A Study on the Influences of Low-Frequency Vorticity on Tropical

OCTOBER 2017 H S I E H E T A L . 4151

A Study on the Inﬂuences of Low-Frequency Vorticity on Tropical Cyclone Formation in the Western North Paciﬁc

YI-HUAN HSIEH Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

CHENG-SHANG LEE Department of Atmospheric Sciences, National Taiwan University, and Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, Taipei, Taiwan

CHUNG-HSIUNG SUI Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

(Manuscript received 31 March 2017, in ﬁnal form 12 July 2017)

ABSTRACT

The WRF Model is used to simulate 52 tropical cyclones (TCs) that formed in the western North Pacific during 2008–09 to study the influence of the low-frequency mode of environmental vorticity on TC formation 21 [Vmax ; 25 kt (;13 m s )]. All simulations, using the same model setting, are repeated at four distinct initial times and with two different initial datasets. These TCs are classified into two groups based on the environmental 850-hPa low-frequency vorticity (using a 10-day low-pass filter) during the period 24–48 h prior to TC formation. Results show that the WRF Model is more capable of simulating the TC formation process, but with larger track errors for TCs formed in an environment with higher low-frequency vorticity (HTC). In contrast, the model is less capable of simulating the TC formation process for TCs formed in an environment with lower low-frequency vorticity (LTC), but with smaller track errors. Fourteen selected TCs are further simulated to examine the sensitivity of previous results to different cumulus parameterization schemes. Results show that the capability of the WRF Model to simulate HTC formation is not sensitive to the choice of cumulus scheme. However, for an LTC, the simulated convection pattern is very sensitive to the cumulus scheme used; therefore, model simulation capability for LTC depends on the cumulus scheme used. Results of this study reveal that the convection process is not a dominant factor in HTC formation, but is very important for LTC formation.

1. Introduction convective vortex dynamics, that is, TC formation (Hendricks et al. 2004; Montgomery et al. 2006; Tropical cyclone (TC) formation is a multiscale Houze et al. 2009). However, it is difficult to identify process during which a tropical disturbance over the the dominant processes from various possible TC ocean intensifies into a self-sustaining system. Previous formation mechanisms because of the complexity of studies have revealed that atmospheric conditions, in- multiple convection variables and tropical dynam- cluding tropical waves (Frank and Roundy 2006; Ching ics. Furthermore, TC formation generally occurs far et al. 2010; Gall et al. 2010; Gall and Frank 2010)and offshore, inhibiting direct observations. Under- the synoptic environment (Ritchie and Holland 1999; standing the mechanisms driving TC formation in the Lee et al. 2006; Lee et al. 2008; Dunkerton et al. 2009; westernNorthPacific(WNP)isparticularlydifficult Chang et al. 2010), can play a significant role in TC because background flows in this region are more formation. These conditions, either individually or complicated. combined, provide a favorable environment for the Previous field campaigns studying TC formation, such initial vortex to intensify to the stage of self-maintained as the Hurricane Rainband and Intensity Change Ex- periment (RAINEX; Houze et al. 2006), and the Pre- Corresponding author: Cheng-Shang Lee, [email protected] Depression Investigation of Cloud-Systems in the

DOI: 10.1175/MWR-D-17-0085.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4152 MONTHLY WEATHER REVIEW VOLUME 145

Tropics (PREDICT; Montgomery et al. 2012) experi- model settings are described in section 2. The classifi- ment, have focused on the Atlantic. As such, observa- cation of TCs is described in section 3, and the simulated tions over the WNP are scarce. Advanced numerical results for each TC class are discussed in section 4. Re- modeling is an effective approach for studying the sults of sensitivity experiments using different cumulus physical mechanisms associated with TC formation. parameterization schemes are presented in section 5. Detailed information regarding these mechanisms can Finally, section 6 presents the discussion and our be obtained using TC hindcasting and theoretical ex- conclusions. periments (Liang et al. 2014; Xu et al. 2014). For example, Li and Pu (2014) used the nonhydrostatic 2. Data and experimental design Weather Research and Forecasting (WRF) Model to examine the sensitivity of Typhoon Nuri’s (2008) for- Four datasets were used in this study. First, TC data mation to different initial conditions. Their results are taken from the Joint Typhoon Warning Center best showed that the WRF Model required appropriate data track dataset (http://www.usno.navy.mil/NOOC/nmfc-ph/ for the initial and lateral boundary conditions to accu- RSS/jtwc/best_tracks/wpindex.php). TC formation was rately simulate TC formation. Furthermore, Li and Pu defined as the time when the first 25-kt maximum wind 2 (2014) found that the replication of intense convection speed was recorded (note that 1 kt 5 0.5144 m s 1). near the center of the wave pouch was necessary for the Second, Gridded Satellite (GridSat-B1) infrared window model to successfully simulate the formation of Ty- (IR) channel data (Knapp et al. 2011) were used for phoon Nuri, supporting the results of previous studies by determining cloud-top temperatures. Third, National Dunkerton et al. (2009) and Wang et al. (2010). Centers for Environmental Prediction (NCEP) Final Results of previous numerical studies on TC forma- Operational Global Analysis (FNL) data, which are tion have demonstrated that further research is required available 6-hourly on 18318 grids (NCEP 2000), were to fully understand the various driving mechanisms used as the source for the model initial conditions. Fi- of TC formation. For example, Tsai et al. (2013) and nally, ECMWF Year of Tropical Convection (YOTC) Elsberry et al. (2014) showed that the European Centre data on 0.25830.258 grids (ECMWF 2010), available for Medium-Range Weather Forecasts (ECMWF) 32-day only for the period May 2008–April 2010, were used as a ensemble forecast model resolved the formation of most second source for the model initial conditions. During TCs during 2009–10; however, some weak and short- 2008–09, 55 TCs formed; however, 3 TCs were excluded lived TCs were not resolved. Nakano et al. (2015) also from this analysis. Two TCs occurred before the YOTC found that the formation of weak and short-lived TCs data were available (prior to May 2008) and the third TC could be difficult to simulate or predict. However, they was excluded because the location of the circulation also determined that some systems could be success- center determined using ECMWF-YOTC data varied fully reproduced within 1–2 weeks of model integration, unreasonably prior to TC formation. for example, Typhoon Songda (2004), which formed The formation processes for the remaining 52 TCs within an active monsoon shear line environment. These during 2008 and 2009 were simulated using version 3.2 of results suggest that the capability of a numerical model the WRF-ARW Model (Skamarock et al. 2008) with two to accurately simulate the TC formation process is influ- nested domains, and horizontal grid spacings of 36 and enced by different atmospheric conditions. However, the 12 km, respectively. The 36-km-resolution domain ex- predictability of TC formation using numerical simula- tended from eastern Africa to western North America tions is complicated by the many natural and artificial (590 3 340 grid cells), and the inner domain covered the factors involved, including model initial conditions, pa- WNP (706 3 400 grid cells), as shown in Fig. 1. There are rameterization schemes, and settings used to perform the 31 levels in the vertical with the model top at 50 hPa. The simulation. average thicknesses are 0.07 and 1 km at the lower and In this study, we systematically simulate TC formation upper levels, respectively. using the WRF Model and analyze the relationship be- All simulations (except for those in the cumulus sen- tween simulation results and environments to un- sitivity experiment) used the same physical parameter- derstand the dominant mechanisms for TC formation ization schemes. These schemes were determined by under certain conditions. To achieve this, TCs that referring to recently published studies of TC simulations formed in the WNP during 2008–09 are simulated using using the WRF Model, including Crosbie and Serra uniform model settings. The sensitivity of the WRF (2014), Li et al. (2014), and Xu et al. (2014). They appear Model to the choice of cumulus scheme is then tested to be appropriate for the simulation of TC formation for a selection of TC cases during the 2008–09 seasons. and include the Kain–Fritsch cumulus scheme (Kain and The data used in this study, experimental design, and Fritsch 1993), the Yonsei University PBL scheme (Hong

