1. Introduction
Statistical and dynamical models were developed over the past decade to predict tropical cyclone (TC) activity on the intraseasonal time scale (Frank and Roundy 2006; Leroy and Wheeler 2008; Vitart et al. 2010). This is due to the modulation of TC activity by the eastward-propagating Madden–Julian oscillation (MJO) (Madden and Julian 1971; Wang 2005; Zhang 2005). The MJO centers of action shift to the Northern Hemisphere during boreal summer, with a dominant northward-propagating component over the Indo–western Pacific sector. The MJO modulates TC activity over the Indian Ocean (Kikuchi et al. 2009), the South China Sea and the western North Pacific (WNP) (Liebmann et al. 1994; Nakazawa 2006), the eastern North Pacific, the Gulf of Mexico, and the North Atlantic (Molinari et al. 1997; Maloney and Hartmann 2000; Mo 2000; Higgins and Shi 2001; Klotzbach 2010; Schreck et al. 2012). The recurrent nature of the MJO and its influence on large-scale environmental conditions for TC formation make an important contribution to the predictability of TC activity on the intraseasonal time scale.
In addition to favorable large-scale environmental conditions, it has been well known that TCs always develop from a low-level synoptic-scale disturbance (Zehr 1992; Gray 1998). The recent marsupial concept provides new insights into the influence of tropical cyclogenesis precursors on tropical cyclogenesis (Dunkerton et al. 2009; Wang et al. 2010; Montgomery et al. 2009, 2010). In this framework, tropical cyclogenesis precursors play an important role in the establishment of the marsupial pouch—a closed gyre favorable for tropical cyclogenesis. Although the parent synoptic-scale system in the marsupial concept was originally proposed for easterly waves, Wang et al. (2012) recently suggested that the framework can provide useful guidance on early tropical cyclogenesis detection.
The low-frequency low-level background in the WNP is characterized by the monsoon trough (MT), which consists of a confluence zone between the monsoon westerlies and the trade easterlies, and a shear line separating the westerlies to the south from the easterlies to the north (Li 2012). Synoptic-scale disturbances are usually associated with northwest-propagating wave trains in the MT, which are characterized by alternating regions of cyclonic and anticyclonic circulations along the MT axis. A few mechanisms were proposed for the development of synoptic-scale wave trains, including Rossby wave energy dispersion of a preexisting TC (Holland 1995; Ritchie and Holland 1999; Li et al. 2003; Li and Fu 2006; Li et al. 2006), transition of mixed Rossby–gravity (MRG) waves to off-equatorial tropical depression–type (TD type) waves (Dunkerton 1993; Takayabu and Nitta 1993; Liebmann and Hendon 1990; Dunkerton and Baldwin 1995; Dickinson and Molinari 2002; Aiyyer and Molinari 2003; Chen and Huang 2009), scale contraction and energy accumulation of off-equatorial Rossby-like waves (Tai and Ogura 1987; Holland 1995; Kuo et al. 2001; Sobel and Maloney 2000; Hartmann and Maloney 2001; Maloney and Hartmann 2001; Maloney and Dickinson 2003; Tam and Li 2006; Gall and Frank 2010), and instability of summer mean flows in the presence of a convection–frictional convergence (CFC) feedback (Li 2006).
Idealized numerical studies have been conducted on the influence of low-frequency background on the development of TC-precursor disturbances. Aiyyer and Molinari (2003) examined the role of the idealized MJO flow in the growth of off-equatorial disturbances that were initiated by equatorial MRG waves in a shallow water model. They found that the TD-type disturbance can develop by obtaining kinetic energy from the MJO flow, suggesting the importance of the MJO flow for the development of off-equatorial disturbances. Gall et al. (2010) and Gall and Frank (2010) investigated the roles of equatorial Rossby (ER) waves in TC formation using a series of numerical experiments. They found that the shear region of the MT led to breaking of the ER waves owing to the nonlinear deformation of ER waves and the development of tropical disturbances. While these studies stressed the roles of equatorial waves, Li (2006) investigated numerically the origin of the summertime synoptic wave train in the WNP with the summer-mean MT pattern by introducing a small perturbation initially to the model, suggesting that the synoptic wave train may arise from instability of the summer mean flow in the presence of a CFC feedback. In fact, the MJO and MT can be treated as the low-frequency background for synoptic-scale disturbances. Hsu et al. (2011) examined the effects of the intraseasonal oscillation (ISO) and a background state (longer than 90 days) and found that on the intraseasonal time-scale synoptic eddies extract (lose) energy from (to) the ISO flow during the ISO active (suppressed) phase, while the background state always contributes positively toward the synoptic eddy kinetic energy. The importance of the ISO was confirmed by Zhou and Li (2010) and Cao et al. (2014). These studies suggest that the influence of the low-frequency background on the development of tropical disturbances that are necessary for TC formation may further increase the predictability of TC activity on the intraseasonal time scale.
The capability of simulating the MJO modulation of TC activity has been recently examined in high-resolution global models (Satoh et al. 2012; Jiang et al. 2012; Kim et al. 2014) and regional models (Xu et al. 2014; Crosbie and Serra 2014). Although these recent studies suggest that current high-resolution models have certain skills in reproducing some aspects of the relationship between MJO and TCs and the statistical behavior of TCs, little attention has been paid to how the low-frequency background affects the development of synoptic-scale disturbances or TC-precursor disturbances.
The objective of this study is to examine the influence of the low-frequency background state on the development of tropical disturbances associated with synoptic-scale wave trains and TC formation through numerical experiments. Compared to the previous idealized experiments, the observed synoptic-scale precursors to TC formation modulated by the low-frequency MJO activity in the MT are selected and the full cycle of the TC formation processes are simulated with a relatively high-resolution regional model, which is justified by the multiscale nature of this study. Moreover, additional numerical experiments are conducted to examine the sensitivity of the development of the wave train to the MT structure by removing individual equatorial waves and the MJO component in the model initial and boundary conditions.
The rest of the paper is organized as follows. The observed large-scale circulations including the low-frequency background and synoptic-scale wave trains associated with the TC formation events are first described in section 2. In section 3, a series of numerical experiments are designed by focusing on the influence of the low-frequency background on the development of TC-precursor disturbances, followed by the results of these numerical experiments in section 4. The conclusions are presented in section 5.
