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A Real-Time Regional Forecasting System Established for the South China Sea and Its Performance in the Track Forecasts of Tropical Cyclones during 2011–13

1 # SHIQIU PENG,* YINENG LI,* XIANGQIAN GU, SHUMIN CHEN,* DONGXIAO WANG,* HUI WANG, @ & @, 11, SHUWEN ZHANG, WEIHUA LV, CHUNZAI WANG,** BEI LIU, *DUANLING LIU, * ,## 11 ZHIJUAN LAI,* WENFENG LAI,* SHENGAN WANG,* YERONG FENG, ## AND JUNFENG ZHANG * State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China 1 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China # National Marine Environmental Forecasting Center, Beijing, China @ College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China & Maoming Meteorological Bureau, Maoming, China ** NOAA/Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida 11 Meteorological Center of Guangdong province, Guangzhou, China ## South China Sea Marine Prediction Center, State Oceanic Administration, Guangzhou, China

(Manuscript received 24 June 2014, in ﬁnal form 28 December 2014)

ABSTRACT

A real-time regional forecasting system for the South China Sea (SCS), called the Experimental Platform of Marine Environment Forecasting (EPMEF), is introduced in this paper. EPMEF consists of a regional atmosphere model, a regional ocean model, and a wave model, and performs a real-time run four times a day. Output from the Global Forecast System (GFS) from the NationalCentersforEnvironmentalPrediction(NCEP)isused as the initial and boundary conditions of two nested domains of the atmosphere model, which can exert a constraint on the development of small- and mesoscale atmospheric perturbations through dynamical downscaling. The forecasted winds at 10-m height from the atmosphere model are used to drive the ocean and wave models. As an initial evaluation, a census on the track predictions of 44 tropical cyclones (TCs) during 2011–13 indicates that the performance of EPMEF is very encouraging and comparable to those of other ofﬁcial agencies worldwide. In particular, EPMEF successfully predicted several abnormal typhoon tracks including the sharp recurving of Megi (2010) and the looping of Roke (2011). Further analysis reveals that the dynamically downscaled GFS forecasts from the most updated forecast cycle and the optimal combination of different microphysics and PBL schemes primarily contribute to the good performance of EPMEF in TC track forecasting. EPMEF, established primarily for research purposes with the potential to be implemented into operations, provides valuable information not only to the operational forecasters of local marine/meteorological agencies or international TC forecast centers, but also to other stakeholders such as the ﬁshing industry and insurance companies.

1. Introduction et al. 2006; Du and Qu 2010). It is also an area where tropical cyclones (TCs) or typhoons form and pass Being the largest tropical marginal sea and connecting through during the summer and fall seasons. These have the western Paciﬁc Ocean and the eastern Indian Ocean, the potential to cause damage to property and loss of life the South China Sea (SCS) plays an important role in the in East Asian countries such as the Philippines, Viet- exchange of water mass and energy between the two nam, and China (Wu and Kuo 1999). For instance, be- ocean basins (Qu et al. 2005; Qu and Du 2006; Wang tween seven and eight typhoons make landfall in China each year, impacting a population of 250 million with Corresponding author address: Dr. Shiqiu Peng, LTO/SCSIO, property losses of over $10 trillion (Liu et al. 2009). 164 West Xingang Rd., Guangzhou 510301, China. Although the forecasting of tropical cyclone tracks E-mail: [email protected] and intensity has improved during the past several