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC OCTOBER 2017 H S I E H E T A L . 4153

FIG. 1. Nested model domain for WRF simulation. The horizontal resolutions are 36 and 12 km for the larger and smaller domains, respectively. et al. 2006), WRF double-moment 6-class microphysics with a 10-day cutoff period to both the NCEP-FNL and (Lim and Hong 2010), the Rapid Radiative Transfer ECMWF-YOTC data to obtain the low- and high- Model longwave radiation scheme (Mlawer et al. 1997), frequency winds at 850 hPa (Duchon 1979; Wu et al. and the Dudhia shortwave radiation scheme (Dudhia 2013). The average vorticity inside the 58 radius of the 1989). For each TC, two different sets of initial condi- circulation center for each pre-TC disturbance during tions and lateral boundary conditions (NCEP-FNL and the period 24–48 h prior to TC formation is computed ECMWF-YOTC data) were used and the simulation using the low- and high-frequency 850-hPa winds, and was initialized at four different times; 48, 72, 96, and the results are termed the (relative) low- and high- 120 h before TC formation (for convenience, denoted frequency vorticities, respectively. as 248, 272, 296, and 2120 h, respectively). Each The scatter diagram of the computed low- versus high- simulation was run until 24 h (denoted as 124 h) after frequency vorticity using NCEP-FNL data for 288 pre- the observed TC formation time (denoted as 0 h). In TC disturbances during 2000–09 is shown in Fig. 2,in summary, there were 8 simulations for each TC and 416 which circles represent systems that formed during simulations in total. 2008–09 and black dots are for those that formed during 2000–07. The mean value of low-frequency vorticity for 2 2 all 2000–09 cases is 1.39 3 10 5 s 1, with a standard 3. Synoptic environments for TC formation 2 2 deviation of 0.84 3 10 5 s 1; while for the high- 2 2 As discussed in section 1, differences in environmen- frequency vorticity, the mean value is 0.76 3 10 5 s 1, 2 2 tal conditions appear to affect the model performance with a standard deviation of 0.68 3 10 5 s 1. Values 2 2 2 when simulating TC formation. Therefore, TCs were range between 20.88 3 10 5 and 2.68 3 10 5 s 1 for 2 classified into different groups based on the low-level high-frequency vorticity, and 21.12 3 10 5 and 4.32 3 2 2 atmospheric conditions during their formation. Previous 10 5 s 1 for low-frequency vorticity. The values of low- studies have identified several synoptic patterns associ- frequency vorticity for pre-TC disturbances are spread ated with TC formation, including monsoon gyres, over a much wider range when compared to those of monsoon shear lines, easterly waves, and trade wind high-frequency vorticity. surges (Lander 1994; Ritchie and Holland 1999; Kuo When the 55 pre-TC disturbances during 2008–09 are et al. 2001; Lee et al. 2008; Chang et al. 2010). Further- considered, results are similar (passing the F test at 95%, more, TC formation is affected by the various time but not the t test at 95%). Therefore, for further analysis, scales of atmospheric circulation systems, including the TCs that formed during 2008–09 were classified into two 10-day period of WNP monsoons (Wu et al. 2013) and groups: TCs formed in an environment with higher low- the 3–8-day period of easterly waves (Fu et al. 2012). frequency vorticity (HTC) and TCs formed in an envi- Rather than using the large-scale patterns associated ronment with lower low-frequency vorticity (LTC). A TC with tropical cyclogenesis as discussed by Ritchie and was classified as an HTC (LTC) if the value of its low- Holland (1999), we applied a low-pass–high-pass filter frequency vorticity was among the top (bottom) half of all

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4154 MONTHLY WEATHER REVIEW VOLUME 145

25 21 FIG. 2. Scatter diagram of 10-day low-pass-filtered 850-hPa vorticity (310 s , abscissa) vs 2 2 10-day high-pass-filtered 850-hPa vorticity (310 5 s 1, ordinate) inside 58 radius at 224 to 248 h for 288 TCs during 2000–09 using NCEP-FNL data. Circles (black dots) indicate the TCs in 2008–09 (2000–07), gray dots indicate TCs Dujuan (2009) and Nuri (2008). Numbers in the corners indicate the TC number in 2000–09 for each quadrant, and the dashed line indicates the divide between HTC and LTC. cases. In addition to the three TCs previously excluded, a drier and the cyclonic circulation only occurs over a further eight TCs were excluded because they were clas- limited area (Fig. 3c). sified into different groups when different analysis datasets Composite results show that HTCs and LTCs form in were used (NCEP-FNL or ECMWF-YOTC). Therefore, distinct synoptic environments. HTCs form in a mon- there are 22 HTCs and 22 LTCs, with a low-frequency soonlike environment, while LTCs form in an easterly 2 2 vorticity threshold of 1.15 3 10 5 s 1. wave–like environment. The characteristics of these two Composites of 6-hourly cloud-top temperatures and synoptic environments are similar to the typical features 850-hPa flow during 248 to 0 h over a domain of 3083 of monsoon-related (with a broad westerly wind to the 308, centered at the 850-hPa circulation center, were south side) and easterly wave (with an inverse trough computed for HTCs and LTCs, respectively. Results structure in the easterly wind) environments associated show that HTCs and LTCs are embedded in quite dif- with TC formations, as discussed by Ritchie and Holland ferent environments during their formation stage, as (1999). Although the synoptic environment of each in- shown in Fig. 3.At248 h, the distribution of cloud-top dividual TC is somewhat different from that of the temperature (Tb) for HTCs (Fig. 3a) shows that active composite, the composite results effectively demon- convection (,260 K) is located to the south of the cir- strate the general low-level features of HTCs and LTCs. culation center and spread over an area approximately Specifically, the composites highlight the contrasts be- 308 wide. For LTCs, convection is spread over a much tween these two classifications of TCs. smaller area (approximately 108 wide) or confined to the Figure 3 also shows that the 850-hPa flow to the south proximity of the circulation center (Fig. 3c), and is of the circulation center at 248 h is predominantly weaker as compared to HTC convection. At 0 h westerly for HTCs, but more easterly for LTCs. This (Figs. 3b,d), convection becomes more concentrated for difference is more evident in the difference field HTCs and stronger near the center for LTCs; however, (Fig. 4a) and persists up to the time of TC formation the patterns of convection are similar to those at 248 h (Fig. 4b). This difference is significant using a t test at the for both LTCs and HTCs. Active convection is still 95% confidence level and is mainly due to the difference spread over a wider area for HTCs compared to that of in the 10-day low-pass-filtered 850-hPa wind field, as LTCs. Results also show that the pre-TC disturbances shown in Figs. 4c–f. The differences in the high-pass- for HTCs are embedded in an environment with high filtered 850-hPa wind fields between HTCs and LTCs specific humidity and a broad cyclonic circulation at at 248 and 0 h (Figs. 4c,d) are much smaller than those 850 hPa (Fig. 3a). For LTCs, the environment is much in the low-pass-filtered 850-hPa wind fields (Figs. 4e,f).

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC OCTOBER 2017 H S I E H E T A L . 4155

FIG. 3. Composites relative to the TC centers for the cloud-top temperature (K, shaded), 850-hPa vorticity 2 2 2 2 2 (.5 3 10 5 s 1, blue contours with interval of 5 3 10 5 s 1), and 850-hPa wind vector (m s 1) for (a),(b) HTCs and (c),(d) LTCs at (left) 248 and (right) 0 h. Green contour indicates the wet area (averaged speciﬁc humidity at 2 1000–700 hPa . 0.014 kg kg 1). Distance from the TC center is in degrees latitude and longitude.