2. Large-scale circulations associated with TC formation
a. The low-frequency background
In this study the TC formation information is obtained from the Joint Typhoon Warning Center (JTWC) best-track data. The large-scale circulations associated with TC formation are from the National Centers for Environmental Prediction (NCEP) Final (FNL) Operational Global Analysis with 1° × 1° latitude–longitude grids at 6-h intervals.
Here we focus on the TC formation processes from 14 August to 10 September 2004 during which five TCs formed in the MT (Table 1). Figure 1a shows the observed TC tracks and the 27-day-mean 850-hPa wind field, indicating the association of the TC formation, and subsequent movement, with the MT. Measured with the boundary between the westerlies and easterlies, the observed mean MT extended southeastward to about 160°E. In this study the MT is treated as the low-frequency background relative to synoptic-scale flows such as tropical disturbances and TCs (Wu et al. 2013). As we know, the quasi-biweekly oscillation (QBWO) is also an important component of the low-frequency flow over the WNP. The westward-propagating QBWO over the WNP may be linked to the equatorial Rossby (ER) wave (Kikuchi and Wang 2009; Chen and Sui 2010) and can also modulate TC formation and tracks (Li and Zhou 2013a,b). To focus on the MJO time scale, however, the QBWO component is removed by using a 20-day low-pass Lanczos filter (Lanczos 1956; Duchon 1979).
Formation time of the observed TCs and simulated TCs in the CTRL run.
Model domains (D1 and D2) used in the ARW simulations with (a) the observed TC tracks and the 850-hPa winds (vectors; m s−1) averaged over 0000 UTC 14 Aug–0000 UTC 10 Sep 2004 and (b) as in (a), but for the simulation in the CTRL experiment.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
The evolution of the low-frequency MT can be shown in the 20-day low-pass-filtered 850-hPa zonal wind averaged over 8°–17°N (Fig. 2a). The MT line is indicated by the zero contour of the zonal wind speed and its eastward extension and westward retreat range from 120° to 165°E. From 14 August onward, the MT persistently extended eastward and reached its easternmost position around 165°E on 28 August. Four TC formation events were observed during the eastward extension, and one TC formed in the westward retreat. Typhoons Megi and Aere formed within the low-frequency westerly region while others (Typhoons Chaba and Songda and Tropical Storm Sarika) formed near the shear line between the westerly and easterly winds. As shown in Fig. 1a, the TCs took a generally northwestward track in the MT and four of them then recurred northeastward along the western boundary of the subtropical anticyclone, while Typhoon Aere moved southwestward during its late lifetime.
Longitude–time cross section of (a) the 850-hPa 20-day low-pass-filtered zonal wind (shaded and contours; m s−1) and (b) the MJO components of OLR anomalies (shaded; W m−2) and the 850-hPa zonal wind (contours; m s−1) averaged over 8°–17°N with the longitude and time of TC formation during 11 Aug–10 Sep 2004. The blue line in (a) is the zero contour of the low-frequency zonal wind speed, indicating the eastward extension of the monsoon trough.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
The wavenumber–frequency spectral analysis is used to separate the MJO and equatorial waves (section 3) in the FNL analysis data within 20° of the equator (Wheeler and Kiladis 1999). The data for tropical convective activity used in the spectral analysis are from the National Oceanic and Atmospheric Administration (NOAA) OLR dataset with 2.5° × 2.5° grids. Figure 2b shows the MJO component of zonal wind and OLR averaged over 8°–17°N. Compared with Fig. 2a, it is clear that the eastward extension (westward withdrawal) of the MT is closely related to the active (inactive) periods, which is characterized by the westerly wind and negative OLR anomalies (easterly wind and positive OLR anomalies). During 14–28 August 2004, the westerly wind associated with the MJO extended eastward. During this period, the MT extended eastward and reached its easternmost position on 28 August with four genesis events. The westward retreat of the MT was coincident with the easterly phase of the MJO, which was located to the west of 133°E at 0000 UTC 10 September (figure not shown).
b. Synoptic-scale wave trains
Lau and Lau (1990) first documented synoptic-scale wave trains with a typical wavelength of 2500–3000 km and a time scale of 6–10 days in the WNP. Some observational studies also showed that synoptic-scale wave trains over the western Pacific propagate northwestward with a wavelength of 3000 km and a period of 4–6 days (Wallace and Chang 1969; Chang et al. 1970; Reed and Recker 1971; Takayabu and Nitta 1993; Schreck et al. 2012). Dunkerton (1993) and Dunkerton and Baldwin (1995) integrated 20 yr of rawinsonde and ERA-15 data for analysis of the vertical structure, interannual variability, horizontal structure, propagation, and convective coupling of 3–6-day westward-propagating synoptic disturbances over the tropical Pacific and shed additional light on the northwestward curving of wave trains in the western Pacific, the upward and downward propagation of waves, and their coupling to OLR. Based on previous studies, a 10-day high-pass Lanczos filter is used to extract the synoptic-scale wind field. To reduce possible bias from strong TC circulation, following Hsu et al. (2008) and Wu et al. (2013), the TC circulation in the FNL data is first removed with the procedure proposed by Kurihara et al. (1993, 1995).
Figure 3 shows the 10-day high-pass-filtered 850-hPa wind field at the TC formation time. We can see the alternating regions of cyclonic and anticyclonic circulations along the MT line. Except Typhoon Chaba, the other four tropical cyclones formed in the cyclonic circulation of the wave train. For example, Typhoons Megi and Aere formed in the cyclonic circulation region around 125°E on 16 August and around 135°E on 20 August, respectively (Figs. 3b and 3f). As shown in Fig. 3e, Typhoon Chaba formed to the north of the anticyclonic circulation of the wave train.
Observed 10-day high-pass-filtered 850-hPa wind fields (vectors; m s−1) with squares and typhoon symbols indicating the locations of newly formed TCs and previously declared storms, respectively. The zero contour of the zonal wind speed from the 20-day low-pass-filtered winds indicates the monsoon trough axis and subtropical ridge line.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
The wave-train activity can be further demonstrated in the longitude–time cross section of the 850-hPa relative vorticity along 8°–17°N (Fig. 4a). Seven wave trains (W1–W7) can be identified during the 27-day period. The wave train shows a wavelength of 2500–3600 km and a period of 4–6 days, in agreement with previous studies (e.g., Lau and Lau 1990; Chang et al. 1996).
