DECEMBER 2016 L I E T A L . 1817 An Operational Statistical Scheme for Tropical Cyclone 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 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 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 South China, 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 Hong Kong 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 Guangdong 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 South China Sea 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 Hong Kong Observatory 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).