The differences in the low-pass-filtered 850-hPa wind significant difference in the synoptic environment be- fields between HTCs and LTCs are also significant tween the two groups. These results demonstrate that using a t test at the 95% confidence level. the low-frequency component of the wind field has a To test the effectiveness of our classification method, more substantial impact on the environmental condi- TCs were then classified into two groups based on the tions important to TC formation as compared to the high-frequency vorticity of the system, instead of the high-frequency component. low-frequency vorticity used in the previous analysis. Four TCs were excluded from the analysis because they 4. Results of systematic simulations for HTCs and were classified into different groups (TCs with higher LTCs high-frequency vorticity versus TCs with lower high- frequency vorticity) when different analysis datasets To quantify the performance of the numerical model were used (NCEP-FNL or ECMWF-YOTC). Results in simulating the process of TC formation, the presence (not shown) indicate that the synoptic environments for of a TC must first be determined in each simulation. the two groups of TCs are similar, and there is no Although the criteria used to identify TC formation

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4156 MONTHLY WEATHER REVIEW VOLUME 145

FIG. 4. Significant difference (pass 95% t test) between composite results of HTCs and LTCs at (left) 248 and 2 (right) 0 h for cloud-top temperature (K, shaded) and wind vector (m s 1) at 850 hPa. Results are shown for (a),(b) unfiltered fields; (c),(d) 10-day high-pass-filtered fields; and (e),(f) 10-day low-pass-filtered fields. Unit for the abscissa (ordinate) is degrees longitude (latitude).

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC OCTOBER 2017 H S I E H E T A L . 4157

FIG. 5. Time series of the average track errors (km, solid line) and the median of the track errors (km, dashed lines) for simulations with different initial times (as labeled). Time 0 rep- resents the time of TC formation. varies for different studies, the common aim is to at 0 h are 249, 301, 441, and 600 km for simulations ini- detect a large cyclonic circulation system with a strong, tialized at 248, 272, 296, and 2120 h, respectively. All concentrated 850-hPa positive vorticity at the center and simulations with TC formations are further classified to identify the time at which the TC forms (Jourdain into two groups based on the track errors of the simu- et al. 2011; Zhan et al. 2011). In this study, the 850-hPa lated TCs. The first group includes simulations with area-averaged vorticity inside the 1.58,38, and 58 radii at continuous unreasonable track errors exceeding 12 h 0 h for all TC cases are computed using the ECMWF- (for three or more continuous 6-hourly time periods), or YOTC data. The vorticity value of a simulated TC having unreasonable track errors during four or more should be higher than the minimum value of all TCs in 6-hourly time periods during the TC formation period the analysis dataset; therefore, the mean value of the (248 to 112 h). These simulations are classified as computed area-averaged vorticity for all cases minus ‘‘Large_error.’’ The remaining simulations are consid- one standard deviation is used as the threshold to de- ered to have simulated TC formation (probably simu- termine whether a TC has formed in the 12-km-resolution lated) and are labeled as ‘‘Simulated_P.’’ grid of the simulation. A model run is considered to have The 352 simulations for the 44 TCs are classified into successfully simulated TC formation if the area-averaged three groups as shown in Table 1. Approximately 32% vorticity of the simulated vortex meets two criteria for of the 88 simulations started at 248 h are classified as more than 12 h during the period from 212 to 112 h. The Simulated_P. It is reasonable that the percentage of first criterion is that the area-averaged vorticity is greater Simulated_P decreases as the simulations are initialized 2 2 than the threshold of 7.87 3 10 5 s 1 inside the 1.58 ra- at earlier times. Consequently, the percentages of sim- dius, and the second criterion is that the area-averaged ulations classified as Simulated_P are 28%, 26%, and 2 2 vorticity exceeds 3.79 3 10 5 s 1 inside the 38 radius or 27% for simulations initialized at 272, 296, and 2120 h, 2 2 1.50 3 10 5 s 1 inside the 58 radius. A simulation that respectively. The percentages of simulations classified as does not meet the above criteria is defined as ‘‘no TC No_TC are 25%, 25%, 28%, and 32% for simulations formation’’ (No_TC). initialized at 248, 272, 296, and 2120 h, respectively. For those simulations with TC formations, the As expected, these percentages increase as the in- 6-hourly track error (with respect to the observed track tegration time becomes longer. from the ECMWF-YOTC data) of every simulated TC Approximately two-thirds (65%) of all No_TC simu- is computed following Tsai et al. (2013). Figure 5 shows lations use NCEP-FNL data for the initial conditions. the mean track error for all simulated TCs for simula- These results indicate that the overall capability of the tions initialized at 248, 272, 296, and 2120 h. The track WRF Model to simulate TC formation is not only af- error of a TC is considered unreasonable if its value is fected by differences in the initialization time, but also larger than the mean value; otherwise, it is considered by the initial conditions. This is similar to the findings of reasonable. The mean track errors of all simulated TCs Li and Pu (2014), who showed that simulation results

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4158 TABLE 1. Classiﬁcation of simulation results for each TC: O, Simulated_P; L, Large_error; and —, No_TC.

248 h 248 h 272 h 272 h 296 h 296 h 2120 h 2120 h Year Case TC name HTC/LTC (ECMWF) (NCEP) (ECMWF) (NCEP) (ECMWF) (NCEP) (ECMWF) (NCEP) 2008 STY 03W Rammasun HTC L L L L L L — — 2008 TY 05W Halong HTC O O O LLLLL 2008 TY 06W Nakri LTC — — L — L — — — 2008 TY 07W Fengshen LTC L O L O L O O L 2008 TY 08W Kalmaegi HTC O L L L L L — L 2008 TY 09W Fung-Wong HTC L O O L L O L L 2008 TS 12 W Vongfong LTC — O —————— 2008 TY 13W Nuri LTC ———————— 2008 TS 14W LTC L ——————— 2008 TY 15W Sinlaku LTC ———————— 2008 TS 17W HTC O L — LLLLL

2008 TY 18W Hagupit LTC L L O O L — V L L REVIEW WEATHER MONTHLY 2008 STY 19W Jangmi LTC L L O O O — L — 2008 TS 20W Mekkhala HTC O O O LLLLO 2008 TS 21W Higos LTC L L L — O — O O 2008 TS 23W Bavi LTC O — O — O O L L 2008 TS 24W Maysak LTC — — O — O — O O 2008 TS 25W Haishen LTC OOOOO—O— 2008 TS 26W Noul LTC L —————O— 2008 TY 27W Dolphin LTC L O O LLLL— 2009 TY 01W Kujira LTC O — O L O L O O 2009 TY 02W Chan-Hom HTC O — L L L L O L 2009 TY 03W Linfa HTC L L L LLLLL 2009 TS 04W Nangka LTC — — L — — L — — 2009 TS 05W Soudelor HTC O — O — O O — — 2009 TD 06W HTC O L L — O — O — 2009 TS 08W Goni HTC L L L LLLLL 2009 TY 09W Morakot HTC L L L LLLLL 2009 TY 11W Vamco LTC O — L L — — O — Unauthenticated |Downloaded 10/05/21 06:11 AMUTC 2009 TY 12 W Krovanh HTC L L O LLLLL 2009 TS 13 W Dujuan HTC O L O L O L O L 2009 TD 14W Mujigae HTC L L L O L L L O 2009 STY 15W Choiwan LTC O — O L — — O — 2009 TY 17W Ketsana HTC L L O L O L O L 2009 TD 18W HTC O L L O L O L L 2009 STY 19W Parma HTC L L L L L O O L 2009 STY 20W Melor LTC L L L L L — L L 2009 TS 21W Nepartak HTC O — L L O L O L 2009 STY 22W Lupit LTC ———— L — L — 2009 TY 23W Mirinae HTC O O L L O O — O 2009 TD 24W LTC O O O L O — — — OLUME 2009 TS 25W LTC L L L LLLLL 2009 STY 26W Nida HTC L O L O O L O L 2009 TD 27W HTC O L O O O O O O 145 OCTOBER 2017 H S I E H E T A L . 4159

