![Operational Forecast of Rainfall Induced by Landfalling Tropical Cyclones Along Guangdong Coast [J]](http://data.docslib.org/img/5edddff91c372ef1f428adb7589887fa-1.webp)
Vol.26 No.1 JOURNALOFTROPICALMETEOROLOGY March 2020
Article ID: 1006-8775(2020) 01-0001-13
OPERATIONALFORECASTOFRAINFALLINDUCEDBYLANDFALLING TROPICALCYCLONESALONGGUANGDONGCOAST
LI Qing-lan (李晴岚) 1, LIU Bing-rong (刘炳荣) 1, WAN Qi-lin (万齐林) 2, WANG Yu-qing (王玉清) 3, LI Guang-xin (李广鑫) 1, LI Tie-jian (李铁键) 4 , LAN Hong-ping (兰红平)5, FENG Sheng-zhong (冯圣中) 1, LIU Chun-xia (刘春霞) 2 (1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China; 2. Guangzhou Institute of Tropical and Marine Meteorology, CMA, Guangzhou 510641 China; 3. University of Hawaii at Manoa, Honolulu, HI 96822 USA; 4. Department of Hydraulic Engineering, Tsinghua University, Beijing 100084 China; 5. Shenzhen Meteorological Bureau, Shenzhen, Guangdong 518040 China)
Abstract: Following previous studies of the rainfall forecast in Shenzhen owing to landfalling tropical cyclones (TCs), a nonparametric statistical scheme based on the classification of the landfalling TCs is applied to analyze and forecast the rainfall induced by landfalling TCs in the coastal area of Guangdong province, China. All the TCs landfalling with the distance less than 700 kilometers to the 8 coastal stations in Guangdong province during 1950—2013 are categorized according to their landfalling position and intensity. The daily rainfall records of all the 8 meteorological stations are obtained and analyzed. The maximum daily rainfall and the maximum 3 days’accumulated rainfall at the 8 coastal stations induced by each category of TCs during the TC landfall period (a couple of days before and after TC landfalling time) from 1950 to 2013 are computed by the percentile estimation and illustrated by boxplots. These boxplots can be used to estimate the rainfall induced by landfalling TC of the same category in the future. The statistical boxplot scheme is further coupled with the model outputs from the European Centre for Medium-Range Weather Forecasts (ECMWF) to predict the rainfall induced by landfalling TCs along the coastal area. The TCs landfalling in south China from 2014 to 2017 and the corresponding rainfall at the 8 stations area are used to evaluate the performance of these boxplots and coupled boxplots schemes. Results show that the statistical boxplots scheme and coupled boxplots scheme can perform better than ECMWF model in the operational rainfall forecast along the coastal area in south China. Key words: tropical cyclone; coastal area; rainfall forecast; statistical boxplot scheme; coupled boxplot scheme CLC number: P444 Document code: A https://doi.org/10.16555/j.1006-8775.2020.001
Although there are urgent demands, the prediction 1 INTRODUCTION of local rainfall induced by TCs is very hard and Tropical cyclone (TC) is one of the most lethal and challenging (Businger et al. [5]; AMS [6]; Willoughby et powerful natural phenomena, especially in China (Ren et al. [7]). Nowadays, Numerical Weather Prediction (NWP) al. [1]; Xiao et al. [2]). TCs will usually bring heavy models perform well in forecasting the track of TCs, but rainstorm in the coastal area, which may cause the forecast of TC rainfall by NWP models remains catastrophic flood and mudslide, resulting in tremendous problematic (Willoughby et al. [7]; Roy and loss of lives and property (Rappaport et al. [3]; Li et Kovordanyi [8]). NWP is still not good enough to capture al. [4]). Timely warning of the possible disasters caused the complicated intrinsic dynamic processes of TCs and by TCs is therefore essential for the local authority to the environmental conditions, which are strongly related protect people, which demands accurate and timely to the precipitation induced by TCs (Saha et al. [9]). It is forecast of rainfall induced by TCs. reported that the rainfall is related to the geographical location and the intensity of TC (Lonfat et al. [10]). Received 2019-04-28; Revised 2019-12-15; Accepted Precipitation decreases logarithmically with the distance 2020-02-15 from the center of TC (Riehl and Malkus [11]; Simpson Foundation items: Key Research and Development Projects in and Riehe [12]; Kidder et al. [13]). Besides, the Guangdong Province (2019B111101002); Program of Science, precipitation and the intensity of TC have a significantly Technology and Innovation Commission of Shenzhen positive correlation (Pfost [14]). Furthermore, the Municipality (JCYJ20170413164957461, GGFW20170731140 distribution of the precipitation induced by TCs is not 31767) symmetric and the rainfall usually concentrates on one Biography: LI Qing-lan, Ph. D., primarily undertaking research side of the track of a TC (Elsberry [15]; Corbosiero and on quantitatively forecast of wind and rainfall induced by Molinari [16]). In the Northern Hemisphere, landfalling landfalling and offshore tropical cyclones, and tropical TC will usually induce stronger rainfall on the right cyclones’intensity forecast. hand side than on the left hand side, as different surface Corresponding author: LI Qing-lan, e-mail: [email protected] friction between land and water may lead to low level 2 Journal of Tropical Meteorology Vol.26 convergence and bring more moisture to the right hand This paper consists of four parts. Section 2 will side when a TC approaches the coast (Kimball [17]; Li et introduce the data and methodology. Section 3 will al. [18]). Researchers pay much attention to pursue a more detail the procedures, results and discussions. The reliable approach than NWP to forecast the rainfall summary and conclusion will be presented in section 4. induced by TCs. Mark et al. [19] proposed a Rainfall 2 DATAANDMETHODOLOGY Climatology and Persistence model (R-CLIPER) which gave a description of the spatial and temporal 2.1 Data distribution of rainfall induced by a TC. The rainfall Eight coastal stations are involved in this study; climatology was described as a relationship with the namely, they are stations of Nanao, Shanwei, Shenzhen, radius and time after landfall. However, the model Zhuhai, Shangchuan