850-hPa synoptic-scale relative vorticity (shaded and contours; 10−5 s−1) averaged over 8°–17°N for (a) the observation and (b) the control experiment with squares indicating the TC formation time and longitude. The zero contour (thick black) of the 10-day low-pass-filtered zonal wind indicates the eastward extension of the monsoon trough. Thick blue lines schematically show the observed and simulated wave trains (W1–W7).
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
3. Experimental design
To demonstrate the influence of the low-frequency background on the development of TC-precursor disturbances and TC formation, a series of numerical experiments are conducted using the Weather Research and Forecast (WRF) Model (version 2.2.1). The full-physics WRF Model has been widely used in the study of TC activity and can simulate realistic TC formation processes. Two interactive model domains are used with 31 vertical levels (Fig. 1a). The coarse domain covers the region of 19.9°S–49.9°N, 105.0°E–176.8°W with a spacing of 27 km. The nested domain has a horizontal resolution of 9 km, covering the region of the observed TC formation (0°–36.0°N, 118.3°–172.2°E) during 14 August–10 September 2004 (Fig. 1a). The data for deriving the model initial and lateral boundary conditions are also from the NCEP FNL analysis data.
The analysis nudging for the wind components above the lower boundary layer is used in the outer domain to force the simulated large-scale patterns close to the observation. The nudging coefficient is 1.5 × 10−4 s−1 in all of the numerical experiments. The WSM 3-class simple ice microphysics scheme (Dudhia 1989) and the Kain–Fritch convective scheme (Kain and Fritch 1993) are used in the outer coarse domain, and the Lin microphysics scheme (Lin et al. 1983; Rutledge and Hobbs 1984; Chen and Sun 2002) is used in the nested domain with no convection parameterization. The Yonsei University PBL scheme (Noh et al. 2003), the Monin–Obukhov surface-layer scheme (Monin and Obukhov 1954), the Noah land surface scheme, the Dudhia shortwave parameterization (Dudhia 1989), and the Rapid Radiative Transfer Model longwave parameterization (Mlawer et al. 1997) are adopted in both of the meshes.
The results of six numerical experiments are presented in this study (Table 2). All of the numerical simulations are integrated for 27 days from 0000 UTC 14 August to 0000 UTC 10 September 2004. The TC formation time in the simulation is defined when the azimuthal-mean maximum wind speed of a TC first reaches 17.2 m s−1. To determine a simulated TC center, a closed low pressure area is first detected and then a variational approach is used to locate the TC center until the maximum azimuthal-mean tangential wind speed is obtained (Wu et al. 2006). A TC simulated in the 9-km domain is defined if 1) the minimum surface pressure is less than 1000 hPa, 2) the maximum 1000-hPa wind speed within a radius of 450 km from the center is stronger than 17.2 m s−1, 3) a warm core appears between 700 hPa and 300 hPa, and 4) the TC lifetime is at least 2 days.
Initial and lateral boundary conditions in the numerical experiments.
The control (CTRL) run is designed to verify the capability of the model in simulating the observed TC formation events during the 27-day period. The unfiltered FNL data are used to initialize the CTRL experiment. The observed sea surface temperature (SST) is updated every 6 h. To examine the influence of the low-frequency background, two experiments (LOWV and LOWF) are conducted to simulate the development of wave trains and TC formation from an initial low-frequency background. For this purpose, the initial and lateral boundary conditions are derived from the 20-day low-pass-filtered fields, including all of the variables used in the model. The difference between the LOWV and LOWF experiments is in the lateral boundary conditions. In the LOWV run the lateral boundary conditions are allowed to vary with the observed low-frequency background, while the lateral boundary conditions are fixed at the initial time (0000 UTC 14 August) in the LOWF experiment. By comparing the results of the two experiments, we can examine the possible influence of the lateral boundaries.
Three additional experiments (R-ER, R-MJO, and R-KEL) are conducted to examine the sensitivity of the development of wave trains and TC formation to the low-frequency MT structure. To make the structural modification dynamically consistent, we use the wavenumber–frequency spectrum analysis to obtain the structures of the ER, MJO, and Kelvin waves from the low-pass-filtered background within 20° of the equator (Wheeler and Kiladis 1999; Ching et al. 2010; Chen and Chou 2014). It has been suggested that equatorial waves are important to TC formation (Frank and Roundy 2006; Molinari et al. 2007; Schreck and Molinari 2011). Recently Chen and Chou (2014) performed a composite study to evaluate the joint contribution of equatorial waves and they suggested that the joint contribution from more than one wave type favors the creation of a more coherent, favorable environment.
The initial and lateral boundary conditions for the three sensitivity experiments are obtained by progressively removing ER, MJO, and Kelvin waves. That is, the component of ER waves is first removed from the initial and lateral boundary conditions of the LOWV experiment and the resulting fields become the initial and lateral boundary conditions of the R-ER experiment. The initial and lateral boundary conditions of the R-MJO (R-KEL) experiment are obtained by removing the MJO component (Kelvin waves) from those of the R-ER (R-MJO) experiment. In fact, both of the MJO component and ER waves are removed in the initial and lateral boundary conditions of the R-MJO experiment while the MJO component, ER waves, and Kelvin waves are all removed in the initial and lateral boundary conditions of the R-KEL experiment.
4. Numerical results
a. CTRL experiment
As observed, the model simulates five TCs in the CTRL experiment (Table 1). Figure 1b shows the simulated TC tracks and the 27-day-mean 850-hPa wind field in this experiment. The MT is fairly well simulated in terms of its mean orientation and eastward extension. While the simulated Megi and Aere form earlier than the observed ones, the other three TCs reach tropical storm intensity later than the observation. As shown in Fig. 1, the formation location of the simulated Songda is about 11° longitude west of the observation owing to the 96-h delay in formation. Following Xiang et al. (2015), a correct forecast of a TC genesis event is defined when a model TC occurs within a 1000-km-radius region and 3-day time window. Close inspection indicates that the model made correct forecasts for Megi, Chaba, Aere, and Sarika. Although the formation time and location are different from the observation, each simulated TC can be related to the corresponding observed one (Table 1). Four TCs take a recurving track and one makes a southwestward turn, fairly well comparable to the observed ones. The results of the control experiment indicates that the 27-day extended simulation can fairly well reproduce the observed TC formation processes with the initial and lateral conditions derived from the FNL analysis.
