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  • View in gallery

    The JTWC best track of Megi from 13 to 24 Oct 2010.

  • View in gallery

    The wavelet power spectrum (105 W2 m−4) of OLR over the region 5°–23°N, 113°–130°E from 1 Aug 2010 to 1 Jan 2011. The black contour is the significant level. The red line indicates Megi turning time.

  • View in gallery

    The evolution of 10–60-day bandpass-filtered wind (vectors) and vorticity (shaded; 10−5 s−1) fields averaged between 850 and 300 hPa. The black dots and red typhoon marks denote the centers of the low-frequency monsoon gyre and the TC center, respectively.

  • View in gallery

    The patterns of (a) the unfiltered initial wind field, (b) the 10-day high-pass-filtered wind field, and (c) the initial wind field in the NO_MG experiment averaged from 850 to 300 hPa at 0000 UTC 18 Oct 2010. The red sign indicates the typhoon location.

  • View in gallery

    The JTWC best track (black) and simulated Megi tracks in the control (red) and NO_MG (blue) experiments. The pink and purple ovals indicate the before-turning and after-turning periods, respectively.

  • View in gallery

    The simulated low-frequency wind (vectors) and vorticity (shaded; 10−5 s−1) averaged between 850 and 300 hPa in the control experiment from (a) 18 to (f) 23 Oct. The red marks indicate the typhoon location.

  • View in gallery

    The track of the monsoon gyre center in the control experiment (black) and in the experiment where Megi was removed (red).

  • View in gallery

    (left) The tracks of the MG and the typhoon centers in (a) the NCEP analysis and (c) the simulation; (right) the relative positions of the MG and typhoon centers in (b) the NCEP analysis and (d) the simulation. The origin (0, 0) in (b) and (d) indicates the middle point between the MG and typhoon center at each time (from 18 to 22 Oct).

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    As in Fig. 8b, but starting from 15 Oct and ending on 24 Oct.

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    The radial profile of the tangential wind (m s−1) across the center of Megi in the east–west direction at 700 hPa for the control (red) and NO_MG experiment (blue).

  • View in gallery

    The wavenumber-1 wind in (a),(c) the control and (b),(d) the NO_MG simulation at (top) 1200 UTC 18 Oct and (bottom) 0000 UTC 19 Oct at 500 hPa. The shading indicates the wind speed (m s−1).

  • View in gallery

    The wavenumber-1 wind in (a),(c) the control and (b),(d) the NO_MG simulation at (top) 1200 UTC 19 Oct and (bottom) 1200 UTC 20 Oct at 500 hPa. The shading indicates the wind speed (m s−1).

  • View in gallery

    The composite wavenumber-1 vorticity tendency fields (shaded; 10−9 s−2) averaged between 850 and 300 hPa during (a),(c) before-turning and (b),(d) after-turning periods in (top) the control and (bottom) the NO_MG experiments. The black vector indicates the direction and magnitude of the maximum vorticity tendency averaged over a 400-km radius.

  • View in gallery

    The composite wavenumber-1 vorticity tendency fields (shaded; 10−9 s−2) averaged between 850 and 300 hPa during the before-turning period in the control simulation: (a) sum of terms B, C, and D; (b) term B; (c) term C; and (d) term D. (e) The azimuthal distribution of each vorticity tendency term averaged over a 400-km radius, with the red dashed line denoting the direction of TC movement. The black vector in (a)–(d) indicates the direction and magnitude of the maximum wavenumber-1 vorticity tendency averaged over a 400-km radius.

  • View in gallery

    As in Fig. 14, but for the after-turning period in the control simulation.

  • View in gallery

    As in Fig. 14, but for the before-turning period in the NO_MG simulation.

  • View in gallery

    As in Fig. 14, but for the after-turning period in the NO_MG simulation.

  • View in gallery

    (a),(d) The total horizontal vorticity advection term (10−9 s−2), (b),(e) the mean flow advection term, and (c),(f) the anomalous flow advection term averaged from 850 to 300 hPa during the after-turning period in the (top) control and (bottom) NO_MG experiments. The black vector indicates the direction and magnitude of the maximum vorticity tendency averaged over a 400-km radius.

  • View in gallery

    (top) Track and (bottom) maximum wind (m s−1) of Megi simulated using ECMWF interim reanalysis data. The red (black) dot and line indicates the simulated (JTWC) track and intensity.

  • View in gallery

    The wind (m s−1) associated with Megi at 500 hPa in (a) the NCEP analysis, and (b) the ECMWF interim reanalysis. The shading indicates the vorticity (10−5 s−1).

  • View in gallery

    (top) Track and (bottom) maximum wind (m s−1) of Megi simulated using ECMWF interim reanalysis data, but the TC is replaced with the vortex extracted from the NCEP FNL reanalysis. The red (black) dot and line indicates the simulation (JTWC) track and intensity.

