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An Operational Statistical Scheme for Induced Wind Gust Forecasts

a,e b a,c d,e d,e QINGLAN LI, PENGCHENG XU, XINGBAO WANG, HONGPING LAN, CHUNYAN CAO, a d,e a GUANGXIN LI, LIJIE ZHANG, AND LIQUN SUN a Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, b Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing, China c The Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Victoria, Australia d Shenzhen Meteorological Bureau, Shenzhen, China e Shenzhen Key Laboratory of Severe Weather in , Shenzhen, China

(Manuscript received 25 January 2016, in final form 2 June 2016)

ABSTRACT

This study provides a quantitative forecast method for predicting the potential maximum wind gust at certain automatic weather stations (AWSs) in South China through the investigation of the relationship between the wind gusts observed at the stations and tropical cyclones’ (TCs) main characteristics: TC in- tensity, TC distance to the station, TC azimuth relative to the station, and TC size. Historical TC data from 1968 to June 2014 within a distance of 700 km to several AWSs in South China are analyzed. The wind gust data available for the same period taken from six coastal AWSs: Yantian International Container Terminal (YICT), Mawan Port (MWP), and Shekou Ferry Terminal (SFT) in Shenzhen, and Observatory (HKO), Cheung Chau Island (CCH), and Waglan Island (WGL) in Hong Kong, are used to build the sta- 2 tistical relationship. The probability of gust gale occurrence (wind gust $ 17 m s 1) at these six stations is also computed. Results show that the wind induced by offshore TCs is strongly affected by the surrounding terrain conditions of the stations. Coastal stations open to the wind direction suffer a greater wind influence than do stations with obstructions located in the wind direction. When TCs are approaching the coast in South China, the most dangerous area is the northeast quadrant of TCs. In this quadrant, typhoons might incur gust gales at coastal stations in South China even at a distance of more than 400 km from the stations.

1. Introduction province in China. According to the statistical data de- veloped by the Meteorological Bureau, 314 Tropical cyclones (TCs) are the most destructive TCs overall made landfall or strongly influenced (pass- natural phenomena in China (Duan et al. 2014). Off- ing close to the Guangdong coastline with a shortest shore and landfalling TCs may induce wind gusts, heavy distance of less than 18 latitude) Guangdong Province precipitation, and storm surge, which can take an during the period of 1951–2013, with an average of 5.3 enormous toll in terms of lives and personal properties. TCs per year. Supertyphoon Rammasun in 2014, the (Willoughby et al. 2007; Konrad and Perry 2010; Li et al. strongest typhoon to hit South China in four decades, 2015). Guangdong, the southernmost province in made landfall three times in China. The average wind mainland China, with the adjacent on 2 speeds near the TC center were more than 60 m s 1, its south, has the longest coastline of 4114 km among the when it made its first and second landfalls at Wenchang, country’s provinces. In fact, tropical cyclones make Hainan Province, and Xuwen, Guangdong Province. On landfall more frequently in Guangdong than any other 18 July, the recorded rainfall at Haikou was more than 500 mm. Rammasun was responsible for 62 deaths in China with an additional 21 people reported missing. Up Denotes Open Access content. to 25 July 2014, the typhoon had caused direct economic losses totaling 6.25 billion U.S. dollars (Xinhuanet News 2014). For such a landfalling typhoon as Rammasun, it is Corresponding author address: Dr. Qinglan Li, Shenzhen In- stitute of Advanced Technology, Chinese Academy of Sciences, very important that the local government is able to issue Shenzhen 518055, China. timely and accurate TC warnings for the local residents, E-mail: [email protected] so they may evacuate or to be prepared for the coming

