzzy synthetic evaluation model for typhoon disaster. Meteor Mon, 34: 20–25 (in Chinese)
17	R A Pielke Jr, J Gratz, C W Landsea, D Collins, M A Saunders, R Musulin (2008). Normalized hurricane damage in the United States:1900–2005. Nat Hazards Rev, 9(1): 29–42 https://doi.org/10.1061/(ASCE)1527-6988(2008)9:1(29)
18	M D Powell, T A Reinhold (2007). Tropical cyclone destructive potential by integrated kinetic energy. Bull Am Meteorol Soc, 879(2): 219–221
19	F M Ren, B Gleason, D Easterling (2001). A numerical technique for partitioning cyclone troical precipitation. J Trop Meteorol, 17(3): 308–313 (in Chinese)
20	M Rezapour, T E Baldock (2014). Classification of hurricane hazards: the importance of rainfall. Weather Forecast, 29(6): 1319–1331 https://doi.org/10.1175/WAF-D-14-00014.1
21	STI/CMA (2006). Climatological Atlas for Tropical Cyclones Affecting China (1951–2000). Beijing: Science Press (in Chinese)
22	STI/CMA (2017). Climatological Atlas of Tropical Cyclones over the Western North Pacific (1981–2010). Beijing: Science Press
23	G J Van Oldenborgh, K van der Wiel, A Sebastian, R Singh, J Arrighi, F Otto, K Haustein, S Li, G Vecchi, H Cullen (2017). Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environ Res Lett, 12(12): 124009 https://doi.org/10.1088/1748-9326/aa9ef2
24	Y J Wang, S S Wen, X C Li, F Thomas, B D Su, R Wang, T Jiang (2016). Spatiotemporal distributions of influential tropical cyclones and associated economic losses in China in 1984–2015. Nat Hazards, 84(3): 2009–2030 https://doi.org/10.1007/s11069-016-2531-6
25	H Y Wu, L H Kang, L L Chen, X M Ma (2007). Effect of meteorological observation environment variability on homogeneity of temperature series in Zhiejiang Province. Mater Sci Technol, 35(1): 152–156 (in Chinese)
26	F J Xiao, Y Z Yin, Y Luo, L C Song, D X Ye (2011). Tropical cyclone hazards analysis based on tropical cyclone potential impact index. J Geosci (Prague), 21: 791–800
27	H S Xu, J J Lian, L L Bin, K Xu (2018). Joint distribution of muliple typhoon hazard factors. Scientia Geographica Sinica, 38(12): 2118–2124 (in Chinese)
28	Q Xu, L Wei, Y Jin, Q Y Zhao, J Cao (2015). A dynamically constrained method for determining the vortex centers of tropical cyclones predicted by high-resolution models. J Atmos Sci, 72(1): 88–103 https://doi.org/10.1175/JAS-D-14-0090.1
29	Q Z Yang, M Xu (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: 299–304
30	M Ying, W Zhang, H Yu, X Q Lu, J X Feng, Y Fan, Y Zhu, D Chen (2014). An overview of the China Meteorological Administration tropical cyclone database. J Atmos Ocean Technol, 31(2): 287–301 https://doi.org/10.1175/JTECH-D-12-00119.1
31	Q Zhang, L G Wu, Q F Liu (2009). Tropical cyclone damages in China 1983–2006. Bull Am Meteorol Soc, 90(4): 489–496 https://doi.org/10.1175/2008BAMS2631.1
32	Q H Zhang, Q Wei, L S Chen (2010). Impact of landfalling tropical cyclones in mainland China. Sci China Earth Sci, 53(10): 1559–1564 (in Chinese) https://doi.org/10.1007/s11430-010-4034-8
33	S S Zhao, F M Ren, G Gao, D P Huang (2015). Characteristics of Chinese tropical cyclone disaster in the past 10 years. J Trop Meteorol, 31(03): 424–432 (in Chinese)

