Editorial Type:
Article
Article Type:
Research Article

Doppler Radar Analysis of the Rapid Intensification of Typhoon Goni (2015) after Eyewall Replacement

Udai Shimada Meteorological Research Institute, Tsukuba, Ibaraki, Japan

Search for other papers by Udai Shimada in
Current site
Google Scholar
PubMed Close
,
Masahiro Sawada Meteorological Research Institute, Tsukuba, Ibaraki, Japan

Search for other papers by Masahiro Sawada in
Current site
Google Scholar
PubMed Close
, and
Hiroyuki Yamada University of the Ryukyus, Nishihara, Okinawa, Japan

Search for other papers by Hiroyuki Yamada in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jan 2018
DOI:
https://doi.org/10.1175/JAS-D-17-0042.1
Page(s):
143–162
Full access

Abstract

A ground-based Doppler radar observed the rapid intensification (RI) of Typhoon Goni (2015) for 24 h immediately after it completed an eyewall replacement cycle. Goni’s RI processes were examined by using radar reflectivity and wind fields retrieved by the ground-based velocity track display (GBVTD) technique. The maximum wind at 2-km altitude increased by 30 m s−1 during the first 6 h of RI, and it further increased by 20 m s−1 during the subsequent 12 h. Around the onset of RI, relatively strong outflow (>2 m s−1) was present both inside and outside the radius of maximum wind (RMW) above the boundary layer (BL), suggesting the existence of supergradient flow in and just above the BL. Despite this outflow, angular momentum increased inside the RMW. The low-level RMW contracted rapidly from 50 to 33 km, causing the RMW to slope greatly outward with height. The radius of maximum reflectivity was a few kilometers inside the RMW. A budget analysis of absolute angular momentum showed that the outflow contributed to the contraction of the tangential wind field. During RI, eyewall convection was enhanced, and a well-defined eye appeared. The low-level outflow changed into inflow immediately outside the RMW. Then the tangential wind field and high inertial stability region expanded radially outward, followed by the formation of an outer reflectivity maximum at twice the RMW. The contraction speed of the low-level RMW slowed down.

© 2018 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: Udai Shimada, ushimada@mri-jma.go.jp

Abstract

A ground-based Doppler radar observed the rapid intensification (RI) of Typhoon Goni (2015) for 24 h immediately after it completed an eyewall replacement cycle. Goni’s RI processes were examined by using radar reflectivity and wind fields retrieved by the ground-based velocity track display (GBVTD) technique. The maximum wind at 2-km altitude increased by 30 m s−1 during the first 6 h of RI, and it further increased by 20 m s−1 during the subsequent 12 h. Around the onset of RI, relatively strong outflow (>2 m s−1) was present both inside and outside the radius of maximum wind (RMW) above the boundary layer (BL), suggesting the existence of supergradient flow in and just above the BL. Despite this outflow, angular momentum increased inside the RMW. The low-level RMW contracted rapidly from 50 to 33 km, causing the RMW to slope greatly outward with height. The radius of maximum reflectivity was a few kilometers inside the RMW. A budget analysis of absolute angular momentum showed that the outflow contributed to the contraction of the tangential wind field. During RI, eyewall convection was enhanced, and a well-defined eye appeared. The low-level outflow changed into inflow immediately outside the RMW. Then the tangential wind field and high inertial stability region expanded radially outward, followed by the formation of an outer reflectivity maximum at twice the RMW. The contraction speed of the low-level RMW slowed down.

© 2018 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: Udai Shimada, ushimada@mri-jma.go.jp
Keywords: Tropical cyclones; Radars/Radar observations

1. Introduction

To elucidate dynamic and thermodynamic processes of rapid intensification (RI) and eyewall replacement cycles (ERCs) of tropical cyclones (TCs), temporally and spatially dense observations are indispensable, because both RI and eyewall replacement cause drastic changes in the intensity and structure of TCs. Although observations by aircraft have provided invaluable clues to RI and ERC processes (e.g., Montgomery et al. 2006; Houze et al. 2007; Montgomery et al. 2014; Abarca et al. 2016; Guimond et al. 2016), we have yet to obtain observations that are sufficiently dense both temporally and spatially. This lack of dense observations has prevented our full understanding of RI and ERCs.

The Ishigaki C-band Doppler radar (Fig. 1a) operated by the Japan Meteorological Agency (JMA) observed Typhoon Goni (2015) at 5-min intervals for 24 h while it was undergoing RI, just after completing an ERC, and reaching peak intensity. The ground-based velocity track display (GBVTD) technique (Lee et al. 1999) can retrieve TC wind fields from single ground-based Doppler radar data. We successfully used the GBVTD technique to retrieve wind data for Goni at 5-min intervals with a high spatial resolution of less than 1 km in horizontal grid spacing (see section 2). These data provided us an invaluable opportunity to examine Goni’s RI processes in detail.

Fig. 1.
Fig. 1.

(a) Ocean temperatures at 50-m depth on 22 Aug 2015 and Goni’s track; small blue-colored circles show the track at 6-h intervals, and black circles show the track at 1-day intervals. The large white circle centered at Ishigaki Island denotes the range of the Ishigaki Doppler radar, ~200 km. The ocean temperature data were provided by the JMA. (b) Time evolution of Goni’s intensity (green line: maximum wind; blue line: central pressure). RSMC Tokyo’s best-track data do not include maximum wind speeds for a TC categorized as a tropical depression. The vertical orange bar indicates the period focused on in this study.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

Hendricks et al. (2010) have suggested that whether a TC experiences RI or not is controlled mostly by internal dynamical processes, provided that relatively favorable environmental conditions exist. Moreover, numerical simulation studies have examined the evolution of inner-core structures up to the onset of RI on this basis (Miyamoto and Takemi 2013, 2015; Kieu et al. 2014). Conventionally, the mechanism of vortex spinup has been considered in a balanced vortex framework (e.g., Nolan et al. 2007; Pendergrass and Willoughby 2009; Vigh and Schubert 2009), but recent theoretical studies have emphasized the role of the boundary layer (BL) in vortex spinup (Smith et al. 2009; Montgomery and Smith 2014; Frisius and Lee 2016).

Observational studies have examined inner-core structures favorable for intensification of TCs, such as the distribution of inertial stability, axial symmetry, vortex tilt, and the radial location of deep convection and diabatic heating (e.g., Rogers et al. 2013, 2015, 2016; Jiang and Ramirez 2013; Zagrodnik and Jiang 2014; Stevenson et al. 2014; Susca-Lopata et al. 2015; Tao and Jiang 2015; Zawislak et al. 2016; Shimada et al. 2017). Rogers et al. (2015) examined structural differences between Hurricane Earl (2010), a rapidly intensifying TC, and Hurricane Gustav (2008), a steady-state TC, and showed that inertial stability was higher inside and lower outside the radius of maximum wind (RMW) and that inflow in the BL was deeper and stronger in Earl than in Gustav, leading to a preference for deep vigorous convection inside the RMW in Earl. However, this result was based on a snapshot observation during RI; thus, what processes are involved in the formation of an inner-core structure favorable for RI remains an open question. High-temporal-resolution observations are needed to address this question.