DOI: 10.1175/WAF-D-16-0015.1

Ó 2016 American Meteorological Society Unauthenticated | Downloaded 10/01/21 03:22 AM UTC 1818 WEATHER AND FORECASTING VOLUME 31 disaster. Therefore, quantitative forecasts of rainfall and misleading (Cangialosi and Franklin 2011). Tyner et al. wind gusts caused by TCs are important and required. (2015) conducted an analysis that compared the forecasts However, as a result of the lack of high-resolution from the National Digital Forecast Database with the observations and imperfections within the latest nu- observations and surface winds in mid-Atlantic region. merical weather prediction (NWP) models, the perfor- Results showed there to be a general overprediction of mance of these models when forecasting local severe the sustained wind speeds, especially for areas affected by weather events is still far from satisfactory (Kidder et al. the strongest winds. Therefore, there is still a long way to 2005; Willoughby et al. 2007; Liu et al. 2008; Li et al. go to perform a reliable forecast for the extents of the 34-, 2015). Thus, besides the NWP method, exploring other 50-, and 64- kt winds induced by TCs. ways to predict rainfall and wind for severe weather In contrast to the United States, research efforts re- conditions is important and necessary. Pfost (2000) garding quantitative forecasts of wind due to offshore and presented some operational techniques for real-time landfalling TCs have been rare in China (Xu et al. 2010). quantitative precipitation forecasting for landfalling In China, operational wind forecasting during the passage tropical cyclones along the Florida and central Gulf of of a TC has mainly relied on the experience of fore- Mexico coasts of the United States. Li et al. (2015) casters, along with the forecasts of a TC’s track and in- proposed a statistical scheme for forecasting the 24- and tensity by NWP models. In this study, a novel statistical 72-h rainfall at a certain area or specific station in South approach is proposed to analyze the wind gusts due to China induced by landfalling TCs, considering the TC’s TCs in the area of Shenzhen and Hong Kong, which can main characteristics of landfall intensity, landfall di- be used as a reference when forecasting the winds in- rection, and the distance between the TC’s landfall lo- duced by future offshore and landfalling TCs. Unlike the cation and the station. The scheme has been proven to research into wind investigation performed by NHC, this work well and has accurately forecast the rainfall in- study will focus on wind gust forecasts at certain stations, duced by TCs in Shenzhen for the typhoon seasons of rather than specific areas around the TC centers (i.e., the 2012–14 (Li et al. 2015). Regarding wind forecasting extents of the 34-, 50-, and 64- kt winds induced by TCs). during TCs, Knaff et al. (2007) proposed a climatology and persistence (CLIPER) model for predicting the TC wind structure in terms of significant wind radii (i.e., 34-, 2. Data and methodology 2 50-, and 64-kt wind radii, where 1 kt 5 0.51 m s 1) a. Data through 5 days. Although the model did a good job in forecasting wind radii variations, the average errors for This study focuses on the Shenzhen and Hong Kong each radius were approximately 18%–28% of the aver- areas to explore the potential for wind gusts caused by age radii at 12 h and increased to approximately 29%– offshore TCs. There are six coastal automatic weather 37% of the radii at 72 h (Knaff et al. 2007). Beginning in stations (AWSs) involved in the investigation: Yantian 2006, the National Hurricane Center’s (NHC) Hurri- International Container Terminal (YICT), Mawan Port cane Probability Program (HPP) implemented a new (MWP), Shekou Ferry Terminal (SFT), Hong Kong methodology that estimated the probabilities of winds of Observatory (HKO), Cheung Chau Island (CCH), and at least 34, 50, and 64 kt up to 120 h, and incorporated Waglan Island (WGL). The first three AWSs are prox- uncertainties in the track, intensity, and wind structure imate to Shenzhen and the latter three to Hong Kong. forecasts (DeMaria et al. 2009, 2013). The program The locations of these six stations are illustrated in used a Monte Carlo method to generate 1000 re- Fig. 1. The hourly meteorological data are obtained alizations by randomly sampling from the operational from the Shenzhen Meteorological Bureau (SZMB) and forecast center track and intensity forecast error distri- the respectively. butions generated during the past 5 yr in the Atlantic As can be seen from Fig. 1, all six of the stations are and the eastern, central, and western North Pacific to adjacent to the South China Sea. Among them, YICT 1008E(DeMaria et al. 2009, 2013). Although the new is a natural deep-water terminal and the leading gateway probability model was relatively unbiased and skillful as serving import and export container traffic generated by measured by the Brier skill score, Cangialosi and its immediate cargo-producing hinterlands. As the Franklin (2011) reported that there was insufficient largest and busiest container terminal in South China, surface wind information to allow the forecasters to YICT’s daily operations rely heavily on the weather accurately analyze the size of a tropical cyclone’s wind conditions, especially the wind conditions. field. As a result, poststorm best-track wind radii were Strong winds can cause severe disruptions to con- likely to have errors so large as to render a verification tainer operations (Tsai 2009). It is often the stronger of official radii forecasts unreliable and potentially gusts that cause the most significant damage to buildings

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FIG. 1. Locations of the six AWSs: YICT, MWP, and SFT from Shenzhen and HKO, CCH, and WGL from Hong Kong. and properties, rather than the average winds (Bureau gust observations is 43 935 for HKO and CCH; the of Meteorology 2016). According to the new European minimum number of gust observations is 8597 for YICT. crane design standard EN13001, wind pressure on The corresponding number of historical tropical cyclone cranes, which are very vulnerable to the wind conditions, observations for each station is listed in Table 1 as well. is now explicitly dependent on wind gusts (Bos 2016). In According to the definition recommended by the China this study, the hourly maximum gust recorded at the Meteorological Administration (CMA), a gust is an AWSs is used to consider the wind influence induced by average wind speed over 3 s recorded at a station. In this offshore/landfalling TCs at the six stations. Because of study, gust gale (GG) occurrence is defined when the 2 the differences in their service histories, the durations of gust is more than 17 m s 1. Under these conditions, the meteorological data for the six stations are different. boats and ships might be rocked and damage to people, Detailed information on the wind data for these six buildings, and property might occur. stations is listed in Table 1. Compared to the stations in Besides the wind gust records at the six stations, the Shenzhen, the three stations in Hong Kong have longer historical tropical cyclone attributes of TC intensity observation histories. The maximum number of hourly [sustained maximum wind speed near the center of a TC

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TABLE 1. Locations of the six AWSs and the durations of the corresponding wind data.