[1]	Baofu LI, Jili ZHENG, Xun SHI, Yaning CHEN. Quantifying the impact of mountain precipitation on runoff in Hotan River, northwestern China[J]. Front. Earth Sci., 2020, 14(3): 568-577.
[2]	Sukh TUMENJARGAL, Steven R. FASSNACHT, Niah B.H. VENABLE, Alison P. KINGSTON, Maria E. FERNÁNDEZ-GIMÉNEZ, Batjav BATBUYAN, Melinda J. LAITURI, Martin KAPPAS, G. ADYABADAM. Variability and change of climate extremes from indigenous herder knowledge and at meteorological stations across central Mongolia[J]. Front. Earth Sci., 2020, 14(2): 286-297.
[3]	Hong LI, Jingyao LUO, Mengting XU. Ensemble data assimilation and prediction of typhoon and associated hazards using TEDAPS: evaluation for 2015–2018 seasons[J]. Front. Earth Sci., 2019, 13(4): 733-743.
[4]	Xiaoqin LU, Hui YU, Xiaoming YANG, Xiaofeng LI, Jie TANG. A new technique for automatically locating the center of tropical cyclones with multi-band cloud imagery[J]. Front. Earth Sci., 2019, 13(4): 836-847.
[5]	Xiba TANG, Fan PING, Shuai YANG, Mengxia LI, Jing PENG. On the rapid intensification for Typhoon Meranti (2016): convection, warm core, and heating budget[J]. Front. Earth Sci., 2019, 13(4): 791-807.
[6]	Linna ZHAO, Xuemei BAI, Dan QI, Cheng XING. BMA probability quantitative precipitation forecasting of land-falling typhoons in south-east China[J]. Front. Earth Sci., 2019, 13(4): 758-777.
[7]	Jia ZHU, Jiong SHU, Xing YU. Improvement of typhoon rainfall prediction based on optimization of the Kain-Fritsch convection parameterization scheme using a micro-genetic algorithm[J]. Front. Earth Sci., 2019, 13(4): 721-732.
[8]	Pingzhi FANG, Deqian ZHENG, Ming GU, Haifeng CHENG, Bihong ZHU. Numerical investigation of the wind environment around tall buildings in a central business district[J]. Front. Earth Sci., 2019, 13(4): 848-858.
[9]	Qianqian JI, Feng XU, Jianjun XU, Mei LIANG, Shifei, TU, Siqi, CHEN. Large-scale characteristics of landfalling tropical cyclones with abrupt intensity change[J]. Front. Earth Sci., 2019, 13(4): 808-816.
[10]	Sui HUANG, Shengming TANG, Hui YU, Wenbo XUE, Pingzhi FANG, Peiyan CHEN. Impact of physical representations in CALMET on the simulated wind field over land during Super Typhoon Meranti (2016)[J]. Front. Earth Sci., 2019, 13(4): 744-757.
[11]	Pingzhi FANG, Deqian ZHENG, Liang LI, Wenyong MA, Shengming TANG. Numerical and experimental study of the aerodynamic characteristics around two-dimensional terrain with different slope angles[J]. Front. Earth Sci., 2019, 13(4): 705-720.
[12]	Zhitao WU, Mingyue WANG, Hong ZHANG, Ziqiang DU. Vegetation and soil wind erosion dynamics of sandstorm control programs in the agro-pastoral transitional zone of northern China[J]. Front. Earth Sci., 2019, 13(2): 430-443.
[13]	Qi LI, Xiaolei ZOU. Bias characterization of ATMS low-level channels under clear-sky and cloudy conditions[J]. Front. Earth Sci., 2019, 13(2): 277-289.
[14]	Xianglin WEI, Yuewei DUAN, Yongxue LIU, Song JIN, Chao SUN. Onshore-offshore wind energy resource evaluation based on synergetic use of multiple satellite data and meteorological stations in Jiangsu Province, China[J]. Front. Earth Sci., 2019, 13(1): 132-150.
[15]	Lei ZHANG, Xiaobin YIN, Hanqing SHI, Zhenzhan WANG, Qing XU. Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures[J]. Front. Earth Sci., 2019, 13(1): 124-131.

Viewed

Full text

Abstract

Cited

Shared

Discussed