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Editorial Type:
Article
Article Type:
Research Article

Understanding the Impacts of Upper-Tropospheric Cold Low on Typhoon Jongdari (2018) Using Piecewise Potential Vorticity Inversion

Ziyu Yan Key laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Xuyang Ge Key laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Zhuo Wang Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Chun-Chieh Wu Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

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Melinda Peng University of Colorado, Colorado Springs, Colorado

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Online Publication:
26 Apr 2021
Print Publication:
01 May 2021
DOI:
https://doi.org/10.1175/MWR-D-20-0271.1
Page(s):
1499–1515
Full access

Abstract

Typhoon Jongdari (2018) had an unusual looping path before making landfall in Japan, which posed a forecasting challenge for operational numerical models. The impacts of an upper-tropospheric cold low (UTCL) on the track and intensity of Jongdari are investigated using numerical simulations. The storm track and intensity are well simulated in the control experiment using the GFS analysis as the initial and boundary conditions. In the sensitivity experiment (RCL), the UTCL is removed from the initial-condition fields using the piecewise potential vorticity inversion (PPVI), and both the track and intensity of Jongdari change substantially. The diagnosis of potential vorticity tendency suggests that horizontal advection is the primary contributor for storm motion. Flow decomposition using the PPVI further demonstrates that the steering flow is strongly affected by the UTCL, and the looping path of Jongdari results from the Fujiwhara interaction between the typhoon and UTCL. Jongdari first intensifies and then weakens in the control experiment, consistent with the observation. In contrast, it undergoes a gradual intensification in the RCL experiment. The UTCL contributes to the intensification of Jongdari at the early stage by enhancing the eddy flux convergence of angular momentum and reducing inertial stability, and it contributes to the storm weakening via enhanced vertical wind shear at the later stage when moving closer to Jongdari. Different sea surface temperatures and other environmental conditions along the different storm tracks also contribute to the intensity differences between the control and the RCL experiments, indicating the indirect impacts of the UTCL on the typhoon intensity.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xuyang Ge, xuyang@nuist.edu.cn

Abstract

Typhoon Jongdari (2018) had an unusual looping path before making landfall in Japan, which posed a forecasting challenge for operational numerical models. The impacts of an upper-tropospheric cold low (UTCL) on the track and intensity of Jongdari are investigated using numerical simulations. The storm track and intensity are well simulated in the control experiment using the GFS analysis as the initial and boundary conditions. In the sensitivity experiment (RCL), the UTCL is removed from the initial-condition fields using the piecewise potential vorticity inversion (PPVI), and both the track and intensity of Jongdari change substantially. The diagnosis of potential vorticity tendency suggests that horizontal advection is the primary contributor for storm motion. Flow decomposition using the PPVI further demonstrates that the steering flow is strongly affected by the UTCL, and the looping path of Jongdari results from the Fujiwhara interaction between the typhoon and UTCL. Jongdari first intensifies and then weakens in the control experiment, consistent with the observation. In contrast, it undergoes a gradual intensification in the RCL experiment. The UTCL contributes to the intensification of Jongdari at the early stage by enhancing the eddy flux convergence of angular momentum and reducing inertial stability, and it contributes to the storm weakening via enhanced vertical wind shear at the later stage when moving closer to Jongdari. Different sea surface temperatures and other environmental conditions along the different storm tracks also contribute to the intensity differences between the control and the RCL experiments, indicating the indirect impacts of the UTCL on the typhoon intensity.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xuyang Ge, xuyang@nuist.edu.cn
Keywords: Upper troposphere; Tropical cyclones; Numerical analysis/modeling

1. Introduction

Skillful prediction of typhoon (TY) intensity and sudden track change remains a challenge for numerical models (Carr and Elsberry 1995; Harr et al. 1996; Rappaport et al. 2009; DeMaria et al. 2014; Emanuel and Zhang 2016; Ge et al. 2018; Yan et al. 2019). The challenge is partly related to our limited understanding of the complicated interactions between a typhoon and nearby systems, such as easterly waves, monsoon throughs, and upper-level cold lows (UTCLs) or troughs (Molinari and Vollaro 1989; Lander 1994; Wang and Wu 2004; Davidson et al. 2008; Hendricks et al. 2011; Leroux et al. 2016; Peirano et al. 2016; Fischer et al. 2019). Among them, UTCLs are characterized by upper-level cyclonic potential vorticity (PV) anomalies and occur frequently over the tropical and subtropical western North Pacific (WNP) (Molinari and Vollaro 1989; Patla et al. 2009; Li et al. 2012) during the typhoon season (June–October) (Wei et al. 2016). As indicated by the name, a UTCL has a cold-core structure in the upper troposphere. The cold-core anomalies peak at 300–400 hPa, with the maximum wind speed around 250 hPa (Kelly and Mock 1982; Chen and Chou 1994; Chen et al. 2001; Campetella and Possia 2007). Although most pronounced in the upper troposphere, a UTCL may extend down to the lower troposphere or even to the surface. The mean lifespan of a UTCL is 6.3 days (Chen and Chou 1994). Some UTCLs develop in a tropical upper-tropospheric trough (TUTT; Sadler 1975) and are also known as TUTT cells, while some others are cutoff lows originated from the midlatitudes westerlies (Molinari and Vollaro 1989).

Patla et al. (2009) proposed a conceptual model on how the track and intensity of typhoons may be affected by a UTCL. The authors identified several factors that affect the typhoon–UTCL interaction, including the intensity, horizontal scale and depth of the UTCL, the typhoon intensity, and the separation distance between them. Using reanalysis data, Li et al. (2012) analyzed the sudden track change of Typhoon Meranti (2010). The authors showed that a UTCL modified the environmental vertical wind shear (VWS) and horizontal vorticity advection around the typhoon, leading to its sudden northward turning. Wen et al. (2019) studied the interaction between Typhoon Meranti and a UTCL using the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts. By comparing the success and failure groups, they found that the group with better track predications had a better representation of the UTCL structure. Additionally, past studies have also indicated that the typhoon’s intensity could be affected by upper-level troughs (Molinari et al. 1995; Bosart et al. 2000; Leroux et al. 2013; Chen et al. 2015). Komaromi and Doyle (2018) simulated the interaction between a typhoon and an upper-level trough in an idealized framework. Their results indicated that the equatorward outflow is stronger than the poleward outflow in the simulated typhoon in the absence of an upper-level trough. In contrast, the presence of an upper-level trough north of the typhoon modifies the inertial stability such that the outflow channel shifts poleward. The reduction of inertial stability may lead to enhanced upper-level divergence that promotes typhoon’s intensification. However, the interaction is sensitive to the relative distance between the typhoon and the trough, and the typhoon weakens due to the enhanced vertical wind shear when the trough is too close.