Island, Yangjiang, Zhanjiang, and resulted in a circularly symmetric rainfall distribution, Xuwen, respectively (from east to west). The which was not true in reality. By incorporating the information of these stations is listed in Table 1. impact of vertical shear and topography into the original R-CLIPER, Lonfat et al. [20] developed the Parametric Table 1. The details of the 8 coastal stations. Hurricane Rainfall Model (PHRaM). PHRaM made Stations Abbreviation Duration of rainfall data some improvement in the prediction in terms of the Nanao NA 1957-2017 rainfall spatial distribution and amplitude compared with R-CLIPER. However, the model showed a significant Shanwei SW 1952-2017 bias toward lower rain amount when the rainfall Shenzhen SZ 1952-2017 prediction at a distance far away from the storm center, Zhuhai ZH 1961-2017 [14] such as 300-400km. Phost identified the moving Shangchuan Island SCI 1957-2017 speed of a TC as the key factor for estimating the Yangjiang YJ 1952-2017 maximum landfalling-TC-induced rainfall along the Zhanjiang ZJ 1951-2017 northern Gulf of Mexico coast and Florida peninsula. However, there was no method proposed in the study to Xuwen XW 1956-2017 forecast the rainfall along the TC swath except for the maximum rainfall. The daily rainfall records of these eight stations are Guangdong province, located in south China with obtained from China Meteorological Administration 4114 kilometers of coastline, is one of the most (CMA), which starts from 8pm (Beijing time) of the vulnerable areas to TC disasters in China. The province previous day to 8pm of the current day. From 1951- suffers great loss from landfalling TCs every year (Liang 2017, there are 674 TCs landfalling in south China. The et al. [21]; Ye [22]; Yuan et al. [23]). In fact, even a slight TC characteristics, including TC landfalling intensity, typhoon may lead to severe economic loss in this region landfalling time and position, are obtained from CMA as (Yin et al. [24]). Typhoon Mujigae in 2015 hit Guangdong well. Historical TC and rainfall data before December province in October, bringing powerful winds and heavy 31, 2013 are used for the calibration of the forecasting rainfall as it approached the province. It affected more scheme in this study and the data after 2013 are used for than 3.5 million people and devastated over 3000 houses the validation of the scheme. and 282 thousand hectares of cropland (Xinhuanet 2.2 Methodology News [25]). To protect people from such kind of natural 2.2.1 TC CLASSIFICATION disaster, decision-makers need to issue timely and Following the method of Li et al.[18], the landfalling accurate TC warnings. However, in China, there are few TCs are divided into group A and group B for each of operational quantitative precipitation forecast techniques the coastal stations. Different from Li et al.[18], we and the prediction of local rainfall induced by TC employed a grouping method based on the different mainly relies on the empirical estimation and subjective wind direction at the station, i.e., the onshore wind or the experience of forecasters with the help of NWP models offshore wind, rather than the landing direction of east (Chen et al. [26]; Xu et al. [27]). Therefore, it is necessary or west. Group A contains the TCs which bring onshore to develop and improve the quantitative precipitation wind to those stations. For Nanao (NA), Shanwei (SW), forecast techniques based on the knowledge of rainfall Shenzhen (SZ), Zhuhai (ZH), Shangchuandao Island induced by TCs in this area. Li et al. [18] developed an (SCI) and Yangjiang (YJ), the corresponding TCs of operational statistical scheme to forecast rainfall induced group A are those TCs landing to the west of the specific by TCs in Shenzhen and it performed well. In this study, station; however, for the station of Zhanjiang (ZJ) and the scheme proposed by Li et al. [18] will be further Xuwen (XW), the corresponding TCs of group A are improved to couple with NWP to forecast the those landing to the south of the specific station. In precipitation due to TCs at several coastal contrast, group B contains TCs landing on the other side meteorological stations in Guangdong province. The which brings offshore wind to the corresponding performance of the improved scheme in the study area stations. Also based on Li et al.[18], all the TCs will be evaluated. landfalling within the distance of 700 kilometers to each No.1 LI Qing-lan (李晴岚), LIU Bing-rong (刘炳荣), et al. 3
110°E 115°E
25°N
20°N
Figure 1. Locations of the 8 coastal stations. of the specific station are considered. According to the Meteorological Administration which is shown in Table distance between the TC landfalling position and the 2, there are six categories of TCs, i. e. super typhoon specific station, TCs are further grouped into A1, A2, (SuperTY), severe typhoon (STY), typhoon (TY), severe A3, … , A7, B1, B2, …, B7, based on the criteria of 100 tropical storm (STS), tropical storm (TS) and tropical kilometers and wind directions. For each station, A1 depression (TD) based on the maximum wind speed near group refers to TCs with the distance of 0-100 km the TC center. In accordance with Li et al.[18], all these (including 100 km) to the station, bringing onshore wind TCs are grouped into four categories in this study, i. e., to the corresponding station; A2 group contains TCs TTY(including SuperTY, STY, and TY), STS, TS and with the distance of 101-200 km to the station, bringing TD. Thus, according to their landfalling direction, onshore wind to the corresponding station, etc. Group landfalling distance and landfalling intensity, all the TCs B7 then contains TCs with the distance of 601-700 km are classified into A1-TTY, A1-STS, A1-TS, A1-TD, A2- to the station, bringing offshore wind to the TTY, A2-STS, A2-TS, A2-TD, … , B7-TTY, B7-STS, corresponding station. B7-TS, and B7-TD. Finally, all the landfalling TCs are According to the TC intensity criterion of China classified into 56 groups.
Table 2. Categories of TC based on the intensity criterion of China Meteorological Administration.