DOI: 10.1175/WAF-D-14-00070.1

Ó 2015 American Meteorological Society Unauthenticated | Downloaded 10/01/21 07:42 AM UTC 472 WEATHER AND FORECASTING VOLUME 30 decades, considerable uncertainty still exists. This is due to a poor understanding of the physical processes related to tropical cyclone intensity as well as the inherent predictability limit in numerical models, which still falls short in capturing the intricacies of the underlying mechanisms (Fraedrich and Leslie 1989; Plu 2011; Peng et al. 2014). In addition to high quality initial conditions (Kurihara et al. 1995; Leslie et al. 1998; Bender et al. 2007), factors contributing to improvements in the TC forecasting accuracy of a model may include the domain settings related to size and location (Landman et al. 2005; Giorgi 2006), the choice of horizontal resolutions for the nested domain, the selected microphysics parameterization scheme (Rao and Prasad 2007; Kanada et al. 2012; Kepert 2012) or/and planetary boundary layer (PBL) parameterization scheme (Khain and Lynn 2011; Pattanayak et al. 2012), as well as their combination (Zhou et al. 2013). This paper introduces a real-time regional forecasting system established for the SCS whose atmosphere model features a ‘‘dynamical downscaling’’ initialization scheme and an ‘‘optimal’’ combination of the Ferrier microphysics scheme and the FIG. 1. Flowchart for EPMEF including the Oregon State Yonsei University (YSU) PBL scheme with two nested University (OSU) Tidal Prediction Software (OTPS). domains centered in the SCS, and presents a preliminary evaluation of its performance in TC track forecasts during 2011–13. weather prediction system that was designed to serve The paper is organized as follows. Section 2 gives both operational forecasting and atmospheric research a brief description to the establishment of the real-time needs (Michalakes et al. 1998; Skamarock et al. 2008). forecasting system. An evaluation of the performance of Dynamical downscaling techniques are commonly used the forecasting system in TC track forecasts is presented for a regional atmospheric model to obtain higher- in section 3, followed by a detailed discussion in section 4. resolution output with small- and mesoscale features A summary is given in the final section. from the lower-resolution output of a global atmospheric model (Lo et al. 2008; Zhang et al. 2009). To realize this, a two-domain one-way-nested configuration 2. Establishment of EPMEF has been designed, as shown in Fig. 2. The outer domain A real-time air–sea–wave forecasting system for the for the atmosphere model covers the western Pacific SCS, also known as the Experimental Platform of Marine Ocean, the entire SCS, and the eastern Indian Ocean, Environment Forecasting (EPMEF), was established in with a horizontal grid resolution of 72 km. The inner the State Key Laboratory of Tropical Oceanography domain covers the entire SCS and southern China, with (LTO), South China Sea Institute of Oceanology a horizontal grid resolution of 24 km. Both domains (SCSIO), in October 2010. The system consists of three have 27 layers in the vertical. The horizontal resolutions main components: an atmosphere model, an ocean of 72 (outer domain) and 24 km (inner domains), along model, and a sea wave model. These are the Weather with the 27 vertical layers, are chosen mainly because of Research and Forecasting (WRF) Model, version 3.2 limited computer resources (currently EPMEF is run- (Michalakes et al. 1998; Skamarock et al. 2008); the ning on a rack server with 32 cores of Intel Xeon E5 with Princeton Ocean Model (POM), 2002 version (Blumberg 2.6 Hz), and they can be updated to higher resolutions in and Mellor 1987; Mellor 2003); and the WAVEWATCH the future when more computer resources are available. III (WWIII) model (Tolman 1997, 1999, 2002), re- The output from the Global Forecast System (GFS) spectively. Figure 1 shows a flowchart of EPMEF. maintained by NCEP with horizontal grid resolution of The WRF Model, version 3.2, developed by the Na- 18318 is used to provide initial conditions (ICs) and tional Center for Atmospheric Research (NCAR) and lateral boundary conditions (BCs) for the outer domain. the National Centers for Environmental Prediction The Ferrier microphysics scheme (Ferrier et al. 2002), (NCEP), is a next-generation mesoscale numerical the Kain–Fritsch cumulus scheme (Kain and Fritsch