FIG. 6. Stacked column chart (percentage of stack’s total) of three types of simulation results for (left) LTCs and (right) HTCs at different initial times. using the WRF Model were sensitive to the initial data at 248 h, 22 are No_TC simulations and 86% of those used in simulations of Typhoon Nuri (2008). This can be (19/22) are LTCs. attributed to the fact that the NCEP-FNL dataset has a For HTCs, 92% of the 176 simulations can simulate coarser grid compared to that of the ECMWF-YOTC TC formation; however, this percentage varies slightly dataset. Table 1 also shows that simulations classified as for simulations with different initial times (Fig. 6). For No_TC or Large_error tend to be for specific TCs. For LTCs, this percentage is only 54%, which is substantially example, all eight simulations of Nuri (2008) are classi- lower than that of HTCs. Moreover, among the 92% of fied as No_TC, and all eight simulations of Morakot simulations that can simulate the TC formation process (2009) are classified as Large_error. It is important to for HTC, 66% are classified as Large_error. This per- note that the low-frequency vorticities of Nuri are rel- centage is higher than that of LTCs, which is at 54%. atively smaller than those of Morakot, which are 0.37 3 These results indicate that simulation results concerning 2 2 2 10 5 and 3.65 3 10 5 s 1, respectively, suggesting that TC formation are affected by the environment in which the performance of the numerical model in simulating TC formation occurs. It is more likely that the WRF TC formation is affected by the low-frequency vorticity Model will successfully simulate TC formation in an associated with the pre-TC disturbance. It has to be environment with a larger low-frequency vorticity noted that different thresholds in determining a simu- (similar to a monsoon system); however, the simulated lated TC (i.e., the minimum value of all cases or the TC tends to have a larger track error (Fig. 6). Con- mean value plus one standard deviation) were tested, versely, the probability that the WRF Model will suc- and the relative numbers of No_TC results were similar. cessfully simulate TC formation in an environment with Among the 28 Simulated_P simulations initialized smaller low-frequency vorticity (an easterly wave–like at 248 h, 11 are for LTC cases, accounting for 25% of environment) is lower; however, the track error tends to all 44 simulations for LTC cases, and 17 are for HTC be smaller. Furthermore, there is a positive correlation cases, accounting for 39% of all simulations, as shown (r 5 0.4) between the low-frequency vorticity in the in Fig. 6. These percentages vary only slightly for analysis data (ECMWF-YOTC) and the mean vorticity simulations with different initial times. For LTCs, errors (No_TC results are excluded) for all 52 TCs at 0 h. they are 30%, 20%, and 25% for simulations initial- Therefore, the mean intensity errors for different areas ized at 272, 296, and 2120 h, respectively. For HTCs, (inside 1.58,38, and 58 radii) are also larger for HTCs they are 27%, 32%, and 27% for simulations initial- than for LTCs (pass t test at 95%). In other words, the ized at 272, 296, and 2120 h, respectively (as shown overall simulation results using the intensity errors in Fig. 6). However, most No_TC simulations are for as the classification criteria might be similar to cur- LTCs. For example, of the 88 simulations initialized rent results. However, using the intensity errors as the

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4160 MONTHLY WEATHER REVIEW VOLUME 145

TABLE 2. Classifications and names of TCs in CU_EXP. in section 2; therefore, these results may differ if other parameterization schemes are used. Because simulation Classification Case name results are likely to be sensitive to the choice of cumulus HHTC Halong (2008), Kalmaegi (2008), Fung-Wong scheme, an additional set of experiments (CU_EXP) is (2008), Mekkhala (2008), Morakot (2009), Dujuan (2009), and Kestasna (2009) carried out for 14 selected TCs. These experiments vary LLTC Nakri (2008), Nuri (2008), TS14W (2008), the cumulus scheme while using the same model set- Maysak (2008), Haishen (2008), Noul (2008), tings, initial conditions, and initial times discussed in and Lupit (2009) section 4. In addition to the Kain–Fritsch scheme used previously, three other cumulus schemes are evaluated, namely the Betts–Miller–Janjic´ scheme (Janjic´ 2000), classifications criteria is likely not appropriate in this the Grell–Dévényi ensemble scheme, and the Grell study because there are more complicated processes 3D ensemble scheme (Grell and Dévényi 2002). There- involved in the TC intensity changes (such as the effect fore, there are 24 more simulations for each of the of a nearby TC, the landmass and convection patterns, 14 selected TCs in the CU_EXP experiments. Seven etc.), which deserve further study in the future. selected cases are HTCs with the highest low-frequency Similar analyses, with TCs classified into two groups vorticity during TC formation, called HHTCs, and an- based on the high-frequency vorticity of the system, other seven selected cases are LTCs with the lowest have also been applied to the simulation results. These low-frequency vorticity during TC formation, called results (not shown) reveal that there is almost no dif- LLTCs (Table 2). ference between the two classifications of TCs regarding For the CU_EXP experiments, the simulation classi- the percentage of each type of simulated result (No_TC, fication criteria and analysis procedures are the same as Simulated_P, and Large_error). These results suggest those used in section 4. Stacked column charts display- that high-frequency vorticity only affects the accuracy of ing the results for these 14 TCs are shown in Fig. 7. The the simulated TC (location, intensity, and convection), overall classification proportions for the HHTCs are but not the actual occurrence of TC formation. These similar to those of the HTCs (Fig. 6). However, the results further support our classification of TCs accord- overall classification proportions for the LLTCs differ ing to low-frequency vorticity. from those of the LTCs. Specifically, the percentage of No_TC results is 75% for LLTCs and 46% for LTCs, 5. Cumulus scheme experiments and the percentage of Simulated_P results is 12% for The results discussed in section 4 are based on simula- LLTCs and 25% for LTCs. However, the percentage of tions using the model parameterization options described Simulated_P results for HHTCs is only slightly larger

FIG. 7. Stacked column chart (percentage of stack’s total) of three types of simulation results of cumulus-scheme experiments for (left) LLTCs and (right) HHTCs at different initial times.