Category Abbreviation Sustained maximum wind speed near center
Super Typhoon SuperTY >=51m s-1 Severe Typhoon STY 41.5 m s-1 ～50.9 m s-1 Typhoon TY 32.7 m s-1 ～41.4 m s-1 Severe Tropical Storm STS 24.5 m s-1 ～32.6 m s-1 Tropical Storm TS 17.2 m s-1 ～24.4 m s-1 Tropical Depression TD 10.8 m s-1 ～17.1 m s-1
2.2.2 RAINFALL DATA PROCESSING daily rainfall caused by TCs is the maximum value of In the late 1980s, China Meteorological the rainfall records during the landfalling period, which Administration began to record hourly weather is defined as the period from two days before TC variables. Before the 1980s, rainfall was recorded on a landfall to three days after landfall[18]. As the landing daily basis. In order to have sufficient rainfall record, the time for a TC might vary from 00 to 23 in a day, only influence of rainfall induced by landfalling TCs is computing the maximum daily rainfall is not enough to computed on a daily basis in this study. The maximum reflect the influence due to a landfalling TC. Therefore, 4 Journal of Tropical Meteorology Vol.26 the maximum 3-day accumulative rainfall is computed forecast for the corresponding station (ECst) and rainfall as well, which is the maximum three consecutive days extreme forecast, including the maximum of the accumulative rainfall during the landfalling period. predicted rainfall (ECmax) and the minimum of the 2.2.3 BOXPLOT predicted rainfall (ECmin), for the city area (the city Due to the daily rainfall’s non-normal distribution which contains the station) by ECMWF model can be characteristics, non-parametric statistical methods are used as references to adjust the rainfall forecast by the more appropriate for analyzing the rainfall data (Pett[28]; boxplot scheme. Three ways are proposed to adjust the Wilks[29]; Li et al.[18]). Boxplot is an efficient and widely boxplot scheme, which are described in detail as follows. used graphic tool for data analysis (Tukey[30]). There are The 50% quantile scheme six elements in a boxplot, including the median, the The key step for this scheme is to adjust the upper lower quartile, the upper quartile, the lower whisker, the bound and the lower bound according to the ECst. While upper whisker and the outliers, all of which can be the ECst is greater than the 75% percentile of the calculated from the samples. boxplot, the upper bound is the bigger one of the ECmax In this study, after the landfalling TCs are and the maximum of the boxplot. And the lower bound categorized for each station, the corresponding rainfall is the median of the boxplot. While the ECst is less than records at the station due to each TC group can be the 25% percentile of the boxplot, the lower bound is the analyzed by boxplot method. In practical application, smaller one of the ECmin and the minimum of the according to the track and intensity forecast by NWP boxplot. And the upper bound is the median of the models and the experience of forecasters, the category of boxplot. Otherwise, the upper bound is the 75% the TC, i.e. TTY, STS, TS, TD, can be determined 12-36 percentile of the boxplot and the lower bound is 25% hours in advance if the TC is going to land along the percentile of the boxplot. coast of south China. Then, referring to the boxplot of The 100% quantile with ECMWF model the specific TC group that the landfalling TC belongs to, extreme minimum scheme forecasters can obtain an estimation of the maximum The upper bound is the maximum of the boxplot daily rainfall and the maximum 3-day accumulated and the lower bound is the smaller one of the ECmin and rainfall that the landfalling TC may cause, including the the minimum of the boxplot. Through this adjustment, medians, the interquartile ranges, etc. the scheme will include more information of the 2.2.4 COUPLINGECMWFNWPOUTPUTWITHTHE unbalanced rainfall distribution which is neglected by HISTORICAL RECORDS the boxplot method, although the lower bound of the Rainfall is a stochastic process in time and in space rainfall is not the key point of typhoon rainfall forecast. (Johnson et al.[31]; Chowdhury et al.[32]; Onyutha and The full quantile scheme with ECMWF model Willems[33]). Although the distribution for a particular extreme outputs rainfall event is stochastic, the rainfall amount at a In this scheme, the upper bound is the bigger one of station for long time is stable and will have similar the EC and the maximum of the boxplot. The lower pattern with its nearby area. Therefore, the boxplot of max bound is the smaller one of the EC and the minimum the historical rainfall induced by a TC group which min of the boxplot. This scheme can reflect the potential reflects the stochastic rainfall characteristics in time at maximum area's rainfall range for the coming the corresponding station can approximately be treated precipitation due to a TC. as the spatial rainfall distribution pattern for the nearby 2.2.5 THEESTIMATIONOFACCURACY area at a rainfall event. In order to evaluate the performance of the coupled Meanwhile, NWP model can roughly reflect the statistical boxplot scheme and the ECMWF model in atmospheric condition of a TC process although the forecasting TC induced rainfall, quantitative estimation model is not good enough to capture the complicated of the accuracy is conducted. In each specific city which intrinsic dynamic processes of the TCs. Therefore, the contains a coastal national basic meteorological station, rainfall prediction provided by the NWP model can be there are numbers of other automatic weather stations also used as an important reference for forecasters. recording rainfall. For each rainfall event, it is not easy European Centre for Medium-Range Weather Forecasts to predict well the exact rainfall at a specific station. (ECMWF) model is renowned worldwide for providing However, people and the local authorities will usually the most accurate medium-range global weather forecast care about the potential maximum rainfall in a certain [34] [35] [36] (Halperin et al. ; Wang et al. ; Magnusson et al. ). area. ECMWF model can provide a rainfall range for the This study will couple the weather forecast information whole urban area, while the coupled statistical boxplot from ECMWF model with the historical observation scheme can also provide a rainfall range. Suppose there data to generate a more accurate forecast for TC rainfall are altogether a stations in the city and there are x at the coastal area. Generally, ECMWF model tends to stations with the rainfall record between the upper bound underestimate the amount of rainfall induced by heavy and the lower bound of the forecast, the accuracy of the storm; however, it performs well in forecasting light x [38] [37] forecasting would then be (Yates et al. ). rainfall (Zhong et al. ). For each TC, the rainfall a No.1 LI Qing-lan (李晴岚), LIU Bing-rong (刘炳荣), et al. 5