While previous studies have provided valuable insights on the typhoon–UTCL interaction, the impacts of UTCL on the intensity and track evolution of typhoons are mostly qualitative in the studies based on observational data and real-storm simulations. The objective of this study is to quantitatively assess the impacts of a UTCL on the track and intensity evolution of a typhoon using semi-idealized simulations. A notable difference between a trough and UTCL is that the propagation of a trough is largely determined by the midlatitude westerlies, while the motion of a cutoff low may be strongly affected by its interaction with a typhoon, which introduces an additional degree of freedom. We will focus on Typhoon Jongdari in 2018. The typhoon has an unusual counterclockwise track and rapid intensity changes likely tied to the interaction with a UTCL. We will employ the piecewise potential vorticity inversion (PPVI) method to separate the flow component associated with the UTCL (Davis and Emanuel 1991). The effects of UTCL on the track and intensity change of Typhoon Jongdari are quantified through the analysis of a series of numerical simulations.

The remaining sections of this paper are organized as follows. An overview of Typhoon Jongdari is presented in section 2. The numerical model, experiment designs, and PPVI method are described in section 3. The effects of a UTCL on the track and intensity of Typhoon Jongdari are analyzed in sections 4 and 5, respectively. The summary and discussion are given in section 6.

2. Overview of Typhoon Jongdari

Typhoon Jongdari is a strong, long-lived typhoon with an unusual track in late July and early August 2018. Typhoon Jongdari formed over the Northwest Pacific at 0500 UTC 25 July. From 26 to 30 July, Typhoon Jongdari experienced a rare counterclockwise turning southeast of Japan. Jongdari made landfall in Japan on 29 July, produced heavy precipitation, and then moved toward eastern China after a small looping track (Fig. 1a). The typhoon had a relatively large meridional translation speed before reaching Japan, with its center moved from 22° to 35°N in 3 days. It intensified rapidly on 26 July and reached its peak intensity at 1200 UTC 27 July, with the minimum central pressure of 950 hPa (Fig. 1b) and the maximum surface wind speed of 45 m s−1 (Fig. 1c) according to the JTWC best track data. The storm then weakened rapidly.

Fig. 1.
Fig. 1.

(a)The observed tracks of Typhoon Jongdari (black) and the UTCL (red) from 0000 UTC 26 Jul to 0000 UTC 1 Aug (Typhoon Jongdari and the UTCL are collocated at 1200 UTC 31 Jul). (b) Time series of the minimum central sea level pressure (CMSLP; units: hPa) of Typhoon Jongdari. (c) Time series of the maximum surface wind speed (Vmax; units: m s−1) of Typhoon Jongdari. (d) A longitude–pressure cross section of meridional wind (contours; units: m s−1) and temperature anomalies (shading; units: K) of the UTCL along 33°N at 0000 UTC 26 Jul. The temperature anomalies in (d) are defined with respect to the average over 120°–150°E along 33°N.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

Due to the unusual counterclockwise track and rapid intensity changes, Typhoon Jongdari was a forecasting challenge for operational numerical models. Lei et al. (2020) showed that both the ECMWF and NCEP ensemble forecasts had low skill for the track and intensity forecast of Jongdari (2018) when the forecasts were initiated before the typhoon and UTCL started interacting, but the forecast skill initialized after the typhoon–UTCL interaction was improved. The contrast suggests that the interaction between the storm and UTCL is an important source of uncertainty. Close examinations of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) Final (FNL) analysis (1° × 1°) show that the UTCL moved southwestward and approached Typhoon Jongdari, and that these two systems were nearly collocated on 1200 UTC 31 July (Fig. 1a; the UTCL center is defined as the position of the maximum relative vorticity at 250 hPa). The UTCL had a maximum wind speed of about 20 m s−1 at 250 hPa, and the diameter of the outer closed circulation was about 2000 km at 0000 UTC 26 July (Fig. 1d). The maximum cold core occurred around 300 hPa, and the cold anomalies extended downward to the surface. These characteristics are consistent with those documented in previous studies for a UTCL (Sadler 1975; Chen and Chou 1994; Patla et al. 2009; Li et al. 2012). The question here is how much the UTCL affected Typhoon Jongdari’s track and intensity. A set of numerical experiments are conducted to investigate this question.

3. Methodology and experiment designs

a. Experiment designs

The Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW), version 3.9.1 (Skamarock et al. 2008), is used in this study. Three two-way interactive domains are used, with horizontal resolutions of 27, 9, and 3 km, respectively. The outer domain covers 0°–57°N, 95°–174°E. The model has 55 sigma levels with more vertical levels in the boundary layer and near the tropopause, and the model top is set at 50 hPa. The initial and boundary conditions are obtained from 6-hourly NCEP GFS FNL analysis with 1° × 1° resolution. The Kain–Fritsch convective scheme (Kain and Fritsch 1993) is applied to the outer two domains, and turned off for the inner domain of 3 km, in which microphysics is used. In addition, the Rapid Radiation Transfer Model (RRTM) longwave radiation (Mlawer et al. 1997), Dudhia shortwave radiation (Dudhia 1989) schemes, YSU planetary boundary scheme (Hong et al. 2006), and the unified Noah LSM surface scheme (Tewari et al. 2004) are used.

A total of seven numerical experiments are conducted to examine the impacts of UTCL on the track and intensity evolution of Typhoon Jongdari (Table 1). In the control experiment (CTL), the UTCL and the TY are present in the GFS analysis. In the sensitivity experiment denoted as RCL, the UTCL circulation is removed in the initial and boundary fields using the PPVI method (described in section 3b). As such, the comparison between CTL and RCL runs helps reveal the influences of UTCL on the intensity and track of Typhoon Jongdari. The CTL and RCL runs both employ the WRF single-moment 6-class (WSM-6) microphysics scheme (Hong and Lim 2006). To test the sensitivity of the prediction to microphysics schemes, additional experiments were conducted with the Purdue Lin microphysics scheme (LIN) and the ETA Grid-scale Cloud and Precipitation microphysics scheme (Ferrier) for both the CTL and RCL runs (Lin et al. 1983; Ferrier et al. 2002). Both LIN and WSM6 schemes are 6-class single-moment schemes, containing the following species: water vapor, cloud water, cloud ice, snow, rain, and graupel. The WSM6 can be regarded as an updated LIN scheme with the reduced sensitivity to the model time step and is more suitable for high-resolution simulations. In contrast, the Ferrier scheme is a 4-class double-moment scheme and includes water vapor, cloud water, rain, and ice (cloud ice/snow/graupel). Additionally, another experiment, “Rland,” is carried out to examine the effects of terrain. In this experiment, the Japanese Archipelago is replaced with ocean of SST 300.7 K, which is the averaged SST over 10-km radius of the typhoon center at 60 h (right before landfall) and at 66 h (after the typhoon reemerges over the ocean). All the simulations were integrated from 0000 UTC 26 July to 0000 UTC 30 July.

Table 1.

Description of different experiments.