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Interactions between Typhoon Megi (2010) and a Low-Frequency Monsoon Gyre

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  • 1 International Laboratory on Climate and Environment Change, and Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China, and International Pacific Research Center, and Department of Atmospheric Sciences, University of Hawai‘i at Mānoa, Honolulu, Hawaii
  • 2 International Pacific Research Center, and Department of Atmospheric Sciences, University of Hawai‘i at Mānoa, Honolulu, Hawaii
  • 3 Naval Research Laboratory, Monterey, California
  • 4 International Laboratory on Climate and Environment Change, and Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
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Abstract

The ARW Model is used to investigate the sharp northward turn of Super Typhoon Megi (2010) after it moved westward and crossed the Philippines. The NCEP analyzed fields during this period are separated into a slowly varying background-flow component, a 10–60-day low-frequency component representing the monsoon gyre, and a 10-day high-pass-filtered component representing Megi and other synoptic-scale motion. It appears that the low-frequency (10–60 day) monsoon gyre interacted with Megi and affected its track. To investigate the effect of the low-frequency mode on Megi, numerical experiments were designed. In the control experiment, the total fields of the analysis are retained in the initial and boundary conditions, and the model is able to simulate Megi’s sharp northward turn. In the second experiment, the 10–60-day monsoon gyre mode is removed from the initial and lateral boundary fields, and Megi moves westward and slightly northwestward without turning north. Tracks of the relative positions between the Megi and the monsoon gyre centers suggest that a Fujiwhara effect may exist between the monsoon gyre and Megi. The northward turning of both Megi and the monsoon gyre occurred when the two centers were close to each other and the beta drift was enhanced.

A vorticity budget analysis was conducted. It is noted that the Megi moves toward the maximum wavenumber-1 vorticity tendency. The sharp change of the maximum vorticity tendency direction before and after the track turning point is primarily attributed to the change of the horizontal vorticity advection. A further diagnosis shows that the steering of the vertically integrated low-frequency flow is crucial for the change of the horizontal advection tendency.

School of Ocean and Earth Science and Technology Contribution Number 9310, International Pacific Research Center Contribution Number 1110, and Earth System Modelling Center Contribution Number 50.

Corresponding author address: Tim Li, International Pacific Research Center, and Department of Meteorology, University of Hawai‘i at Mānoa, 1680 East-West Road, Honolulu, HI 96822. E-mail: timli@hawaii.edu

Abstract

The ARW Model is used to investigate the sharp northward turn of Super Typhoon Megi (2010) after it moved westward and crossed the Philippines. The NCEP analyzed fields during this period are separated into a slowly varying background-flow component, a 10–60-day low-frequency component representing the monsoon gyre, and a 10-day high-pass-filtered component representing Megi and other synoptic-scale motion. It appears that the low-frequency (10–60 day) monsoon gyre interacted with Megi and affected its track. To investigate the effect of the low-frequency mode on Megi, numerical experiments were designed. In the control experiment, the total fields of the analysis are retained in the initial and boundary conditions, and the model is able to simulate Megi’s sharp northward turn. In the second experiment, the 10–60-day monsoon gyre mode is removed from the initial and lateral boundary fields, and Megi moves westward and slightly northwestward without turning north. Tracks of the relative positions between the Megi and the monsoon gyre centers suggest that a Fujiwhara effect may exist between the monsoon gyre and Megi. The northward turning of both Megi and the monsoon gyre occurred when the two centers were close to each other and the beta drift was enhanced.

A vorticity budget analysis was conducted. It is noted that the Megi moves toward the maximum wavenumber-1 vorticity tendency. The sharp change of the maximum vorticity tendency direction before and after the track turning point is primarily attributed to the change of the horizontal vorticity advection. A further diagnosis shows that the steering of the vertically integrated low-frequency flow is crucial for the change of the horizontal advection tendency.

School of Ocean and Earth Science and Technology Contribution Number 9310, International Pacific Research Center Contribution Number 1110, and Earth System Modelling Center Contribution Number 50.

Corresponding author address: Tim Li, International Pacific Research Center, and Department of Meteorology, University of Hawai‘i at Mānoa, 1680 East-West Road, Honolulu, HI 96822. E-mail: timli@hawaii.edu

1. Introduction

Tropical cyclones, to a large degree, move with the environmental steering flow (Chan and Gray 1982), while the beta effect and tropical cyclone (TC) structures also play a role (Fiorino and Elsberry 1989; Li and Zhu 1991). Despite the prevailing control of the large-scale environmental flow and the great improvement made in track prediction, some cases of large track error still occur because of complex interactions of TCs with other scales of motion, such as the low-frequency mode (Carr and Elsberry 1990).

It has been noticed that the largest error in the prediction of TC tracks is observed during TC recurvature and sudden turns. Previous studies have examined the relationship between midlatitude waves and TC recurvature. For example, George and Gray (1977) found that, if the upper-level westerlies are greater than 25 m s−1 within 20° poleward of a typhoon, the typhoon may recurve. Hodanish and Gray (1993) compared the sharply and gradually recurving cases and found that typhoons begin to turn when upper-tropospheric westerlies penetrate to within 6° from the typhoon’s center. Holland and Wang (1995) found that typhoons tend to recurve into the midlatitudes when a synoptic-scale trough moves away from East Asia into the subtropical ocean.

Some TCs in the tropical western North Pacific (WNP) occasionally experienced a sudden northward track change. Megi (2010) is one example wherein most operational numerical models failed to predict the sharp turn at the right time (see a more detailed description of this super typhoon in section 2). Using a barotropic model, Carr and Elsberry (1995) investigated the sudden northward turning of a vortex when it approached a large-scale monsoon gyre. They suggested that Rossby wave energy dispersion associated with the monsoon gyre is critical in causing the sudden northward-turning track.