and along the coastline of south China. The detailed 3 RESULTSANDDISCUSSIONS information of these landfalling TCs is listed in Table 3. 3.1 Boxplots for the precipitation These TCs are used to evaluate the performance of the All the TCs landfalling within the distance of 700 coupled statistical boxplot scheme. kilometers to the eight meteorological stations are taken For a specific station, according to the landfalling into account. Following the procedures mentioned in position and intensity, the TCs in table 3 will be section 2, the historical landfalling TCs from 1950 to classified individually. Then the TC-induced rainfall at a 2013 are grouped into 56 categories. For all the city area can be figured out according to the boxplot historical rainfall records, the non-parametric statistical scheme. Meanwhile, the rainfall forecast by ECMWF method is employed in the analysis. The maximum daily model can also provide a rainfall range for the city area, rainfall (R24) and the 3-day accumulated rainfall (R72) including the ECst, ECmin and ECmax. With the reference during the TC landfalling period for each TC category at of ECMWF models outputs, the lower bound and the each station are estimated and the results are described upper bound of rainfall forecast for each of the coupled by the statistical boxplot scheme. statistical boxplot scheme can be computed as well, Figure 2 shows the boxplots of R24 (Fig. 2a) and according to the methods mentioned in section 2.2.4. R72 (Fig. 2b) for each TC category during the TCs By comparing the forecast accuracy of the rainfall landfalling period at Shenzhen and Zhuhai, for instance. forecast by ECMWF model and by the coupled In each figure, the subgraphs from the top to the bottom statistical boxplot schemes, we can estimate the illustrate the rainfall characteristic of the groups which performance of each scheme. The average forecasting belong to TTY, STS, TS, and TD, respectively. In each accuracy for each of the forecasting schemes is subgraph, the thick black solid line in the middle presented in Table 4 and Table 5. Furthermore, the represents the position of the meteorological station, forecasting accuracy at the national basic stations by while the ticks of the horizontal coordinate-axis indicate each of the forecasting schemes is computed, and the the distance to the station. For example, A1 represents results are presented in Table 4 and Table 5 as well. the TCs landfalling within the distance of 100 km which Compared with the rainfall forecast at the AWSs bring onshore wind to the stations; B1 represents the over the city area by ECMWF model, the forecasts by TCs landfalling within the distance of 100 km which the coupled statistical schemes are generally better. The bring offshore wind to the station. From Fig. 2, it can be 50% quantile scheme and the 100% quantile scheme seen that the maximum daily rainfall and 3-day without extreme ECMWF output perform better and accumulated rainfall induced by different types of TCs have higher accuracy at 7 cities compared with the at Shenzhen and Zhuhai have similar patterns. In forecast by ECMWF model except for ZJ city. The full general, with the increase of distance, the rainfall quantile scheme with the extreme output of ECMWF induced by landfalling TCs tends to be weaker and the and the 100% quantile scheme with ECmin, which decreasing rate of this tendency becomes slower. With consider the advantages of ECMWF model and the same landfalling distance and direction, TTY will statistical scheme, overwhelm the ECMWF model’s generally bring more rainfall to the stations than STS forecast at all the 8 cities. As the statistical boxplot and TS do, especially within the distance of 300 km to scheme mainly considers the maximum daily rainfall or the stations. The boxplots of the precipitation due to TCs maximum 3-day accumulative rainfall, the high rainfall landfalling on the left side and the right side are not range over the area might be covered by the scheme. For symmetric along the central thick black solid line, the coupled scheme including the ECmin, as well as the especially for the TD category. Compared with the TCs scheme including ECMWF extreme output (including landfalling on the right side which bring offshore wind ECmax and ECmin), the rainfall range certainly becomes to the corresponding station, TCs landfalling on the left larger, especially enlarging the lower rainfall range, side which bring on shore wind to the station may which will therefore improve the forecast accuracy over generally induce stronger rainfall. the city area. Furthermore from Table 4 and Table 5, it It is worth mentioning that the weather observation can be seen that the forecasting accuracy at the national in Zhuhai starts from September 1961, which is later basic stations is generally higher than the forecasting than the observation in Shenzhen. Zhuhai station is accuracy at the AWSs by the forecasting schemes. This located to the west of Shenzhen station with a distance is reasonable as those forecasting schemes are originally of around 70 kilometers. For this reason, most of the developed based on the historical rainfall observations TCs in group B1 at Zhuhai station will be categorized of the national basic station. into group A1 at Shenzhen station. Similarly, most TCs From Table 4 and Table 5, it can be seen that the in group A1 at Zhuhai station will be categorized into coupled statistical schemes perform well in forecasting group A2 at Shenzhen station. the rainfall induced by TCs along the coastal area in 3.2 Cases evaluation: forecasting and discussion Guangdong province. It should be pointed out that During the year from 2014 to 2017, there are though the extreme minimum of ECMWF model altogether 25 TCs landfalling in the north of Vietnam improves the forecast accuracy of the statistical scheme, 6 Journal of Tropical Meteorology Vol.26
Shenzhen Zhuhai ) ) 300 300 mm ( mm ( 250 250 200 200 150 150 100 100 50 50
0 TTY by induced Rain 0 anidcdb TTY by induced Rain A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 ) ) 300 300 mm mm ( ( 250 250 200 200 150 150 100 100 50 50 anidcdb STS by induced Rain anidcdb STS by induced Rain 0 0 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7
) 300 ) 300 mm mm
( 250 ( 250 200 200 150 150 100 100 50 50
anidcdb TS by induced Rain 0 TS by induced Rain 0 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7
) 300 ) 300 mm mm
( 250 ( 250 200 200 150 150 100 100 50 50
anidcdb TD by induced Rain 0 TD by induced Rain 0 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 (a)
Shenzhen ) Zhuhai ) 500 500 mm mm ( ( 400 400
300 300
200 200
100 100 anidcdb TTY by induced Rain 0 TTY by induced Rrain 0 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 ) 500 ) 500 mm mm ( 400 ( 400
300 300
200 200
100 100 anidcdb TTS by induced Rain 0 TTS by induced Rain 0 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7
) 500 ) 500 mm mm
( 400 ( 400
300 300
200 200
100 100
anidcdb TS by induced Rain 0 TS by induced Rain 0 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 ) 500 ) 500 mm mm ( 400 ( 400
300 300
200 200
100 100 anidcdb TD by induced Rain 0 TD by induced Rain 0 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 A7 A6 A5 A4 A3 A2 A1 B1 B2 B3 B4 B5 B6 B7 (b) Figure 2. Boxplots of the maximum daily rainfall (a) and 3 days accumulated rainfall (b) induced by different TC categories during landfalling period in Shenzhen from 1952 to 2013 (left) and in Zhuhai from 1961 to 2013 (right): the first row is for TTY, the second row is for TTS, the third row is for TS and the fourth row is for TD. No.1 LI Qing-lan (李晴岚), LIU Bing-rong (刘炳荣), et al. 7
Table 3. The details of the 25 TCs landfalling within the distance of 700 km to the 8 coastal stations in Guangdong province during the period from 2014 to 2017.