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U.S. Navy–U.S. Air Force Joint Typhoon Warning Center (JTWC), the Japan Meteorological Agency (JMA), the National Meteorological Center of China (NMCC), and the Central Weather Bureau of Taiwan (CWBT). Since the 2012 TC season, NCEP/Environmental Modeling Center (EMC) has been making real-time forecasts for west Paciﬁc TCs using HWRF (Tallapragada et al. 2013); thus, we also compare our results for 2012–13 TC forecasts with those from NCEP/EMC. Because of limited computer resources, EPMEF only makes 72-h track forecasts for TCs entering the inner domain. TC vortexes are initialized by downscaling the ﬁrst guess from GFS at 18318 resolution to the regional WRF FIG. 2. The model domains of WRF and the JTWC best tracks of Model at 24-km resolution. There are 21, 25, and 31 18 TCs during 2013 that entered the model’s inner domain with life named tropical cyclones in 2011, 2012, and 2013, re- cycles longer than 48 h and that were forecasted by at least four agencies (JTWC, JMA, NMCC, CWBT, NCEP, and EPMEF). spectively. For all of these TCs, only those that entered the inner domain of the atmosphere model of EPMEF with life cycles longer than 48 h and were forecasted by 1990, 1993), the YSU PBL scheme (Hong et al. 2006), at least three other agencies are counted in our statistics, and the Dudhia shortwave (Dudhia 1989) and RRTM which results in counts of 10, 16, and 18 TCs for 2011, longwave (Mlawer et al. 1997) radiation schemes are 2012, and 2013, respectively. As an example, Fig. 2 chosen for both domains. Forecasts of 15 and 3 days are shows the tracks of the 18 TCs for 2013 that entered the made automatically every 6 h (i.e., four times a day, at inner domain with life cycles longer than 48 h. The TC 0200, 0800, 1400, and 2000 LT in Beijing) for the outer center is tracked by a routine position search available in and inner domains, respectively. the fourth release of the Read/Interpolate/Plot (RIP4) The 10-m-height winds from the inner domain of the WRF postprocess program. This takes into account the WRF Model are used to drive the POM and WWIII. criteria in both the upper atmosphere and the sea sur- POM is a three-dimensional (3D) ocean model with face: the predicted minimum sea level pressure, maxi- primitive equations, embedded in a second-moment mum 10-m height winds, maximum vorticities at 650 and turbulence closure model [the Mellor–Yamada level 850 hPa as well as some prescribed thresholds for the 2.5 scheme; Mellor and Yamada (1982)]. The WWIII vorticity at all levels, and the temperature at the surface model is a third-generation wave model developed at and 700 hPa. Based on the observed (best) track issued NOAA/NCEP (Tolman 1997, 1999, 2002). As an initial by JTWC, the track position errors (TPEs) for 24-, 48-, evaluation of EPMEF, we focus on the performance of and 72-h forecasts by each agency are calculated using EPMEF in the TC track forecasts in this paper, and thus the following formula (Neumann and Pelissier 1981; here we omit detailed descriptions of the setup of the Powell and Aberson 2001): ocean and wave models. The evaluation for the perfor- 180 mance of the ocean and wave models will be given in 5 : 21 u u TPE 111 11 p cos [sin O sin S a future publication once enough observations are 1 u u l 2 l available. cos O cos S cos( O S)], (1)

u l where O and O are the latitude and longitude of the 3. Evaluation of the performance in TC track u l storm center in the best-track data, and S and S are forecasts during 2011–13 those of the forecasted storm center from each agency. The SCS and the western Paciﬁc Ocean are the re- To make a relatively fair comparison among the gions most frequently affected by typhoons or tropical agencies, we calculate the relative errors of each TC storms in summer and autumn. For instance, there were track predicted by each agency using the following 77 named and numbered tropical storms/typhoons oc- formula: curring in these areas during 2011–13. Therefore, for an 5 5 ... initial evaluation of the performance of EPMEF, we Ri TPEi/ TPE (i 1, , N), (2) focus on the track forecasts of tropical cyclones during 5 N 2011–13 by EPMEF and make a comparison with the where TPE 1/Nåi 5 1TPEi and N denotes the number forecasts by other major agencies, including the of agencies. If an agency has no forecasting result

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FIG. 3. Mean TPEs (gray bars; km) and Ri (white bars) with the std dev (black vertical lines) of the predicted tracks for the 10 tropical cyclones during 2011 from different agencies for (a) 24-, (b) 48-, and (c) 72-h forecasts.

available for a TC, 1.0 is assigned to it. It is obvious that respectively. The mean TPEs (Ri) over the 10 TCs in the smaller the value of Ri is, the better the corre- 2011 from EPMEF are 97.0 km (0.98), 172 km (1.05), sponding agency performs. We deﬁne the relative fore- and 278 km (1.0) for 24-, 48-, and 72-h forecasts, re- casting skill as the mean Ri of all TCs for each agency for spectively, ranking as third, fourth, and second among 24-, 48-, and 72-h forecasts. the ﬁve agencies. In 2012 and 2013, the forecasts from Figures 3–5 give the mean TPEs of and the relative HWRF are available for comparison alongside the other errors of 10, 16, and 18 TCs for 2011, 2012, and 2013, four agencies. The mean TPEs (Ri) from EPMEF are

FIG.4.AsinFig. 3, but for the 16 tropical cyclones during 2012 with an additional agency (NCEP) and hindcast (EPMEF-6). NCEP/EMC began to make real-time forecasts for west Paciﬁc TCs using HWRF beginning with the 2012 TC season. EPMEF-6 is the same as EPMEF except that EPMEF-6 used the GFS forecasts of the previous cycle (6 h early).