Table 1.

b. Piecewise potential vorticity inversion

PV thinking is a useful approach to study vortex evolution (Hoskins et al. 1985). PV is defined based on absolute vorticity and static stability (potential temperature gradient), and contains the information on both thermodynamic and dynamic states of the atmosphere. Based on the PV invertibility principle, the balanced wind and mass fields can be determined from the PV distribution given proper boundary conditions. In particular, piecewise potential vorticity inversion is a useful diagnostic technique (Davis 1992; Davis and Emanuel 1991). Using PPVI, the total PV field is divided into discrete portions corresponding to different dynamical features of interest, and the PV field for each portion is then inverted separately to obtain the balanced wind and mass fields associated with individual dynamical features. PPVI has been widely used to isolate the balanced circulations associated with individual entities in order to quantitatively examine their characteristics and interactions among them (Wu and Emanuel 1995a,b; Shapiro 1996, 1999; Wu et al. 2003, 2009; Yang et al. 2008). Here we will apply PPVI to isolate the circulation components associated with the UTCL and Typhoon Jongdari and assess how the UTCL affects the intensity and track evolution of Typhoon Jongdari.

The PPVI method is based on the following equations:
q=gkπp[⁡(f+∇2Ψ) ∂2Φ∂2π2−1a2 cos2ϕ∂2Ψ∂λ∂π∂2Φ∂λ∂π−1a2∂2Ψ∂ϕ∂π∂2Φ2∂ϕ∂π],
∇2Φ=∇⋅(f∇Ψ)+2a4⁡ cos2⁡ϕ∂(∂Ψ∂λ,∂Ψ∂ϕ)∂(λ,ϕ),
where q denotes PV; Φ and Ψ represent geopotential height and streamfunction, respectively; κ = Rd/Cp, with Rd the gas constant and Cp the specific heat capacity; π [π = Cp(p/p0)κ] is the Exner function, which defines the vertical coordinate; a is Earth’s radius; f is the Coriolis parameter; and λ and ϕ denote longitude and latitude, respectively. Given the distribution of q, the lateral boundary of Φ and Ψ, and the θ (θ = −∂Φ/∂π) on the upper and lower boundaries, the distribution of Φ and Ψ can be solved using the successive overrelaxation method (Davis and Emanuel 1991). Thus the nondivergent wind and potential temperature can also be obtained by the following two relations:
V=k×∇Ψandθ=−∂Φ/∂π.
Following Shapiro (1996), the axisymmetric average relative to the center of Typhoon Jongdari is calculated as the mean field. The mean streamfunction field Ψ¯ is derived from the azimuthal average of the wind field. The associated mean geopotential height field can be derived using a nonlinear balance equation:
∇2Φ^=∇⋅(f0∇Ψ¯)+2a4⁡ cos2⁡ϕ∂(∂Ψ¯∂λ,∂Ψ¯∂ϕ)∂(λ,ϕ),
and the mean PV field is calculated as follows (Wu et al. 2003):
q^=gkπp[⁡(f+∇2Ψ¯) ∂2Φ^∂2π2−1a2⁡ cos2⁡ϕ∂2Ψ¯∂λ∂π∂2Φ^∂λ∂π−1a2∂2Ψ¯∂ϕ∂π∂2Φ^∂ϕ∂π].

The perturbation fields are defined as Ψ′=Ψ−Ψ¯, Φ′=Φ−Φ^, and q′=q−q^. The balanced perturbation flow field (Ψ′) and mass field (Φ′) associated with a PV perturbation (q′) can be obtained by inverting q′ using the perturbation form of Eqs. (1) and (2). The whole PV perturbation (q′) is then divided into three parts: the PV anomaly associated with the asymmetric structure of Typhoon Jongdari (qTY′), the UTCL (qUTCL′) and other systems (qm′); qUTCL′ is identified as a 3D coherent structure of positive PV anomalies around the UTCL center. The nondivergent wind and potential temperature fields associated with the UTCL are obtained by inverting these positive PV anomalies, and then these fields are removed from the corresponding total fields to obtain the initial conditions for the RCL runs at 0000 UTC 26 July.

To examine how well this method removes the balanced UTCL circulation, the total flow fields before and after the removal of UTCL at 0000 UTC 26 July are compared in Fig. 2. The oblique cross section of PV going through the centers of the typhoon and the UTCL (red line in Fig. 2d) shows that Typhoon Jongdari is characterized by cyclonic PV extending throughout the troposphere, with a maximum around 500 hPa (Fig. 2a). In contrast, the UTCL is mainly confined in the middle to upper troposphere, and its intensity increases nearly monotonically from 500 to 200 hPa. Corresponding to the upper-level PV feature, there are low geopotential anomalies, cold temperature anomalies and a cyclonic circulation at 400 hPa associated with the UTCL, while the circulation component of the typhoon is much weaker at the same level (Figs. 2b–d). In the initial conditions for the RCL experiment (Figs. 2e–h), the PV feature associated with the UTCL is largely gone, and the signals in the geopotential height, temperature and wind fields at 400 hPa are also substantially reduced. Overall, the PPVI technique does a reasonable job removing the UTCL from the initial conditions of the RCL run.

Fig. 2.
Fig. 2.

(a)–(d) Total fields and (e)–(h) fields after the removal of the UTCL using the PPVI method. (a),(e) The oblique cross sections of PV (units: 0.01 PVU) in two experiments that pass through both the typhoon and UTCL centers in the CTL run [as shown by the red lines in (d) and (h)]. The other columns show (b),(f) geopotential height (shaded; units: gpm), (c),(g) air temperature (shaded; units: K), and (d),(h) wind vectors at 400 hPa (units: m s−1).

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

4. Simulated results and track analysis

a. Overview of the simulated track and intensity evolution

The track and intensity evolution of Jongdari in the CTL and RCL runs are shown in Fig. 3. The simulated track of Typhoon Jongdari in CTL is nearly in line with the JTWC best track (Fig. 1a), with the storm taking a counterclockwise path and then making landfall in Japan on 29 July. The track error is less than 70 km before the landfall in Japan. The storm intensifies in the first 42 h and then weakens, which is also largely consistent with the best track (Fig. 1b) but with some quantitative differences. In contrast, when the UTCL is removed, the storm takes a nearly zonal path and tracks westward between 20° and 25°N at a slow speed in RCL. The simulated Typhoon Jongdari in RCL remains over the open ocean throughout the 3-day period, and intensifies steadily up to 72 h. The large differences between the CTL and RCL runs suggest that the typhoon–UTCL interaction is responsible for the unusual movement of Jongdari, as will be analyzed in the subsequent sections.

Fig. 3.
Fig. 3.