Name Year Landing longitude (°E) Landing latitude (°N) Landing intensity (m s-1)
HAGIBIS 2014 116.7 23.2 20 RAMMASUN 2014 110.3 20.3 55 KALMAEGI 2014 110.9 19.8 40 MATMO 2014 119.5 25.2 33 2014TD 2014 110.4 21.1 12 KUJIRA 2015 110.5 18.9 25 LINFA 2015 116.2 22.8 35 SOUDELOR 2015 119.2 25.1 38 DUJUAN 2015 119.1 25.1 33 MUJIGAE 2015 110.6 21.1 50 NEPARTAK 2016 118.7 24.7 25 MIRINAE 2016 110.2 18.7 28 NIDA 2016 114.5 22.5 40 DIANMU 2016 110.5 20.6 20 MERANTI 2016 118.3 24.5 50 MEGI 2016 118.9 25 33 SARIKA 2016 110.4 18.8 45 HAIMA 2016 115.1 22.8 42 2016TD 2016 112.5 21.8 14 MERBOK 2017 114.5 22.5 23 ROKE 2017 114.3 22.4 20 HAITANG 2017 119.5 22.5 20 NESAT 2017 119.5 25.5 33 HATO 2017 112.9 22 42 PAKHAR 2017 112.9 22 30
Table 4. Comparisons of the average accuracy of daily rainfall forecast induced by TCs at the 8 cities by ECMWF model (the sec‐ ond and third column), by the pure boxplot method (the last two columns) and by the coupled statistical schemes (the fourth to the ninth column) from 2014 to 2017. The numbers with normal font style in the table refer to the average accuracy of the rainfall fore‐ cast at the AWSs for the corresponding city, and the italic bold numbers refer to the average accuracy of the rainfall forecast at the na‐ tional basic stations of the corresponding cities. The full quantile The 100% quantile The 50% quantile The 100% quantile City The accuracy of EC scheme with ECMWF scheme without EC‐ scheme scheme with EC min extreme outputs MWF extreme output NA 0.02 0.06 0.40 0.44 0.49 0.69 0.52 0.69 0.49 0.69 SCI 0.12 0.22 0.44 0.56 0.65 0.89 0.65 0.89 0.63 0.83 SW 0.40 0.39 0.44 0.39 0.64 0.74 0.64 0.74 0.50 0.65 SZ 0.33 0.33 0.42 0.54 0.58 0.92 0.61 0.92 0.44 0.92 XW 0.44 0.47 0.50 0.60 0.66 0.93 0.66 1.00 0.45 0.80 YJ 0.55 0.56 0.61 0.56 0.77 0.94 0.78 0.94 0.55 0.88 ZJ 0.60 0.81 0.57 0.31 0.71 0.88 0.72 0.88 0.48 0.69 ZH 0.38 0.35 0.54 0.52 0.66 0.65 0.67 0.74 0.39 0.52 8 Journal of Tropical Meteorology Vol.26
Table 5. The same as Table 4, but for the comparisons of the average accuracy of the 3-day accumulated rainfall forecast. The full quantile The 100% quantile The 50% quantile The 100% quantile City The accuracy of EC scheme with ECMWF scheme without EC‐ scheme scheme with EC min extreme outputs MWF extreme output NA 0.00 0.20 0.20 0.50 0.26 0.79 0.27 0.79 0.25 0.79 SCI 0.03 0.14 0.31 0.50 0.46 0.71 0.46 0.79 0.43 0.71 SW 0.21 0.44 0.27 0.44 0.43 0.72 0.44 0.72 0.34 0.67 SZ 0.11 0.37 0.20 0.37 0.34 0.79 0.35 0.79 0.31 0.68 the extreme minimum still lacks practical significance range might not include the extreme rainfall. Therefore, for its harmlessness of the light rainfall. ECMWF model all the three coupled schemes should be referred to in the often underestimates the local rainfall of the whole area operational TC rainfall forecast. and the forecast by ECMWF model is usually too 3.3 Case studies smooth to reflect the local rainstorm intensity which is Besides the above general evaluations for ECMWF more likely to lead to disastrous events. On the contrary, model and the coupled statistical boxplot schemes in the coupled statistical scheme, which combines the forecasting TC induced rainfall, a particular TC case is outputs of ECMWF model and the historical observation employed in this study to assess the performance of information, can depict the rainfall well induced by TCs. these schemes. TC Kujira was a severe tropical storm In forecasting the 3-day accumulated rainfall that landed in 2015 (Fig. 3) and had a great impact in induced by TCs, the coupled statistical scheme also Guangdong province. The cities of SW, ZH, and YJ are performs better than ECMWF model does. The rainfall used as the cities representing the east, middle and west forecast by the full quantile scheme with ECMWF parts of Guangdong province to illustrate the forecast extreme outputs and the 100% quantile scheme with performances of NWP model and the coupled statistical
ECmin overwhelm the ECMWF output at all the cities. schemes. The 50% quantile scheme and the 100% quantile scheme In SW city, the maximum daily rainfall forecast by without ECMWF extreme output also outperform the ECMWF model during the TC period is 24.58 mm, and ECMWF at 7 cities except for ZJ city. the minimum daily rainfall is 1.97 mm (Fig. 4a). The It is worth noting that ECMWF model generally forecasted rainfall range by ECMWF model cannot underestimates the extreme maximum rainfall and has a reflect the real situation of the rainfall observation small forecasting range in forecasting the rainfall ranging from 0 mm to 97mm (shown in Fig. 4c).On the induced by TCs at a city area. Expanding the range contrary, the original boxplot scheme contains most of appropriately can improve rainfall forecasting accuracy. the range of the observed data (Fig. 4b). All the three However, there is a compromise between the forecasting coupled statistical schemes outperform the ECMWF rainfall range and the operational forecasting model in forecasting the rainfall induced by TC Kujira at applicability. When the range is too large, such as the SW city. The 50% quantile scheme has an accuracy of full quantile scheme with ECMWF extreme outputs, the 61%. The 100% quantile scheme with the ECmin and the applicability of the forecasting would be reduced though full quantile scheme with ECMWF extreme outputs both the accuracy is improved. And when the range is too have an accuracy of 93%, while ECMWF model only small, such as the 50% quantile scheme, the forecast has an accuracy of 36%.