TCs in the WNP are usually accompanied with multiscale waves, including intraseasonal (10–90 day) oscillations (ISOs) and synoptic-scale (3–10 day) disturbances (Li and Wang 2005; Li 2012). A typical example of low-frequency systems in the WNP is the monsoon gyre (Lander 1994). Li et al. (2006) demonstrated the effect of the monsoon gyre in promoting TC genesis in a 3D model. One of the important aspects of the ISOs in the tropical WNP is their interaction with synoptic-scale waves or disturbances (Zhou and Li 2010). On one hand, the ISOs can influence the development of synoptic-scale disturbances through barotropic energy conversion (Maloney and Hartmann 2000). On the other hand, the synoptic-scale perturbations may feed back to the ISO through the nonlinear rectification of surface latent heat flux, diabatic heating, and eddy momentum transport (Hsu and Li 2011; Hsu et al. 2011). In general, the ISO in the WNP exhibits two spectral peaks at periods of 30–60 days and 10–20 days (Chen and Chen 1993; Chen and Sui 2010; Mao and Chan 2005). Harr and Elsberry (1991) found that TC tracks alternate between westward and recurving clusters at the intraseasonal time scale. Kim et al. (2008) revealed a close relationship between landfalling TCs in the WNP and the phase of the MJO. Thus, it is likely that the ISO flows may affect not only TC formation but also TC tracks.

The objective of the current study is to investigate how and to what extent Typhoon Megi (2010) interacted with low-frequency monsoon gyre flow and how such an interaction may have led to its sudden northward-turning track. The rest of the paper is organized as follows. In section 2, we present an overview of the evolution for Typhoon Megi and the nearby low-frequency circulation. In section 3, we describe the experiment design and simulation results. In section 4, we investigate mechanisms responsible for the sudden northward-turning track. Diagnostics of vorticity tendency are given in section 5. In section 6, additional experiments with different initial conditions are presented to verify our hypothesis. The conclusions and discussion are given in the last section.

2. Overview of Megi and associated low-frequency flow

Megi can be traced back to a pregenesis tropical depression that emerged east of the Philippines (around 140°E) early on 13 October 2011 (Fig. 1). The low pressure system strengthened throughout the day and became a named tropical storm, Megi, by 1200 UTC 13 October, when its central pressure fell to 998 hPa. In the following 3 days, Megi continued to develop while moving northwestward. It was upgraded to typhoon category by the Joint Typhoon Warning Center (JTWC) at 1200 UTC 16 October. It then moved west-southwestward and continued to strengthen, reaching its peak intensity of super typhoon by 1200 UTC 17 October, with a central minimum pressure of 895 hPa and maximum wind speed of 72 m s−1. It caused 11 deaths, 16 injuries, and the evacuation of more than 200 000 people.

Fig. 1.
Fig. 1.

The JTWC best track of Megi from 13 to 24 Oct 2010.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

After passing through the northern Philippines, Megi weakened a little and slowed down as it entered the South China Sea. Beyond 19 October, Megi moved in a northwest direction. By 0000 UTC 20 October, Megi’s track turned straight northward from its original westward and northwestward movement. The angle of pre- and postturning tracks from before 0600 UTC 19 October to beyond 20 October was almost 90°. Most operational TC forecast models failed to predict such a rather sudden track change, as most models predicted that Megi would continue moving westward in the following 24–72 h. As a result, track forecast error for Typhoon Megi is among the biggest in recent years. Megi eventually made landfall over the southern China coast, and weakened quickly after landfall. By 1800 UTC 23 October, Megi was downgraded to a tropical depression.

As stated in the introduction, previous studies have indicated that ISOs may affect TCs in various ways. To examine the structure and evolution characteristics of a low-frequency mode during the period, we first analyzed the power wavelet spectrum of outgoing longwave radiation (OLR) averaged over 8°–23°N, 112°–130°E. The data used for this analysis are daily on 2.5° × 2.5° grids for a period of 14 months (1 March 2010–30 April 2011). Our calculations indicate that there is a significant peak at the intraseasonal (10–60 day) period (Fig. 2). The power spectrum analysis results are consistent with previous studies (Li and Wang 2005; Mao and Chan 2005).

Fig. 2.
Fig. 2.

The wavelet power spectrum (105 W2 m−4) of OLR over the region 5°–23°N, 113°–130°E from 1 Aug 2010 to 1 Jan 2011. The black contour is the significant level. The red line indicates Megi turning time.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

The 10–60-day mode is here termed as the low-frequency ISO mode. To investigate the relative importance of different temporal-scale motions in causing the sudden northward turning of Megi, all dynamic and thermodynamic fields are divided into a high-frequency component (with a 10-day high-pass filter), an ISO component (with a 10–60-day bandpass filter), and a slowly varying mean flow (with use of a 60-day low-pass filter). A Lanczos filter (Duchon 1979) was applied in these calculations.

As a part of the steering flow, the ISO flow may affect TC track. Figure 3 shows the evolution of the vertically integrated (from 850 to 300 hPa) ISO flow of the National Centers for Environmental Prediction Final Analysis (NCEP FNL analysis; NOAA/National Centers for Environmental Prediction 2000) from 14 to 24 October. It is interesting to note that the vertically integrated ISO flow has a wavelike structure, propagating from southeast to northwest in the low latitudes. This wavelike structure is mostly evident in the lower troposphere. A black dot in Fig. 3 denotes the center of a cyclonic vortex within the 10–60-day wave. This cyclonic vortex resembles a monsoon gyre (MG) depicted in Lander (1994) and Carr and Elsberry (1995). The most interesting aspect is the spatial phase relation between Typhoon Megi (denoted by a red typhoon mark) and the MG center. On 14 October, Megi was located to the east of the MG center. Megi moved anticlockwise along the MG steering flow, and on 16 October it arrived to the northeast of the MG center. In the subsequent days, Megi moved toward the MG center. At 1200 UTC 19 October, the typhoon center and the MG center nearly overlapped. Afterward, they moved together toward the north.