(a) TY tracks and (b) the time series of TY intensity in terms of CMSLP in the different runs from 0000 UTC 26 Jul to 0000 UTC 30 Jul. Shading in (a) shows the time-independent skin temperature field used in all simulations. The black box in (a) depicts the innermost domain, and the plotting domain roughly represents the intermediate domain. Different line styles and colors represent different simulations as shown in the legends.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

Also shown in Fig. 3 are the track and intensity evolution of the experiments using different microphysics schemes (Table 1). The Lin scheme produces the strongest storm in both the control group and RCL group of experiments. This is consistent with Maw and Min (2017) finding that Lin scheme tends to produce a stronger tropical cyclone than the Ferrier or WSM6 scheme due to more heating between 300 and 100 hPa. Despite the sensitivity to microphysics, the storm intensity differences between the groups of simulations with and without a UTCL are robust. Interestingly, the track evolution is not sensitive to the choice of the microphysics scheme in this case. Previous studies (e.g., Fovell et al. 2009; Choudhury and Das 2017) suggested that different microphysics schemes may produce different outer wind structures for a typhoon and can potentially affect its track by modifying the beta gyres. However, the sensitivity may vary from case to case. In the case of Jongdari, the steering flow seems to play a dominant role in the storm motion and make the track insensitive to the microphysics options. For brevity, our subsequent analysis will focus on the first set of experiments using the WSM6 microphysics (i.e., CTL versus RCL run). We will examine the track differences between the CTL and RCL runs in section 4 and the intensity differences in section 5.

b. Potential vorticity budget analysis

A typhoon generally moves toward the region of the maximum PV tendency (Holland 1983; Wu and Kurihara 1996; Wu and Wang 2000; Chan et al. 2002; Bi et al. 2015). Here we carry out the PV budget analysis to investigate the mechanism accounting for the distinct tracks in the CTL and RCL runs. The PV budget equation in isobaric coordinates can be written as (Wu and Wang 2000):
∂P∂t=−u ∂P∂x−υ ∂P∂y︸HAD−ω ∂P∂p︸VAD−g[⁡(ζ+f) ∂Q∂p+∂Q∂y∂u∂p−∂Q∂x∂υ∂p]︸DH+F*
where π=(p/100 000)R/Cp, Q = Q1/(Cpπ), and Q1 is the total diabatic heating rate including radiation, latent heating, surface flux, and subgrid-scale processes, and can be calculated using the thermodynamic equation.

In Eq. (5), the left-hand-side term is the local PV tendency (PVT); the first and second right-hand-side terms represent the horizontal PV advection (HAD), which includes the advection by the large-scale steering flow, the beta effect, and the nonlinear self-advection (Wu and Emanuel 1995a,b); the third term is the vertical PV advection (VAD); the fourth term is the diabatic heating term (DH); and the residual term F* represents friction and subgrid processes (including diffusion; not shown). We will focus on the wavenumber-1 structure because previous studies suggested that the wavenumber-1 PVT well reflects the typhoon’s motion (Holland 1983; Wu and Wang 2000; Chan and Williams 1987; Bi et al. 2015). When applying Eq. (5) to the wavenumber-1 pattern, the residual term also includes the interaction of the wavenumber-1 pattern with the other wavenumber components.

Figure 4 shows the PVT and different terms for the wavenumber-1 component averaged over 850–600 hPa in both experiments (CTL and RCL runs) at 24 and 48 h. The two snapshots are chosen to represent the typhoon–UTCL interaction at different distances and when the typhoon has different intensities. As shown in Fig. 4, the HAD term has a pattern similar to the PVT and makes the major contribution to the PVT in both simulations. Compared to the HAD term, the VAD and DH terms are more concentrated in the inner-core region of the typhoon. Specifically, HAD term in the CTL run shows a positive tendency northeast of the storm center at 24 h, causing the northeastward motion of the storm at the early stage. This is consistent with the advective impacts of the UTCL, which is located northeast of the typhoon. The DH term has a northwest–southeast-oriented dipole pattern with a negative center in the northwest. This DH pattern is largely consistent with the vertical velocity or convection pattern. Due to the impacts of the vertical wind shear, enhanced convection occurs on downshear left side, which is to the southeast of the storm center at 24 h as reflected by the reflectivity pattern (Fig. 5a). The VAD term is determined by both the vertical velocity distribution and the vertical PV gradient. DH partially cancel HAD and VAD within the radius of 150 km in the CTL run. In the RCL run, the PVT term shows strong positive values to the northwest of the storm center, consistent with the northwestward track of the storm. DH is consistent with the enhanced convection south of the storm center under vertical wind shear (Fig. 5c). The VAD and DH terms in the RCL run are weaker than those in the CTL run, consistent with the weaker storm intensity in RCL at the time.

Fig. 4.
Fig. 4.

Columns show (left to right) the total PV tendency (PVT), the horizontal advection (HAD), the vertical advection (VAD), and the diabatic heating (DH) terms (units: 0.01 PVU s−1). Rows show the (a) CTL and (b) RCL runs at t = 24 h and the (a) CTL and (b) RCL runs at t = 48 h.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

Fig. 5.
Fig. 5.

Wavenumber-1 reflectivity (dBZ; shaded) in (a),(b) CTL and (c),(d) (b) RCL run averaged over 850–600 hPa. The black vector indicates the direction of environmental vertical wind shear averaged over 500 km of the typhoon center.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

At 48 h, the PVT terms in the CTL and RCL indicate northwestward and westward propagation, respectively, consistent with their storm tracks. Compared to 24 h, the PVT terms at 48 h have become strong in both the CTL and RCL runs, which is mainly due to the stronger HAD terms. In addition, the DH and VAD terms in the CTL run are still stronger than those in the RCL run, consistent with the stronger typhoon intensity in the CTL run. The DH terms have a similar spatial pattern as the convection distribution under the impacts of vertical wind shear in both experiments (Fig. 5). The VAD term tends to cancel the DH term in the RCL run while the VAD and DH terms have a nearly 90° phase shift in the CTL run. Overall, the HAD terms have a similar spatial pattern and magnitude to the PVT terms in both runs, indicating the important role of steering flow in determining the typhoon track, which will be further examined in section 4c.

c. Steering flow

Tropical cyclone motion is strongly affected by the steering flow (George and Gray 1976; Chan and Gray 1982; Velden and Leslie 1991). The depth of the steering flow tends to vary with the storm intensity (Velden and Leslie 1991). For simplicity we calculate the steering flow over a fixed layer, 800–200 hPa, as below:
V=∫800hPa200hPaVs⁡(p) dp∫800hPa200hPadp,
where Vs(p) is the averaged wind vector within a 7° × 7° box following the center of Typhoon Jongdari. The 800–200-hPa layer, instead of 850–200-hPa layer, is chosen to avoid terrain effects at the later stage of the CTL simulation. To examine the contribution of different flow components to typhoon motion, we calculated the deep-layer steering flow from the total wind field (Vs), from the UTCL-related flow (Vcl), and from the flow after the removal of the UTCL (Vm) in the CTL simulation. The UTCL-related flow is derived from the UTCL-related PV anomalies using the PPVI (Wu et al. 2003), and its difference from the total flow field is used to calculate Vm. As shown in Fig. 6, the total steering flow displays an overall good agreement with the storm motion in the CTL simulation and suggests that the horizontal advection makes a dominant contribution to the typhoon motion, consistent with the PVT analysis. The comparison between different steering flow components shows that Vcl has a stronger magnitude than Vm and makes a larger contribution to the total steering flow from 18 to 72 h. In particular, Vcl has a strong poleward component during 24–54 h and a westward component afterward. It, thus, first leads to the poleward typhoon motion and then the westward motion. The direction of the steering flow induced by the UTCL is broadly consistent with its relative location to the typhoon.
Fig. 6.
Fig. 6.