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FIG.5.AsinFig. 3, but for the 18 tropical cyclones during 2013 with an additional agency (NCEP). NCEP/EMC began to make real-time forecasts for west Paciﬁc TCs using HWRF beginning with the 2012 TC season.

83 km (0.87), 133 km (0.85), and 232 km (0.95) for of which large TPEs for all forecast times are seen for the16TCin2012(rankingfirst,first,andsecond)for EPMEF (Fig. 8). In an overall assessment of the three 24-, 48-, and 72-h forecasts, respectively; and 86 km TC seasons of 2011–13, the performance of EPMEF is (0.98), 129 km (0.87), and 171 km (0.90) for 18 TCs in very encouraging compared to those of some official 2013 (ranking third, first, and first) for 24-, 48-, and 72-h agencies. forecasts, respectively. As examples, Figs. 6 and 7 show Now let us look into a couple of cases that have ab- the performance of each agency for Nanmadol (2011) normal tracks and are more difficult to predict: Megi and Rumbia (2013), which made landfall along the (2010) and Roke (2011). Megi was the strongest typhoon coasts of Jinjiang in Fujian Province and Zhanjiang in observed during 2010 and the strongest one generated in Guangdong Province, respectively. For Nanmadol (2011), autumn in the western Pacific Ocean for the past 20 2 EPMEF performed slightly better than the others for years, with maximum wind speed of 72 m s 1 and lowest the 24-h forecast, with a mean TPE of 84 km, although sea level pressure of 895 hPa. The most astonishing the TPE increased quickly at the last initialization time feature of its track was the nearly 908 turn from west- (0000 UTC 30 August 2011), which is probably due to ward to northward at 0000 UTC 20 October 2010 after it the initialization quality associated with the accuracy passed through the Philippines and entered the SCS. of the GFS forecasts at that time when the TC was Most of the official agencies did not predict this big turn going to make landfall. EPMEF performed signifi- until 2100 UTC 19 October 2010. EPMEF, however, cantly better than others for the 48- and 72-h forecasts successfully predicted the big turn as early as 0000 UTC 18 with mean TPEs of 137 and 289 km, respectively, October 2010 (Fig. 9a). As revealed by Peng et al. especially for the second half of the period. The 72-h (2014), the cold-air intrusion from the northwest played predicted track by EPMEF initializing at 1200 UTC a key role in the big turn of Megi through its adjustment 27 August 2011 is very close to the observed track to the large-scale circulation. The cold-air intrusion (Fig. 6d). For Rumbia (2013), EPMEF performed much was well predicted by EPMEF and is mainly attributed better than the others, with mean TPEs of 53, 64, and to the proper selection of the PBL and physical 129 km for the 24-, 48-, and 72-h forecasts, respectively. schemes, as indicated by Zhou et al. (2013) and dis- As indicated in Fig. 7d, initializing at 0000 UTC 29 cussed in section 4b. Roke was generated as a tropical June 2013, the EPMEF predicted a track and a landfall storm in the western Pacific (22.18N, 1378E) at 1200 UTC 13 location 3 days ahead that are very close to the ob- September 2011. When it moved westward to the east of servations. In contrast, EPMEF shows the worst per- Okinawa Island (26.48N, 129.78E) at 0600 UTC 16 formance in some TCs, such as the strong Nesat (2011), September, it turned around and looped cyclonically