To obtain the synoptic-scale wind fields in the simulation, the Savitzky–Golay filter that can well preserve the shape and height of waveform peaks is used in this study (Savitzky and Golay 1964; Press et al. 2007). The filter essentially performs a local polynomial regression to determine the smoothed value for each point and can be used to estimate smoothed signal at all points of the signal, including the end points where there are no preceding/following data points. The synoptic-scale 850-hPa wind field at the TC formation time is obtained by subtracting the low-pass-filtered data (Fig. 5).
The 850-hPa synoptic-scale wind fields (vectors; m s−1) in the CTRL experiment. The zero contour of the zonal wind speed from the 10-day low-pass-filtered winds indicates the monsoon trough axis and subtropical ridge line.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
In addition to the relatively realistic simulation of the observed TC activity, the model also well reproduces the observed wave trains (Fig. 4b) compared to the observation (Fig. 4a). In both the observation and the simulation, we can see that the synoptic-scale wave trains can be initiated and develop far to the east of the MT. It is interesting to note that, as in the observation (Fig. 3), the simulated Chaba also forms near the anticyclonic circulation of the wave train while other four TCs form within the cyclonic circulation (Fig. 5d). Figure 6 shows the comparison of the evolution of the observed and simulated MTs. Note that the observed MT is also obtained from a 10-day low-pass Savitzky–Golay filter. Despite the differences in the eastward extension and westward withdrawal, the MT maintains in the simulation during the 27-day period. The successful simulation of wave trains and TC formation facilitates our following analysis of the influence of the low-frequency background on the development of TC-precursor disturbances and TC formation.
Longitude–time cross sections of (a) the 850-hPa 10-day low-pass-filtered zonal wind averaged over 8°–17°N from a Savitzky–Golay smoothing filter for (a) the observation and (b) the control experiment. The blue line is the zero contour of the zonal wind speed, indicating the eastward extension of the monsoon trough.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
We also compare the results in the CTRL experiment with the numerical experiment without nudging (figure not shown) and find that the successful simulation of the MT is largely due to the analysis nudging for the wind components above the lower boundary layer. However, we should note that the evolution of the simulated fields in the inner domain is based fully on the model dynamics and physics. Note that, as shown in Fig. 1b, the simulated TC formation events are well within the inner domain.
b. LOWV and LOWF experiments
As mentioned in the last section, the LOWV and LOWF experiments are designed to examine the TC formation from an initial low-frequency background. In other words, the components with periods of shorter than 20 days are generated by the model dynamics and physics, as well as their interaction with the low-frequency background. Figure 7 shows the simulated wave-train patterns and TC formation events in the LOWV experiment. Since the simulated TCs cannot be exactly found in the observation, here a new numbering convention (TC1–TC6) is introduced. At 0600 UTC 17 August, the model has been integrated for 78 h and the wave train can be seen (Fig. 7a). The first TC (TC1) forms in the cyclonic circulation around 140°E. As the TC1 and the wave train propagate northwestward, the second TC (TC2) forms around 155°E at 1200 UTC 18 August (Fig. 7b). As the wave train continues to propagate northwestward, new cyclonic circulations constantly emerge and four more TCs form in each of the new cyclonic circulation. Comparing Fig. 7 with Fig. 5, we can find that the wavelength (about 2000 km) of the simulated wave train in the LOWV experiment is substantially shorter than that in the CTRL experiment owing to the absence of higher-frequency (shorter than 20 days) influences. For this reason, the model produces one more TC than the observation and the CTRL run during this period.
Synoptic-scale 850-hPa wind fields (vectors; m s−1) associated with the tropical cyclone formation at (a) 0600 UTC 17 Aug, (b) 1200 UTC 18 Aug, (c) 0000 UTC 19 Aug, (d) 1800 UTC 25 Aug, (e) 1200 UTC 29 Aug, and (f) 1200 UTC 3 Sep 2004 in the LOWV experiment. The simulated TCs are indicated by TC1–TC6 with the letter A showing the center of the anticyclonic circulation.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
Figure 8 compares the simulated TC formation and subsequent tracks in the LOWV run with those in the LOWF run. In the latter experiment, the lateral boundary conditions are fixed at the model initial time. In the LOWV run, low-frequency waves can propagate into the model domains through the lateral conditions. As in the LOWV experiment, the model also simulates six TC formation events in the LOWF experiment. The formation time and location for the first two TCs are nearly the same in the two experiments since the impact of lateral boundary conditions has not reached the area during the first 4 days. Since 19 August, the formation time and location for the next four TCs are slightly different. However, the wave-train pattern (e.g., wavelength) simulated in the LOWF run is very similar to that in the LOWV run (figure not shown), suggesting that the low-frequency influence from the lateral boundary is not essential for the development of the synoptic-scale wave train.
The 20-day low-pass-filtered 850-hPa wind fields (vectors; m s−1) at the initial time and the simulated TC tracks in the (a) LOWV and (b) LOWF experiments. The TC formation time is indicated in time/day/month format.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
As mentioned in the introduction, the wave train can result from the Rossby wave energy dispersion of a preexisting TC (Holland 1995; Li et al. 2003; Li and Fu 2006; Li et al. 2006) and transition of MRG waves to off-equatorial TD-type waves (Takayabu and Nitta 1993; Liebmann and Hendon 1990; Dickinson and Molinari 2002; Aiyyer and Molinari 2003; Chen and Huang 2009). In the LOWV experiment, neither of the two mechanisms works, because of the low-frequency initial and lateral boundary conditions during the first 3 days. By comparing the results from the LOWV and LOWF experiments with those from the CTRL run, we can conclude that synoptic-scale wave trains can develop from the low-frequency background without high-frequency signals from the initial and lateral boundary conditions, although the wavelength is substantially reduced in the former experiments.
Li (2006) suggested that the summertime synoptic wave train over the WNP could result from instability of the summer mean flow in the presence of the CFC feedback. His numerical experiments indicate that the summer mean flow alone cannot excite baroclinic or barotropic instability for the growth of tropical perturbations, but the mean flow determines a preferred length scale. He suggested that the synoptic-scale wave train can develop in situ and does not need upstream precursors. Our numerical experiments support his suggestion.
c. R-ER, R-MJO, and R-KEL experiments
The 850-hPa wind components of ER waves, the MJO and Kelvin waves in the initial conditions are shown in Fig. 9. The periods (wavenumbers) of the wavenumber–frequency spectrum analysis are 6.25–48 days (wavenumbers 1–10) for ER waves, 30–96 days (wavenumbers 1–5) for the MJO, and 2.5–30 days (wavenumbers 1–14) for Kelvin waves. Note that some components of ER and Kelvin waves with the periods shorter than 20 days have been removed. As shown in this figure, the major 850-hPa signal of the ER wave is easterly winds on the northern side of the MT while the MJO and the Kelvin wave are mainly indicated by the westerly winds on the southern side of the MT. It is expected that the removal of the individual wave components leads to changes in the MT structure.