Some TCs experience RI after eyewall replacement. Sitkowski et al. (2011) extracted 24 ERC events of 14 hurricanes that were observed by aircraft and examined the climatological characteristics of changes in intensity and structure associated with the ERCs. Their study revealed that after ordinary intensification, an outer wind maximum is observed in the incipient secondary eyewall on average 9 h prior to a suspension of intensification. Then, during the period from the local intensity maximum to the local intensity minimum, wind speed around the inner eyewall decreases by 10 m s−1 on average, whereas wind speed around the outer eyewall increases. A reintensification period follows, during which the wind speed in the outer eyewall becomes greater than that in the inner eyewall and the wind peak around the inner eyewall gradually disappears, signaling the completion of the ERC. This reintensification takes 11 h on average. The RMW associated with the secondary eyewall rapidly contracts during the ERC. After the completion of the ERC, the TC often continues to intensify, and in some cases, RI occurs. Whether RI processes after an ERC differ from those not associated with an ERC has not yet been investigated.

The purpose of this study was to conduct an in-depth examination of processes occurring in Goni during RI just after the eyewall replacement using radar reflectivity and retrieved wind data with the aim of contributing to our understanding of the dynamical processes of RI. In particular, we focused on drastic structural changes around the onset of and during RI from an axisymmetric perspective and compared the kinematic structures of Goni with those that have been shown in observations and simulations for TCs that undergo RI without an ERC. To our knowledge, no case of RI just after an ERC has been examined by using both high-resolution temporal and high-resolution spatial observations obtained over a 24-h period.

This paper consists of six sections. We describe the wind dataset used and its limitations, data quality, and the method of TC intensity estimation in section 2, and we present an overview of Goni’s evolution and the associated environmental conditions in section 3. In section 4, we present RI processes of Goni. We discuss the results in section 5, and section 6 is a summary.

2. Data and methodology

a. Wind dataset and its limitations

We applied the GBVTD technique to Doppler velocity VD data observed by the Ishigaki C-band Doppler radar and constructed a two-dimensional wind dataset for Goni at 1-km-altitude intervals from 1 to 10 km of altitude from 2200 UTC 22 August to 2200 UTC 23 August 2015 (see appendix for details). The Doppler observation parameters of the JMA radars are listed in Table 1 of Shimada et al. (2016). In this study, we set the retrieval resolution to 0.7° of azimuth and 500 m of radius in storm-centered cylindrical coordinates. The dataset includes tangential winds with wavenumbers up to 3 and radial winds with wavenumbers up to 1. Because radar coverage was sparse at high altitudes when Goni was far from the radar location, however, we were not able to obtain the wind field for all altitudes at all times. In addition, we obtained vertical velocities w by upward integration of the mass continuity equation from the surface, where vertical velocity is 0. The mean hurricane season density profile of Jordan (1958) was used to compute w. Because there were no retrieved winds below 1-km altitude, 1-km winds were used as winds between the surface and 1-km altitude. This is a limitation of the vertical velocities used in this study.

The method of Shimada et al. (2016) was used to construct the constant-altitude plan position indicator (CAPPI) data, on which the GBVTD analysis was performed. In this method, plan position indicator (PPI) data with an elevation angle of less than 10.0° (specifically, 0.2°, 1.1°, 2.7°, 4.0°, 5.8°, and 8.3°) were interpolated to CAPPI data with a radial range of up to 200 km. For example, for radar ranges greater than ~60 km, VD from the 0.2°-elevation PPI scan, whose height was between 1 and 4 km, was projected onto the 1-km CAPPI. Because vertical profiles of wind speed in TCs generally increase downward from 3 km to below 1 km (e.g., Franklin et al. 2003), there is a possibility that this CAPPI method contributes to a negatively biased wavenumber-0 tangential wind when the TC center is far from the radar location. Similarly, the retrieved wavenumber-0 radial wind at 1-km altitude can be regarded as representing above 1-km altitude. In this study, we defined a strong inflow layer as the BL, which is generally seen below 1-km altitude. Thus, we regarded the wind field at 1-km altitude as virtually above the BL.

The GBVTD technique has some intrinsic limitations with respect to retrieval accuracy because it retrieves two-dimensional winds from only one component of the near-horizontal wind (i.e., VD). The accuracy of the GBVTD-retrieved and is affected by the aliasing of the wavenumber-2 radial wind into and (Lee et al. 1999, 2006; Murillo et al. 2011; Bell and Lee 2012). The GBVTD technique assumes that the asymmetric radial wind is much smaller than the corresponding tangential wind. However, if the wavenumber-2 radial wind is dominant within the eye region and its distribution moves around the eyewall cyclonically together with mesovortices (e.g., Kossin and Schubert 2004; Braun et al. 2006) or vortex Rossby waves (e.g., Montgomery and Kallenbach 1997; Wang 2002), an artificial fluctuation in the retrieved and with a period of a few hours may be found. Because of this possibility, in our analysis, we ignored fluctuations in and with a period of a few hours.

The accuracy of the GBVTD technique is also sensitive to the position accuracy of the TC center. We used the GBVTD-simplex algorithm (Lee and Marks 2000; Bell and Lee 2012) to detect the TC center at 2-km altitude at 5-min intervals, following the procedure of Shimada et al. (2016), and then we used the 2-km TC center for the TC center at other altitudes. For maximum accuracy of the GBVTD analysis, it is actually desirable to use TC centers derived from VD at each altitude. However, because there are few radar scatterers at upper altitudes, using the simplex algorithm, we were unable to detect centers at every altitude. Lee and Marks (2000) showed that the accuracy of the GBVTD-retrieved is less sensitive to the deviation from the true center than that of asymmetric tangential wind. Although a TC vortex generally tilts to the left of the downshear with height and the difference between centers at 2 and 10 km can be up to several kilometers (e.g., Reasor et al. 2013), this range of vortex tilt may not significantly affect the accuracy of . Moreover, because the magnitude of often changes drastically during an ERC, those changes can be captured despite the retrieval errors that may exist.

Furthermore, the GBVTD technique can only retrieve the wind field within the interval between the TC center and the radar location. For this reason, no winds were retrieved while the radar site was located within the TC’s eye region, because no VD observations could be made. Accordingly, no TC centers were detected during this period, which lasted about 2 h, from 1140 to 1400 UTC 23 August 2015. For this reason, we subjectively determined the center of the eye for this period while viewing radar reflectivity.

Because of these limitations and uncertainties, we focused mainly on the axisymmetric component of the retrieved winds.

b. Data quality

The accuracy of the retrieved wind obtained by the GBVTD technique is defined as the overall average of the root-mean-square difference (RMSD) between VD resampled from the GBVTD-retrieved winds and observed VD, and it is identical to the root-mean-square error (RMSE) in Zhao et al. (2012, their Fig. 3). In general, the RMSD at 2-km altitude was smaller than 3 m s−1 except when Goni passed near Ishigaki Island (Fig. 2). The horizontal distribution of the RMSD shows that it tended to be larger inside the RMW (not shown). It is likely that the presence of a small-scale extreme wind (e.g., Aberson et al. 2006) or small eyewall vorticity maximum (e.g., Marks et al. 2008) along the inner edge of the eyewall, the existence of which is not taken account of by the GBVTD technique, lowered the accuracy of the GBVTD analysis result. In addition, if the retrieval range is inside the RMW, then there can be significant geometrical distortion in the GBVTD technique (Lee et al. 1999; Jou et al. 2008). Furthermore, noise contamination in the VD field near the outermost range of the radar reduced the accuracy of the analysis (not shown). For these reasons, the overall average RMSD increased when Goni was located far from the radar site and when most of retrieval range was inside the RMW (i.e., when Goni approached the radar site). However, because the RMSD at 2-km altitude was generally less than 3 m s−1 where the wind speed was greater than 30 m s−1, that is, because the RMSD was generally less than 10% of the actual wind speed, this level of accuracy is acceptable for the wind field analysis.

Fig. 2.
Fig. 2.