Duration of No. of gust observations Corresponding No. of historical Station Lat (8N) Lon (8E) Elevation (m) wind data corresponding to historical TCs TC observations YICT 22.57 114.28 26 Jun 2007–14 8597 88 MWP 22.50 113.87 25 Jun 2006–14 9631 102 SFT 22.48 113.91 36 Jun 2004–14 11 486 113 HKO 22.30 114.17 74 Jun 1968–2014 43 935 532 CCH 22.20 114.03 99 Jun 1968–2014 43 935 532 WGL 22.18 114.30 83 Jun 1989–2014 21 672 275

(SMWNCTC)], TC position, and TC size (in terms of Winds are generally stronger when they are closer to 2 the radius of gale force winds of 17 m s 1) are also used the TC center, except within the TC eye area (Frank in this study. These data are obtained from the CMA. 1977; Lin et al. 2006; Zhang et al. 2010; Chen 2012). Therefore, the distance between the station and the TC b. Methodology center is considered to be a very important factor In general, the wind impact of a TC is dependent on its influencing the wind at a station. Knowing the latitudes intensity and size (Powell and Reinhold 2007; Irish et al. and longitudes of two points on the earth, the distance 2008; Knaff et al. 2014; Wu et al. 2015). Usually, TCs with between the two points can be computed as follows stronger intensities will result in more damage to prop- (Meeus 1999): erties. In addition, a TC’s size, in terms of wind field, often p determines the TC’s potential impacts. According to the S 5 R 3 3 arccos[sinu sinu 180 1 2 criteria of the CMA (Table 2), there are six categories of 1 u u 2 TCs: supertyphoon (SuTY), severe typhoon (STY), ty- cos 1 cos 2 cos(L1 L2)], (1) phoon (TY), severe tropical storm (STS), tropical storm S (TS), and tropical depression (TD) (Li et al. 2015). As the where is the distance between two points (A and B) on R numbers of SuTY and STY that approached close to the earth’s sphere (km); refers to the radius of the L u South China or landed in China are smaller than any of earth (km); 1 and 1 are the longitude and latitude for L u the other TC categories, the wind influences at the sta- point A and 2, respectively; and 2 are the longitude and latitude for the point B. Here, A is the center of a tions impacted by SuTYs and STYs are analyzed and TC and B is the position of an AWS. discussed together. SuTYs and STYs are combined and In addition, it has been reported that when TCs are renamed SSTYs in this study. All the historical TC data close to the coast, the wind is asymmetric: bigger on the are then grouped into these five categories: TD, TS, STS, TY, and SSTY. The method for processing the data is landward wind side (wind flows from the sea to the land) shown by using one TC as an example. Figure 2 shows the compared to on the seaward wind side (wind flows from track of Typhoon Vicente (2012). Vicente (2012) formed the land to the sea) (Chen and Meng 2001; Chen and at 0800 local time (LT; Beijing local time, which is used Yau 2003; Kimball 2008). Thus, the azimuth of the TCs relative to the weather station is another important hereafter) 20 July 2012 and dissipated at 1100 LT 25 July factor that influences local wind and rainfall. To com- 2012. During this time period, its intensity transitioned pute the azimuth, we employ (Meeus 1999) from TD (0800 LT 20 July–2000 LT 21 July) to TS (2100 LT 21 July–0500 LT 22 July), then to STS (0600 LT sin(90 2 u ) sin(L 2 L ) 22 July–0900 LT 23 July), then to TY (1000 LT 23 July– a 5 2 2 1 arcsin u , (2) 0800 LT 24 July), then weakened to STS (0900 LT sin( ) 24 July–1200 LT 24 July), then to TS (1300 LT 24 July– 2200 LT 24 July), and finally to TD (2300 LT 24 July 24– 11 25 July). Typhoon Vicente (2012) is separated into TABLE 2. The tropical cyclone intensity scale according to four parts according to its different intensity life spans: the CMA. 2 TD, TS, STS and TY (there is no SSTY life span for Category Abbrev SMWNCTC (m s 1) Vicente). Similar to Vicente, the other historical TCs are Supertyphoon SuTY $51 also separated into different parts. Next, the TC parts Severe typhoon STY 41.5 ; 50.9 with the same intensity levels are analyzed as a group. Typhoon TY 32.7 ; 41.4 Meanwhile, the corresponding wind gust observations at Severe tropical storm STS 24.5 ; 32.6 the six AWSs during the TCs periods are selected from Tropical storm TS 17.2 ; 24.4 ; the wind record datasets. Tropical depression TD 10.8 17.1

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FIG. 2. Track of Typhoon Vicente (line with points referring to the TY category; yellow refers to STS, green to TS, and blue to TD). where u satisfies location of TY Vicente’s track relative to YICT. Meanwhile, the hourly maximum wind gusts at YICT u 5 2 u 2 u cos( ) cos(90 2) cos(90 1) corresponding to each of the TC location points are 1 sin(90 2 u ) sin(90 2 u ) cos(L 2 L ). (3) picked out and illustrated by different colors for those 2 1 2 1 locations (Fig. 3b). The colors range from blue to green, When point B is located in the first quadrant with then yellow to red, showing the wind strength from weak respect to point A, the azimuth 5 a; when point B is to strong. In fact, Fig. 3 cannot only be plotted in polar located in the second quadrant, the azimuth 5 360 1 a; coordinates, but can also be plotted in Cartesian co- and when point B is located in the third or fourth ordinates, with the knowledge of the locations of the quadrant, the azimuth 5 180 2 a. station and the TC. Similar to the studies conducted by After computing the distance and azimuth of a TC the NHC, who use polar coordinates to predict TC wind relative to any AWS, the track of the TC can then be structure in terms of significant wind radii (Knaff et al. plotted on a polar coordinate grid. The center of the 2007; DeMaria et al. 2009, 2013), polar coordinates are circle refers to the location of a weather station. The TY used as well in this study to reflect the maximum wind category of Typhoon Vicente (Fig. 2), which occurred gusts at the AWS induced by TCs. from 1000 LT 23 July to 0800 LT 24 July 2012, is taken as In the future, when a TC with a certain intensity passes an example to show its wind influence on the weather the location at a distance of S and an azimuth of A rel- station at YICT. Each point in Fig. 3a refers to the ative to the weather station, the historical wind gusts

FIG. 3. (a) The location of the TY category for Vicente relative to YICT. (b) The maximum hourly wind gusts observed at YICT corresponding to each of the locations of TY Vicente.