Observed 850-hPa wind fields (vectors; m s−1) at the model initial time (0000 UTC 14 Aug) associated with (a) ER, (b) MJO, and (c) Kelvin waves with shading indicating zonal winds less than 0.0 m s−1, zonal winds exceeding 1.0 m s−1, and zonal winds exceeding 0.2 m s−1.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
Figure 10 shows the 850-hPa low-frequency wind fields in these numerical experiments at 0000 UTC 17 August 2004, after 72-h integration. While the MT in the R-ER experiment is very similar to that in the LOWV experiment, as expected, the monsoon westerly winds in the R-MJO and R-KEL experiments are weaker than those in the LOWV and R-ER experiments. The MT can be identified with the wind field and positive relative vorticity in all of the three experiments on 17 August 2004, extending southwestward around 160°E (Fig. 10). The MT can be identified in the LOWV and R-ER experiments by 19 August, while it disappears in the R-MJO and R-KEL experiments (figure not shown). By 27 August, the westerly winds cannot be seen and the MT area is replaced with weak winds at 850 hPa, but the positive relative vorticity region is located to the south of the subtropical anticyclone (Fig. 11). Note that at this moment the observed monsoon westerly winds reached its easternmost position around 170°E. It is suggested that lack of the circulations with periods shorter than 20 days leads to the MT evolution that is different from the observation. Zhou and Li (2010) conducted an observational analysis and suggested that synoptic-scale variability can exert an upscale feedback to ISO through the nonlinearly rectified surface latent heat flux. This upscale feedback is substantially reduced in our numerical experiments with low-frequency initial and lateral conditions.
The 850-hPa 10-day low-pass-filtered wind fields at 0000 UTC 17 Aug 2004 in the (a) LOWV, (b) R-ER, (c) R-MJO, and (d) R-KEL experiments.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
The 10-day low-pass-filtered wind fields at 0000 UTC 27 Aug 2004 in the (a) LOWV, (b) R-ER, (c) R-MJO, and (d) R-KEL experiments.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
The different initial structure of the MT also reflects in the development of synoptic-scale wave trains. Figure 12 shows the 850-hPa synoptic-scale wind field. At 0000 UTC 18 August, after the 4-day integration, it is clear that the wave train develops in the LOWV and R-ER experiment, while the wave-train patterns are relatively weak in the R-MJO and R-KEL experiments. As we mentioned above, the initial monsoon westerly winds are substantially reduced in the R-MJO and R-KEL experiments and the MT structure cannot be identified by 18 August (Fig. 10).
Synoptic-scale 850-hPa wind fields (vectors; m s−1) at 0000 UTC 18 Aug in the (a) LOWV, (b) R-ER, (c) R-MJO, and (d) R-KEL experiments.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
Figure 13 shows the synoptic-scale 850-hPa wind fields at 0000 UTC 25 August. While the low-frequency MT disappears, it is interesting that the synoptic-scale wave train patterns can still be seen in the LOWV, R-MJO, and R-KEL experiments and TCs form in the cyclonic circulation. In the R-ER experiment, although TCs still can form even without the presence of the MT, no synoptic-scale wave train can be identified. As the model integrations proceed (Fig. 14), while the wave train can still be seen in the LOWV and R-KEL experiments, the wave-train patterns cannot be seen in the other two experiments. Figures 13 and 14 suggest that the wave train can occurs in the absence of the MT. Further investigation is needed to understand the development of the wave train in the absence of the low-frequency MT.
Synoptic-scale 850-hPa wind fields (vector; m s−1) at 0000 UTC 25 Aug in the (a) LOWV, (b) R-ER, (c) R-MJO, and (d) R-KEL experiments.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
Synoptic-scale 850-hPa wind fields (vectors; m s−1) at 0000 UTC 31 Aug in the (a) LOWV, (b) R-ER, (c) R-MJO, and (d) R-KEL experiments.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-14-0375.1
5. Conclusions
The low-frequency background can modulate TC activity on the intraseasonal time scale by providing large-scale conditions favorable for TC formation over the WNP (Liebmann et al. 1994; Nakazawa 2006). Synoptic-scale disturbances that are necessary for TC formation are usually associated with northwest-propagating wave trains in the MT over the WNP, and their development is closely associated with the low-frequency background state (e.g., Aiyyer and Molinari 2003; Li 2006; Gall and Frank 2010). In this study, we demonstrate that the influence of the low-frequency background on the development of synoptic-scale wave trains, which can provide an additional source of the predictability of TC activity on the intraseasonal time scale.
In the extended control simulation over the western North Pacific during 14 August–10 September 2004 with the initial and lateral boundary conditions derived from the unfiltered FNL analysis, the model can well reproduce the observed wave trains and the observed TC formation processes that can be related to the corresponding observed ones. Sensitivity experiments are designed with the initial and lateral boundary conditions derived from the low-frequency background (longer than 20 days). In the LOWV and LOWF experiments, the synoptic-scale wave train can be well simulated although its wavelength is substantially reduced in the absence of high-frequency signals with a period shorter than 20 days. Six simulated TCs form in the cyclonic circulations of the wave train as it propagates northwestward. Numerical experiments are also conducted through the modification of the MT structure, in which the dynamically consistent structures of ER waves, Kelvin waves, and the MJO are removed from the low-frequency initial and lateral boundary conditions. These sensitivity experiments indicate that the evolution of the MT is sensitive to its initial structure. Although further investigation is needed to understand the mechanism for the development of synoptic-scale wave trains in the MT, this study suggests that synoptic-scale wave trains observed in the MT over the WNP can develop in the low-frequency background without high-frequency signals from the initial and lateral boundary conditions.
Acknowledgments
This research was jointly supported by the National Basic Research Program of China (2013CB430103, 2015CB452803), the National Natural Science Foundation of China (Grant 41275093), and the project of the specially appointed professorship of Jiangsu Province.