(top) The time–height profiles of the steering flow averaged within a 7° × 7° box following the typhoon center, which is defined as the location of minimum SLP. (bottom) The deep-layer steering flow components averaged over 800–200 hPa: “T” denotes the TY translation speed, “Vs” is the total steering flow, “Vcl” is the steering flow component induced by the UTCL, and “Vm” is the steering flow after removing the UTCL. (a) The CTL run and (b) the RCL run. The vectors represent the zonal and meridional flow components, with the vector scale shown in the lower right in the units of m s−1.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

The deep-layer steering flow is also calculated for the RCL simulation. It also shows a good agreement with the typhoon motion. In particular, the steering flow does not show a strong poleward component after the removal of the UTCL. Although the environmental flow field from the CTL run with the UTCL winds removed (Vm) is close to the environmental flow field from the RCL run (Vs), the storm tracks in the two experiments are different and contribute to the differences between Vm from the CTL and Vs from the RCL, which are calculated along the storm tracks. The results suggest that the track differences between the two simulations are mainly due to the direct impacts of the UTCL on the steering flow, and the differences can be further amplified when a storm moves to a different location and is subject to different environmental flow.

d. Fujiwhara effect

The impacts of the UTCL on Jongdari’s track can be explained by the Fujiwhara effect (Fujiwhara 1921; 1923). The Fujiwhara effect has been widely used to interpret the interaction between two tropical cyclones. Additionally, Bi et al. (2015) suggested that the interaction between a typhoon and a monsoon gyre can be explained by the Fujiwhara effect as well. The interaction between the two involved cyclonic systems is sensitive to their sizes, depth, intensities, and the environmental conditions such as the vertical wind shear, moisture, and sea surface temperature (Chang 1983; Ritchie and Holland 1993; Wang and Holland 1995; Wu et al. 2003, 2009, 2010; Yang et al. 2008; Peng and Reynolds 2005, 2006). The interaction is also sensitive to the distance between two vortices. When the distance is less than a critical distance (i.e., ~1400 km), the two vortices rotate around the vorticity centroid (Lander and Holland 1993; Liou et al. 2016); when the relative distance is shorter (i.e., ~750 km), the two vortices attract each other (Brand 1970).

Figures 7a–e shows the time evolution of the UTCL-related horizontal flow averaged over 800–200 hPa and the typhoon and UTCL centers in the CTL run. The typhoon (UTCL) center is defined as the location of the maximum relative vorticity at 850 (250) hPa. The typhoon is southwest of the UTCL at 0000 UTC 26 July. Jongdari and the UTCL rotate counterclockwise around each other over the following few days, and the distance between the two systems decreases with time. Figure 7f shows the orbiting tracks of the two cyclonic vortices with respect to the midpoint between the two vorticity centers. Although the typhoon and UTCL have very different vertical structures, the orbiting motions of the two vortices suggest that the Fujiwhara effect is still largely valid: the two systems rotate around each other cyclonically while moving closer together from 26 to 30 July. The relative distance decreases from more than 1400 km on 26 July to about 600 km on 30 July. As they move closer to each other, the intensity of UTCL (represented by the 250-hPa PV) and the typhoon both decrease with time (Fig. 3b). We will examine the intensity evolution of Typhoon Jongdari in the next section.

Fig. 7.
Fig. 7.

Interaction between the UTCL and Typhoon Jongdari in CTL. (a)–(e) The 250-hPa PV (shading; units: 0.1 PVU) and averaged 800–200-hPa wind field (vectors; units: m s−1) associated with the UTCL. The blue star and the red TY symbol represent the UTCL and typhoon centers, respectively. (f) The relative positions of the UTCL and typhoon with respect to their vorticity centroid at different times with 6-h intervals.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

5. Storm intensity and the impacts of the upper-level cold low

As described in section 4a, the simulated intensity of Typhoon Jongdari in the CTL run increases initially and then decreases, similar to that observed. The impacts of the ULCL on the typhoon intensity evolution are examined in this section.

a. Upper-level outflow

Previous studies suggested that an upper-level trough may contribute to typhoon’s intensification by enhancing the upper-level outflow and thus the secondary circulation (Shi et al. 1997; Leroux et al. 2013; Komaromi and Doyle 2018). In general, the upper-level outflow of a storm is related to convection in the storm inner-core region and is also affected by the environmental conditions (Holland and Merrill 1984; Rappin et al. 2011; Komaromi and Doyle 2018). The latter can be understood in terms of inertial stability, which is a measure of resistance to the radial motion (Schubert and Hack 1982, Holland and Merrill 1984; Molinari and Vollaro 2014), and reduced inertial stability helps enhance the upper-level outflow and convection. Inertial stability is defined as (Alaka 1961):
I2=(f+2υtr)⁡(f+ζ).

Figure 8 shows the 200-hPa horizontal flow in CTL and RCL averaged during 12–24 h, a time period when the storm intensity clearly diverges between the two simulations (Fig. 3b). A prominent feature in the CTL run is a strong southwesterly jet north of the typhoon center. The jet is located east of the UTCL. Although the strong wind does not extend above the typhoon center, it is connected to the outflow of the typhoon. Figure 8a also shows a region of reduced inertial stability north of the typhoon center and near the UTCL in the CTL run. In other words, the UTCL modifies the upper-level absolute vorticity and reduces the inertial stability north of the typhoon, which contributes to stronger poleward outflow (Fig. 8a). It helps enhance the secondary circulation of the typhoon and favors its development (Rodgers et al. 1991; Shi et al. 1997; Fischer et al. 2017). In contrast, Fig. 8b shows that the outflow in RCL is much weaker and is directed equatorward toward the region of lower inertial stability (Rappin et al. 2011; Barrett et al. 2016; Komaromi and Doyle 2018).

Fig. 8.
Fig. 8.

The 200-hPa flow (vectors; units: m s−1), the inertial stability (shading; units:10−5 s−1), and the jet (contours; wind speed > 20 m s−1 with an interval of 5 m s−1) averaged over 12–24 h. The center of the domain represents the typhoon center, and the tick marks show the distance from the typhoon center in km.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

b. Eddy fluxes convergence

Previous studies have suggested that an upper-level trough may enhance upper-level outflow and contribute to storm intensification via eddy angular momentum convergence (Molinari and Vollaro 1989; DeMaria et al. 1993; Molinari et al. 1995; Hanley et al. 2001; Komaromi and Doyle 2018). More specifically, the asymmetric structures of the outflow associated with upper-level synoptic systems could produce radial imports of eddy angular momentum, enhance the secondary circulation, and thus affect the typhoon intensification. The eddy flux convergence of angular momentum (EFC) is calculated following Molinari and Vollaro (1989):
EFC=−1r2∂∂r r2υr′υt′¯,
where r is the radial distance from the storm center (defined as the location of the minimum sea level pressure); the overbar denotes the azimuthal mean computed in storm-relative coordinates; and υr′ and υt′ represent the perturbation radial wind and perturbation tangential wind, respectively, with respect to the azimuthal mean of the corresponding wind component.