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FIG. 6. TPEs (km) of Nanmadol from different agencies for multiple (a) 24-, (b) 48-, and (c) 72-h forecasts initializing every 6 h starting at 0000 UTC 24 Aug 2011 (the interval of the abscissa is 6 h), and (d) the best and predicted tracks of Nanmadol from different agencies for a single 72-h forecast initializing at 1200 UTC 27 Aug 2011. and intensified into a typhoon. After this looping, it 4. Discussion moved northward and then northeastward, and made a. The initialization process landfall over Shizuoka County in Japan at 0600 UTC 21 September 2011. As early as 1200 UTC 14 September The above results indicate that the performance of 2011, EPMEF successfully predicted the cyclonical EPMEF appears to be comparable to or even better loop that is usually very hard to predict (Fig. 9b). than the performance of most of the official agencies. Figures 9c and 9d show the 48- and 72-h TPEs of Roke However, one should be aware of the following points: for each of the forecasting agencies (forecasting data 1) the comparison is based on only those TCs that en- from JTWC are not available), in which the perfor- tered the inner domain of EPMEF and lasted longer mance of EPMEF is seen to be very encouraging with than 48 h in the domain and 2) the track forecasts from a minimum mean 48-h TPE of 130.15 km and a 72-h EPMEF are purely from its numerical model initialized TPE of 194.03 km, compared to the mean 48-h TPEs of at the starting time of the current forecast cycle, but 241.3, 195.8, and 219.6 km, and the 72-h TPEs of 342.7, those released online by the official agencies are usually 304.4, and 340.9 km for NMCC, JMA, and CWBT, based on results from a number of guidance models respectively. including multilayer dynamical models, single-layer

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FIG.7.AsinFig. 6, but for Rumbia starting at 1200 UTC 28 Jun in (a)–(c) and at 0000 UTC 29 Jun 2013 in (d). trajectory models, consensus models, and statistical process produces an early version of a late model for the models, as well as the experiences of forecasters. There- current forecast cycle that is based on the most current fore, the comparison here is not strictly among numerical available guidance. The adjusted version of a late model models. Guidance models are characterized as either is known, mostly for historical reasons, as an interpolated early or late, depending on whether or not they are model. Since the initial and boundary conditions in the available to forecasters during the forecast cycle. For ex- real atmosphere may vary significantly at different ample, the GFS forecast made for the 1200 UTC forecast times, the forecast of a dynamical (late) model starting cycle would be considered to be a late model since it is not from the previous forecast cycle (i.e., initializing at 0600 complete and available to forecasters until about 1600 UTC, or 0000 UTC) for the current forecast cycle (1200 UTC) or about an hour after the NHC forecast is released; that (on which the interpolated model is based) is generally is, it could not be used to help prepare the 1200 UTC of- less accurate than that starting from the current forecast ficial forecast. Multilayer dynamical models are gener- cycle (i.e., initializing at 1200 UTC). This may be the ally, if not always, late models. To allow the forecast to main reason why the forecasts of EPMEF are generally benefit from a late model as much as possible while more accurate than those of most official agencies based keeping it timely, a technique is adopted that adjusts the on interpolated models. However, EPMEF achieves this most recent available run of a late model to the current in a cost of about 2 h in its late releases of forecasts. For synoptic time and initial conditions. The adjustment instance, at the 1200 UTC forecast cycle, EPMEF waits

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FIG.8.AsinFig. 6, but for Nesat starting at 0000 UTC 24 Sep in (a)–(c) and at 1200 UTC 25 Aug 2011 in (d). until 1515 UTC for the arrival of GFS forecasts ini- model) to create ICs/BCs, we perform an ensemble of tializing at 1200 UTC, which are not available until hindcasts for the 16 TCs during 2012 using a different 1500 UTC. Running on a rack server with 32 cores of initialization scheme (denoted as EPMEF-6). The ini- Intel Xeon E5 with 2.6 Hz, it generally takes about 1.5 h tialization scheme for EPMEF-6 uses GFS forecasts of for EPMEF to download the GFS data, prepare ICs/BCs, the previous forecast cycle (6-h early) for generating and finish a 72-h forecast, so the track forecast from ICs/BCs, which is similar to the early model except that EPMEF could be released at about 1630 UTC [i.e., 1.5 h it does not carry out any adjustment or projection to the late compared to the forecast release time (1500 UTC) GFS forecasts of the previous forecast cycle. For proposed by the official agencies]. For forecasts longer EPMEF-6, the mean TPEs for 24-, 48-, and 72-h fore- than 24 h, the negative impact of a 2-h delay in forecast casts are 96.6, 159.4, and 270.3 km, respectively, an ap- release is nearly negligible. Thus, in our opinion, it is parent degrade compared to those of 82.8, 133.4, and worth to gain an improvement in TC track forecasts at the 232.3 km for EPMEF (Fig. 4). Therefore, the reduction cost of a ;1–2-h delay in the forecast release (the de- in TPEs in EPMEF is obviously attributed to a better laying time may be shortened to less than 1 h if a faster representation of the initial large-scale environmental computer is used in the future), especially for a forecast circulation in the late model. The adjustment or pro- period that is longer than 24 h. jection according to the latest information of the live TC To see how EPMEF may benefit from using the GFS implemented in the standard early model is believed to forecasts of the current forecast cycle (i.e., the late improve the small- and mesoscale features of the