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TABLE 3. The location and intensity of Vicente from 1400 to 1900 LT 23 Jul 2012 and the hourly maximum wind gust recorded at YICT during the same period. Note: SMWNCTC refers to sustained maximum winds near the center of the TC. The data for the maximum wind gust are shown in bold.

2 2 Time (UTC 23 Jul 2012) Lat (8N) Lon (8E) SMWNCTC (m s 1) Distance (km) Azimuth Gust at YICT (m s 1) 1400 20.4 114.9 35 249.67 165.00 15 1500 20.6 114.8 35 225.55 166.11 14.1 1600 20.7 114.6 38 210.54 170.90 14.8 1700 20.8 114.5 38 198.12 173.37 17.7 1800 20.8 114.4 38 197.20 176.37 18.2 1900 20.9 114.3 38 185.70 179.36 17.2 induced by all the nearby TCs with the same intensity 3) It will usually take a TC of a certain intensity several can be referenced to help forecast the potential wind hours to pass through a 18318 grid within the study gust at the station. Considering the historical sample size area. During this period, the maximum wind gust and the forecasting precision, the nearby area used in record at the AWS is screened out and taken as a this study for forecasting reference is defined to cover reference to forecast the potential maximum wind gust the region of (S 2 50 km, S 1 50 km), (A 2 158, A 1 158). at the station when a TC with the same intensity passes Suppose there are N historical TC samples with the that 18318 grid in the future. Taking Typhoon Vicente same intensity included in the region. All of the wind (2012) as an example, the storm passed through the grid gusts fx1, x2,.:: xi,.::, xNg (1 # i # N) at a weather sta- of [208N, 218N), [1148E, 1158E) during 1400 LT 23 July– tion induced by these N TCs are supposed to have the 1900 LT 23 July 2012 (Table 3). During these 6 h, the 2 same statistical characteristics. For a given value y,an maximum wind gust recorded at YICT was 18.2 m s 1 observed wind gust x satisfies the probability (this value appears in boldface in Table 3). This value p 5 P(x . y). Then, all of the observed samples xi # y can then be used as a reference to forecast the maxi- satisfy the following equation (Klenke 2013): mum wind gust induced by a future typhoon passing through the same 18318 grid. n f (n, p) 5 (1 2 p) . (4) 4) The numbers of all categories of TCs passing each 183 18 grid that are within a distance of 700 km from the For a new observation that is larger than all the ob- weather station are counted as N (i, j), N (i, j), served samples, we get the probability (Klenke 2013) SSTY TY N (i, j), N (i, j), and N (i, j)(1# i # 13; 1 # j # 15). ð STS TS TD 1 1 Here, i and j refer to the order of the latitude and f (n) 5 (1 2 p)n dp 5 . (5) 83 8 n 1 1 longitude of the 1 1 grid. Taking the station YICT 0 (22.578N, 114.288E) as an example, the latitudes are Similarly, for a new observation that is smaller than all from 16.278 to 28.878N and the longitudes are from the observed samples, we get the same probability of 107.468 to 121.18E for the area with a distance of less 1/(n 1 1). Therefore, for a future new observation that is than 700 km to YICT. Hence, the maximum i is 13 and located between the maximum and minimum of the maximum j is 15. If the maximum hourly wind gust 21 fx1, x2,.:: xi,.::, xN g, we get the probability (confidence record at the AWS is more than 17 m s for a TC level) of (n 2 1)/(n 1 1). with a certain intensity passing that 18318 grid (i, j), Furthermore, the probability of occurrence of GG at the number of GG occurrences is counted to M(i, j): the AWSs induced by different categories of TCs is MSSTY(i, j) for SSTY, MTY(i, j) for TY, etc. computed. The detailed procedures are as follows: 5) Then, for each 18318 grid, the probability of GG occurrence due to the different categories of TCs can 1) For a weather station, all TCs passing within a be calculated by distance of 700 km to the station are considered. 2) The considered TCs are separated into different cate- R (i, j) 5 M (i, j)/N (i, j) for SSTY, gories according to their intensity and, then regrouped SSTY SSTY SSTY into new categories of SSTY (including SuTY and 5 RTY(i, j) MTY(i, j)/NTY(i, j) for TY, STY), TY, STS, TS, and TD. For example, TC1 is separated into SSTY1, TY1, STS1, TS1, and TD1; TC2 etc. is separated into SSTY2, TY2, STS2, TS2, and TD2; 6) With the knowledge of the latitude and longitude of SSTY1, SSTY2, SSTY3, etc. are grouped into SSTY; the TC’s center for the corresponding hourly maxi- and TY1, TY2, TY3, etc. are grouped into TY. mum wind gust recorded at the AWS, and the GG