The left column in Fig. 9 shows the radius–time plots of EFC in the CTL and RCL runs. The EFC is vertically averaged between 100 and 300 hPa. As expected, the EFC shows marked differences between CTL and RCL runs (Figs. 9a,d). In the CTL run, the EFC maximum occurs around the radius of 1200 km during the first few hours of the model integration. It then propagates inward and moves to ~300 km by 40 h (Fig. 9a), accompanied by an increase in the low-level tangential wind. The time–radius plot of the outflow averaged over 100–200 hPa is shown in Fig. 9b, and the vertical motion averaged over 500–200 hPa is shown in Fig. 9c. Since the upper-level outflow not only influences convection but also responds to convection, one should not expect a perfect agreement between EFC and υr due to the stochastic nature of convection. Nevertheless, it is worth noting that the inward propagation of the EFC is accompanied by the inward propagation of upper-level outflow (Fig. 9b). In addition, strong upward motion in the inner-core region occurs from 18 h onward, about 10 h after the inward propagation of EFC (Fig. 9c). The strong upward motion is concomitant with the intensification of Typhoon Jongdari in the CTL run prior to 40 h, then the storm weakens, and w declines accordingly (Figs. 9c and 3b; also see the tangential wind in Fig. 9a). In contrast, with the removal of the UTCL, the EFC and the outflow at the large radii in RCL are much weaker than those in the CTL prior to 48 h, and there is no apparent inward propagation of EFC or upper-level outflow. The storm undergoes a steady but slow intensification before 48 h, and then intensifies more rapidly and has strong upward motions after 48 h (Figs. 9f and 3b; also see the tangential wind in Fig. 9d). The results suggest that the UTCL can enhance the storm intensity via eddy angular momentum convergence, consistent with the balanced vortex theory (e.g., Molinari and Vollaro 1989).

Fig. 9.
Fig. 9.

Time–radius plots of azimuthally averaged (a),(d) 300–100-hPa layer-mean eddy flux convergence (EFC; shaded; units: m s−1 day−1) and 800-hPa tangential wind (Vt; contours; units: m s−1); (b),(e) 200–100-hPa layer-mean radial wind (Vr; shaded; units: m s−1); and (c),(f) 500–200-hPa layer-mean vertical wind (w; shaded; units: m s−1) in the (a)–(c) CTL and (d)–(f) RCL runs.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

c. Impacts of vertical wind shear

The impacts of vertical wind shear on typhoon development have been widely studied (Gray 1968; DeMaria 1996; Frank and Ritchie 2001; Tang and Emanuel 2012; Yan et al. 2019). Typhoon intensification is generally inhibited by strong shear through venting moisture out of the typhoon inner-core region (Frank and Ritchie 2001), transporting low-entropy air from midlevel into the boundary through downdrafts (Riemer et al. 2010), or generating asymmetric eddies (Nguyen and Molinari 2015). The ventilation index (Tang and Emanuel 2012) is calculated in this subsection to examine the impacts of vertical wind shear on the storm intensity evolution. The ventilation index is defined as
Λ=UshearχmUPI,
where Ushear is the magnitude of vertical wind shear (defined as the magnitude of the vector wind difference between 200 and 800 hPa), χm is the nondimensional entropy deficit, which is defined as
χm=sm*−smsSST*−sb,
where sm* is the saturation entropy at 600 hPa in the inner core of the TC, sm is the environmental entropy at 600 hPa, sSST* is the saturation entropy at the sea surface temperature, and sb is the entropy of the boundary layer. The pseudoadiabatic entropy s is calculated according to Bryan (2008). In the denominator of Eq. (9), UPI is the potential intensity (Bister and Emanuel 2002). The ventilation index is averaged within a 500-km radius from the typhoon center. A smaller value of the ventilation index, due to weak shear, small entropy deficit or large potential intensity, represents a favorable environment for typhoon intensification. As shown in Fig. 10a, the ventilation index in the CTL run is generally smaller than that in the RCL run before 36 h and larger afterward. As such, the difference in the ventilation index can help explain the intensity difference between the two experiments (Fig. 3b). Figures 10b–d illustrate the evolution of individual terms. The vertical wind shear in the CTL run is smaller than that in the RCL run before 36 h and larger afterward, contributing to the difference of the ventilation index between CTL and RCL. Additionally, the typhoon in the RCL run is associated with a higher potential intensity than that in the CTL run after 24 h, which also contributes to a smaller ventilation index. Potential intensity essentially measures how thermodynamically favorable the environment is for the TC, and is a function of the SST. Although the same time-independent SST field is specified in all experiments (Fig. 3a), SST and other thermal conditions along the simulated typhoon tracks are different between CTL and RCL after the storm tracks diverge. Figure 10b shows that the CTL run has a smaller entropy deficit than the RCL run after 18 h. It contributes to the stronger storm in the CTL run between 18 and 36 h but is offset by the stronger vertical wind shear and lower potential intensity afterward. Therefore, the intensity differences are also due to the indirect impacts of the UTCL by changing the storm track.
Fig. 10.
Fig. 10.

Time series of (a) the ventilation index, (b) entropy deficit (Xm), (c) potential intensity (MPI; units: m s−1), and (d) vertical wind shear (VWS; units: m s−1) in the CTL (black solid) and RCL (red dashed) simulations averaged within 500 km from the typhoon center.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

Overall the UTCL has two direct, competing influences on the typhoon intensity: strong vertical wind shear that hinders typhoon intensification (Gray 1968; DeMaria and Kaplan 1994; Riemer et al. 2010), and enhanced upper-level outflow that strengthens the secondary circulation and facilitates storm intensification (e.g., Holland and Merrill 1984; Komaromi and Doyle 2018). The impacts of the UTCL on the typhoon intensity in the CTL run are consistent with previous studies: a UTCL may strengthen storm intensity at the early stage via enhanced upper-level outflow, but the negative impacts of strong vertical wind shear become dominant and result in storm stagnant or weakening when the UTCL moves too close to the storm (e.g., DeMaria and Kaplan 1994; Komaromi and Doyle 2018). In addition, the UTCL steers the typhoon to a different track and affects the typhoon intensity indirectly via the different SST and other thermal conditions along the altered track.