110°E 115°E 120°E 125°E
25°N
20°N
15°N
Figure 3. The track of TC Kujira (2015). No.1 LI Qing-lan (李晴岚), LIU Bing-rong (刘炳荣), et al. 9
(b) (a) >200 150-200 150 25°30 ′ N 120-150 100-120 80-100 125 60-80 50-60 100 25°10 ′ N 40-50 35-40 30-35 75 25-30 20-25 15-20 50 22°50 ′ N 10-15 5-10 3-5 25 0.1-3 <0.1 0 114°50 ′ E 115°10 ′ E 115°30 ′ E 115°50 ′ E 116°10 ′ E
Shanwei 2015-06-23 20:00:00 200 (c) 150.0 120.0 100.0 80.0 60.0 50.0 40.0 23°N 35.0 30.0 25.0 20.5 15.0 10.5 5.0 3.0 0.1 22.5°N 114.5°E 115°E 115.5°E 116°E
Figure 4. (a) The forecasted daily rainfall induced by TC Kujira at Shanwei by ECMWF model. (b) The boxplot of historical precipi‐ tation of the specific TC group. The red shadow represents the range of ECMWF output and the blue shadow represents the observed rainfall range. (c) The actual daily rainfall distribution in Shanwei.
In ZH and YJ, the situation is quite similar. In ZH, could provide more accurate estimations of the local the maximum rainfall prediction by ECMWF model is rainfall induced by TCs. 21mm and the minimum rainfall prediction is 7 mm, The situation is similar for the 3-day accumulated while the maximum observed rainfall is 45.5 and the rainfall forecast along the coastal area. The ECMWF minimum is 1. In YJ, the maximum rainfall prediction model forecasts are still too smooth to reflect the real by ECMWF model is 30 and the minimum is 5; while situation and cannot predict the extreme situation. the observed maximum rainfall is 84.5 and the minimum However, the statistical boxplot scheme and the coupled is 0. As Fig. 5 and Fig. 6 show, the ranges of the statistical schemes perform better. The comparisons are boxplots are larger than ECMWF model outputs, illustrated in Fig. 7, Fig. 8 and Fig. 9. compared with the actual rainfall observation. The 4 CONCLUSIONS boxplots can therefore provide a good reference for weather forecasters. Actually in ZH, the 50% quantile This study explores the methods to forecast the scheme has an accuracy of 54%; the 100% quantile maximum daily rainfall and 3-day accumulated rainfall scheme with the ECmin and the full quantile scheme with induced by landfalling TCs at the coastal cities in ECMWF extreme outputs, both have an accuracy of Guangdong province. The historical TCs landfalling in 87.5%, while ECMWF model only has an accuracy of south China and in North Vietnam from 1951 to 2013 33%. In YJ, the 50% quantile scheme has the accuracy and the corresponding historical rainfall data at the of 44%; the 100% quantile scheme with ECmin and the coastal stations are used for the method calibration. The full quantile scheme both have the accuracy of 84.4%, TCs and rainfall data from 2014 to 2017 are used for the while ECMWF model has the accuracy of 31%. method validation. Results show that the boxplot scheme From the above cases in the three cities, it can be based on the historical observation data can usually inferred that ECMWF model has the shortage while provide a better prediction of the rainfall range along the forecasting the local rainfall induced by TCs and often coastal city area than ECMWF model does. The underestimates the real rainfall range. Coupling performance of the coupled statistical boxplot schemes, ECMWF model forecast with the statistical boxplot with the integration of ECMWF model outputs into the 10 Journal of Tropical Meteorology Vol.26
(a) >200 150-200 (b) 120-150 60 100-120 80-100 50 60-80 50-60 40-50 40 22°10 ′ N 35-40 30-35 25-30 30 20-25 15-20 20 10-15 5-10 3-5 10 21°50 ′ N 0.1-3 <0.1 0 113°10 ′ E 113°30 ′ E
Zhuhai 2015-06-23 20:00:00 (c) 200 150.0 22.5°N 120.0 100.0 80.0 60.0 50.0 40.0 35.0 30.0 22°N 25.0 20.5 15.0 10.5 5.0 3.0 0.1 21.5°N 113°E 113.5°E 114°E Figure 5. The same as Fig. 4, but for the city of Zhuhai.
(a) >200 (b) 150-200 80 120-150 22°30 ′ N 100-120 80-100 60-80 60 50-60 22°10 ′ N 40-50 35-40 30-35 40 25-30 21°50 ′ N 20-25 15-20 10-15 5-10 20 3-5 21°30 ′ N 0.1-3 <0.1 0 111°10 ′ E 111°30 ′ E 111°50 ′ E 112°10 ′ E 112°30 ′ E Yangjiang 2015-06-23 20:00:00 (c) 200 150.0 22.5°N 120.0 100.0 80.0 60.0 50.0 40.0 22°N 35.0 30.0 25.0 20.5 15.0 10.5 21.5°N 5.0 3.0 0.1 111°E 111.5°E 112°E Figure 6. The same as Fig. 4, but for the city of Yangjiang. No.1 LI Qing-lan (李晴岚), LIU Bing-rong (刘炳荣), et al. 11
(b) 175 (a) >200 150-200 23°30 ′ N 120-150 150 100-120 80-100 60-80 125 50-60 40-50 100 23°10 ′ N 35-40 30-35 25-30 75 20-25 15-20 10-15 50 22°50 ′ N 5-10 3-5 0.1-3 25 <0.1 0 114°50 ′ E 115°10 ′ E 115°30 ′ E 115°50 ′ E 116°10 ′ E Shanwei R72H (c) 200 150.0 120.0 100.0 80.0 60.0 50.0 40.0 23°N 35.0 30.0 25.0 20.5 15.0 10.5 5.0 3.0 0.1 22.5°N 115°E 115.5°E 116°E Figure 7. The same as Fig. 4, but for the 3-day accumulated rainfall in Shanwei.