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FIG. 9. The best track and the predicted tracks for a single 72-h forecast from different agencies for (a) Megi, initializing at 0000 UTC 18 Oct 2010, and (b) Roke, initializing at 0600 UTC 14 Sep 2011, and the TPEs (km) during Roke from different agencies for (c) 48- and (d) 72-h forecasts initializing every 6 h starting at 1800 UTC 13 Sep 2011 (the interval of the abscissa is 6 h). meteorological fields in the regional model, but may not component of steering flows from EPMEF drives the cy- be much help in improving the large-scale environ- clone to the south, which is closer to the best track. The mental circulations that steer the TC. Figure 10 displays field of geopotential height in EPMEF also appears to be the steering flows and the 500-hPa potential heights favorable for a southwestward movement of the cyclone, from EPMEF and EPMEF-6 for Typhoon Gaemi (2012) while that in EPMEF-6 seems to facilitate northeastward at the initialization time of 1200 UTC 3 October 2012 movement, which deviates from the observed track of the and 30-h forecast time, imposed by the TC best track and cyclone. The biases of the zonal u and meridional y com- the simulated tracks. The steering flows are obtained ponents of steering flows from the 72-h forecasts of through averaging the wind field between 925 and EPMEF and EPMEF-6 against the NCEP Final Analysis 300 hPa and over a radial band from ;38 to 88 centered (FNL) are displayed in Fig. 11, which are averaged over at the TC eye, based on the suggestions or experiences in an ensemble of model runs initialized at every 6 h some previously published studies (Chan and Gray 1982; from 0000 UTC 2 October to 1200 UTC 5 October 2012. Dong and Neumann 1983). Although the initial vortex A significant reduction of the biases in the y component of position from EPMEF-6 is slightly closer to the one from the steering flows is found for EPMEF, implying that the the best track than that from EPMEF, the northward late model used in the initialization of EPMEF can better component of steering flows from EPMEF-6 makes capture the large-scale environmental circulation not only the cyclone move to the north, while the southward at the initial time but also at the forecast times. Therefore,

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FIG. 10. The steering ﬂows and the 500-hPa geopotential heights [geopotential meters (GPM)] from the (a),(c) late and (b),(d) early model at (a),(b) the initialization time of 1200 UTC 3 Oct 2012 and (c),(d) the 30-h forecast time valid at 1800 UTC 4 Oct 2012 for Gaemi, imposed by the best (black) and simulated (red) tracks as well as the vectors of steering ﬂows at the corresponding time.

EPMEF benefits from the GFS forecasts of the current Ferrier microphysics scheme and the YSU PBL scheme forecast cycle (the most updated), which bring better in the model. As shown in Zhou et al. (2013), various steering flow than those of the previous forecast cycle (6 h combinations of microphysics and PBL schemes may early), leading to better track forecasts of TCs. have different influences on TC track simulation, and the combination of a Ferrier scheme and the YSU b. The effect of microphysics and PBL schemes scheme is the optimal pairing that leads to a best-track Another important factor contributing to the good simulation of Supertyphoon Megi. The influence of performance of EPMEF could be the choice of the various combinations of the microphysics and PBL