REFERENCES
Aiyyer, A., and J. Molinari, 2003: Evolution of mixed Rossby–gravity waves in idealized MJO environments. J. Atmos. Sci., 60, 2837–2855, doi:10.1175/1520-0469(2003)060<2837:EOMRWI>2.0.CO;2.
Cao, X., T. Li, M. Peng, W. Chen, and G. Chen, 2014: Effects of monsoon trough intraseasonal oscillation on tropical cyclogenesis in the western North Pacific. J. Atmos. Sci., 71, 4639–4660, doi:10.1175/JAS-D-13-0407.1.
Chang, C.-P., V. F. Morris, and J. M. Wallace, 1970: A statistical study of easterly waves in the western Pacific: July–December 1964. J. Atmos. Sci., 27, 195–201, doi:10.1175/1520-0469(1970)027<0195:ASSOEW>2.0.CO;2.
Chang, C.-P., J. Chen, P. Harr, and L. Carr, 1996: Northwestward propagating wave patterns over the tropical western North Pacific during summer. Mon. Wea. Rev., 124, 2245–2266, doi:10.1175/1520-0493(1996)124<2245:NPWPOT>2.0.CO;2.
Chen, G. H., and R. H. Huang, 2009: Interannual variations in mixed Rossby–gravity waves and their impacts on tropical cyclogenesis over the western North Pacific. J. Climate, 22, 535–549, doi:10.1175/2008JCLI2221.1.
Chen, G. H., and C. H. Sui, 2010: Characteristics and origin of quasi-biweekly oscillation over the western North Pacific during boreal summer. J. Geophys. Res., 115, D14113, doi:10.1029/2009JD013389.
Chen, G. H., and C. Chou, 2014: Joint contribution of multiple equatorial waves to tropical cyclogenesis over the western North Pacific. Mon. Wea. Rev., 142, 79–92, doi:10.1175/MWR-D-13-00207.1.
Chen, S.-H., and W.-Y. Sun, 2002: A one-dimensional time dependent cloud model. J. Meteor. Soc. Japan, 80, 99–118, doi:10.2151/jmsj.80.99.
Ching, L., C.-H. Sui, and M.-J. Yang, 2010: An analysis of the multiscale nature of tropical cyclone activities in June 2004: Climate background. J. Geophys. Res., 115, D24108, doi:10.1029/2010JD013803.
Crosbie, E., and Y. Serra, 2014: Intraseasonal modulation of synoptic-scale disturbances and tropical cyclone genesis in the eastern North Pacific. J. Climate, 27, 5724–5745, doi:10.1175/JCLI-D-13-00399.1.
Dickinson, M., and J. Molinari, 2002: Mixed Rossby–gravity waves and western Pacific tropical cyclogenesis. Part I: Synoptic evolution. J. Atmos. Sci., 59, 2183–2196, doi:10.1175/1520-0469(2002)059<2183:MRGWAW>2.0.CO;2.
Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 1016–1022, doi:10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.
Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107, doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.
Dunkerton, T. J., 1993: Observation of 3–6-day meridional wind oscillations over the tropical Pacific, 1973–1992: Vertical structure and interannual variability. J. Atmos. Sci., 50, 3292–3307, doi:10.1175/1520-0469(1993)050<3292:OODMWO>2.0.CO;2.
Dunkerton, T. J., and M. P. Baldwin, 1995: Observation of 3–6-day meridional wind oscillations over the tropical Pacific, 1973–1992: Horizontal structure and propagation. J. Atmos. Sci., 52, 1585–1601, doi:10.1175/1520-0469(1995)052<1585:OODMWO>2.0.CO;2.
Dunkerton, T. J., M. T. Montgomery, and Z. Wang, 2009: Tropical cyclogenesis in a tropical wave critical layer: Easterly waves. Atmos. Chem. Phys., 9, 5587–5646, doi:10.5194/acp-9-5587-2009.
Frank, W. M., and P. E. Roundy, 2006: The role of tropical waves in tropical cyclogenesis. Mon. Wea. Rev., 134, 2397–2417, doi:10.1175/MWR3204.1.
Gall, J. S., and W. M. Frank, 2010: The role of equatorial Rossby waves in tropical cyclogenesis. Part II: Idealized simulations in a monsoon trough environment. Mon. Wea. Rev., 138, 1383–1398, doi:10.1175/2009MWR3115.1.
Gall, J. S., W. M. Frank, and M. C. Wheeler, 2010: The role of equatorial Rossby waves in tropical cyclogenesis. Part I: Idealized numerical simulations in an initially quiescent background environment. Mon. Wea. Rev., 138, 1368–1382, doi:10.1175/2009MWR3114.1.
Gray, W. M., 1998: The formation of tropical cyclones. Meteor. Atmos. Phys., 67, 37–69, doi:10.1007/BF01277501.
Hartmann, D. L., and E. D. Maloney, 2001: The Madden–Julian oscillation, barotropic dynamics, and North Pacific tropical cyclone formation. Part II: Stochastic barotropic modeling. J. Atmos. Sci., 58, 2559–2570, doi:10.1175/1520-0469(2001)058<2559:TMJOBD>2.0.CO;2.
Higgins, R. W., and W. Shi, 2001: Intercomparison of the principal modes of interannual and intraseasonal variability of the North American monsoon system. J. Climate, 14, 403–417, doi:10.1175/1520-0442(2001)014<0403:IOTPMO>2.0.CO;2.
Holland, G. J., 1995: Scale interaction in the Western Pacific Monsoon. Meteor. Atmos. Phys., 56, 57–79, doi:10.1007/BF01022521.
Hsu, H.-H., C.-H. Hung, A.-K. Lo, C.-C. Wu, and C.-W. Hung, 2008: Influence of tropical cyclones on the estimation of climate variability in the tropical western North Pacific. J. Climate, 21, 2960–2975, doi:10.1175/2007JCLI1847.1.
Hsu, P.-C., T. Li, and C.-H. Tsou, 2011: Interactions between boreal summer intraseasonal oscillations and synoptic-scale disturbances over the western North Pacific. Part I: Energetics diagnosis. J. Climate, 24, 927–941, doi:10.1175/2010JCLI3833.1.
Jiang, X. A., M. Zhao, and D. E. Waliser, 2012: Modulation of tropical cyclones over the eastern Pacific by the intraseasonal variability simulated in an AGCM. J. Climate, 25, 6524–6538, doi:10.1175/JCLI-D-11-00531.1.