(a) 140 >200 (b) 150-200 120-150 120 100-120 80-100 60-80 100 50-60 22°10 ′ N 40-50 80 35-40 30-35 25-30 60 20-25 15-20 40 10-15 5-10 3-5 20 21°50 ′ N 0.1-3 <0.1 0
113°10 ′ E 113°30 ′ E Zhuhai R72H (c) 200 150.0 22.5°N 120.0 100.0 80.0 60.0 50.0 40.0 35.0 22°N 30.0 25.0 20.5 15.0 10.5 5.0 3.0 0.1 21.5°N 113°E 113.5°E 114°E Figure 8. The same as Fig. 5, but for the 3-day accumulated rainfall in Zhuhai. 12 Journal of Tropical Meteorology Vol.26
(a) >200 150-200 (b) 120-150 140 22°30 ′ N 100-120 80-100 120 60-80 50-60 100 22°10 ′ N 40-50 35-40 30-35 80 25-30 21°50 ′ N 20-25 60 15-20 10-15 40 5-10 3-5 20 21°30 ′ N 0.1-3 <0.1 0 111°10 ′ E 111°30 ′ E 111°50 ′ E 112°10 ′ E 112°30 ′ E Yangjiang R72H (c) 200 150.0 22.5°N 120.0 100.0 80.0 60.0 50.0 40.0 22°N 35.0 30.0 25.0 20.5 15.0 10.5 21.5°N 5.0 3.0 0.1 111°E 111.5°E 112°E Figure 9. The same as Fig. 6, but for the 3-day accumulated rainfall in Yangjiang. boxplot scheme, is further improved in forecasting the 10.1175/1520-0434(1990)005<0378:sfcopa>2.0.co;2. rainfall range at the coastal city area. The boxplot [6] AMS. Policy statement: Hurricane research and forecasting scheme and the coupled boxplot schemes are therefore [J]. Bull Amer Meteor Soc, 2000, 81(6): 1341-1346, https:// valuable for forecaster in the real weather forecast doi.org/10.1175/1520-0477(2000)081<1341:pshraf>2.3.co; operation during TC seasons. 2. [7] WILLOUGHBYHE, RAPPAPORT E N, MARKSFD. REFERENCES: Hurricane forecasting: The state of the art [J]. Natural Hazards Review, 2007, 8(3): 45-49, https://doi.org/10.1061/ [1] RENFM, GLEASONB, EASTERLINGD. Typhoon (ASCE)1527-6988(2007)8:3(45). impacts on China’s precipitation during 1957-1996 [J]. [8] ROYC, KOVORDANYIR. Tropical cyclone track Adv Atmos Sci, 2002, 19(5): 943-952, https://doi. org / forecasting techniques-A review [J]. Atmos Res, 2012, 104- 10.1007/s00376-002-0057-1. 105: 40-69, https://doi.org/10.1016/j.atmosres.2011.09.012. [2] XIAOFJ, XIAOZN. Characteristics of tropical cyclones [9] SAHAKK, HASANMM, QUAZIA. Forecasting tropical in China and their impacts analysis [J]. Natural Hazards, cyclone-induced rainfall in coastal Australia: implications 2010, 54(3): 27-837, https://doi. org / 10.1007 / s11069-010- for effective flood management [J]. Australasian J Environ 9508-7. Management, 2015, 22(4): 446-457, https://doi. org / [3] RAPPAPORT E N, FUCHSM, LORENSTON M. The 10.1080/14486563.2015.1028109. threat to life in inland areas of the United States from [10] LONFAT M, MARKS Jr. FD, CHENSS. Precipitation Atlantic tropical cyclones [C]// 23d Conference on distribution in tropical cyclones using the Tropical Hurricanes and Tropical Meteorology, Dallas: American Rainfall Measuring Mission (TRMM) microwave imager: Meteorological Society, 1999, 811-814. A global perspective [J]. Mon Wea Rev, 2004, 132(7): [4] LIQL, XUPC, WANG X B, et al. An operational 1645-1660, https://doi.org/10.1175/1520-0493(2004)132< statistical scheme for tropical cyclone induced wind gust 1645:pditcu>2.0.co;2. forecast [J]. Wea Forecasting, 2016, 31(6): 1817-1832, [11] RIEHLH, MALKUSJ. Some aspects of Hurricane Daisy https://doi.org/10.1175/waf-d-16-0015.1. [J]. Tellus, 1961, 13(2): 181-213, https://doi.org/10.1111/ [5] BUSINGERS, KNAPPDI, WATSON G F. Storm j.2153-3490.1961.tb00077.x. following climatology of precipitation associated with [12] SIMPSONRH, RIEHLH. The Hurricane and Its Impact winter cyclones originating over the Gulf of Mexico [J]. [M]. Louisiana State University Press, 1981: 135-140. Wea Forecasting, 1990, 5(3): 378-403, https://doi. org / [13] KIDDERSQ, KNAFFJA, KUSSELSONSJ, et al. The No.1 LI Qing-lan (李晴岚), LIU Bing-rong (刘炳荣), et al. 13
tropical rainfall potential (TRaP) technique, Part I: and forecasting on rainfall associated with landfalling Description and examples [J]. Wea Forecasting, 2005, 20 tropical cyclones [J]. Adv Atmos Sci, 2010, 27(5): 967- (4): 456-464, https://doi.org/10.1175/waf860.1. 976, https://doi.org/10.1007%2Fs00376-010-8171-y. [14] PFOSTRL. Operational tropical cyclone quantitative [27] XUYL, ZHANGL, GAOSZ. The advances and precipitation forecasting [J]. National Weather Digest, discussions on China operational typhoon forecasting [J]. 2000, 24(1-2): 61-66. Meteorol Mon, 2010, 36(7): 43-49, https://doi. org / [15] ELSBERRY R L. Predicting hurricane landfall 10.3788/HPLPB20102207.1462. precipitation: Optimistic and pessimistic views from the [28] PETTMA. Nonparametric Statistics for Health Care symposium on precipitation extremes [J]. Bull Amer Research-Statistics for Small Samples and Unusual Meteor Soc, 2002, 83(9): 1333-1339, https://doi. org / Distributions [M]. Thousand Oaks: Sage Publications, 10.1175/1520-0477(2002)083<1333:phlpoa>2.3.co;2. 