Time evolution of the overall average of the RMSD between VD resampled from the GBVTD-retrieved winds at 2-km altitude and observed VD at 2-km altitude.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

c. Intensity estimation

The central pressures of Goni were estimated by the method used by Shimada et al. (2016). With this method, GBVTD-retrieved at 2-km altitude, sea level pressure observations near the radar site, and the gradient wind equation are used to estimate the central pressure. The RMSE and bias of this method are 8.37 and 1.51 hPa, respectively, compared with the best-track data of the Regional Specialized Meteorological Center (RSMC) Tokyo. In particular, for TCs with an RMW of 20–70 km, the estimation accuracy is outstanding, with an RMSE of only 5.55 hPa relative to the best-track data.

3. Overview of Goni

a. Storm history and environmental conditions

According to the best-track data of RSMC Tokyo, Typhoon Goni moved westward along ~19°N from 18 to 20 August and then remained nearly stationary just northeast of Luzon Island on 21 August before starting to move north-northeast after 1800 UTC 21 August (Fig. 1a). Goni weakened slightly just before the onset of intensification at 0300 UTC 23 August (Fig. 1b) and reached its lifetime-maximum intensity at 1800 UTC 23 August, just after it passed near Ishigaki Island. Subsequently, it moved northeastward over the East China Sea.

During the intensification beginning at 0300 UTC 23 August, the magnitude of vertical wind shear between 850 and 200 hPa was less than 6 m s−1, with the direction of the shear being nearly southeastward (Fig. 3). In addition, ocean temperatures at 50-m depth east of Taiwan were greater than 28°C (Fig. 1a). These environmental conditions were favorable for TC intensification.

Fig. 3.
Fig. 3.

Vertical wind shear (black line) and its heading direction (red line) between 200 and 850 hPa averaged within 500 km from the center. The shear was calculated by using JRA-55 data (Kobayashi et al. 2015). The shading indicates the radar observation period.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

b. Intensity analysis

The intensity estimation results show that the evolution of central pressure estimated from Doppler radar data was consistent with that in the best-track data (Fig. 4a). The 2-h running mean (black line) indicated that the central pressure first increased temporarily up to ~960 hPa at 0030 UTC 23 August and then decreased rapidly by 30 hPa over the next 18 h. Thus, Goni’s intensification met the definition of RI, that is, a 30-hPa decrease in central pressure in 24 h (Shimada et al. 2017). Hereafter, we define the period from 0030 to 1830 UTC 23 August as the RI period. The temporary increase in the central pressure at around 1030 UTC during Goni’s passage near Ishigaki Island is due to the GBVTD-related limitations, as shown by the relatively large RMSDs at that time (Fig. 2). The central pressure decreased at an almost constant rate (−1.7 hPa h−1) except for that temporary increase. In contrast, the maximum wind at 2-km altitude, despite some erratic fluctuations, rapidly increased by at least 30 m s−1 from 35 m s−1 during only 6 h from 0100 to 0700 UTC 23 August while the RMW contracted greatly from 45 to 30 km (Fig. 4b). The maximum wind eventually reached at least 85 m s−1 at around 1900 UTC 23 August.

Fig. 4.
Fig. 4.

(a) Time evolutions of estimated central pressures (blue, red, green, and purple lines), their 2-h running mean (black line), and the best-track central pressure (brown line) of Goni. The blue, red, green, and purple lines indicate central pressures derived from sea level pressure observations at Yonaguni, Iriomote, Ishigaki, and Miyako, respectively. (b) Time evolutions of maximum wind speed (blue line) and RMW (red line) at 2-km altitude of Goni. Periods I–IV, defined in section 4a and Table 1, are also shown at the bottom of each panel.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

c. Structural evolution

Microwave satellite imagery showed that Goni had concentric eyewalls when it reached a point southeast of Taiwan at 0530 UTC 22 August (Fig. 5a). After 1700 UTC 22 August, there appeared to be active convection in both the inner and outer eyewalls because brightness temperatures became lower (Fig. 5b). Because 1700 UTC corresponds to 0100 local standard time, this temporary convective activity may have been associated with a diurnal cycle of convection as described by Dunion et al. (2014). Then, at 0435 UTC 23 August, Goni had a well-defined axisymmetric primary eyewall (Fig. 5c), suggesting that an eyewall replacement occurred.

Fig. 5.
Fig. 5.

Brightness temperatures from the Global Change Observation Mission 1st–Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2; at 89 GHz A-horn, vertically polarized wave) polar-orbiting microwave satellite at (a) 0529 and (b) 1739 UTC 22 Aug and (c) 0435 UTC 23 Aug.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

Figures 6–8 show the structural evolution of the inner core before and after the eyewall replacement. At 2200 UTC 22 August, cloud-top temperatures associated with the inner eyewall were still low (Fig. 6a), and radar imagery showed a well-defined inner eyewall (Fig. 7a), but there was little evidence of a wind maximum at the inner eyewall (Fig. 8a), probably because of the limitations of the GBVTD analysis. Centers used in the GBVTD analysis were those determined relative to the RMW associated with the outer eyewall. Around that time, because the center of the inner eyewall wobbled relative to that of the outer eyewall, the centers were different, and as a result, the accuracy of the GBVTD retrieval around the inner eyewall was poor. After 0000 UTC 23 August, when Goni had reached a point ~200 km east of Taiwan, the storm underwent the eyewall replacement as the inner eyewall weakened and an outer eyewall developed (Fig. 7b). The disappearing inner eyewall merged with the outer eyewall on the southeast side at 0100 UTC 23 August (Fig. 7b). Brightness temperatures associated with the eyewall decreased, and a well-defined eye became visible on satellite imagery after 0330 UTC 23 August (Figs. 6b,c,d). Radar imagery showed a polygonal eyewall structure for a while after the replacement (Figs. 7c,d), and the wind field at 2-km altitude showed local wind maxima near high reflectivity in the eyewall (Figs. 8c,d and 7c,d).

Fig. 6.
Fig. 6.

Infrared brightness temperatures (at 10.4 μm) from the Himawari-8 geostationary satellite: (a)–(g) 2200 UTC 22 Aug to 2105 UTC 23 Aug.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

Fig. 7.
Fig. 7.

As in Fig, 6, but for radar rainfall rate provided by the JMA. The red dot indicates the radar site. The black circle denotes the RMW.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

Fig. 8.
Fig. 8.

As in Fig, 6, but for retrieved wind speed at 2-km altitude. The black line indicates Goni’s track, which is composed of the centers (from 2200 UTC 22 Aug to 2200 UTC 23 Aug) used in the GBVTD analysis and axisymmetric analyses. The red dot indicates the radar site. The black circle denotes the RMW. There was no GBVTD-retrieved wind field at 1200 UTC 23 Aug.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

When Goni approached maturity, at 1730 UTC 23 August, there were many rainbands located around the primary eyewall (Fig. 7f). Note that, because of radar attenuation, radar reflectivity distant from a radar site tends to be weaker than that near the site. Nonetheless, active rainbands were observed in the southeast quadrant where wind speed was also relatively strong (Fig. 8e). After Goni reached maturity (1800 UTC 23 August), the storm had a concentric eyewall structure (Fig. 7g). However, no outer wind maximum was yet associated with the outer reflectivity maximum at that time (Fig. 8f).

4. Goni’s RI

In this section, we document RI processes of Goni in detail. Radius–time Hovmöller diagrams of , , azimuthal-mean radar reflectivity, and azimuthal-mean absolute angular momentum (Fig. 9), where = (½)fr2 +, r is the radius from the TC center, and f is the Coriolis parameter, show the following:

  1. The 2-km RMW contracted from 60 to 25 km as in the vicinity of the RMW increased. Isopleths of inside the RMW moved inward with time such that the radial gradient of increased, whereas isopleths of outside the RMW moved outward during the first half of the time period shown on the Hovmöller diagram.