Kain, J. S., and J. M. Fritch, 1993: Convective parameterization for mesoscale models: The Kain–Fritch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.
Kikuchi, K., and B. Wang, 2009: Global perspective of the quasi-biweekly oscillation. J. Climate, 22, 1340–1359, doi:10.1175/2008JCLI2368.1.
Kikuchi, K., B. Wang, and H. Fudeyasu, 2009: Genesis of tropical cyclone Nargis revealed by multiple satellite observations. Geophys. Res. Lett., 36, L06811, doi:10.1029/2009GL037296.
Kim, D., M.-I. Lee, H.-M. Kim, and J. H. Yoo, 2014: The modulation of tropical storm activity in the western North Pacific by the Madden–Julian Oscillation in GEOS-5 AGCM experiments. Atmos. Sci. Lett., 15, 335–341, doi:10.1002/asl2.509.
Klotzbach, P. J., 2010: On the Madden–Julian oscillation–Atlantic hurricane relationship. J. Climate, 23, 282–293, doi:10.1175/2009JCLI2978.1.
Kuo, H.-C., J.-H. Chen, R. T. Williams, and C.-P. Chang, 2001: Rossby waves in zonally opposing mean flow: Behavior in northwest Pacific summer monsoon. J. Atmos. Sci., 58, 1035–1050, doi:10.1175/1520-0469(2001)058<1035:RWIZOM>2.0.CO;2.
Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 2030–2045, doi:10.1175/1520-0493(1993)121<2030:AISOHM>2.0.CO;2.
Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1995: Improvements in the GFDL hurricane prediction system. Mon. Wea. Rev., 123, 2791–2801, doi:10.1175/1520-0493(1995)123<2791:IITGHP>2.0.CO;2.
Lanczos, C., 1956: Applied Analysis. Prentice Hall, 539 pp.
Lau, K.-H., and N.-C. Lau, 1990: Observed structure and propagation characteristics of tropical summertime synoptic scale disturbances. Mon. Wea. Rev., 118, 1888–1913, doi:10.1175/1520-0493(1990)118<1888:OSAPCO>2.0.CO;2.
Leroy, A., and M. C. Wheeler, 2008: Statistical prediction of the weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 136, 3637–3654, doi:10.1175/2008MWR2426.1.
Li, R. C. Y., and W. Zhou, 2013a: Modulation of western North Pacific tropical cyclone activity by the ISO. Part I: Genesis and intensity. J. Climate, 26, 2904–2918, doi:10.1175/JCLI-D-12-00210.1.
Li, R. C. Y., and W. Zhou, 2013b: Modulation of western North Pacific tropical cyclone activity by the ISO. Part II: Tracks and landfalls. J. Climate, 26, 2919–2930, doi:10.1175/JCLI-D-12-00211.1.
Li, T., 2006: Origin of the summertime synoptic-scale wave train in the western North Pacific. J. Atmos. Sci., 63, 1093–1102, doi:10.1175/JAS3676.1.
Li, T., 2012: Synoptic and climatic aspects of tropical cyclogenesis in western North Pacific. Cyclones: Formation, Triggers and Control, K. Oouchi and H. Fudeyasu, Eds., Nova Science Publishers, 61–94.
Li, T., and B. Fu, 2006: Tropical cyclogenesis associated with Rossby wave energy dispersion of a preexisting typhoon. Part I: Satellite data analyses. J. Atmos. Sci., 63, 1377–1389, doi:10.1175/JAS3692.1.
Li, T., B. Fu, X. Ge, B. Wang, and M. Peng, 2003: Satellite data analysis and numerical simulation of tropical cyclone formation. Geophys. Res. Lett., 30, 2122, doi:10.1029/2003GL018556.
Li, T., X. Ge, B. Wang, and Y. T. Zhu, 2006: Tropical cyclogenesis associated with Rossby wave energy dispersion of a preexisting typhoon. Part II: Numerical simulations. J. Atmos. Sci., 63, 1390–1409, doi:10.1175/JAS3693.1.
Liebmann, B., and H. H. Hendon, 1990: Synoptic-scale disturbances near the equator. J. Atmos. Sci., 47, 1463–1479, doi:10.1175/1520-0469(1990)047<1463:SSDNTE>2.0.CO;2.
Liebmann, B., H. H. Hendon, and J. D. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden-Julian oscillation. J. Meteor. Soc. Japan, 72, 401–412.
Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702–708, doi:10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.
Maloney, E. D., and D. L. Hartmann, 2000: Modulation of eastern North Pacific hurricanes by the Madden–Julian oscillation. J. Climate, 13, 1451–1460, doi:10.1175/1520-0442(2000)013<1451:MOENPH>2.0.CO;2.
Maloney, E. D., and D. L. Hartmann, 2001: The Madden–Julian oscillation, barotropic dynamics, and North Pacific tropical cyclone formation. Part I: Observations. J. Atmos. Sci., 58, 2545–2558, doi:10.1175/1520-0469(2001)058<2545:TMJOBD>2.0.CO;2.
Maloney, E. D., and M. J. Dickinson, 2003: The intraseasonal oscillation and the energetics of summertime tropical western North Pacific synoptic-scale disturbances. J. Atmos. Sci., 60, 2153–2168, doi:10.1175/1520-0469(2003)060<2153:TIOATE>2.0.CO;2.
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 (D14), 16 663–16 682, doi:10.1029/97JD00237.
Mo, K. C., 2000: The association between intraseasonal oscillations and tropical storms in the Atlantic basin. Mon. Wea. Rev., 128, 4097–4107, doi:10.1175/1520-0493(2000)129<4097:TABIOA>2.0.CO;2.
Molinari, J., D. Knight, M. Dickinson, D. Vollaro, and S. Skubis, 1997: Potential vorticity, easterly waves, and tropical cyclogenesis. Mon. Wea. Rev., 125, 2699–2708, doi:10.1175/1520-0493(1997)125<2699:PVEWAE>2.0.CO;2.
Molinari, J., K. Lombardo, and D. Vollaro, 2007: Tropical cyclogenesis within an equatorial Rossby wave packet. J. Atmos. Sci., 64, 1301–1317, doi:10.1175/JAS3902.1.
Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the atmosphere near the ground. Tr. Geofiz. Inst., Akad. Nauk SSSR, 24, 1963–1987.