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC OCTOBER 2017 H S I E H E T A L . 4161 than that for HTCs (33% compared to 31%), and the three simulations successfully replicate TC formation percentage of No_TC simulations is approximately the for Nuri). All experiments have clear cyclonic circula- same for HHTCs and HTCs (9.0% and 8.0%). In addition at 850 hPa for the Dujuan case, and the simulation tion, more than 81% of Large_error results are for results using the Betts–Miller–Janjic´ scheme produced HHTCs and 89% of No_TC results are for LLTCs. the highest vorticity at 850 hPa (Fig. 9). The numbers For simulations initialized at 248 and 272 h, the of simulation results classified as Simulated_P, Large_ percentage of No_TC results for LLTCs (73% and 79%) error, and No_TC are 13, 17, and 2, respectively. De- is almost double that of LTCs (43% and 39%). These spite the success of the WRF Model in replicating TC differences can be attributed to difficulties with the formation in this case, the positions (and tracks) of the WRF Model in replicating TC formation for the ex- simulated TCs differ markedly between simulations. treme TCs (having very small low-frequency vorticities) Typhoon Nuri (2008) formed in an easterly wave en- simulated for CU_EXP. Consequently, the proportion vironment (Montgomery et al. 2010). Thirty-six hours of No_TC results increases significantly for LLTCs. before TC formation (0600 UTC 15 August 2008), the These results indicate that WRF can simulate the TC pre-Nuri disturbance was a weak inverse trough with a formation process in an environment with large low- small area of convection located near 168N and 1528E, frequency vorticity, using a range of cumulus schemes. as shown in Fig. 10a. The system moved westward and Hence, the choice of cumulus scheme is not a key factor for intensified significantly during the following 24 h. An WRF when simulating TC formation in an environment 850-hPa cyclonic circulation accompanied by convection with large low-frequency vorticity. This suggests that, while was observed at 0600 UTC 16 August (Fig. 10d). On the it is a required process, convection is not a major forcing other hand, Tropical Storm Dujuan (2009) formed in mechanism for this type of TC formation. However, for an environment with a large and long-lasting 850-hPa TCs that have formed in an environment with small low- cyclonic circulation pattern prior to TC formation. In frequency vorticity, the WRF Model is less capable of contrast to the pre-Nuri disturbance, a large low-level cy- simulating TC formation. Furthermore, the capability of clonic circulation pattern with vorticity greater than 5 3 2 2 the model to replicate the formation of this type of TC is 10 5 s 1 existed 36 h prior to the formation of Dujuan heavily dependent on the choice of cumulus scheme. (0600 UTC 2 September 2009; Fig. 11a). Convection was spread over a broader region and was better organized than a. Simulation results for selected cases: Nuri (2008) that of Nuri. During the following 24 h, strong convection and Dujuan (2009) became more concentrated on the southwestern side of the Simulations using different cumulus schemes for the circulation center (Fig. 11d) with a more concentrated same TC case are likely to share a similar synoptic-scale strong vorticity field. The environments of the pre-Nuri and evolution pattern; however, the track and vorticity of the pre-Dujuan disturbances are similar to the composites of simulated disturbance can differ markedly. To account LTCsandHTCs(showninFig. 3), indicating that they are for this, simulation results for the formation times of typical LTC and HTC cases, respectively. Nuri (2008) and Dujuan (2009), which represent an LTC b. Comparisons between simulation results using and HTC, respectively, are shown in Figs. 8 and 9 to different cumulus schemes explore the similarities and differences between simulations and TCs. Only 3 of the 32 CU_EXP simulations Results of CU_EXP experiments also show that the replicate TC formation for Nuri, while the majority (30 percentages of the 112 simulations (14 TCs, four initial of 32) of CU_EXP simulations reproduce TC formation times, and two datasets) that can simulate TC formation for Dujuan. It should be noted that Nuri and Dujuan are are 64%, 77%, 45%, and 47% for simulations using the both extreme TC cases, located at the extremities of Kain–Fritch, Betts–Miller–Janjic´, and Grell–Dévényi Fig. 2, with area-averaged low-frequency vorticities in- ensemble, as well as the Grell 3D (G3D) ensemble 2 2 2 side the 58 radius of 0.37 3 10 5 and 3.95 3 10 5 s 1, scheme, respectively. Note that simulations using the respectively, but with similar high-frequency vorticities Kain–Fritch and Betts–Miller–Janjic´ schemes tend to 2 2 2 inside the 58 radius (0.25 3 10 5 and 0.29 3 10 5 s 1). produce stronger convection; thus, the percentage is For Nuri, the low-level (850 hPa) features of the 32 higher for simulations using these two schemes (espe- simulation results (Fig. 8) are diverse and there is no cially for LLTCs, for which over 90% of the simulation TC-like vortex in many of the simulations, especially results are classified as No_TC when Grell and G3D those using the Kain–Fritsch scheme. Simulations using schemes are used). However, there are several simula- the Betts–Miller–Janjic´ cumulus scheme tend to have tions (especially for HTCs) using the Kain–Fritch stronger vorticity, and two simulations that successfully and Betts–Miller–Janjic´ schemes that produce false TC replicate TC formation use this scheme (note that only formation as indicated in Fig. 9.

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4162 MONTHLY WEATHER REVIEW VOLUME 145

25 21 FIG. 8. The 850-hPa vorticity (310 s ) and streamlines at 1800 UTC 16 Aug 2008 for the simulations started at (a) 248, (b) 272, (c) 296, and (d) 2120 h for NURI (2008), with two initial conditions (shown on the right), and four cumulus schemes (from left to right are the Kain–Fritch, Betts–Miller–Janjic´, Grell–Devenyi ensemble, and Grell 3D ensemble cumulus schemes). The red cross indicates the location of Nuri at 0 h in the ECMWF-YOTC analysis.

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC OCTOBER 2017 H S I E H E T A L . 4163

25 21 FIG. 9. The 850-hPa vorticity (310 s ) and streamlines at 1800 UTC 3 Sep 2009 for the simulations started at (a) 248, (b) 272, (c) 296, and (d) 2120 h for Dujuan (2009), with two initial conditions (shown on the right), and four cumulus schemes (from left to right are the Kain–Fritch, Betts–Miller–Janjic´, Grell–Devenyi ensemble, and Grell 3D ensemble cumulus schemes). Red rectangles mark the false TCs, and the red cross indicates the location of Dujuan at 0 h in the ECMWF-YOTC analysis.

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4164 MONTHLY WEATHER REVIEW VOLUME 145

21 25 21 FIG. 10. The 850-hPa wind vector (m s ), 850-hPa vorticity (blue contours at 5, 10, and 50 3 10 s ), and cloud-top temperature (K, shaded) based on GridSat data at (a) 0600 UTC 15 Aug and (d) 0600 UTC 16 Aug 2008. (b),(c),(e),(f) As in (d), but for model simulations with shading indicating the simulated reﬂectivity (the maximum reﬂectivity at grid column) using different cumulus schemes as labeled. All simulations are started at 248 h (1800 UTC 14 Aug 2008) using ECMWF-YOTC for the initial conditions.

To explore the impact of the choice of cumulus Grell 3D ensemble schemes; therefore, the vorticity of the scheme on the simulated convection and circulation simulated pre-TC disturbance does not reach the thresh- patterns, results of simulations initialized at 248 h using old value (Figs. 10e,f). For the simulation using the Kain– different cumulus schemes are analyzed for Nuri and Fritch scheme, the overall pattern of simulated circulation Dujuan. Simulations for both cases use ECMWF-YOTC and convection appears reasonable; however, active initial conditions. The simulations start at 1800 UTC convection only occurs in a limited region to the north of 16 August 2008 for Nuri and at 1800 UTC 3 September the trough (Fig. 10b), which is not consistent with the 2009 for Dujuan. observations. As a result, the simulated cyclonic circula- Results of 12-h forecasts (valid at 0600 UTC 15 August, tion does not develop into a TC using the Kain–Fritsch or 236 h) show that all simulations for Nuri can replicate scheme. TC formation only occurs in the simulation using the inverse open trough observed near 168N and 1528E the Betts–Miller–Janjic´ scheme (Fig. 10c). (Fig. 10a). However, the convection pattern differs Simulation results for Nuri are consistent with those of markedly between simulations using different cumulus Li et al. (2014) and Li and Pu (2014). They indicate that schemes. For example, the simulation using the Kain– the assimilation of airborne Doppler radar data and the Fritsch scheme produces a reasonable convection pattern, ﬁner initial conditions (especially the low-level water while simulations using the Grell–Dévényi and Grell 3D vapor) would lead to convection occurring near the ensemble schemes (Betts–Miller–Janjic´ scheme) tend to center of the pre-TC disturbance and could help the produce weaker (stronger) convection compared to the formation of Nuri in the WRF simulations. Our results observations. At 0600 UTC 16 August 2008 (212 h), the support this ﬁnding. simulated convection and vorticity are still weaker than For Dujuan, the overall patterns of simulated con- observations for simulations using the Grell–Dévényi and vection appear similar (but with different intensity) for