Fig. 3.
Fig. 3.

The evolution of 10–60-day bandpass-filtered wind (vectors) and vorticity (shaded; 10−5 s−1) fields averaged between 850 and 300 hPa. The black dots and red typhoon marks denote the centers of the low-frequency monsoon gyre and the TC center, respectively.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

The analysis above suggests an MG–TC interaction scenario. To examine the impact of the MG on the TC motion, we design and conduct numerical experiments as described in the next section.

3. Model description and numerical experiments

The model used in this study is the Advanced Research version of the WRF (ARW) Model. The model domain covers a region of 90°–155°E and 5°S–40°N using a Mercator projection. The SST field from daily real-time global sea surface temperature (RTG_SST) analysis data is used to update the SST every 24 h during the simulation. The model has a single mesh with a horizontal resolution of 18 km. The model physics includes a WSM6 microphysics scheme, a Kain–Fritch convective scheme, a Dudhia shortwave radiation parameterization, and an RRTM longwave radiation parameterization.

In the control simulation, the model initial and boundary conditions use the NCEP analysis on 1.0° × 1.0° grids with the boundary values updated every 6 h. The simulation starts at 0000 UTC 18 October 2010, 36 h before Megi made its sharp northward turning, and is integrated for 5 days. Figure 4a shows the initial wind field in the control simulation. To isolate the role of the low-frequency monsoon gyre, we conduct a sensitivity experiment in which we filter out the 10–60-day mode of all the prognostic variables in the initial and lateral boundary fields. This 10–60-day mode represents well the MG structure in the WNP. This sensitivity experiment is named the NO_MG simulation. Figure 4b illustrates the pattern of the initial low-frequency MG flow. The initial wind field without the low-frequency MG flow in the NO_MG experiment is shown in Fig. 4c.

Fig. 4.
Fig. 4.

The patterns of (a) the unfiltered initial wind field, (b) the 10-day high-pass-filtered wind field, and (c) the initial wind field in the NO_MG experiment averaged from 850 to 300 hPa at 0000 UTC 18 Oct 2010. The red sign indicates the typhoon location.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

Figure 5 shows the tracks for both the control and sensitivity experiments. In the control experiment, the TC initially moves westward and crosses the Philippines. It takes a sharp northward turn afterward, at about 117°E, over the South China Sea. The simulated TC track (red line) is very close to the JTWC best track (black line).

Fig. 5.
Fig. 5.

The JTWC best track (black) and simulated Megi tracks in the control (red) and NO_MG (blue) experiments. The pink and purple ovals indicate the before-turning and after-turning periods, respectively.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

To diagnose how well the model captures the observed structure and evolution of the low-frequency mode and its phase relationship with the TC, we plotted in Fig. 6 the model-simulated low-frequency wind field in the control simulation. Here, a 5-day running-mean method (subtracted from a 30-day running mean) was used to extract the 10–60-day low-frequency wind evolution. Since the simulation covers a period from 18 to 23 October, the NCEP analysis 2 days before and 2 days after the integration period was used in the above calculation. It is noted that major structure and evolution characteristics of the low-frequency MG were well captured by the model, as compared with Fig. 3. For example, on 18 October, the MG center was located in the northern tip of the Philippines, while the simulated TC center is slightly to the east. On 20 October, both the MG center and Megi were located to the west of the Philippines. After that, both the MG center and simulated TC moved toward the north. Thus, both the TC movement and the low-frequency MG evolution were well simulated.

Fig. 6.
Fig. 6.

The simulated low-frequency wind (vectors) and vorticity (shaded; 10−5 s−1) averaged between 850 and 300 hPa in the control experiment from (a) 18 to (f) 23 Oct. The red marks indicate the typhoon location.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

In the NO_MG simulation, the TC continues moving westward after crossing the Philippines, and there is no sharp northward turning. The results suggest that the presence of the low-frequency MG mode and its interaction with Megi might be essential to cause its northward turning.

4. Interactions between Megi and the monsoon gyre

The impact of the MG flow on the TC track has been studied by many investigators. For example, Carr and Elsberry (1995) demonstrated in a barotropic model that a vortex placed to the east of a large-scale MG may undergo a sharp northward turning because of steering of the southerly flow caused by MG energy dispersion. In this scenario, the vortex has little impact on the MG flow.

The low-frequency circulation evolutions illustrated in Figs. 3 and 6 seem to suggest a two-way interaction scenario; that is, on the one hand, the MG influences the TC track, and, on the other hand, it is influenced by the TC. After the TC cyclonically moved toward the MG center and they became close to each other on 20 October, they moved together to the north. To demonstrate the effect of the TC on the low-frequency flow, we conducted another sensitivity experiment in which we retained the low-frequency flow (with a time scale longer than 10 days) while removing the higher-frequency eddies that included Megi in both the initial and lateral boundary conditions. All the experiment settings are the same as those in the control run, except for the different initial and lateral boundary conditions stated above. In this new experiment, we can examine the movement of the low-frequency MG without the impact of TC Megi and other higher-frequency perturbations.

Figure 7 shows how the MG center moves in this experiment. Instead of the northward movement in the control simulation, now the MG center is moving westward and northwestward. Therefore, this experiment indicates there is an interaction between Megi and the MG during the northward journey of both.

Fig. 7.
Fig. 7.