Montgomery, M. T., Z. Wang, and T. J. Dunkerton, 2009: Intermediate and high resolution numerical simulations of the transition of a tropical wave critical layer to a tropical storm. Atmos. Chem. Phys. Discuss., 9, 26 143–26 197, doi:10.5194/acpd-9-26143-2009.
Montgomery, M. T., Z. Wang, and T. J. Dunkerton, 2010: Coarse, intermediate and high resolution numerical simulations of the transition of a tropical wave critical layer to a tropical storm. Atmos. Chem. Phys., 10, 10 803–10 827, doi:10.5194/acp-10-10803-2010.
Nakazawa, T., 2006: Madden-Julian Oscillation activity and typhoon landfall on Japan in 2004. SOLA, 2, 136–139, doi:10.2151/sola.2006-035.
Noh, Y., W. G. Cheon, S.-Y. Hong, and S. Raasch, 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 401–427, doi:10.1023/A:1022146015946.
Press, W. H., S. A. Teukolsky, W. T. Vertterling, and B. P. Flannery, 2007: Numerical Recipes. 3rd ed. Cambridge University Press, 1256 pp.
Reed, R. J., and E. E. Recker, 1971: Structure and properties of synoptic-scale wave disturbances in the equatorial western Pacific. J. Atmos. Sci., 28, 1117–1133, doi:10.1175/1520-0469(1971)028<1117:SAPOSS>2.0.CO;2.
Ritchie, E. A., and G. J. Holland, 1999: Large-scale patterns associated with tropical cyclogenesis in the western Pacific. Mon. Wea. Rev., 127, 2027–2043, doi:10.1175/1520-0493(1999)127<2027:LSPAWT>2.0.CO;2.
Rutledge, S. A., and P. V. Hobbs, 1984: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. J. Atmos. Sci., 41, 2949–2972, doi:10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2.
Satoh, M., and Coauthors, 2012: The intra-seasonal oscillation and its control of tropical cyclones simulated by high-resolution global atmospheric models. Climate Dyn., 39, 2185–2206, doi:10.1007/s00382-011-1235-6.
Savitzky, A., and M. J. E. Golay, 1964: Soothing and differentiation of data by simplified least squares procedures. Anal. Chem., 36, 1627–1639, doi:10.1021/ac60214a047.
Schreck, C. J., and J. Molinari, 2011: Tropical cyclogenesis associated with Kelvin waves and the Madden–Julian oscillation. Mon. Wea. Rev., 139, 2723–2733, doi:10.1175/MWR-D-10-05060.1.
Schreck, C. J., J. Molinari, and A. Aiyyer, 2012: A global view of equatorial waves and tropical cyclogenesis. Mon. Wea. Rev., 140, 774–788, doi:10.1175/MWR-D-11-00110.1.
Sobel, A. H., and E. D. Maloney, 2000: Effect of ENSO and the MJO on western North Pacific tropical cyclones. Geophys. Res. Lett., 27, 1739–1742, doi:10.1029/1999GL011043.
Tai, K. S., and Y. Ogura, 1987: An observational study of eastern waves over the eastern Pacific in the northern summer using FGGE data. J. Atmos. Sci., 44, 339–361, doi:10.1175/1520-0469(1987)044<0339:AOSOEW>2.0.CO;2.
Takayabu, Y. N., and T. Nitta, 1993: 3-5 day period disturbances coupled with convection over the tropical Pacific Ocean. J. Meteor. Soc. Japan, 71, 221–245.
Tam, C. Y., and T. Li, 2006: The origin and dispersion characteristics of the observed tropical summertime synoptic-scale waves over the western Pacific. Mon. Wea. Rev., 134, 1630–1646, doi:10.1175/MWR3147.1.
Vitart, F., A. Leroy, and M. C. Wheeler, 2010: A comparison of dynamical and statistical predictions of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 138, 3671–3682, doi:10.1175/2010MWR3343.1.
Wallace, J. M., and C.-P. Chang, 1969: Spectrum analysis of large-scale wave disturbances in the tropical lower troposphere. J. Atmos. Sci., 26, 1010–1025, doi:10.1175/1520-0469(1969)026<1010:SAOLSW>2.0.CO;2.
Wang, B., 2005: Theory. Intraseasonal Variability in the Atmosphere-Ocean Climate System, W. K.-M. Lau and D. E. Waliser, Eds., Springer, 307–360.
Wang, Z., M. T. Montgomery, and T. J. Dunkerton, 2010: Genesis of pre–Hurricane Felix (2007). Part II: Warm core formation, precipitation evolution, and predictability. J. Atmos. Sci., 67, 1730–1744, doi:10.1175/2010JAS3435.1.
Wang, Z., T. J. Dunkerton, and M. T. Montgomery, 2012: Application of the marsupial paradigm to tropical cyclone formation from northwestward-propagating disturbances. Mon. Wea. Rev., 140, 66–76, doi:10.1175/2011MWR3604.1.
Wheeler, M., and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. J. Atmos. Sci., 56, 374–399, doi:10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.
Wu, L., S. A. Braun, J. Halverson, and G. Heymsfield, 2006: A numerical study of Hurricane Erin (2001). Part I: Model verification and storm evolution. J. Atmos. Sci., 63, 65–86, doi:10.1175/JAS3597.1.
Wu, L., H. Zong, and J. Liang, 2013: Observational analysis of tropical cyclone formation associated with monsoon gyres. J. Atmos. Sci., 70, 1023–1034, doi:10.1175/JAS-D-12-0117.1.
Xiang, B., and Coauthors, 2015: Beyond weather time-scale prediction for Hurricane Sandy and Super Typhoon Haiyan in a global climate model. Mon. Wea. Rev., 143, 524–535, doi:10.1175/MWR-D-14-00227.1.
Xu, Y., T. Li, and M. Peng, 2014: Roles of synoptic-scale wave train, intraseasonal oscillation, and high-frequency eddies in genesis of Typhoon Manyi (2001). J. Atmos. Sci., 71, 3706–3722, doi:10.1175/JAS-D-13-0406.1.
Zehr, R., 1992: Tropical cyclogenesis in the western North Pacific. NOAA Tech. Rep. NESDIS 16, 181 pp.
Zhang, C., 2005: Madden–Julian oscillation. Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158.
Zhou, C., and T. Li, 2010: Upscale feedback of tropical synoptic variability to intraseasonal oscillations through the nonlinear rectification of the surface latent heat flux. J. Climate, 23, 5738–5754, doi:10.1175/2010JCLI3468.1.