  2. While the RMW was rapidly contracting (from 2200 UTC 22 August to 0415 UTC 23 August), at 1-km altitude was outflow. After the contraction speed slowed down, just outside the RMW became inflow. Then, became outflow again at around 1800 UTC 23 August when Goni reached its peak intensity.

  3. The radius of maximum reflectivity (RMR) at 2-km altitude was located inside the RMW during the period of rapid contraction. After Goni’s passage near Ishigaki Island, the RMR was located outside the RMW, and an outer reflectivity maximum formed at radii of ~60–80 km.

Fig. 9.
Fig. 9.

(a) Radius–time Hovmöller diagram of (color scale), (105 m2 s−1; purple lines), and RMW (thick black line) at 2-km altitude. The blank areas are where the GBVTD technique could not retrieve (i.e., during the TC’s passage near Ishigaki Island and in the eye region). (b) Radius–time Hovmöller diagram of at 1-km altitude (color scale), radius of maximum radar reflectivity at 2-km altitude (green line), and RMW at 2-km altitude (thick black line). The blank areas are where the GBVTD technique could not retrieve . (c) Radius–time Hovmöller diagram of azimuthal-mean radar reflectivity at 2-km altitude (color), radius of maximum radar reflectivity (red line), and RMW (thick black line). The black horizontal lines indicate the boundaries of the periods defined in Table 1.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

Hereafter, we focus on four specific periods (Table 1) during which enough wind data were retrieved to examine the processes at work. The boundary between periods I and II was determined by the difference in data density above 5-km altitude, and the boundary between periods II and III was determined by the timing of the change in at 1-km altitude from outflow to inflow just outside the RMW. Periods I and II encompass the onset of RI, period III falls within the period of RI, and during period IV, Goni was in its mature stage.

Table 1.

Definitions of the four periods.

Table 1.

a. RI onset

During period I, the eyewall replacement had just been completed while the RMW rapidly contracted (Fig. 9a). There was relatively strong outflow of greater than 3 m s−1 inside the RMW at 1-km altitude (Fig. 9b). In radius–height plots of time-averaged , , azimuthal-mean radar reflectivity, and (Figs. 10a–c), it can be seen that an axis of outflow was present that sloped radially outward with height at altitudes from 1 to 4 km (Fig. 10b). This structure may reflect the remnants of the low-level outflow above the inflow peak associated with the contracting RMW if it is assumed that inflow primarily occurs below 1-km altitude, as shown by the composite-based studies (Zhang et al. 2011; Rogers et al. 2012). The RMW sloped radially outward from 1- to 3-km altitude and inward from 3- to 5-km altitude (Fig. 10a). Stern et al. (2014) showed similar RMW slopes in their observations and numerical simulations and attributed these features to unbalanced flow in the vicinity of the RMW. During period I, the RMR was located 5 km inside the RMW, and below 5-km altitude, it was almost upright (Fig. 10a). Assuming that the reflectivity distribution is similar to the diabatic heating distribution, then this configuration was favorable for vortex spinup and RMW contraction (Shapiro and Willoughby 1982; Hack and Schubert 1986; Nolan et al. 2007; Pendergrass and Willoughby 2009; Rogers et al. 2013, 2015, 2016; Smith and Montgomery 2016), and likely led to the subsequent intensification, observed after this period.

Fig. 10.
Fig. 10.

Radius–height plots of (a) time-averaged (m s−1; black contours), azimuthal-mean radar reflectivity (color scale), and (105 m2 s−1; red contours); (b) time-averaged (m s−1; black contours positive, white contours negative), azimuthal-mean radar reflectivity (color scale), and (105 m2 s−1; red contours); and (c) time-averaged (color scale), azimuthal-mean radar reflectivity (black contours but the zero contour is omitted), and (105 m2 s−1; green contours) during period I. The dashed line is the RMW, and the white line is the RMR. Contours were drawn in areas where there were observations averaged at least over 1 h in total. (d)–(f) As in (a)–(c), respectively, but for period II. (g)–(i) As in (a)–(c), respectively, but for period III. (j)–(l) As in (a)–(c), respectively, but for period IV.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

During period II, Goni started to intensify again, and the RMW continued to contract rapidly (Figs. 4 and 9a). The low-level outflow at and above 1-km altitude became weaker than it was during period I (Fig. 10e). The RMW was vertical up to 5-km altitude, and it sloped slightly outward above that altitude (Fig. 10d). The RMR was located a few kilometers inside the RMW below 9-km altitude, and the slope of the RMR was more upright than slopes of surfaces in the vicinity of the RMR (Fig. 10d). This feature is consistent with the result of Hazelton et al. (2015), who showed the slopes of reflectivity and surfaces from composites of airborne Doppler radar and found that intensifying TCs tend to have dBZ surfaces that are more upright than surfaces. There was inflow just outside the RMW above 5-km altitude (Fig. 10e). Geostationary satellite imagery showed a well-defined eye from 0330 UTC 23 August (Fig. 6c) and a decrease over time in brightness temperatures associated with the eyewall (Figs. 6c,d). These features suggest that the secondary circulation driven by diabatic heating in the eyewall had started to strengthen.

However, the distribution of calculated from upward integration of the mass continuity equation showed downdrafts just inside the RMW below 6-km altitude (Fig. 10f). Because BL models for TCs predict that a frictionally forced updraft lies near the RMW (e.g., Kepert 2001; Kepert and Wang 2001; Kepert and Nolan 2014) and because observations have shown that there are azimuthal-mean updrafts near the RMW (Bell and Montgomery 2008; Reasor et al. 2009; Rogers et al. 2012, 2013; Bell et al. 2012; Sanger et al. 2014; Stern et al. 2014), these downdrafts were most likely artificial products of the analysis. Using numerical simulation results as truth, Nolan (2013) revealed that, in the case of tornadoes, vertical velocities derived from Doppler radar data are problematic. He demonstrated that both the lack of a BL mass flux into the core region in the radar data and a positive bias of caused by outward centrifuging of debris by a tornado are likely to produce anomalously large downward motion inside the RMW. In the case of Goni, no inflow was retrieved in the vicinity of the RMW even at 1-km altitude during period II, leading to a lack of the BL convergence that should be present around the RMW (e.g., Kepert and Wang 2001). As a result, updrafts that should exist in and just above the BL were not calculated. Instead, downdrafts were computed because at altitudes from 1 to 4 km around the RMW, divergence was mainly retrieved. For comparison, if it is assumed that the vertical velocity at 4-km altitude just inside the RMW is 0.5 m s−1, then downward integration of the mass continuity equation from 4-km altitude yields a vertical velocity of ~0.8 m s−1 at 1-km altitude. The analysis result poses a challenge in obtaining the wind field below 1-km altitude. To resolve this issue, the development of new techniques, including the scan strategy, data processing, and C-band phased array radar, should be addressed in future studies.