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC OCTOBER 2017 H S I E H E T A L . 4165

21 25 21 FIG. 11. The 850-hPa wind vector (m s ), 850-hPa vorticity (blue contours at 5, 10, and 50 3 10 s ), and cloud-top temperature (K, shaded) based on GridSat data at (a) 0600 UTC 2 Sep and (d) 0600 UTC 3 Sep 2009. (b),(c),(e),(f) As in (d), but for model simulations with shading indicating the simulated reﬂectivity (the maximum reﬂectivity at grid column) using different cumulus schemes as labeled. All simulations are started at 248 h (1800 UTC 1 Sep 2009) using ECMWF-YOTC for the initial conditions.

simulations using the different cumulus schemes and These results indicate that the location and strength of the patterns match the observations. The strongest convection prior to TC formation is very important to convection is located on the southwestern side of the enabling the model to simulate the formation of Nuri circulation center (178N, 1308E) at 236 h. Convection (an LTC) but is less important for the model to simu- either becomes stronger or spreads over a larger area late the formation of Dujuan (an HTC). Results are during the following 24 h, and is better organized similar for the other six LTCs and six HTCs, analyzed at 212 h, as shown in Figs. 11b,c,e,f. Similar to those in for CU_EXP. Therefore, it is reasonable to conclude Nuri’s simulations, the simulated convection is that simulation results regarding TC formation are stronger (weaker) than observed for simulations using highly (less) sensitive to simulated convections for LTCs the Betts–Miller–Janjic´ scheme (Grell–Dévényi and (HTCs). Furthermore, the WRF Model can successfully Grell 3D ensemble schemes). However, in this case, simulate the formation of a HTC using a variety of cu- all simulations can simulate the TC formation. Aside mulus schemes; however, not all cumulus schemes facili- from the differences in the intensity of simulated tate the formation of an LTC. An appropriate cumulus convection, the vorticities of the simulated TCs scheme is required to simulate LTC formation. at 212 h (the 36-h forecast) for simulations using the Grell–Dévényi and Grell 3D ensemble schemes 6. Discussion and conclusions (Figs. 11e,f) are much weaker than the observations. Additionally, the centers for the simulated TCs for Following previous studies (e.g., Ritchie and Holland these two simulations are located far from the ob- 1999; Lee et al. 2008; Teng 2016), this study aims to served location; thus, these two simulations are clas- advance our understanding regarding the environmental siﬁed as Large_error. inﬂuences on TC formation in the WNP by means of

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4166 MONTHLY WEATHER REVIEW VOLUME 145 systematic simulations of TC formation using the WRF Nuri, which formed in an environment with smaller low- Model. The simulations used fixed model domains and frequency vorticity, the choice of cumulus scheme is settings. Two different datasets, ECMWF-YOTC and critical for simulating the increase in low-level vorticity. NCEP-FNL, were used for the initial and boundary Hence, the cumulus scheme and initial conditions are conditions. The simulations were initialized at four dif- critical for determining whether a disturbance can de- ferent times (48, 72, 96, and 120 h) prior to TC formation velop to TC intensity. Previous studies, including the and were run for up to 24 h after formation. Therefore, work of Cheung and Elsberry (2006), Li et al. (2014), eight simulations with four different integration times and Li and Pu (2014), have also determined that an were produced for TCs that occurred during the period appropriate cumulus scheme and initial conditions are of 2008–09, totaling 416 simulations for 52 TCs. This vital for accurate TC simulation. However, for Dujuan, approach minimized the impact of artificial factors on an HTC case that formed in an environment with large the simulation results and allowed for the analysis of low-frequency vorticity, the overall pattern of convec- statistical relationships between the simulation results tion was less sensitive to the choice of cumulus scheme. and synoptic environments. In this case, all simulations were able to simulate the To highlight the important environmental factors formation of a TC, regardless of cumulus scheme; influencing TC formation, all TCs were classified into however, the simulated TCs displayed different in- two groups (HTC and LTC) based on the environmental tensities (vorticities) and track errors. low-frequency vorticity prior to TC formation. This These results indicate that the choice of cumulus approach follows the findings of Ching et al. (2010), Wu scheme is not a key factor for the WRF Model when and Duan (2015), and Zong and Wu (2015), who iden- simulating TC formation in an environment with large tified the importance of low-frequency vorticity on TC low-frequency vorticity; however, the TC track error formation. Analysis of the systematic simulation results may be large. In other words, convection does not play a shows that for HTCs that form in a monsoon-related dominant role in the formation of this type of TC. system (with higher low-frequency vorticity) the WRF However, as found by Tory et al. (2007), it does affect Model demonstrated a high degree of skill in simulating the timing and location of TC center development. TC formation; however, the simulated TCs tended to Based on our analysis and on previous studies (e.g., have large track errors. In contrast, the WRF Model was Zong and Wu 2015), we hypothesize that in a monsoon- less capable of simulating LTC formation; however, the related environment where the background low- track errors of simulated TCs tended to be smaller. frequency vorticity is large, organized convection can To address the effect of the cumulus parameterization easily concentrate vorticity, leading to the formation of a scheme on the simulation results, cumulus experiments TC. Thus, the WRF Model can more readily simulate (CU_EXP) were conducted. Seven extreme HTCs and TC formation in this environment. However, such an seven extreme LTCs (called HHTCs and LLTCs) were environment is also conducive to the initiation of con- simulated using three alternate cumulus schemes vection (Lee et al. 2008; Chang 2013) and the stochastic (Betts–Miller–Janjic´, Grell–Dévényi, and Grell 3D) in nature of convection results in larger simulated TC track addition to the Kain–Fritsch scheme used in the pre- errors, similar to the argument of Tory et al. (2007). vious systematic simulations. An additional 336 simu- Furthermore, Wu and Duan (2015) found that TC for- lations (CU_EXP) were conducted to test the sensitivity mation only occurred when using the low-frequency of TC formation to the choice of cumulus scheme. The background as the initial conditions. Therefore, in- analysis procedures for CU_EXP followed those of the teractions between the pre-TC disturbance and its en- previous systematic simulations. The overall simulation vironmental low-frequency background appear to be results for HHTC were similar to those for HTCs, but critical for the formation of this type of TC (Zong and the overall simulation results for LLTCs differed from Wu 2015). It is also important to understand the role of those for LTCs. More than 80% of the simulations with tropical waves, synoptic perturbations, and convection large track errors were for HHTCs and 89% of No_TC processes on HTC formation, as has been established by simulations were for LLTCs. Furthermore, the per- Park et al. (2015) for Tropical Storm Mekkhala (2008). centage of No_TC simulation results for LLTCs was On the other hand, the WRF Model is less capable of much greater than that for LTCs. simulating TC formation in an environment with small The CU_EXP simulation results for Nuri (2008) and low-frequency vorticity. In this environment, the capa- Dujuan (2009), representing an LTC and HTC case, bility of the WRF Model to simulate TC formation is respectively, were also analyzed. Results showed that strongly influenced by the choice of cumulus scheme. This the intensity of simulated convection was very sensitive suggests that, for LTCs, convection might represent to the choice of the cumulus scheme. In the case of TC a critical low-level vorticity source, which is strongly