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occurrence probability for each 18318 grid, the coastal area of South China are compared. Here, the TY probability of a GG occurrence at the AWS induced category is shown. Figures 5a–f show the wind gusts at by all categories of TCs can then be plotted. Differ- these six stations for all historical TYs up to June 2014 ent from Fig. 3, the GG occurrence probability is within 700 km of the corresponding stations. The histor- plotted through the use of Geographic Information ical wind gust periods for these six stations can be found System (GIS) data in order to have the background in Table 1.FromFig. 5, it can be seen that the winds of the geographical information included in the figure. two island stations near Hong Kong (CCH and WGL) are generally bigger than those of the other four stations. This should be due to their different local geographical con- 3. Results and discussions ditions. As island weather stations, CCH and WGL are more prone to wind impacts as compared to the other a. Potential wind gust at YICT induced by all four stations. Examining the wind gust influence on the categories of TCs three stations in Shenzhen, we find that the gusts at YICT As mentioned before, YICT is the largest and busiest are the biggest. Generally, the gust influence at SFT is the container terminal in South China. The wind conditions lowest. As the wind gust recording periods for the three heavily affect YICT’s daily operations. Therefore, the po- stations near Hong Kong are longer than those of the tential wind gusts at YICT induced by all categories of TCs three stations around Shenzhen, the wind gust records of are discussed first. Following the procedures mentioned in the three Hong Kong stations for a shorter period are section 2, the wind gusts at YICT corresponding to all the further plotted. Figures 5g–i show the maximum gust hourly positions of TYs that are within 700 km of YICT are influence at CCH, HKO, and WGL induced by all TYs plotted in Fig. 4a. This figure shows that the wind gusts may for the period from January 2004 to June 2014. Com- change significantly when the TC’s position changes only paring Fig. 5d with Fig. 5g for CCH, Fig. 5e with Fig. 5h slightly. For example, the wind gusts vary from 18 to for HKO, and Fig. 5f with Fig. 5i for WGL, it can be seen 2 30 m s 1 when TYs are near the center of Fig. 4a (i.e., that the patterns of the gusts at the three Hong Kong withinadistanceof100kmfromYICT).Ascanbeseen coastal stations for different periods are similar, which from Table 3, the hourly maximum wind gusts at YICT are means the results from this spatial statistical gust plot are not at the same level when a TY is in the same 18318 grid. robust. However, the maximum wind gust observations Figure 4b is obtained by considering the maximum wind for HKO are different for the two periods (Figs. 5e,h). gusts at YICT due to all the historical TYs passing each 183 The locations of the TCs for the maximum gust occur- 18 grid within a distance of 700 km from YICT. Therefore, if rences at HKO for the two periods remain the same, but

N1 TCspassa18318 grid with N2 positions (N2 . N1), the values do change. That should be due to changes in there will be N1 positionslefttobeplottedinthat18318 the surrounding environment. With the development of grid in Fig. 4b. Compared to Fig. 4a, the wind pattern in Hong Kong’s urban area, Hong Kong Observatory is now Fig. 4b is smoother and can provide more valuable in- surrounded by tall buildings. Therefore, the wind gust formation for predicting the potential maximum winds at influence due to TCs is lower than before. Generally, the YICT induced by future TYs passing in the offshore area. more historical wind gust observations there are, the Similarly, the maximum wind gusts at YICT due to SSTY, more confidence there will be in predicting the potential STS, TS, and TD events passing each 18318 grid are wind gusts at an AWS as a result of a TC. However, for plotted in Figs.4c,4d,4e,and4f, respectively. When a TC HKO, which has experienced environmental changes in with a certain intensity passes a location within 700 km of the surrounding area, Fig. 5h is preferred when predicting YICT in the future, the wind gusts at YICT induced by the wind gusts at this station by a future TY. historical nearby TCs can be referenced to forecast the wind Furthermore, it can be seen from Fig. 5 that the gust gusts due to that TC. The probability (confidence level) that influence of TCs at the six stations is asymmetric. The the forecasted wind gust will be located between the his- gusts are stronger when the TYs are in the quadrant torical maximum and minimum gust observations varies from 1808 to 2708, compared to the other three quad- with the number of historical TCs referenced. For example, rants. The strongest gust influence at each station will the probability is 90% [(20 2 1)/(20 1 1) 5 0:9048] if there generally occur at a distance of around 50–140 km from are 20 reference samples and the probability is 60% the corresponding station in the 1808–2708 quadrant. [(4 2 1)/(4 1 1) 5 0:6]ifthereareonly4referencesamples. Then, the gust will decay around this most influen- tial center. When the station is within the TC’s inner b. Potential wind gusts at six stations induced by TYs core (generally less than 50 km from the TC’s center), The wind gust records at these six stations resulting the gust will be weaker compared with the maximum from TCs passing near or making landfall around the gust at locations between 50 and 140 km in the third

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FIG. 4. Spatial relationship between the positions of historical TCs and the wind gusts at YICT. The black dots refer to the positions of the historical TCs with certain intensity categories, and the different colors refer to different levels of wind gusts at YICT. (a) The TY category, which considers all of the historical hourly positions. (b) The TY category, showing the maximum wind gusts due to each TC passing through a 1838grid box within a distance of 700 km. (c) As in (b), but for SSTYs. (d) As in (b), but for STSs. (e) As in (b), but but for TSs. (f) As in (b), but for TDs.