From period I to period II, the 2-km RMW rapidly contracted from 50 to 33 km. Along with this contraction, the tangential wind field contracted. For example, the 40 m s−1 isopleth outside the RMW contracted from a radius of 60 to 50 km (Fig. 9a). Weak reflectivity ranging from 25 to 30 dBZ was distributed from the 60- to the 70-km radius in isolation from the eyewall (Fig. 9c). This reflectivity may indicate inner rainbands moving with the outflow in the lower troposphere, as proposed by Moon and Nolan (2015).

b. RI phase

During period III, Typhoon Goni continued to intensify, but the contraction speed of the RMW slowed down (Figs. 4 and 9a). Just outside the RMW, at 1-km altitude changed to inflow, although weak outflow remained at around 60-km radius in the lower troposphere (Figs. 9b and 10h). At the same time, the 45 m s−1 isopleth of outside the RMW started to expand radially outward (Fig. 9a). There was a strong updraft inside the RMW through the free atmosphere (Fig. 10i), and the magnitude of reflectivity in the eyewall increased during the second half of period III (Fig. 9c). These features are consistent with decrease in brightness temperatures in the satellite imagery (Figs. 5c and 6c,d,e), though part of the increase in reflectivity was likely due to the effect of radar attenuation. The RMR at altitudes between 2 and 9 km was located inside the outward-sloping RMW (Fig. 10g). In contrast, reflectivity outside the RMW between 40- and 60-km radius became weaker with time, signaling the formation of a moat and the potential development of another secondary eyewall (Fig. 9c).

From 0530 to 0725 UTC, Goni’s eyewall passed over Hateruma Island (Fig. 8c), where there is a weather station operated by the JMA. We estimated the gradient wind speed at 1-km altitude from the gradient wind equation by using sea level pressure data at 5-min intervals observed at Hateruma Island during that period, under the assumption that radial pressure gradients at the surface and 1-km altitude are almost the same and that the pressure observations are representative of axisymmetric pressure in the vicinity of the eyewall. Here, the radial pressure gradient at each time was derived from the pressure observation and the distance between the storm center and the weather station, both of which were smoothed in time (by applying a 1–2–1 filter 20 times). Estimated gradient winds were, in general, smaller than the corresponding GBVTD-retrieved at 1-km altitude (Fig. 11), indicating that the flow around and inside the RMW at 1-km altitude was supergradient. This result is consistent with the existence of outflow inside the RMW at 1-km altitude (Figs. 9b and 10h).

Fig. 11.
Fig. 11.

Radial profile of gradient wind (black) and GBVTD-retrieved (red) at 1-km altitude.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

The slope of the RMW became more outward slanting, reaching ~45° from the vertical. This slanting was caused by the more rapid contraction of the RMW at 1-km altitude, from 38 km during period II to 30 km during period III, compared with its contraction at 10-km altitude, from 42 km during period II to 39.5 km during period III (Table 2). Stern et al. (2015) demonstrated by using a diagnostic equation for RMW contraction that the speed of RMW contraction can be determined from the radial gradient of the time rate of change of , (∂/∂r)(∂/∂t), and the sharpness of peak in the radial profile of , ∂2/∂r2, at the RMW:
e1
Contraction is faster when (∂/∂r)(∂/∂t) has a larger negative value and the peak is broader. From period I to period II, the outflow just outside the RMW in the lower troposphere above the BL suppressed the increase in there (see section 4d for more details), causing (∂/∂r)(∂/∂t) to become more negative and leading to the more rapid contraction at 1-km altitude than at 10-km altitude (Table 3).
Table 2.

Contraction speed (km h−1) of the RMW at 1- and 10-km altitude. The speed was calculated from the difference between the mean RMWs of two periods.

Table 2.
Table 3.

Values of the numerator and denominator of the term on the right-hand side of Eq. (1) and the diagnosed time tendency of the RMW at altitudes of 1 and 10 km between periods II and III, computed by using time-averaged during periods II and III. Values of were radially filtered using the 10-km running mean.

Table 3.

c. Mature stage

Goni reached its peak intensity after it passed near the Ishigaki radar (Fig. 4). An outer reflectivity maximum was formed from the radius of 60 to 80 km after 1000 UTC 23 August (Figs. 9c and 10j). Maximum at 2-km altitude peaked from 1430 to 1700 UTC 23 August (Fig. 9a). The tangential wind field expanded radially outward, and isopleths of greater than 25 × 105 m2 s−1 moved radially inward from 1430 to 2130 UTC. The again became outflow above the BL (Figs. 9b and 10k). The RMR was located outside the RMW (Figs. 9c and 10j). The updraft axis associated with the eyewall was located just inside the RMW (Fig. 10l) but weakened. This weakening is consistent with increase in brightness temperatures in the satellite imagery (Figs. 6f,g). From period III to period IV, the contraction speed of the RMW in the upper level was the same as before, whereas the speed in the lower troposphere slowed down (Table 2). As a result, the RMW sloped less outward with height (~38° from the vertical) in the mature stage (Fig. 10j). After 1800 UTC, the region with greater than 35 m s−1 expanded radially outward from 70 to 80 km, and isopleths of around the outer reflectivity peak moved radially inward (Fig. 9a). However, there was no local maximum in around the outer reflectivity peak at this time (Fig. 9a).

A region of inertial stability I, where I 2 ≡ (1/r3)of more than 1 × 10−3 s−1 (~20f ) at 2-km altitude outside the eyewall expanded from 40 to 50 km from 0800 to 2200 UTC 23 August (Fig. 12a), whereas I at 2-km altitude outside the outer reflectivity maximum decreased (Figs. 12b,c). The I in the midtroposphere increased (Figs. 12b,c) in the vicinity of the outer reflectivity maximum because increased greatly there (Fig. 10j). This increase in I is expected to increase the ratio of energy retained as wind kinetic energy to injected heat energy around the outer reflectivity peak (Rozoff et al. 2012), although the GBVTD analysis did not confirm the development of an outer wind maximum during the analysis period.

Fig. 12.
Fig. 12.

(a) Hovmöller diagram of I (10−3 s−1; color scale), (105 m2 s−1; red lines), and RMW (thick black line) at 2-km altitude. The blank areas are where the GBVTD technique could not retrieve (i.e., during the TC’s passage near Ishigaki Island and in the eye region). The black horizontal lines indicate the boundaries of the periods defined in Table 1. (b),(c) Radius–height plots of time-averaged I (10−3 s−1; black contours) and azimuthal-mean radar reflectivity (color scale) during (b) period III and (c) period IV.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

d. Absolute angular momentum budget

Smith and Montgomery (2016) illustrated vortex spinup and spindown by considering how the distribution of is changed by the secondary circulation induced by forced diabatic heating. Because absolute angular momentum is, to good approximation, materially conserved except in the BL, examining changes in the distribution of is useful for understanding the evolution of a TC vortex. We here examined changes in during the onset of and during RI by performing a budget analysis of .

The angular momentum budget equation can be expressed as follows (e.g., Rozoff et al. 2012):
e2
where ζa is the vertical component of absolute vorticity, ρ is density, p is pressure, λ is the azimuthal angle, and Fλ is the tangential component of friction/diffusion. The azimuthal mean (axisymmetric component) of each variable is denoted by an overbar, and the deviation from the azimuthal mean (storm-relative asymmetric flow) is denoted by a prime. We calculated the first four terms on the right-hand side of Eq. (2). Additionally, because the sum of the four terms was inconsistent with the actual local time tendency, , the residual difference between the actual local time tendency and the sum from the first term to the fourth term was computed to evaluate analysis errors. We focused on the budget just outside the RMW (up to 80-km radius) because the retrieval accuracy of inside the RMW and near the edge of the analysis field was not good. In general, although values of the eddy terms (the third and fourth terms) were much smaller than those of the azimuthal-mean terms (the first and second terms), they were nonnegligible (Figs. 13d,e and 14d,e). This is consistent with previous studies (Reasor et al. 2009; Rozoff et al. 2012; Sun et al. 2013; Persing et al. 2013; Miyamoto and Takemi 2015). Note that in the GBVTD technique, asymmetric radial winds are aliased into the other wind components (Lee et al. 1999), which introduces uncertainty into the values of the eddy terms. Also, note that the uncertainty in the vertical velocity calculation leads to uncertainty in the budget.
Fig. 13.
Fig. 13.