The track of the monsoon gyre center in the control experiment (black) and in the experiment where Megi was removed (red).

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

It is worth pointing out that the MG track in the above sensitivity experiment resembles the track of Megi in the NO_MG case. Given that both the 10–60-day mode in the former case and the TC vortex in the latter case have the environmental mean-flow steering, it is likely that the seasonal mean flow steers both the TC and the MG in the two experiments. An examination of the seasonal mean flow (i.e., 60-day low-pass-filtered wind) confirms that the mean flow in the region is indeed west-northwestward.

In an observational data analysis, Carr and Elsberry (2000) showed that 39 out of 69 NOGAPS TC large-error cases were related to TC interaction with a surrounding cyclonic flow or vortex. This provides observational evidence to support the argument that it is the interaction of Megi with the 10–60-day MG mode that leads to its unusual track.

Now the question becomes why Megi and the MG moved northward after 19 October. Examining Figs. 3 and 6, we see that the northward movement began when the centers of the MG and Megi became very close, but not before. It appears that the center of Megi and the MG are rotating cyclonically with respect to each other and getting closer. Could a Fujiwhara effect exist between Megi and the MG, even though their sizes and intensities are quite different?

The Fujiwhara effect (Fujiwhara 1921, 1923) has long been identified as a fascinating effect caused by the binary interaction between two tropical cyclones. An early observational study of tropical cyclones shows that, when the distance between two cyclones is within 1400 km, they circle around each other; when the distance is less than 740 km, they could attract each other. The effect is also different when the two involved systems have different sizes and intensities. Many research efforts have been made to study the interaction of two tropical cyclones through numerical simulations (Chang 1983; Ritchie and Holland 1993; Wang and Holland 1995) and quantitative diagnostics (Wu et al. 2003).

In Fig. 8, the tracks of the MG center and the Megi center are plotted in the Earth reference frame from 18 to 22 October in Figs. 8a and 8c, and the trajectories of them in a reference frame centered at the middle point between the centers for Megi and the MG (centroid points) are plotted in Figs. 8b and 8d. The tracks from the NCEP analysis (Figs. 8a,b) and those from our model simulations (Figs. 8c,d) are very similar, so we will use the tracks from the NCEP analysis for discussion.

Fig. 8.
Fig. 8.

(left) The tracks of the MG and the typhoon centers in (a) the NCEP analysis and (c) the simulation; (right) the relative positions of the MG and typhoon centers in (b) the NCEP analysis and (d) the simulation. The origin (0, 0) in (b) and (d) indicates the middle point between the MG and typhoon center at each time (from 18 to 22 Oct).

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

The tracks on the Earth reference (Figs. 8a,c) show that the centers of the MG and Megi are far apart on 18 October, with Megi located on the eastern side of the Philippines, while the center of MG is on the western side. In the next 24 h, Megi moved rather quickly over the Philippines, while the MG moved slowly northward, and they became much closer by 19 October. Beyond this point, the two centers’ movement was almost parallel, with Megi lagging behind for about 1 day. By 21 October, Megi caught up with the MG and moved ahead of it.

When the positions of Megi and the MG are plotted relative to each other (Figs. 8b,d), it becomes clear that Megi and the MG are attracted to each other and moved toward each other in a rotating manner. For the 24 h between 18 and 19 October, the movement was fast, but it slowed down after 19 October. The rotating motion between them made a turn after 1800 UTC 20 October. By 21 October, the two centers were very close to each other (within computational errors of identifying the two centers), but it is clear that they never merged into one. Instead, the two centers started to move away from each other beyond this point. One of the reasons that Megi and the MG did not merge may be the significantly different scales and characteristics each possessed. This is different from the traditional Fujiwhara effect between two tropical cyclones of similar characteristics. The rotating and attracting motions between Megi and the MG are further illustrated in a domain covering a larger area and longer time frame starting on 15 October (Fig. 9). Beyond the early counterclockwise rotation motion between Megi and the MG, the attraction between them started around 1200 UTC 16 October, when the two systems came within 1000 km of each other. By 19 October, the two centers were so close that Megi actually followed the track of the MG to the north.

Fig. 9.
Fig. 9.

As in Fig. 8b, but starting from 15 Oct and ending on 24 Oct.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

Lander and Holland (1993, hereafter LH) added more details to the typically observed interaction of two tropical cyclones (binary interaction) to include phases of approach, capture, and orbit, followed by merger or escape. Megi and the MG did not follow the typical interaction observed between two tropical cyclones as outlined in LH. In LH, the interaction begins with a gradual approach while undergoing an anticyclonic orbit, then there is a capture, at which time a faster cyclonic mutual orbit ensues at relatively constant distance, which is followed by a merger or an escape. In the Megi–MG case, there is an initial slow cyclonic orbit at far separation, then a rapid approach with an anticyclonic orbit (which is consistent with LH), and then the two systems get close enough to be considered merged. While two TCs that merged could become one entity, Megi and the MG remained separated even when their centers were very close. This may be due to the very different scales and characteristic of the two systems. The interesting Fujiwhara effect between Megi and a monsoon gyre warrants future in-depth investigation and is beyond the scope of the present study.