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FIG. 5. Maximum gusts induced by TCs in the TY category passing through a 18318 grid box during the periods shown in Table 1 within a distance of 700 km. Shown are (a) YICT, (b) MWP, and (c) SFT stations, which are near Shenzhen, and (d) CCH, (e) HKO, and (f) WGL, which are near Hong Kong. Also shown are results for (g) CCH, (h) HKO, and (i) WGL, but for the period from Jan 2004 to June 2014. The black dots refer to the positions of the historical TCs with TY intensity and the different colors refer to different levels of wind gusts at AWSs. quadrant. Among the three stations near Shenzhen, less of an influence from TCs than does YICT. Fur- the strongest wind generally occurs at YICT. When a thermore, it can be seen in Fig. 1 that there are two TC is at an azimuth between 1808 and 2408, the wind mountains located to the north and west of YICT. direction at those stations should be from the south- When a TC is in the third quadrant relative to YICT, east. It can be seen in Fig. 1 that YICT faces open water the southeast wind will prevail at that station. With in the southeast direction; however, MWP and SFT these two mountains located downstream, there would face Hong Kong from the southeast direction. This be a narrow channel effect, resulting in an intensified might be the reason that MWP and SFT generally feel gust at YICT.

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TABLE 4. The maximum wind gust ever recorded at the six weather stations during historical TCs up to June 2014.

2 Station Time and date TC lat (8N) TC lon (8E) TC ID SMWNCTC Distance (km) Azimuth (8) Gust (m s 1) YICT 0200 UTC 24 Jul 2012 21.7 113.3 1208 40 139.81 226.4 35.7 MWP 0200 UTC 24 Jul 2012 21.7 113.3 1208 40 106.07 213.66 29.6 SFT 0400 UTC 24 Jul 2012 22 113 1208 40 107.71 240.47 25.6 CCH 0800 UTC 17 Sep 1993 21.7 113.6 199301619 35 71.01 218.41 54.8 WGL 0800 UTC 17 Sep 1993 21.7 113.6 199301619 35 90.13 233.47 43.6 HKO 0200 UTC 17 Aug 1971 22.1 113.7 197101825 40 53.62 245.07 54.5

2 In addition, we compare the maximum wind gusts of gale force winds (17 m s 1) in this study. The TD 2 ever recorded at these six weather stations due to his- category, which has an upper bound of 17.1 m s 1 for torical TCs (Tables 4 and 5). Table 4 indicates that the SMWNCTC, is not considered in the comparison. It can 2 maximum gust is at CCH with a value of 54.8 m s 1. The be seen from Fig. 6 that a TC’s size ranges from 100 to maximum record was induced by TC 199301619 at 550 km. Comparing the medians of the four different TC 0800 LT 17 September 1993. The wind gust records at categories, SSTY has the largest size of 330 km, followed the three stations near Shenzhen before 2004 are not by TY with a size of 300 km and STS at 260 km. The available. The maximum recorded gusts at the three median of the TS size is the smallest, with a value of stations in the Shenzhen area were all due to TC 1208, 180 km. The median value of each of the TC categories is Typhoon Vicente. This event occurred in July 2012. The tested and found to be significantly different from one of maximum wind gusts recorded at the six weather sta- the other TC categories by use of a Kruskal–Wallis test, tions during the period from January 2004 to June 2014 which is a nonparametric version of the classical are compared, and the results are shown in Table 5.It ANOVA test (McDonald 2014). Figure 6 indicates that can be seen from this table that during this period the the sizes of TCs can be implicitly reflected by their in- maximum gusts at these stations are all induced by TC tensity categories. 1208, Typhoon Vicente. With the knowledge of the medians for different TC Table 5 shows that the maximum gusts at CCH and categories, each TC category can be separated into WGL due to Typhoon Vicente are bigger than the gust large- and small-sized groups. In the previous section, observations at the three stations around Shenzhen. CCH is found to suffer the most from TC impacts, and When the maximum gusts are observed at these six CCH has relatively more wind observations compared stations, the range of the azimuth is from 197.618 to with the stations near Shenzhen. Therefore, CCH is used 245.078. The corresponding distances between the TC as an example to explore the influence of TC size on centers and the stations are from 50 to 140 km. wind gusts at AWSs. Figure 7 shows the maximum gust at CCH induced by TYs for the period from 2006 to June c. The influence of TC size on AWS winds 2014. Figure 7a is for the gust observations at CCH in- Since the TC size information is absent from our data duced by TYs that are of large size (TY size $ 300 km), source before 2006, to use the TC observation data be- and Fig. 7b is for the gust observations at CCH induced fore 2006 and to get as many samples as possible, the by TYs of small size (TY size # 300 km). It can be seen sizes of TCs in our previous subsections have not been from Fig. 7 that larger TYs would usually incur bigger distinguished explicitly. In this section, the influence of a gusts than smaller TYs. However, all of this size influ- TC’s size on an AWS’s winds will be investigated by ence information has already been included in Fig. 5. using the TC dataset from 2006 to June 2014. As usual Figure 5g for CCH is actually a combination of Figs. 7a (Wu et al. 2015), the TC’s size is measured by the radius and 7b. The purpose in using Fig. 5 is to find the

TABLE 5. The maximum wind gust at the six stations during the period from Jan 2004 to June 2014.