Radius–height plots of time-averaged (a) local time tendency, (color scale), (b) axisymmetric radial advection (color scale), (c) axisymmetric vertical advection (color scale), (d) eddy radial advection (color scale), (e) eddy vertical advection (color scale), and (f) residual difference between the local time tendency and the sum from the first term to the forth term (color scale) during period II. The black contours are azimuthal-mean radar reflectivity (the zero contour is omitted), the dashed line is the RMW, and the green contours are (105 m2 s−1). In (b)–(f), the region inside the RMW and at radii > 80 km are masked out.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

Fig. 14.
Fig. 14.

As in Fig. 13, but for period III.

Citation: Journal of the Atmospheric Sciences 75, 1; 10.1175/JAS-D-17-0042.1

During period II (Fig. 13), increased greatly inside the RMW. Outside the RMW, increased at and above 5-km altitude, whereas decreased below 5-km altitude. The residual difference shows a lack of a positive advective tendency outside the RMW below 3-km altitude. The lack of updraft around the RMW during period II (Fig. 10f) and data in the BL most likely led to the budget inconsistency. A local time tendency of outside the RMW was consistent with the distribution of the first term in terms of sign: the low-level outflow above the BL contributed to the decrease in below 5-km altitude, and upper-level inflow contributed to the increase in at and above 5-km altitude. In addition, this local decrease in outside the RMW below 5-km altitude led to the maintenance of relatively low I outside the RMW (Fig. 12a) through the decrease in the radial gradient of , because I 2 ≡ (1/r3).

During period III (Fig. 14), both just outside the RMW and inside the RMW increased. Just outside the RMW, the local time tendency was consistent with the distribution of the first term in terms of sign: that is, inflow just outside the RMW contributed to the increase in there, leading to the expansion of the tangential wind field.

5. Discussion

There were two notable features in our analysis results. During the onset of RI, just after the completion of an ERC, Goni had a broad region of outflow from the inside of the RMW to 100-km radius at altitudes between 1 and 5 km. As RI proceeded, changed from outflow to inflow just outside the RMW. These features are discussed below.

Relatively strong outflow just above the BL around the RMW suggests that there was supergradient flow in and above the BL. A low-level outflow jet associated with supergradient winds has been shown by many previous studies (Zhang et al. 2001; Kepert and Wang 2001; Kepert 2006a,b, 2013; Zhang et al. 2011). In particular, the existence of unbalanced flow in the BL during intensification and SEF has been demonstrated (Smith et al. 2009; Huang et al. 2012; Bell et al. 2012; Abarca and Montgomery 2013, 2014, 2015; Abarca et al. 2016). Abarca and Montgomery (2013) showed by numerical simulation that there is outflow above the BL around and outside the newly formed secondary eyewall in the presence of a strong BL inflow and supergradient flow during SEF.

In the case of Goni, however, the outflow outside the RMW around the onset of RI occupied a broad region from the inside of the RMW to 100-km radius at altitudes between 1 and 5 km. This feature is different from the distribution of that is generally seen in intensifying TCs. Observational and modeling studies have shown that an outflow jet associated with supergradient flow is confined to a narrow layer around the RMW just above the BL inflow, that intensifying TCs have weak inflow outside the RMW above the BL, and that this inflow advects radially inward to spin up a TC vortex (Reasor et al. 2009; Persing et al. 2013; Rogers et al. 2013, 2015; Wang and Wang 2014; Stern et al. 2015; Kilroy et al. 2016; Zhang et al. 2017). Such weak inflow is represented by the secondary circulation induced by imposed diabatic heating in the eyewall (Shapiro and Willoughby 1982; Hack and Schubert 1986; Pendergrass and Willoughby 2009). The existence of weak inflow above the BL has been theoretically modeled to explain vortex spinup, although roles of weak inflow in intensification differ among theories (Ooyama 1969; Emanuel 1989; Smith et al. 2009).

There is a possibility that the wide outflow region was partly due to analysis errors. The number of PPI scans observed by the operational Doppler radar may be insufficient to resolve a narrow outflow jet just above the BL and insufficient to retrieve weak inflow in a broad region outside the RMW. Meanwhile, Wu et al. (2012) and Huang et al. (2012) showed that a broad outflow region exists from 1- to 5-km altitude outside the secondary eyewall up to ~180-km radius just after SEF. Zhang et al. (2011) showed from a composite dropsonde dataset that for category 4–5 hurricanes, an outflow region exists up to the 3 × RMW just above 1.5-km altitude, although the number of samples was too few for the results to be considered robust. Liu et al. (1999) and Zhang et al. (2001) showed that outflow associated with supergradient flow exists within radii of more than the 3 × RMW just above the BL during RI. In addition, recent studies have shown that supergradient flow exists not only in the BL but also at around 2-km altitude (Montgomery et al. 2014; Rogers et al. 2015). In such a case, outflow may occur at altitudes up to 4 km.

In Goni, the outflow just outside the RMW at low levels played a role in decreasing (Fig. 13) and lowering I there (Fig. 12a) around the onset of RI, just after the ERC, but not during RI. Rogers et al. (2013, 2015) hypothesized that low I outside the RMW is favorable for strong inflow, and thus greater mass fluxes into the eyewall region, because of less resistance to radial displacement, and this strong inflow and greater mass fluxes can lead to intensification. Whether such outflow is present in other intensifying TCs around the onset of RI just after an ERC, and what roles such outflow might play in subsequent intensification, should be addressed in a future study.

During RI, just outside the RMW at low levels above the BL became inflow. An updraft peak and the RMR between 2- and 9-km altitude were located inside the RMW. Thus, the secondary circulation became well established. These features are consistent with those of intensifying TCs (Rogers et al. 2013, 2015).

6. Summary and conclusions

An operational ground-based Doppler radar located at Ishigaki Island observed Typhoon Goni (2015) for 24 h just after it completed an ERC. During this observation period, Goni experienced RI and then reached peak intensity. We examined a series of RI processes of Goni mainly from an axisymmetric perspective by using radar reflectivity and retrieved wind by the GBVTD technique with high resolution in both time and space. The central pressure was estimated to fall by 30 hPa from 960 hPa at an almost constant rate during 18 h by using the retrieved tangential wind at 2-km altitude, sea level pressure observations near the radar site, and the gradient wind equation, whereas the maximum wind at 2-km altitude increased by 30 m s−1 from 35 m s−1 during the first 6 h of RI and increased by 20 m s−1 during the subsequent 12 h.

Around the onset of RI, during periods I and II when Goni temporarily weakened and started to intensify again, and when the inner eyewall disappeared, the following features were observed:

  1. Relatively strong outflow (>2 m s−1) was present both inside and outside the RMW above the BL.

  2. Immediately before RI onset, the RMW sloped radially outward from 1- to 3-km altitude and sloped inward from 3- to 5-km altitude.

  3. Despite the outflow, inside the RMW was increased.

  4. The maximum wind at 2-km altitude started to increase after the inner eyewall disappeared.

  5. The 2-km RMW rapidly contracted from 50 to 33 km within ~5 h.

  6. The tangential wind field and high-I region at 2-km altitude also contracted.

  7. The RMR was located a few kilometers inside the RMW below 9-km altitude.

The outflow in the vicinity of the RMW suggests that there was supergradient flow in and just above the BL. The absolute angular momentum budget analysis showed that the outflow just outside the RMW above the BL contributed to the decrease in , that is, the contraction of the tangential wind field and high-I region.