The question remains as to why both Megi and the MG moved northward when they were in close proximity to each other. Our hypothesis is that the near overlapping of Megi and the MG caused a superposition effect of the beta drift. The effect of a planetary vorticity gradient (beta) that causes a north-northwest movement of tropical cyclones has been well studied (Rossby 1939, 1948; Adem and Lezama 1960; Anthes and Hoke 1975; Kitade 1981; Holland 1983, 1984; Chan and Williams 1987; Fiorino and Elsberry 1989). Figure 10 shows the radial profile of the tangential wind across the center of Megi in the east–west direction for the control and NO_MG experiments. On 19 October, when Megi was changing its course from westward to northwestward, the wind profile was larger overall in the control than the one in the experiment without the presence of the MG. We extract the azimuthal wavenumber-1 wind field from the two experiments. The patterns at 500 hPa are displayed in Fig. 11 for 1200 UTC 18 October and 0000 UTC 19 October. These two time snapshots represent the period while Megi moved mostly westward both in the control and in the NO_MG experiments. The wavenumber-1 asymmetry displays mostly westward ventilation flow that corresponds well with the movement. The same wavenumber-1 wind fields at 1200 UTC 19 October and 1200 UTC 20 October are shown in Fig. 12. During this period, Megi moved north-northwestward (1200 UTC 19 October) and northward (at 1200 UTC 20 October), and the orientation of the wavenumber-1 asymmetry corresponds with the TC movement. On the other hand, when the MG is absent, the asymmetry is weak and not organized (as indicated within the 800-km-radius circle).

Fig. 10.
Fig. 10.

The radial profile of the tangential wind (m s−1) across the center of Megi in the east–west direction at 700 hPa for the control (red) and NO_MG experiment (blue).

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

Fig. 11.
Fig. 11.

The wavenumber-1 wind in (a),(c) the control and (b),(d) the NO_MG simulation at (top) 1200 UTC 18 Oct and (bottom) 0000 UTC 19 Oct at 500 hPa. The shading indicates the wind speed (m s−1).

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

Fig. 12.
Fig. 12.

The wavenumber-1 wind in (a),(c) the control and (b),(d) the NO_MG simulation at (top) 1200 UTC 19 Oct and (bottom) 1200 UTC 20 Oct at 500 hPa. The shading indicates the wind speed (m s−1).

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

5. Diagnosis of maximum vorticity tendencies

To understand in detail the role of the TC–low-frequency MG interaction in Megi’s sudden northward turning, we conducted a vorticity budget analysis based on the argument that the TC moves toward the direction of maximum vorticity tendency (Holland 1983; Li and Zhu 1991). In both the control and NO_MG experiments, we focus our diagnosis on two periods: before turning and after turning. The before-turning period includes 18 time periods at a 1-h interval from 1900 UTC 18 October to 1200 UTC 19 October, as indicated by a pink circle in Fig. 5. The after-turning period also includes 18 time periods from 0300 to 2000 UTC 20 October, as indicated by a purple circle in Fig. 5. Special attention is paid to the vorticity tendency before and after turning in the control simulation and the vorticity tendency between the control and NO_MG experiments.

The vorticity tendency equation in a pressure coordinate can be written as follows:
e1

Here, t is time; p is pressure surface; u and υ are zonal and meridional wind, respectively; ω is vertical p velocity; f is the Coriolis parameter; and ζ is vorticity. Equation (1) states that the vorticity tendency is determined by three major terms: the 3D vorticity advection term (B), the tilting term (C), and the divergence term (D). The friction term in the free atmosphere was neglected. For each term above, a vertical integration (from 850 to 300 hPa) operator is applied. Besides, we mainly focus on the azimuthal wavenumber-1 component of the vorticity tendency, because other higher-wavenumber components do not contribute to the TC motion (Holland 1983; Li and Zhu 1991; Wu and Wang 2000). The left-hand side of Eq. (1) was calculated based on the vorticity difference between hour +1 and hour 0 (current time).

The simulated wavenumber-1 vorticity tendency fields [corresponding to the left-hand side (LHS) of Eq. (1)] averaged during the before- and after-turning periods in the control and NO_MG simulations are shown in Fig. 13. The black arrows in Fig. 13 represent the direction and magnitude of maximum vorticity tendency averaged over a 400-km radius. As one can see, the maximum vorticity tendency in the control experiment directs to the west before turning and changes toward the north after turning. This differs from the NO_MG experiment, in which the maximum vorticity tendency is always toward the west. Therefore, the maximum wavenumber-1 vorticity tendency direction represents well the TC moving direction in both simulations.

Fig. 13.
Fig. 13.

The composite wavenumber-1 vorticity tendency fields (shaded; 10−9 s−2) averaged between 850 and 300 hPa during (a),(c) before-turning and (b),(d) after-turning periods in (top) the control and (bottom) the NO_MG experiments. The black vector indicates the direction and magnitude of the maximum vorticity tendency averaged over a 400-km radius.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

Given that the wavenumber-1 vorticity tendency can well depict the direction of TC movement, we further analyzed specific physical processes/terms that give rise to such a vorticity tendency change. Figure 14 shows the wavenumber-1 component of each vorticity budget term on the right-hand side of Eq. (1) during the before-turning period in the control simulation. As in Fig. 13, a radial average from 0 to 400 km has been applied in calculating the direction and amplitude of the maximum vorticity tendency in Figs. 14a–e. Given that major wavenumber-1 vorticity tendency is almost concentrated near a radius of 350 km from the TC center, such a radial domain selection is reasonable. Note that the sum of three terms in the right-hand side of the vorticity equation has the same direction and amplitude as the LHS of the vorticity equation (Fig. 7a), indicating that our vorticity budget analysis is reliable. Before Megi’s turning, the vorticity advection term (including the beta effect) has the largest contribution projecting into the TC moving direction (red dashed line in Fig. 8e), while the tilting term and the convergence term are relative small. In particular, the divergence term has an opposite effect on TC movement (Fig. 8e).