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC OCTOBER 2017 H S I E H E T A L . 4167 affected by the choice of cumulus scheme. These results Cheung, K. K. W., and R. L. Elsberry, 2006: Model sensitivities may explain why some weak and short-lived TCs are in numerical simulations of the formation of Typhoon difficult to simulate or predict in global models during TC Robyn (1993). Terr. Atmos. Oceanic Sci., 17, 53–89, https://doi.org/10.3319/TAO.2006.17.1.53(SWS). formation, as was found by Tsai et al. (2013), Elsberry Ching, L., C.-H. Sui, and M.-J. Yang, 2010: An analysis of et al. (2014),andNakano et al. (2015). It is likely that the multiscale nature of tropical cyclone activities in June convection processes are crucial for the formation of 2004: Climate background. J. Geophys. Res., 115, D24108, these weak systems, but not only in the WNP, because https://doi.org/10.1029/2010JD013803. similar simulation results have been found for Atlantic Crosbie, E., and Y. L. Serra, 2014: Intraseasonal modulation of synoptic-scale disturbances and tropical cyclone genesis easterly wave cases (Wang et al. 2010; Suzuki-Parker in the eastern North Pacific. J. Climate, 27, 5724–5745, 2012; Thatcher and Pu 2013). However, the stochastic https://doi.org/10.1175/JCLI-D-13-00399.1. nature of convection makes it difficult for models to ac- Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. curately simulate the convection process during TC for- J. Appl. Meteor., 18, 1016–1022, https://doi.org/10.1175/ , . mation, such that the model tends to easily miss the 1520-0450(1979)018 1016:LFIOAT 2.0.CO;2. Dudhia, J., 1989: Numerical study of convection observed formation of these TCs. Therefore, it is important to in- during the Winter Monsoon Experiment using a meso- vestigate convective processes during TC formation to scale two-dimensional model. J. Atmos. Sci., 46, 3077– understand the key forcing mechanisms for the formation 3107, https://doi.org/10.1175/1520-0469(1989)046,3077: of this type of system. NSOCOD.2.0.CO;2. Our results show that the percentage of simulations Dunkerton, T. J., M. T. Montgomery, and Z. Wang, 2009: Tropical cyclogenesis in a tropical wave critical layer: Easterly waves. that can simulate TC formation, is higher for simulations Atmos. Chem. Phys., 9, 5587–5646, https://doi.org/10.5194/ using the Kain–Fritch or Betts–Miller–Janjic´ schemes acp-9-5587-2009. than those using the Grell–Dévényi or Grell 3D en- ECMWF, 2010: WCRP and WWRP THORPEX YOTC (Year of semble schemes (especially for LTCs). However, the Tropical Convection) Project. Research Data Archive, Com- Kain–Fritsch and Betts–Miller–Janjic´ schemes are not putational and Information Systems Laboratory, National necessarily more suitable for simulating TC formation Center for Atmospheric Research, Boulder, CO, accessed 30 March 2017, https://doi.org/10.5065/D6R20ZDD. because these schemes simulate several false TC for- Elsberry, R. L., H.-C. Tsai, and M. S. Jordan, 2014: Extended- mations owing to more active convection (especially range forecasts of Atlantic tropical cyclone events dur- for HTCs). Therefore, understanding the detailed evo- ing 2013 using the ECMWF 32-day ensemble predictions. lution of simulated convection using different cumu- Wea. Forecasting, 29, 271–288, https://doi.org/10.1175/ lus schemes, as well as their effects on TC formation, WAF-D-13-00104.1. Frank, W. M., and P. E. Roundy, 2006: The role of tropical waves is important for determining the key factors and trig- in tropical cyclogenesis. Mon. Wea. Rev., 134, 2397–2417, gering mechanisms for TC formation. An understand- https://doi.org/10.1175/MWR3204.1. ing of these processes will form the basis of future Fu, B., M. S. Peng, T. Li, and D. E. Stevens, 2012: Developing investigations. versus nondeveloping disturbances for tropical cyclone formation. Part II: Western North Pacific. Mon. Wea. Rev., 140, 1067–1080, https://doi.org/10.1175/2011MWR3618.1. Acknowledgments. CS Lee is supported by the Na- Gall, J. S., and W. M. Frank, 2010: The role of equatorial Rossby tional Taiwan University and the Taiwan Typhoon waves in tropical cyclogenesis. Part II: Idealized simulations Flood Research Institute, National Applied Research in a monsoon trough environment. Mon. Wea. Rev., 138, 1383– Laboratories, and CH Sui and YH Hsieh are supported 1398, https://doi.org/10.1175/2009MWR3115.1. by the National Taiwan University. This research is ——, ——, and M. C. Wheeler, 2010: The role of equatorial Rossby waves in tropical cyclogenesis. Part I: Idealized numerical supported by the Ministry of Science and Technology simulations in an initially quiescent background environ- (MOST) of the Republic of China (Taiwan) under ment. Mon. Wea. Rev., 138, 1368–1382, https://doi.org/10.1175/ Grants MOST 102-2111-M-002-001-, MOST 103-2111- 2009MWR3114.1. M-002-003-, MOST 104-2111-M-002-003-, MOST 105- Grell, G. A., and D. Dévényi, 2002: A generalized approach 2111-M-002-004-, and MOST 106-2111-M-002-009-. to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693, https://doi.org/10.1029/2002GL015311. REFERENCES Hendricks, E. A., M. T. Montgomery, and C. A. Davis, 2004: On the role of ‘‘vortical’’ hot towers in the formation of Chang, L.-Y., 2013: Tropical cyclone formation associated with Tropical Cyclone Diana (1984). J. Atmos. Sci., 61, 1209– trade-wind surges. Ph.D. thesis, National Taiwan University, 1232, https://doi.org/10.1175/1520-0469(2004)061,1209: 206 pp. TROVHT.2.0.CO;2. ——, K. K. W. Cheung, and C.-S. Lee, 2010: The role of trade wind Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion surges for tropical cyclone formations in the western North package with an explicit treatment of entrainment processes. Pacific. Mon. Wea. Rev., 138, 4120–4134, https://doi.org/10.1175/ Mon. Wea. Rev., 134, 2318–2341, https://doi.org/10.1175/ 2010MWR3152.1. MWR3199.1.

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC 4168 MONTHLY WEATHER REVIEW VOLUME 145