During RI, period III, the following features were seen:

  1. The RMW sloped more outward with height, reaching ~45° from the vertical.

  2. The contraction speed of the low-level RMW slowed down.

  3. The in the lower troposphere changed from outflow to inflow immediately outside the RMW.

  4. An updraft axis associated with the eyewall was located inside the RMW.

  5. The RMR was located inside the RMW at altitudes between 2 and 9 km.

  6. A well-defined eye appeared, and active convection was enhanced in the eyewall.

  7. Reflectivity outside the RMW between 40- and 60-km radius became weaker with time, leading to the formation of a moat and signaling the potential development of another secondary eyewall.

  8. The tangential wind field around the RMW started to expand.

The absolute angular momentum budget analysis showed that the inflow above the BL immediately outside the RMW transported inward and contributed to the expansion of the tangential wind field.

During the mature stage, period IV, the following features were found:

  1. The RMR was located outside the RMW.

  2. The outward slope of the RMW became less (~38° from the vertical).

  3. The tangential wind field expanded.

  4. The I outside the RMW was increased in the midtroposphere.

  5. An outer reflectivity maximum was formed at twice the RMW.

Although we could examine Goni’s RI using high-resolution observations for 24 h, which is one of the novel features of this study, the study has some limitations. We lacked observations of BL flow and the wind field immediately before the maturity of Goni. In addition, there are also some limitations inherent in the GBVTD technique. Furthermore, we lacked thermodynamic observations. These limitations made it difficult to capture completely the RI processes of Goni. Next, we plan to examine the detailed processes of RI associated with ERCs by using numerical simulations to reproduce the RI observed in Goni. In particular, we are interested in the roles of outflow and supergradient wind in the onset of RI, the reason that changed from outflow to inflow immediately outside the RMW, and how BL processes interact with the intensification of the interior vortex.

Acknowledgments

U. Shimada is deeply grateful to Mr. S. Tsujino, Dr. K. Ito, Dr. Y. Miyamoto, Dr. A. Wada, and Dr. R. F. Rogers for fruitful discussions. U. Shimada and M. Sawada also extend their gratitude to their colleagues at the Meteorological Research Institute. The authors thank three anonymous reviewers for valuable comments that have greatly improved the manuscript. This work was partially supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) KAKENHI Grant 16H04053.

APPENDIX

GBVTD

The GBVTD technique is based on the assumption that there is one primary circular vortex around the TC center and that the asymmetric radial wind is much smaller than the corresponding tangential wind. These assumptions allow the retrieval of a two-dimensional TC wind field from single-Doppler velocities [for more details, see Lee et al. (1999)]. The technique provides tangential winds with wavenumbers up to 3, radial wind with only wavenumber 0, and the component of the mean environmental wind parallel to a line connecting the TC center with the radar location at each TC radius in GBVTD nonlinear coordinates. The technique does not provide the cross-beam component of the mean environmental wind VM⊥. Instead, VM⊥ is aliased into the wavenumber-0 (i.e., the azimuthal mean) tangential wind, ; as a result, the retrieval accuracy of is decreased at outer TC radii (Lee et al. 1999; Harasti et al. 2004; Chen et al. 2013). To resolve this aliasing issue, following Harasti et al. (2004) and Shimada et al. (2016), we used the cross-beam component of the TC translational speed as a proxy for VM⊥ to dealias . Shimada et al. (2016) demonstrated that dealiasing of in this way can greatly decrease its biases. Finally, the retrieved winds in GBVTD nonlinear coordinates are converted into winds in storm-centered cylindrical coordinates. The retrieved VM is decomposed into wavenumber-1 tangential and radial winds. In our retrieval procedure, tangential winds with wavenumbers higher than 3 in storm-centered cylindrical coordinates were azimuthally filtered out. The GBVTD technique has been successfully applied to the data of many TCs (Lee et al. 2000; Harasti et al. 2004; Lee and Bell 2007; Zhao et al. 2008, 2012, 2016).