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a detailed description in Skamarock et al. (2008)] and initializing at every 6 h from 1200 UTC 21 July to 1800 UTC 23 July for Typhoon Vicente (2012). The mean TPEs for various combinations are given in Table 1. It is found that the combination of the Ferrier and YSU schemes yields the smallest mean TPEs for both 24- and 72-h forecasts. Although the combinations of the MYJ PBL scheme with most microphysics schemes perform well, they do not work (blow up) for the 72-h forecast. Therefore, in an overall assessment, the combination of the Ferrier and YSU schemes performed the best among all combinations, as indicated by the rank based on the sum of the relative errors of all forecast periods for each combination (Table 1). This can be attributed to a more accurate forecasting of the large-scale environmental circulations (i.e., the steering flows) from the combination of the Ferrier and YSU schemes, as indicated in Fig. 12. Considering that the influence of various combinations may be case dependent in the configuration of EPMEF, we have carried out an ensemble of experiments using different combinations of microphysics and FIG. 11. The mean biases of the (a) u and (b) y components of PBL schemes for a number of typhoon cases. The results steering flows from the 72-h forecasts of EPMEF and EPMEF-6 also show that the combination of the Ferrier and against the FNL analysis averaged over an ensemble of model runs YSU schemes produces more accurate forecasts of the initialized every 6 h from 0000 UTC 2 Oct to 1200 UTC 5 Oct 2012 for Gaemi. steering flows, which are governed by the large-scale circulations, leading to better TC track forecasts (figures schemes on TC track forecasts may depend on the model not shown). Hence, EPMEF obviously benefits from configuration such as the horizontal grid resolution, the the choice of the Ferrier microphysics scheme and the domain size, topography, etc. To investigate their in- YSU PBL scheme, resulting in a better performance in fluences on TC track forecasts in the configuration of the TC track forecasts. EPMEF, we carry out a number of experiments using c. Other factors influencing the performance of different combinations of four microphysics schemes EPMEF [i.e., Ferrier, Goddard, WSM6, and Lin; see a detailed description in Skamarock et al. (2008)] and three PBL There may be other factors influencing the per- schemes [i.e., YSU, MYJ, and the level-2.5 Mellor– formance of EPMEF, such as the domain settings re- Yamada Nakanishi Niino PBL scheme (MYNN2); see garding size and location, the choices of the horizontal

TABLE 1. TPEs (km) and Ri of 24-, 48-, and 72-h forecasts, as well as the total rank for different combinations of four microphysics schemes and three PBL schemes, averaged over an ensemble of forecasts initializing every 6 h from 1200 UTC 21 Jul to 1800 UTC 23 Jul 2012 for Vicente.

TPE (Ri) Forecast (h) Ferrier Goddard WSM6 Lin 24 YSU 69.9 (0.80) 81.7 (0.93) 84.8 (0.97) 88.0 (1.01) MYJ 94.6 (1.08) 90.1 (1.03) 87.4 (1.00) 93.5 (1.07) MYNN2 83.5 (0.95) 84.3 (0.96) 96.9 (1.11) 95.2 (1.09) 48 YSU 156.2 (0.94) 184.0 (1.10) 170.5 (1.02) 171.1 (1.02) MYJ 144.0 (0.86) 149.8 (0.90) 142.4 (0.85) 157.3 (0.94) MYNN2 185.0 (1.11) 176.9 (1.06) 188.0 (1.13) 178.8 (1.07) 72 YSU 134.5 (0.50) 330.3 (1.22) 325.9 (1.21) 444.2 (1.64) MYJ — (—) — (—) — (—) — (—) MYNN2 182.5 (0.68) 235.2 (0.87) 228.9 (0.85) 280.7 (1.04) Rank (sum) YSU 1 (2.23) 7 (3.26) 5 (3.20) 8 (3.67) MYJ 9 (—) 9 (—) 9 (—) 9 (—) MYNN2 2 (2.74) 3 (2.89) 4 (3.10) 6 (3.20)