Houze, R. A., Jr., and Coauthors, 2006: The Hurricane Rainband J. Geophys. Res., 102, 16 663–16 682, https://doi.org/10.1029/ and Intensity Change Experiment: Observations and 97JD00237. modeling of Hurricanes Katrina, Ophelia, and Rita. Bull. Montgomery, M. T., M. E. Nicholls, T. A. Cram, and A. B. Amer. Meteor. Soc., 87, 1503–1521, https://doi.org/10.1175/ Saunders, 2006: A vortical hot tower route to tropical cyclo- BAMS-87-11-1503. genesis. J. Atmos. Sci., 63, 355–386, https://doi.org/10.1175/ ——, W.-C. Lee, and M. M. Bell, 2009: Convective contribution to JAS3604.1. the genesis of Hurricane Ophelia (2005). Mon. Wea. Rev., 137, ——, L. L. Lussier III, R. W. Moore, and Z. Wang, 2010: 2778–2800, https://doi.org/10.1175/2009MWR2727.1. The genesis of Typhoon Nuri as observed during the Janjic´, Z. I., 2000: Comments on ‘‘Development and evaluation Tropical Cyclone Structure 2008 (TCS-08) field experi- of a convection scheme for use in climate models.’’ ment—Part 1: The role of the easterly wave critical layer. J. Atmos. Sci., 57, 3686–3686, https://doi.org/10.1175/ Atmos. Chem. Phys., 10, 9879–9900, https://doi.org/10.5194/ 1520-0469(2000)057,3686:CODAEO.2.0.CO;2. acp-10-9879-2010. Jourdain, N. C., P. Marchesiello, C. E. Menkes, J. Lefévre, E. M. ——, and Coauthors, 2012: The Pre-Depression Investigation of Vincent, M. Lengaigne, and F. Chauvin, 2011: Mesoscale Cloud-Systems in the Tropics (PREDICT) Experiment: Sci- simulation of tropical cyclones in the South Pacific: entific basis, new analysis tools, and some first results. Bull. Climatology and interannual variability. J. Climate, 24, 3–25, Amer. Meteor. Soc., 93, 153–172, https://doi.org/10.1175/ https://doi.org/10.1175/2010JCLI3559.1. BAMS-D-11-00046.1. Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for Nakano, M., M. Sawada, T. Nasuno, and M. Satoh, 2015: mesoscale models: The Kain–Fritsch scheme. The Represen- Intraseasonal variability and tropical cyclogenesis in the tation of Cumulus Convection in Numerical Models, Meteor. western North Pacific simulated by a global nonhydrostatic Monogr., No. 46, Amer. Meteor. Soc., 165–170. atmospheric model. Geophys. Res. Lett., 42, 565–571, Knapp, K. R., and Coauthors, 2011: Globally gridded satellite https://doi.org/10.1002/2014GL062479. observations for climate studies. Bull. Amer. Meteor. Soc., 92, NCEP, 2000: NCEP FNL Operational Model Global Tropo- 893–907, https://doi.org/10.1175/2011BAMS3039.1. spheric Analyses, continuing from July 1999. Research Kuo, H.-C., J.-H. Chen, R. T. Williams, and C.-P. Chang, 2001: Data Archive, Computational and Information Systems Rossby waves in zonally opposing mean flow: Behavior Laboratory, National Center for Atmospheric Research, in northwest Pacific summer monsoon. J. Atmos. Sci., 58, Boulder, CO, accessed 30 March 2017, https://doi.org/10.5065/ 1035–1050, https://doi.org/10.1175/1520-0469(2001)058,1035: D6M043C6. RWIZOM.2.0.CO;2. Park, M.-S., H.-S. Kim, C.-H. Ho, R. L. Elsberry, and M.-I. Lee, Lander, M. A., 1994: Description of a monsoon gyre and its effects 2015: Tropical Cyclone Mekkhala’s (2008) formation over the on the tropical cyclones in the western North Pacific during South China Sea: Mesoscale, synoptic-scale, and large-scale August 1991. Wea. Forecasting, 9, 640–654, https://doi.org/10.1175/ contributions. Mon. Wea. Rev., 143, 88–110, https://doi.org/10.1175/ 1520-0434(1994)009,0640:DOAMGA.2.0.CO;2. MWR-D-14-00119.1. Lee, C.-S., Y.-L. Lin, and K. K.-W. Cheung, 2006: Tropical cyclone Ritchie, E. A., and G. J. Holland, 1999: Large-scale patterns formations in the South China Sea associated with the mei-yu associated with tropical cyclogenesis in the western Pacific. front. Mon. Wea. Rev., 134, 2670–2687, https://doi.org/10.1175/ Mon. Wea. Rev., 127, 2027–2043, https://doi.org/10.1175/ MWR3221.1. 1520-0493(1999)127,2027:LSPAWT.2.0.CO;2. ——, K. K. W. Cheung, J. S. N. Hui, and R. L. Elsberry, 2008: Skamarock, W., and Coauthors, 2008: A description of the Ad- Mesoscale features associated with tropical cyclone forma- vanced Research WRF version 3. NCAR Tech. Note NCAR/ tions in the western North Pacific. Mon. Wea. Rev., 136, 2006– TN-4751STR, 113 pp., http://dx.doi.org/10.5065/D68S4MVH. 2022, https://doi.org/10.1175/2007MWR2267.1. Suzuki-Parker, A., 2012: Uncertainties and Limitations in Simu- Li, Z., and Z. Pu, 2014: Numerical simulations of the genesis of lating Tropical Cyclones. Springer, 78 pp. Typhoon Nuri (2008): Sensitivity to initial conditions and Teng, H.-F., 2016: Formation and development of tropical cloud implications for the roles of intense convection and moisture cluster in the western North Pacific. Ph.D. thesis, National conditions. Wea. Forecasting, 29, 1402–1424, https://doi.org/10.1175/ Taiwan University, 178 pp. WAF-D-14-00003.1. Thatcher, L., and Z. Pu, 2013: Evaluation of tropical cyclone gen- ——, ——, J. Sun, and W.-C. Lee, 2014: Impacts of 4DVAR esis precursors with relative operating characteristics (ROC) assimilation of airborne Doppler radar observations on numerical in high-resolution ensemble forecasts: Hurricane Ernesto. simulations of the genesis of Typhoon Nuri (2008). J. Appl. Trop. Cyclone Res. Rev., 2, 131–148. Meteor. Climatol., 53, 2325–2343, https://doi.org/10.1175/ Tory, K. J., M. T. Montgomery, and N. E. Davidson, 2007: Pre- JAMC-D-14-0046.1. diction and diagnosis of tropical cyclone formation in an NWP Liang, J., L. G. Wu, and H. J. Zhong, 2014: Idealized numerical system. Part III: Developing and nondeveloping storms. simulations of tropical cyclone formation associated with mon- J. Atmos. Sci., 64, 3195–3213, https://doi.org/10.1175/ soon gyres. Adv. Atmos. Sci., 31, 305–315, https://doi.org/10.1007/ JAS4023.1. s00376-013-2282-1. Tsai, H.-C., R. L. Elsberry, M. S. Jordan, and F. Vitart, 2013: Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective Objective verifications and false alarm analyses of western double-moment cloud microphysics scheme with prognostic North Pacific tropical cyclone event forecasts by the cloud condensation nuclei (CCN) for weather and climate ECMWF 32-day ensemble. Asia-Pac. J. Atmos. Sci., 49, 409– models. Mon. Wea. Rev., 138, 1587–1612, https://doi.org/10.1175/ 420, https://doi.org/10.1007/s13143-013-0038-6. 2009MWR2968.1. Wang, Z., M. T. Montgomery, and T. J. Dunkerton, 2010: Genesis Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. of pre–Hurricane Felix (2007). Part I: The role of the Clough, 1997: Radiative transfer for inhomogeneous atmospheres: easterly wave critical layer. J. Atmos. Sci., 67, 1711–1729, RRTM, a validated correlated-k model for the longwave. https://doi.org/10.1175/2009JAS3420.1.

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC OCTOBER 2017 H S I E H E T A L . 4169

Wu, L., and J. Duan, 2015: Extended simulation of tropical cy- in the genesis of Typhoon Manyi (2001). J. Atmos. Sci., 71, clone formation in the western North Pacific monsoon 3706–3722, https://doi.org/10.1175/JAS-D-13-0406.1. trough. J. Atmos. Sci., 72, 4469–4485, https://doi.org/10.1175/ Zhan, R. F., Y. Wang, and C.-C. Wu, 2011: Impact of SSTA in East JAS-D-14-0375.1. Indian Ocean on the frequency of northwest Pacific tropical ——, H. Zong, and J. Liang, 2013: Observational analysis cyclones: A regional atmospheric model study. J. Climate, 24, of tropical cyclone formation associated with monsoon 6227–6242, https://doi.org/10.1175/JCLI-D-10-05014.1. gyres. J. Atmos. Sci., 70, 1023–1034, https://doi.org/10.1175/ Zong, H., and L. Wu, 2015: Synoptic-scale influences on tropical JAS-D-12-0117.1. cyclone formation within the western North Pacific monsoon Xu, Y., T. Li, and M. Peng, 2014: Roles of the synoptic-scale wave trough. Mon. Wea. Rev., 143, 3421–3433, https://doi.org/10.1175/ train, the intraseasonal oscillation, and high-frequency eddies MWR-D-14-00321.1.

Unauthenticated | Downloaded 10/05/21 06:11 AM UTC