2 Station Time and date TC lat (8N) TC lon (8E) TC ID SMWNCTC Distance (km) Azimuth (8) Gust (m s 1) YICT 0200 UTC 24 Jul 2012 21.7 113.3 1208 40 139.81 226.40 35.7 MWP 0200 UTC 24 Jul 2012 21.7 113.3 1208 40 106.07 213.66 29.6 SFT 0400 UTC 24 Jul 2012 22 113 1208 40 107.71 240.47 25.6 CCH 0100 UTC 24 Jul 2012 21.6 113.5 1208 40 86.15 219.23 51 WGL 2300 UTC 23 Jul 2012 21.3 114 1208 40 103.09 197.61 41.4 HKO 0000 UTC 24 Jul 2012 21.4 113.7 1208 40 111.70 205.94 32.3

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categories of TCs. As the number of occurrences and level of destruction induced by TYs and STSs are rela- tively higher than for other TC categories, the GG oc- currence probabilities for TYs and STSs are shown here (Fig. 8). It is worth noting that for the 18318 grid area across mainland China, where there is no historical re- cord of TCs passing through the region, the GG occur- rence is assigned a value of 0. The gust gale probabilities at YICT due to TYs and STSs during 2007 –14 are plotted in Fig. 8. From the figure, it can be seen that the GG probability at YICT is extremely asymmetric with respect to the locations of the TCs. GG will probably occur at YICT only when FIG. 6. Boxplots of the TC sizes for SSTYs, TYs, STSs, and TSs TYs/STSs are within a distance of 150 km if TCs are (the red horizontal line in each of the boxplots refers to the median approaching the station from the southeast direction. value of the size for each TC category). However, if TYs are from the west and southwest of YICT, GG might occur even when TYs are at a distance maximum possible wind gust at an AWS. Because TYs of more than 400 km from the station (Fig. 8a). From the of all sizes are included in Fig. 5, the gusts for a certain figure, it can be seen that the area of high GG proba- area are not uniform. When a TY of a certain size passes bility ( p . 0.9) due to TYs is bigger than the probability through an area within 700 km of a station, the larger due to STSs. gust value in that area should be referred to if the TY is Similarly, the GG probabilities for the other five bigger than 300 km, and the smaller gust value in that AWSs based on all of their corresponding historical area should be referred to if the TY is smaller than observations up to June 2014 (Table 1) are computed 300 km. Similar processes can be followed for SSTY, and plotted. Here, only the cases due to the TY category STS, and TS cases. (Fig. 9) are shown. From the figure, it can be seen that the area and shape of the high GG probability ( p . 0.9) d. Probability of occurrence of gales are different from one another. The stations of CCH, To guarantee the safety and continuity of the termi- WGL, and YICT are more vulnerable to the impacts of nal’s daily operations, the probability of occurrence of TCs compared with the other three stations. As a ferry gust gales under different categories of TCs also need to terminal for ship passengers, SFT is the station least be computed in addition to Fig. 4, which can reflect the vulnerable to TYs, which can be seen from Figs. 9 and 5. potential gust influence at the station due to different Similar to Fig. 5, all of the plots of GG occurrence

FIG. 7. Maximum gusts at CCH induced by TYs passing through a 18318 grid box within a distance of 700 km for TYs with size (a) larger and (b) smaller than or equal to 300 km.

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FIG. 8. Probability of GG occurrences at YICT due to (a) TYs and (b) STSs. The black dot indicates the location of YICT. probabilities for these six AWSs are asymmetric relative For the TC’s intensity, it will usually take at least 12 h to the location of the corresponding AWSs. For CCH, for a TC to change appreciably (Willoughby et al. 2007; WGL, and YICT, when TYs are from the southwest Li et al. 2015). Therefore, the TC’s intensity for the fu- relative to the stations, the GG occurrence probability is ture 12 and 24 h can be derived by rules of thumb fol- very high even if the storm is as much as 400 km away. lowed by the forecasters, as well as from the NWP. Figure 5 can be used as a reference when forecasting the Knowing the TC’s track and intensity, the potential potential maximum gust at the AWS due to a future TC maximum gust at YICT can then be estimated by ref- passing a certain place within 700 km of the station; erencing Fig. 4. Similarly, the maximum wind gust at the while Fig. 9 can provide operational forecasters and other five stations induced by different categories of TCs policy-makers with information related to GG occur- can be referenced by their corresponding figures show- rence probabilities. ing the spatial wind gust distribution (figures similar to Fig. 4). The boxplots in Figs. 10b–e show the gust fore- casts produced by the statistical scheme at these six 4. Application of the statistical relationship and stations as influenced by Rammasun, TD 465, Kalmaegi, discussion and Fung-Wong, respectively. The historical gust sam- In the previous section, a relationship has been ple sizes due to the corresponding TC used for the gust established between the maximum wind gust and GG forecast at each station are indicated in parentheses occurrence probability at an AWS with the TC’s dis- along the x axis and the confidence levels for the fore- tance, azimuth, intensity, and size based on the historical casts are shown in parentheses as well. For example, the observations up to June 2014. In this section, the per- first entry along the x axis in Fig. 10b, YICT (2, 33%), formance of our statistical scheme will be tested. From represents two historical samples that have been used to July 2014 to the end of that year, there were four TCs forecast the maximum gust at YICT induced by Ram- that passed nearby or made landfall across South masun; the confidence level for this forecasting is 33%. China. Figure 10a shows the tracks of these four TCs: In addition, the predictions of the maximum wind at the Rammasun (illustrated by the diamond line), TD 465 six stations from the latest forecast (the model forecast (illustrated by the square line), Kalmaegi (illustrated produced 24 h before the maximum wind occurrence) by the triangular line), and Fung-Wong (illustrated by from the European Centre for Medium-Range Weather thecircleline). Forecasts (ECMWF) model is shown by the magenta In practice, if there is a TC passing over the South rectangles in the figures. The real wind gust observations China Sea in the next several days, the information on at the six stations are illustrated as well by green ellipses. the TC’s possible track can be obtained from the latest From the figure, it can be seen that for most of the cases, numeral weather prediction model, as the NWP forecast the wind forecasts produced by the ECMWF model at is reliable for providing the TC’s track for 12 and 24 h the six stations are far less than the real wind gust ob- into the future (DeMaria and Gross 2013; Cecil et al. servations, especially as a result of the TCs with high 2004; Knaff et al. 2005; Rogers et al. 2006; Li et al. 2015). intensity passing through the third quadrant relative to