Fig. 14.
Fig. 14.

The composite wavenumber-1 vorticity tendency fields (shaded; 10−9 s−2) averaged between 850 and 300 hPa during the before-turning period in the control simulation: (a) sum of terms B, C, and D; (b) term B; (c) term C; and (d) term D. (e) The azimuthal distribution of each vorticity tendency term averaged over a 400-km radius, with the red dashed line denoting the direction of TC movement. The black vector in (a)–(d) indicates the direction and magnitude of the maximum wavenumber-1 vorticity tendency averaged over a 400-km radius.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

The maximum vorticity tendency after turning exhibits a clear northward direction (Fig. 9a). The northward vorticity tendency is primarily contributed by the vorticity advection and the tilting term. Particularly, comparing the before- (Fig. 14) and after-turning (Fig. 15) periods, one may find that the most significant change appears in the advection term: it changes by more than 90° in vorticity tendency direction between the two periods, while the tilting term only alters by about 20°. Again, the divergence term is mostly opposed to the direction of TC movement.

Fig. 15.
Fig. 15.

As in Fig. 14, but for the after-turning period in the control simulation.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

The diagnosis of the wavenumber-1 vorticity tendency budget for the NO_MG case shows a consistent result: that is, the vorticity advection term plays the biggest role in determining the maximum tendency direction and magnitude (Figs. 16e and 17e). In the absence of the low-frequency MG, the advection term continues forcing the vortex to move toward the west during the two periods. While the divergence term always tends to oppose the TC moving direction, the contribution from the tilting term is positive but small in magnitude (Figs. 16 and 17).

Fig. 16.
Fig. 16.

As in Fig. 14, but for the before-turning period in the NO_MG simulation.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

Fig. 17.
Fig. 17.

As in Fig. 14, but for the after-turning period in the NO_MG simulation.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

To sum up, the vorticity budget analysis of both the control and NO_MG experiments points out that the most important cause of Megi’s sharp northward turning is the three-dimensional vorticity advection associated with the atmospheric low-frequency flow. The three-dimensional advection term consists of horizontal vorticity advection, the planetary vorticity advection, and the vertical vorticity advection. Our calculation shows that the second and third terms are small. Thus, in the following diagnosis, we focus on the horizontal vorticity advection.

To demonstrate the role of the mean-flow steering effect, the wind field at each level is simply divided into two components: an area-mean flow and a perturbation component deviated from the mean. For reanalysis data, the steering flow is often defined as the average wind over a deep layer (850–300 hPa) within the radius of 5–7° from the TC center (Carr and Elsberry 1990). For higher-resolution model data, because of greater wind speed and smaller radius of maximum wind, the average wind in a smaller radius is more reasonable. Thus, in this study, the steering flow is defined as a layer average of 850–300 hPa in a radius of 400 km from the typhoon center. With such a mean-flow definition, the vorticity of the mean flow is always zero. Therefore, the horizontal advection term (HAV) may be decomposed into two terms: namely, the mean-flow advection (MA) and the anomalous-flow advection (AA):
e2
In the equation above, and indicate the mean zonal and meridional velocity at each level, and u′, υ′, and ζ′ indicate the perturbation zonal wind, meridional wind, and vorticity deviated from the mean. Figure 18 shows the layer-averaged (from 850 to 300 hPa) MA and AA terms during the after-turning period for both the control and NO_MG cases. Again, only the wavenumber-1 component is retained. As shown in Fig. 18, the major difference between the control and NO_MG cases lies in the mean-flow advection (one directing toward the north and northeast, another directing toward the west and northwest), while the vorticity tendency due to the anomalous advection points to the same direction. Therefore, our sensitivity numerical experiments clearly demonstrate the important role of steering of the low-frequency mean flow in causing the sharp northward turning of Megi.
Fig. 18.
Fig. 18.

(a),(d) The total horizontal vorticity advection term (10−9 s−2), (b),(e) the mean flow advection term, and (c),(f) the anomalous flow advection term averaged from 850 to 300 hPa during the after-turning period in the (top) control and (bottom) NO_MG experiments. The black vector indicates the direction and magnitude of the maximum vorticity tendency averaged over a 400-km radius.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

It is worth mentioning that there are some errors associated with the vorticity tendency calculation. For example, the maximum vorticity tendency at the after stage in Fig. 15d is nearly twice as large as that in the before stage (Fig. 15c), even though the westward speeds of the No_MG experiment in the before stage (1900 UTC 18 October–1200 UTC 19 October) and after stage (0300–2000 UTC 20 October) (shown in Fig. 5) were nearly the same. Such an error is likely due to a small TC center position error. Thus, what we rely on more in the vorticity tendency calculation is its direction (rather than its amplitude) and its relationship with the TC moving direction.