1997: 307. [16] CORBOSIEROKL, MOLINARIJ. The relationship [29] WILKSDS. Statistical Methods in the Atmospheric between storm motion, vertical wind shear, and Sciences (Vol. 100) [M]. Academic Press, 2011: 23-24. convective asymmetries in tropical cyclones [J]. J Atmos [30] TUKEYJW. Exploratory Data Analysis [M]. Menlo Park, Sci, 2003, 60(2): 366-376, https://doi.org/10.1175/1520- Cal, London, Amsterdam, Don Mills, Ontario: Addison ‐ 0469(2003)060<0366:trbsmv>2.0.co;2. Wesley Publishing Company Reading, Mass, 1977: 688. [17] KIMBALLSK. Structure and evolution of rainfall in [31] JOHNSONGL, DALY C, TAYLOR G H, et al. Spatial numerically simulated landfalling hurricanes [J]. Mon variability and interpolation of stochastic weather Wea Rev, 2008, 136(10): 3822-3847, https://doi. org / simulation model parameters [J]. J Applied Meteorol, 10.1175/2008mwr2304.1. 2000, 39(6): 778-796, https://doi.org/10.1175/1520-0450 [18] LIQL, LANHP, CHANJC, et al. An operational (2000)039<0778:SVAIOS>2.0.CO;2. statistical scheme for tropical cyclone induced rainfall [32] CHOWDHURY A F M K, LOCKART N, WILLGOOSE forecast [J]. J Trop Meteor, 2015, 21(2): 101-110, https:// G, et al. Development and evaluation of a stochastic daily doi.org/10.16555/j.1006-8775.2015.02.001. rainfall model with long-term variability [J]. Hydrology [19] MARKSFD, KAPPLERG, DEMARIAM. Development and Earth System Sciences, 2017, 21(12): 6541-6558, of a tropical cyclone rainfall climatology and persistence https://doi.org/10.5194/hess-21-6541-2017. (R-CLIPER) model [C]// 25th Conference on Hurricanes [33] ONYUTHAC, WILLEMSP. Space-time variability of and Tropical Meteorology, San Diego: American extreme rainfall in the River Nile basin [J]. International Meteorological Society, 2002. Journal of Climatology, 2017, 37(14): 4915-4924, https:// [20] LONFAT M, ROGERSR, MARCHOK, T, et al. A doi.org/10.1002/joc.5132. parametric model for predicting hurricane rainfall [J]. [34] HALPERINDJ, FUELBERGHE, HART R E, et al. An Mon Wea Rev, 2007, 135(9): 3086-3097, https://doi.org/ evaluation of tropical cyclone genesis forecasts from 10.1175/mwr3433.1. global numerical models [J]. Wea Forecasting, 2013, 28 [21] LIANGBQ, LIANGJQ, WENZP. The characteristics (6): 1423-1445, https://doi. org / 10.1175 / WAF-D-13- of typhoon disasters and its effects on economic 00008.1. development in Guangdong province [J]. Natural Disaster [35] WANG Y, BELLUSM, GELEYNJF, et al. A new Reduction in China, 1994, 49(4): 145-150 (in Chinese). method for generating initial condition perturbations in a [22] YEW. The Characteristics of typhoon disaster and regional ensemble prediction system: Blending [J]. Mon disaster countermeasures in Guangdong Province [J]. Wea Rev, 2014, 142(5): 2043-2059, https://doi. org / Journal of Catastrophology, 2002, 17(3): 54-59 (in 10.1175/MWR-D-12-00354.1. Chinese). [36] MAGNUSSONL, BIDLOTJR, LANGS, et al. [23] YUANJN, ZHENGB. An analysis on interdecadal Evaluation of medium-range forecasts for hurricane variations of tropical cyclone precipitation in Guangdong Sandy [J]. Mon Wea Rev, 2014, 142(5): 1962-1981, province of China [J]. J Trop Meteor, 2012, 18(1): 65-71, https://doi.org/10.1175/MWR-D-13-00228.1. https://doi.org/10.3969/j.issn.1006-8775.2012.01.007. [37] ZHONG Shui-xin, YANG Shuai and CHEN Zi-tong. [24] YINJ, WUS, DAIE. Assessment of economic damage Evaluation of the parameterization schemes and risks from typhoon disasters in Guangdong, China [J]. J nudging techniques in GRAPES for warm sector Resources and Ecology, 2012, 3(2): 144-150, https://doi. torrential rains using surface observations [J]. J Trop org/10.5814/j.issn.1674-764x.2012.02.006. Meteor, 2019, 25(3): 353-364, https://doi. org / 10.16555 / [25] XINHUANETNEWS. Typhoon Mujigae kills 19 in south j.1006-8775.2019.03.007. China, 06 Oct 2015. Accessed September 22, 2017 [38] YATES E, ANQUETINS, DUCROCQV, et al. Point and [Available Online at: http://news.xinhuanet.com/english/ areal validation of forecast precipitation fields [J]. 2015-10/06/c_134687953.htm] Meteorol Appl, 2006, 13(1): 1-20, https://doi.org/10.1017/ [26] CHENLS, LIY, CHENGZQ. An overview of research S1350482705001921.
Citation: LI Qing-lan, LIU Bing-rong, WAN Qi-lin, et al. Operational forecast of rainfall induced by landfalling tropical cyclones along Guangdong coast [J]. J Trop Meteor, 2020, 26(1): 1-13, https://doi.org/10.16555/j.1006-8775.2020.001.