d. Terrain effects

Terrain may affect typhoon track via the separation of low-level and upper-level vortex centers and cause track deflection or discontinuity (Bender et al. 1985; Yeh and Elsberry 1993a,b; Wu 2001). Additionally, typhoons usually weaken quickly after landfall (Yu et al. 2017). In the CTL run, Typhoon Jongdari moves over Japan shortly after 60 h, and its intensity decreases rapidly (Fig. 3b). This is also the time period when the typhoon and UTCL are very close to each other, and the UTCL may weaken the typhoon by inducing strong vertical wind shear as suggested by previous studies (DeMaria et al. 1993; Komaromi and Doyle 2018). To quantify the effect of UTCL on the typhoon evolution after 60 h, the “Rland” experiment was carried out by replacing the Japanese Archipelago with ocean. As shown in Fig. 11, the simulated storm track in Rland is similar to that in the CTL run. Small track differences occur only after the storm has passed the location of the Japanese Archipelago, and the track in the CTL run turns slightly leftward compared to the track in the Rland run, which is consistent with typhoon track deflections reported in previous studies (Lin et al. 1999; Hsu et al. 2018). The storm intensity in the Rland run is close to that in the CTL run before 60 h, but diverges sharply afterward. In the Rland run, the typhoon still weakens after 60 h (albeit at a slower rate) even without the presence of Japanese Archipelago. This suggests that the weakening of Typhoon Jongdari at the later stage in the CTL run cannot be completely attributed to landfall, and another possible factor is the moderate vertical wind shear induced by the UTCL when it gets close to the typhoon (Fig. 10d).

Fig. 11.
Fig. 11.

(a) Typhoon tracks and (b) time series of CMSLP in the CTL and Rland simulations.

Citation: Monthly Weather Review 149, 5; 10.1175/MWR-D-20-0271.1

6. Summary and discussion

The impacts of an upper-level cold low (UTCL) on the track and intensity evolution of Typhoon Jongdari (2018) is examined in this study using numerical simulations. Typhoon Jongdari had an unusual counterclockwise looping track before making landfall over Japan. The CTL run that is initialized using the GFS analysis captures the track and intensity change reasonably well. In the RCL run, the UTCL is removed from the initial and boundary conditions using the piecewise potential vorticity inversion (PPVI) method, and the track and intensity evolution of the storm is substantially different from that in the CTL run. The PV budget analysis suggests that the motion of the storm is mainly controlled by horizontal PV advection, and that the storm motion is also affected by the vertical motion and diabatic heating fields, which are modified by the interaction between the UTCL and typhoon as well. The analysis of the steering flow in combination with PPVI suggests that the UTCL strongly modulates the steering flow and contributes to the looping track of the storm. Typhoon Jongdari and the UTCL rotate counterclockwise around each other as they move close together, a typical evolution under the Fujiwhara effect.

Further analysis showed that the UTCL could enhance the upper-level outflow around Typhoon Jongdari through eddy flux convergence of angular momentum and by reducing the inertial stability, and thus contributes to the intensification of Typhoon Jongdari. Typhoon Jongdari weakens at the later stage of the interaction in the CTL run due to strong ventilation. The strong ventilation can be attributed to increased vertical wind shear and reduced SST, which are related to the close proximity of the UTCL to the typhoon as well as a more northern typhoon track due to the impacts of the UTCL on the environmental steering flow. In addition, an experiment in which the Japanese Archipelago is replaced with ocean shows that the Typhoon weakens at a slow rate when it reaches Japan area and suggests that the environmental conditions, including the UTCL, and the land/terrain effects both contribute to the weakening of Typhoon Jongdari at its later stage.

This study provides quantitative evidence for the strong impacts of a UTCL on the track and intensity evolution of a nearby typhoon. The analysis of this particular case suggests that realistic representation of the upper-level low (or trough) and its interaction with a typhoon is important for skillful prediction of typhoon track and intensity. Our conclusion is based on one case study. Idealized simulations with UTCLs of different size and intensity and at different relative locations from the typhoon would provide more insight into the complex nature of the typhoon–UTCL interaction, which merits further study.

Acknowledgments

This work was jointly sponsored by the Science and Technology Innovation Project of Ningbo (Grant 2019B10025), the National Key R&D Program of China (2017YFC1502000), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and Graduate research and innovation projects of Jiangsu Province (KYCX20_0912).

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  • Yu, Z. F., Y. Wang, H. Xu, N. Davidson, Y. Chen, Y. Chen, and H. Yu, 2017: On the relationship between intensity and rainfall distribution in tropical cyclones making landfall over China. J. Appl. Meteor. Climatol., 56, 2883–2901, https://doi.org/10.1175/JAMC-D-16-0334.1.

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  • <sc>Fig</sc>. 1.
    View in gallery
    Fig. 1.

    (a)The observed tracks of Typhoon Jongdari (black) and the UTCL (red) from 0000 UTC 26 Jul to 0000 UTC 1 Aug (Typhoon Jongdari and the UTCL are collocated at 1200 UTC 31 Jul). (b) Time series of the minimum central sea level pressure (CMSLP; units: hPa) of Typhoon Jongdari. (c) Time series of the maximum surface wind speed (Vmax; units: m s−1) of Typhoon Jongdari. (d) A longitude–pressure cross section of meridional wind (contours; units: m s−1) and temperature anomalies (shading; units: K) of the UTCL along 33°N at 0000 UTC 26 Jul. The temperature anomalies in (d) are defined with respect to the average over 120°–150°E along 33°N.

  • <sc>Fig</sc>. 2.
    View in gallery
    Fig. 2.

    (a)–(d) Total fields and (e)–(h) fields after the removal of the UTCL using the PPVI method. (a),(e) The oblique cross sections of PV (units: 0.01 PVU) in two experiments that pass through both the typhoon and UTCL centers in the CTL run [as shown by the red lines in (d) and (h)]. The other columns show (b),(f) geopotential height (shaded; units: gpm), (c),(g) air temperature (shaded; units: K), and (d),(h) wind vectors at 400 hPa (units: m s−1).

  • <sc>Fig</sc>. 3.
    View in gallery
    Fig. 3.

    (a) TY tracks and (b) the time series of TY intensity in terms of CMSLP in the different runs from 0000 UTC 26 Jul to 0000 UTC 30 Jul. Shading in (a) shows the time-independent skin temperature field used in all simulations. The black box in (a) depicts the innermost domain, and the plotting domain roughly represents the intermediate domain. Different line styles and colors represent different simulations as shown in the legends.

  • <sc>Fig</sc>. 4.
    View in gallery
    Fig. 4.

    Columns show (left to right) the total PV tendency (PVT), the horizontal advection (HAD), the vertical advection (VAD), and the diabatic heating (DH) terms (units: 0.01 PVU s−1). Rows show the (a) CTL and (b) RCL runs at t = 24 h and the (a) CTL and (b) RCL runs at t = 48 h.

  • <sc>Fig</sc>. 5.
    View in gallery
    Fig. 5.

    Wavenumber-1 reflectivity (dBZ; shaded) in (a),(b) CTL and (c),(d) (b) RCL run averaged over 850–600 hPa. The black vector indicates the direction of environmental vertical wind shear averaged over 500 km of the typhoon center.

  • <sc>Fig</sc>. 6.
    View in gallery
    Fig. 6.