6. Experiments using ECMWF analysis

It is arguable that the MG is part of the large-scale flow and certainly would impact the movement of Megi so that removing the MG would impact Megi. In this section, we examine the interaction between Megi and the MG using different analysis fields as the initial and boundary conditions to further demonstrate the interaction between Megi and the MG. In this set of experiments, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ECMWF 2009) is used. The reason for choosing this analysis is that the ECMWF global model was not able to catch the northward motion of Megi. When the ECMWF analysis is used, our model also failed to produce the northward motion of Megi (Fig. 19a), and the track is very similar to the track in the operational ECMWF model (figure not shown). Our hypothesis is that the intensity of Megi in the ECMWF analysis may be too weak (Fig. 19b) to allow proper interactions between Megi and the MG. With our filtering technique, the TC vortex associated with Megi in the NCEP and ECMWF analyses is extracted, and it is displayed in Fig. 20. The intensity for Megi in the NCEP analysis is much stronger than that in the ECMWF analysis. To demonstrate that the interaction between Megi and the MG would impact the movement of Megi, we remove Megi in the ECMWF analysis and replace it with the Megi vortex extracted from the NCEP analysis. The track of Megi in this experiment is shown in Fig. 21, and it shows a new perfect track of Megi in the model. This new experiment reinforces our hypothesis that proper interactions between Megi and the MG are responsible for their movement.

Fig. 19.
Fig. 19.

(top) Track and (bottom) maximum wind (m s−1) of Megi simulated using ECMWF interim reanalysis data. The red (black) dot and line indicates the simulated (JTWC) track and intensity.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

Fig. 20.
Fig. 20.

The wind (m s−1) associated with Megi at 500 hPa in (a) the NCEP analysis, and (b) the ECMWF interim reanalysis. The shading indicates the vorticity (10−5 s−1).

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

Fig. 21.
Fig. 21.

(top) Track and (bottom) maximum wind (m s−1) of Megi simulated using ECMWF interim reanalysis data, but the TC is replaced with the vortex extracted from the NCEP FNL reanalysis. The red (black) dot and line indicates the simulation (JTWC) track and intensity.

Citation: Journal of the Atmospheric Sciences 72, 7; 10.1175/JAS-D-14-0269.1

7. Conclusions and discussion

Through a series of numerical experiments using the WRF Model, we investigated the role of the low-frequency monsoon gyre (MG) in the sharp northward turn of Super Typhoon Megi (2010) beyond 19 October, after Megi passed over the Philippines. The analysis fields from NCEP and ECMWF are divided into a slowly varying background mean state (with a 60-day low-pass filter), a 10–60-day ISO component representing the MG, and a 10-day high-pass-filtered component representing Megi and other synoptic-scale components. The NCEP analysis data were first used as the initial and boundary conditions. In the first experiment (the control experiment), complete analysis containing all three component flows is included, and the WRF Model is able to capture the observed northward turning of Megi. In the second experiment, the 10–60-day flow containing mainly the nearby MG is removed from the initial and lateral boundary conditions (NO_MG). In the absence of the low-frequency monsoon gyre, Megi experiences a westward and slightly northwestward journey without turning northward, which is very different from the control experiment. In the third experiment, the higher-frequency eddies containing Megi were removed (NO_Megi). In this case, the MG center moves westward and has a similar track to that of the TC vortex in the NO_MG experiment. The results suggest the importance of the TC–MG interaction in causing the northward turning of both the TC vortex and the MG.

When tracks of the Megi center and the MG center were plotted in a centroid reference frame relative to each other, a Fujiwhara effect appeared to be in play, modulating the motions of both the MG and Megi. In the early stage, the two systems rotated counterclockwise with each other. When the two systems were roughly within 1000-km distance, attraction between them sped up. In the Earth reference frame, Megi and the MG moved parallel to each other, with Megi lagging behind the MG by about a day, after both turned to the north. In the centroid reference frame, the two centers closest to one another around 21 October and then drifted away from each other without merging into one system like some pairs of tropical cyclones do with the conventional Fujiwhara effect. This may be because the scale and characteristics of the two systems are very different. Idealized simulations will be conducted to investigate and verify this interaction, which is similar to the conventional Fujiwhara effect between two tropical cyclones.

The northward turning of both Megi and the MG was attributed to the enhanced beta drift when the two centers are nearly collocated, as shown by the wavenumber-1 wind fields before and after the turning. A vorticity budget analysis was performed for both the control and NO_MG experiments. It was found that the directions of TC movement in both the before- and after-turning periods were consistent with maximum wavenumber-1 vorticity tendency directions. The change of the maximum vorticity tendency direction before and after the turning in the control experiment was primarily attributed to the change of horizontal advection. A further diagnosis showed that the mean-flow steering in the presence of the 10–60-day mode was crucial for the change of the maximum vorticity tendency.

To further demonstrate that interactions between Megi and the MG played a critical role in their movement, we conducted additional experiments using data from the ECMWF interim reanalysis as the initial and boundary conditions. The reason for these experiments is that the ECMWF model was not able to predict the northward turn of Megi. When the ECMWF reanalysis was used, our model was not able to simulate the northward movement of Megi either. Examining the difference between the ECMWF and NCEP analyses shows that the vortex representing Megi in the former is much weaker than in the latter. When the Megi vortex was removed from the ECMWF analysis and replaced by the Megi vortex extracted from the NCEP reanalysis, our model successfully simulated the northward turning of Megi.

The results of the current study suggest that an accurate TC track forecast requires proper representations of both low-frequency ISO flows and the TC vortex itself. The low-frequency MG flow or the ISO flow, in general, may affect not only TC genesis, but also TC track. The failure in predicting Megi’s sharp northward turning by many operational models may result from a lack of accurate representation of the atmospheric low-frequency (10–60 day) mode, the TC vortex, or both.

Acknowledgments

This work was supported by China National 973 Projects 2015CB453200 and 2013CB430103, NSFC Grants 41475084 and 41375058, NRL Grant N00173-13-1-G902, the International Pacific Research Center, which is sponsored by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), the Jiangsu Shuang-Chuang Team, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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