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FIG. 9. Probability of GG occurrences at the six AWSs due to TYs: (a) CCH, (b) HKO, (c) WGL, (d) YICT, (e) MWP, and (f) SFT. The black dots refer to the locations of the respective AWSs. the six stations, such as Rammasun and Kalmaegi. By Therefore, the lower sections of the corresponding comparison, the wind gust range forecasts produced by boxplots could be referenced to forecasts of the wind the statistical scheme encompass the real gust observa- gusts at the six stations due to Rammasun, and the tions at those six stations for most of the time. Fur- upper parts of the corresponding boxplots could be thermore, the sizes of the four TCs could be used to referenced to forecasts of the wind gusts at the six narrow the forecasting gust range estimated by the sta- stations due to Kalmaegi, yielding more accurate gust tistical scheme. For example, the sizes of Rammasun predictions. From Fig. 10, it can be seen that the sta- and Kalmaegi were 260 and 320 km, respectively, around tistical scheme can provide better predictions of the the time of maximum gust occurrence. Rammasun was a maximum gusts at the six AWSs induced by TCs com- small-sized SuTY and Kalmaegi was a large-sized TY. pared with the NWP method.

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FIG. 10. (a) The tracks of four TCs, Rammasun (diamond line), TD 465 (square line), Kalmaegi (triangular line), and Fung-Wong (circle line), from July 2014 to September 2014. (b) The maximum wind gust observations at the six stations induced by Rammasun; the maximum wind gusts forecasted at the six stations by the statistical scheme and the ECMWF model are also shown; the y axis refers to the gust value 2 (m s 1) and the x axis presents results from different stations. The parentheses under each of the station names include two numbers: the first number refers to the samples used for gust forecasting and the second number refers to the confidence level for the gust forecasting. (c) As in (b), but for TD465. (d) As in (b), but for Kalmaegi. (e) As in (b), but for Fung-Wong.

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In addition to Figs. 4 and 5, Fig. 9 is also valuable in to the sea in the south/southeast directions, are generally operational practice. For example, Kalmaegi was pre- larger than the wind gusts at HKO, MWP, and SFT dicted to enter the area of high probability ( p . 0.9) of GG stations, which have land existing in those two di- occurrence at 2200 LT 15 September (see Fig. 9d). SZMB rections. For YICT, CCH, and WGL, gust gales might therefore hoisted a yellow warning (issued when average occur when TYs are at the distance of around 400 km 2 wind speeds may exceed 17 m s 1 or wind gusts may ex- from the stations with the azimuths of 1808–2408. 2 ceed 20.8 m s 1; all outdoor work should be stopped) at The statistical methods proposed in this study can be 2000 LT 15 September. The whole warning process in the used as references for operational forecasters when pre- Shenzhen region for Kalmaegi was later proven to be very dicting the potential wind influences at those AWSs due timely and correct. to future TCs. As of this writing, this technique is already The plots from the statistical scheme can provide in use at the Shenzhen Meteorological Bureau and serves valuable information to operational forecasters when as a valuable reference for predicting the wind gusts due predicting the potential maximum gust at certain sta- to TCs at YICT, MWP, and SFT. During the 2014 TC tions due to a TC. However, it must be acknowledged season, the technique successfully provided accurate that there are still some uncertainties for the plots be- forecasts of maximum gusts and the durations of the cause of the small sample sizes of some of the TC cate- winds influencing the Shenzhen region during Rammasun gories as a result of the natural features, as well as the and Kalmaegi; these events resulted in great damage history of the stations. Generally, the gust predictions at across Guangdong Province. Due to the timely and ac- the AWSs near Hong Kong are more reliable compared curate forecasts, no lives were lost in Shenzhen and the with the gust predictions at the AWSs close to Shenzhen loss of property was not as high as it could have been. as a result of the sample sizes. With more observations of TCs passing close to or landfalling along the southeast Acknowledgments. This paper is supported by the China coast in the future, the database of TCs will ex- Natural Science Foundation of Guangdong Province with pand and the wind gust predictions made by the statis- Grants 2015A030313742 and 2016A050503035, and the tical scheme will become more accurate. Generally, the Innovation of Science and Technology Commission of latest records should help to more accurately reflect the Shenzhen Municipality with Grant JCYJ20120617115926138 conditions of the changed environment. Therefore, and JCYJ20150521144320984. when the sample is big enough in the future, the dataset should be updated to include the latest 30–40 yr of his- torical observations, and the older entries will be REFERENCES removed. Bos, W. V. D., 2016: Wind influence on container handling, It is worth noting that although this study focuses on equipment and stacking. 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