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FIG. 12. The mean biases of the (a) u and (b) y components of steering flows from the 72-h forecasts by different combinations of four microphysics schemes (Ferrier, Goddard, WSM6, and Lin) and three PBL schemes (YSU, MYJ, and MYNN2) against the FNL analysis for Vicente, which are averaged over an ensemble of model runs initialized every 6 h from 1200 UTC 21 Jul to 1800 UTC 23 Jul 2012. resolutions for the nested domain, and so on. As in- microphysics schemes (figure omitted). Considering dicated in the study of Landman et al. (2005), model both the model performance and the computation cost, domain choice is important in the simulation of TC-like the current model configuration regarding the domain vortices in the southwestern Indian Ocean. Giorgi sizes and locations, as well as the horizontal resolution, (2006) also pointed out that one should carefully con- are thus nearly optimal. With more computer resources sider the choice of domain size and location in relation available in the future, we will update the current hori- to model resolution and the placement of domain zontal resolution of EPMEF to a higher level (say, 10 km boundaries in the design of regional climate simulation in the inner domain), which we believe will benefit the experiments. Nevertheless, the results from a set of ex- performance of EPMEF. periments with different domain sizes and locations It is obvious that EPMEF performed much better for (shifting the domain to the east or west), as well as in- the 2012 and 2013 TC seasons than for the 2011 TC creased resolution (from 24 to 10 km in the inner do- season in terms of TC track forecasts. The better per- main), indicate that, though differences among these formance of the EPMEF may be attributed to the fol- experiments exist, they are relatively small compared lowing factors. 1) The large-scale forecasts from GFS to those among different combinations of PBL and are probably improved in the 2012 and 2013 TC seasons

Unauthenticated | Downloaded 10/01/21 07:42 AM UTC APRIL 2015 P E N G E T A L . 483 compared to those in 2011, as a result of the use of one-way-downstream version, with a higher grid resolution ensemble forecast information as background error es- for the atmospheric component. Furthermore, a data timation in NCEP GSI through the hybrid ensemble– assimilation package will be incorporated into EPMEF GSI, which in turn improves the ICs/BCs in EPMEF and for assimilating both the atmospheric and oceanic obser- results in more accurate TC track forecasts for the 2012 vations (including in situ and satellite-derived observa- and 2013 TC seasons. 2) The network for EPMEF was tions) using three- and four-dimensional variational data upgraded to a higher speed since 2012, which allows assimilation (3DVAR and 4DVAR, respectively). In more most-updated GFS data to be downloaded before particular, a scale-selective data assimilation (SSDA) the starting of the model run for each forecast cycle in scheme (Peng et al. 2010) will be employed to assimilate the 2012 and 2013 TC seasons than during 2011. As the large-scale atmospheric circulation from the fore- demonstrated in section 4a, the performance of EPMEF casts of a global model into WRF for improving the can benefit from the most updated GFS forecasts. track prediction of tropical cyclones (Xie et al. 2010). It is expected that the TC track forecasts from EPMEF will continue to improve in the future. On the other hand, 5. Summary the forecasts of storm surges and waves from the ocean A real-time air–sea–wave regional forecasting system and wave model appear good when compared to a few for the SCS has been established. It consists of a regional limited observations, but an overall assessment is still atmosphere model (WRF), a regional ocean model not ready yet, which is due to a lack of observations. This (POM), and a sea wave model (WWIII). The output is to be reported in our future work. from the Global Forecast System (GFS) of NCEP is used as the initial and boundary conditions for WRF, Acknowledgments. This work was jointly supported which exerts a constraint on the development of small- by the MOST of China (Grants 2011CB403505 and and mesoscale atmosphere perturbations through dy- 2014CB953904); the China Special Fund for Meteorologi- namical downscaling in two nested domains. The wind cal Research in the Public Interest (GYHY201406008); field at 10-m height forecasted by WRF is used to drive the Strategic Priority Research Program of the Chinese the ocean and sea wave models. The preliminary results Academy of Sciences (Grant XDA01020304); the Chinese from its near-real-time run (four times a day) for the last Academy of Sciences through Project KZCX2-EW-208; 3 years demonstrate that it is stable and reliable during the National Natural Science Foundation of China various situations. In particular, the performance of (Grant 41376021); the Hundred Talent Program of the EPMEF in typhoon track prediction during the 2011–13 Chinese Academy of Sciences; and Guangdong Marine’s TC seasons is very encouraging compared to results disaster emergency response technology research center from other major agencies around the world, although at (2012A032100004). a cost of about a 2-h delay in forecast release time. The dynamically downscaled GFS forecasts from the most REFERENCES updated forecast cycle and the optimal combination of Bender, M. A., I. Ginis, R. Tuleya, B. Thomas, and T. 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