    (top) The time–height profiles of the steering flow averaged within a 7° × 7° box following the typhoon center, which is defined as the location of minimum SLP. (bottom) The deep-layer steering flow components averaged over 800–200 hPa: “T” denotes the TY translation speed, “Vs” is the total steering flow, “Vcl” is the steering flow component induced by the UTCL, and “Vm” is the steering flow after removing the UTCL. (a) The CTL run and (b) the RCL run. The vectors represent the zonal and meridional flow components, with the vector scale shown in the lower right in the units of m s−1.

  • <sc>Fig</sc>. 7.
    View in gallery
    Fig. 7.

    Interaction between the UTCL and Typhoon Jongdari in CTL. (a)–(e) The 250-hPa PV (shading; units: 0.1 PVU) and averaged 800–200-hPa wind field (vectors; units: m s−1) associated with the UTCL. The blue star and the red TY symbol represent the UTCL and typhoon centers, respectively. (f) The relative positions of the UTCL and typhoon with respect to their vorticity centroid at different times with 6-h intervals.

  • <sc>Fig</sc>. 8.
    View in gallery
    Fig. 8.

    The 200-hPa flow (vectors; units: m s−1), the inertial stability (shading; units:10−5 s−1), and the jet (contours; wind speed > 20 m s−1 with an interval of 5 m s−1) averaged over 12–24 h. The center of the domain represents the typhoon center, and the tick marks show the distance from the typhoon center in km.

  • <sc>Fig</sc>. 9.
    View in gallery
    Fig. 9.

    Time–radius plots of azimuthally averaged (a),(d) 300–100-hPa layer-mean eddy flux convergence (EFC; shaded; units: m s−1 day−1) and 800-hPa tangential wind (Vt; contours; units: m s−1); (b),(e) 200–100-hPa layer-mean radial wind (Vr; shaded; units: m s−1); and (c),(f) 500–200-hPa layer-mean vertical wind (w; shaded; units: m s−1) in the (a)–(c) CTL and (d)–(f) RCL runs.

  • <sc>Fig</sc>. 10.
    View in gallery
    Fig. 10.

    Time series of (a) the ventilation index, (b) entropy deficit (Xm), (c) potential intensity (MPI; units: m s−1), and (d) vertical wind shear (VWS; units: m s−1) in the CTL (black solid) and RCL (red dashed) simulations averaged within 500 km from the typhoon center.

  • <sc>Fig</sc>. 11.
    View in gallery
    Fig. 11.

    (a) Typhoon tracks and (b) time series of CMSLP in the CTL and Rland simulations.

Fig. 1.

(a)The observed tracks of Typhoon Jongdari (black) and the UTCL (red) from 0000 UTC 26 Jul to 0000 UTC 1 Aug (Typhoon Jongdari and the UTCL are collocated at 1200 UTC 31 Jul). (b) Time series of the minimum central sea level pressure (CMSLP; units: hPa) of Typhoon Jongdari. (c) Time series of the maximum surface wind speed (Vmax; units: m s−1) of Typhoon Jongdari. (d) A longitude–pressure cross section of meridional wind (contours; units: m s−1) and temperature anomalies (shading; units: K) of the UTCL along 33°N at 0000 UTC 26 Jul. The temperature anomalies in (d) are defined with respect to the average over 120°–150°E along 33°N.
Fig. 2.

(a)–(d) Total fields and (e)–(h) fields after the removal of the UTCL using the PPVI method. (a),(e) The oblique cross sections of PV (units: 0.01 PVU) in two experiments that pass through both the typhoon and UTCL centers in the CTL run [as shown by the red lines in (d) and (h)]. The other columns show (b),(f) geopotential height (shaded; units: gpm), (c),(g) air temperature (shaded; units: K), and (d),(h) wind vectors at 400 hPa (units: m s−1).
Fig. 3.

(a) TY tracks and (b) the time series of TY intensity in terms of CMSLP in the different runs from 0000 UTC 26 Jul to 0000 UTC 30 Jul. Shading in (a) shows the time-independent skin temperature field used in all simulations. The black box in (a) depicts the innermost domain, and the plotting domain roughly represents the intermediate domain. Different line styles and colors represent different simulations as shown in the legends.
Fig. 4.

Columns show (left to right) the total PV tendency (PVT), the horizontal advection (HAD), the vertical advection (VAD), and the diabatic heating (DH) terms (units: 0.01 PVU s−1). Rows show the (a) CTL and (b) RCL runs at t = 24 h and the (a) CTL and (b) RCL runs at t = 48 h.
Fig. 5.

Wavenumber-1 reflectivity (dBZ; shaded) in (a),(b) CTL and (c),(d) (b) RCL run averaged over 850–600 hPa. The black vector indicates the direction of environmental vertical wind shear averaged over 500 km of the typhoon center.
Fig. 6.

(top) The time–height profiles of the steering flow averaged within a 7° × 7° box following the typhoon center, which is defined as the location of minimum SLP. (bottom) The deep-layer steering flow components averaged over 800–200 hPa: “T” denotes the TY translation speed, “Vs” is the total steering flow, “Vcl” is the steering flow component induced by the UTCL, and “Vm” is the steering flow after removing the UTCL. (a) The CTL run and (b) the RCL run. The vectors represent the zonal and meridional flow components, with the vector scale shown in the lower right in the units of m s−1.
Fig. 7.

Interaction between the UTCL and Typhoon Jongdari in CTL. (a)–(e) The 250-hPa PV (shading; units: 0.1 PVU) and averaged 800–200-hPa wind field (vectors; units: m s−1) associated with the UTCL. The blue star and the red TY symbol represent the UTCL and typhoon centers, respectively. (f) The relative positions of the UTCL and typhoon with respect to their vorticity centroid at different times with 6-h intervals.
Fig. 8.

The 200-hPa flow (vectors; units: m s−1), the inertial stability (shading; units:10−5 s−1), and the jet (contours; wind speed > 20 m s−1 with an interval of 5 m s−1) averaged over 12–24 h. The center of the domain represents the typhoon center, and the tick marks show the distance from the typhoon center in km.
Fig. 9.

Time–radius plots of azimuthally averaged (a),(d) 300–100-hPa layer-mean eddy flux convergence (EFC; shaded; units: m s−1 day−1) and 800-hPa tangential wind (Vt; contours; units: m s−1); (b),(e) 200–100-hPa layer-mean radial wind (Vr; shaded; units: m s−1); and (c),(f) 500–200-hPa layer-mean vertical wind (w; shaded; units: m s−1) in the (a)–(c) CTL and (d)–(f) RCL runs.
Fig. 10.

Time series of (a) the ventilation index, (b) entropy deficit (Xm), (c) potential intensity (MPI; units: m s−1), and (d) vertical wind shear (VWS; units: m s−1) in the CTL (black solid) and RCL (red dashed) simulations averaged within 500 km from the typhoon center.
Fig. 11.

(a) Typhoon tracks and (b) time series of CMSLP in the CTL and Rland simulations.
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