REFERENCES

  • Abarca, S. F., and M. T. Montgomery, 2013: Essential dynamics of secondary eyewall formation. J. Atmos. Sci., 70, 3216–3230, https://doi.org/10.1175/JAS-D-12-0318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Abarca, S. F., and M. T. Montgomery, 2014: Departures from axisymmetric balance dynamics during secondary eyewall formation. J. Atmos. Sci., 71, 3723–3738, https://doi.org/10.1175/JAS-D-14-0018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Abarca, S. F., and M. T. Montgomery, 2015: Are eyewall replacement cycles governed largely by axisymmetric balance dynamics? J. Atmos. Sci., 72, 82–87, https://doi.org/10.1175/JAS-D-14-0151.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Abarca, S. F., M. T. Montgomery, S. Braun, and J. Dunion, 2016: On the secondary eyewall formation of Hurricane Edouard (2014). Mon. Wea. Rev., 144, 3321–3331, https://doi.org/10.1175/MWR-D-15-0421.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Aberson, S. D., M. T. Montgomery, M. Bell, and M. Black, 2006: Hurricane Isabel (2003): New insights into the physics of intense storms. Part II: Extreme localized wind. Bull. Amer. Meteor. Soc., 87, 1349–1354, https://doi.org/10.1175/BAMS-87-10-1349.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bell, M. M., and M. T. Montgomery, 2008: Observed structure, evolution, and potential intensity of category 5 Hurricane Isabel (2003) from 12 to 14 September. Mon. Wea. Rev., 136, 2023–2046, https://doi.org/10.1175/2007MWR1858.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bell, M. M., and W. Lee, 2012: Objective tropical cyclone center tracking using single-Doppler radar. J. Appl. Meteor. Climatol., 51, 878–896, https://doi.org/10.1175/JAMC-D-11-0167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bell, M. M., M. T. Montgomery, and W.-C. Lee, 2012: An axisymmetric view of concentric eyewall evolution in Hurricane Rita (2005). J. Atmos. Sci., 69, 2414–2432, https://doi.org/10.1175/JAS-D-11-0167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Braun, S. A., M. T. Montgomery, and Z. Pu, 2006: High-resolution simulation of Hurricane Bonnie (1998). Part I: The organization of eyewall vertical motion. J. Atmos. Sci., 63, 19–42, https://doi.org/10.1175/JAS3598.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chen, X., K. Zhao, W.-C. Lee, B. J.-D. Jou, M. Xue, and P. R. Harasti, 2013: The improvement to the environmental wind and tropical cyclone circulation retrievals with modified GBVTD (MGBVTD) technique. J. Appl. Meteor. Climatol., 52, 2493–2508, https://doi.org/10.1175/JAMC-D-13-031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dunion, J. P., C. D. Thorncroft, and C. S. Velden, 2014: The tropical cyclone diurnal cycle of mature hurricanes. Mon. Wea. Rev., 142, 3900–3919, https://doi.org/10.1175/MWR-D-13-00191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Emanuel, K. A., 1989: The finite-amplitude nature of tropical cyclogenesis. J. Atmos. Sci., 46, 3431–3456, https://doi.org/10.1175/1520-0469(1989)046<3431:TFANOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Franklin, J. L., M. L. Black, and K. Valde, 2003: GPS dropwindsonde wind profiles in hurricanes and their operational implications. Wea. Forecasting, 18, 32–44, https://doi.org/10.1175/1520-0434(2003)018<0032:GDWPIH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Frisius, T., and M. Lee, 2016: The impact of gradient wind imbalance on tropical cyclone intensification within Ooyama’s three-layer model. J. Atmos. Sci., 73, 3659–3679, https://doi.org/10.1175/JAS-D-15-0336.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Guimond, S. R., G. M. Heymsfield, P. D. Reasor, and A. C. Didlake, 2016: The rapid intensification of Hurricane Karl (2010): New remote sensing observations of convective bursts from the Global Hawk platform. J. Atmos. Sci., 73, 3617–3639, https://doi.org/10.1175/JAS-D-16-0026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hack, J. J., and W. H. Schubert, 1986: Nonlinear response of atmospheric vortices to heating by organized cumulus convection. J. Atmos. Sci., 43, 1559–1573, https://doi.org/10.1175/1520-0469(1986)043<1559:NROAVT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Harasti, P. R., C. J. McAdie, P. P. Dodge, W.-C. Lee, J. Tuttle, S. T. Murillo, and F. D. Marks, 2004: Real-time implementation of single-Doppler radar analysis methods for tropical cyclones: Algorithm improvements and use with WSR-88D display data. Wea. Forecasting, 19, 219–239, https://doi.org/10.1175/1520-0434(2004)019<0219:RIOSRA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hazelton, A. T., R. Rogers, and R. E. Hart, 2015: Shear-relative asymmetries in tropical cyclone eyewall slope. Mon. Wea. Rev., 143, 883–903, https://doi.org/10.1175/MWR-D-14-00122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hendricks, E. A., M. S. Peng, B. Fu, and T. Li, 2010: Quantifying environmental control on tropical cyclone intensity change. Mon. Wea. Rev., 138, 3243–3271, https://doi.org/10.1175/2010MWR3185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Houze, R. A., Jr., S. S. Chen, B. F. Smull, W.-C. Lee, and M. M. Bell, 2007: Hurricane intensity and eyewall replacement. Science, 315, 1235–1239, https://doi.org/10.1126/science.1135650.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Huang, Y.-H., M. T. Montgomery, and C.-C. Wu, 2012: Concentric eyewall formation in Typhoon Sinlaku (2008). Part II: Axisymmetric dynamical processes. J. Atmos. Sci., 69, 662–674, https://doi.org/10.1175/JAS-D-11-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jiang, H., and E. M. Ramirez, 2013: Necessary conditions for tropical cyclone rapid intensification as derived from 11 years of TRMM data. J. Climate, 26, 6459–6470, https://doi.org/10.1175/JCLI-D-12-00432.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jordan, C. L., 1958: Mean soundings for the West Indies area. J. Meteor., 15, 91–97, https://doi.org/10.1175/1520-0469(1958)015<0091:MSFTWI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jou, B. J.-D., W. C. Lee, S. P. Liu, and Y. C. Kao, 2008: Generalized VTD retrieval of atmospheric vortex kinematic structure. Part I: Formulation and error analysis. Mon. Wea. Rev., 136, 995–1012, https://doi.org/10.1175/2007MWR2116.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part I: Linear theory. J. Atmos. Sci., 58, 2469–2484, https://doi.org/10.1175/1520-0469(2001)058<2469:TDOBLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., 2006a: Observed boundary layer wind structure and balance in the hurricane core. Part I: Hurricane Georges. J. Atmos. Sci., 63, 2169–2193, https://doi.org/10.1175/JAS3745.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., 2006b: Observed boundary layer wind structure and balance in the Hurricane core. Part II: Hurricane Mitch. J. Atmos. Sci., 63, 2194–2211, https://doi.org/10.1175/JAS3746.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., 2013: How does the boundary layer contribute to eyewall replacement cycles in axisymmetric tropical cyclones? J. Atmos. Sci., 70, 2808–2830, https://doi.org/10.1175/JAS-D-13-046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., and Y. Wang, 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part II: Nonlinear enhancement. J. Atmos. Sci., 58, 2485–2501, https://doi.org/10.1175/1520-0469(2001)058<2485:TDOBLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., and D. S. Nolan, 2014: Reply to “Comments on ‘How does the boundary layer contribute to eyewall replacement cycles in axisymmetric tropical cyclones?’”. J. Atmos. Sci., 71, 4692–4704, https://doi.org/10.1175/JAS-D-14-0014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kieu, C., V. Tallapragada, and W. Hogsett, 2014: Vertical structure of tropical cyclones at onset of the rapid intensification in the HWRF model. Geophys. Res. Lett., 41, 3298–3306, https://doi.org/10.1002/2014GL059584.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kilroy, G., R. K. Smith, and M. T. Montgomery, 2016: Why do model tropical cyclones grow progressively in size and decay in intensity after reaching maturity? J. Atmos. Sci., 73, 487–503, https://doi.org/10.1175/JAS-D-15-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kobayashi, S., and Coauthors, 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kossin, J. P., and W. H. Schubert, 2004: Mesovortices in Hurricane Isabel. Bull. Amer. Meteor. Soc., 85, 151–153, https://doi.org/10.1175/BAMS-85-2-151.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., and F. D. Marks Jr., 2000: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part II: The GBVTD-simplex center finding algorithm. Mon. Wea. Rev., 128, 1925–1936, https://doi.org/10.1175/1520-0493(2000)128<1925:TCKSRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., and M. M. Bell, 2007: Rapid intensification, eyewall contraction, and breakdown of Hurricane Charley (2004) near landfall. Geophys. Res. Lett., 34, L02802, https://doi.org/10.1029/2006GL027889.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., B. J.-D. Jou, P.-L. Chang, and S.-M. Deng, 1999: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part I: Interpretation of Doppler velocity patterns and the GBVTD technique. Mon. Wea. Rev., 127, 2419–2439, https://doi.org/10.1175/1520-0493(1999)127<2419:TCKSRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., B. J.-D. Jou, P.-L. Chang, and F. D. Marks Jr., 2000: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part III: Evolution and structure of Typhoon Alex (1987). Mon. Wea. Rev., 128, 3982–4001, https://doi.org/10.1175/1520-0493(2000)129<3982:TCKSRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., P. R. Harasti, M. Bell, B. J.-D. Jou, and M.-H. Chang, 2006: Doppler velocity signatures of idealized elliptical vortices. Terr. Atmos. Oceanic Sci., 17, 429–446.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, Y., D. Zhang, and M. Yau, 1999: A multiscale numerical study of Hurricane Andrew (1992). Part II: Kinematics and inner-core structures. Mon. Wea. Rev., 127, 2597–2616, https://doi.org/10.1175/1520-0493(1999)127<2597:AMNSOH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Marks, F. D., P. G. Black, M. T. Montgomery, and R. W. Burpee, 2008: Structure of the eye and eyewall of Hurricane Hugo (1989). Mon. Wea. Rev., 136, 1237–1259, https://doi.org/10.1175/2007MWR2073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Miyamoto, Y., and T. Takemi, 2013: A transition mechanism for the axisymmetric spontaneous intensification of tropical cyclones. J. Atmos. Sci., 70, 112–129, https://doi.org/10.1175/JAS-D-11-0285.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Miyamoto, Y., and T. Takemi, 2015: A triggering mechanism for rapid intensification of tropical cyclones. J. Atmos. Sci., 72, 2666–2681, https://doi.org/10.1175/JAS-D-14-0193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., and R. J. Kallenbach, 1997: A theory for vortex Rossby waves and its application to spiral bands and intensity changes in hurricanes. Quart. J. Roy. Meteor. Soc., 123, 435–465, https://doi.org/10.1002/qj.49712353810.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., and R. K. Smith, 2014: Paradigms for tropical cyclone intensification. Aust. Meteor. Oceanogr. J., 64, 37–66, https://doi.org/10.22499/2.6401.005.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., M. M. Bell, S. D. Aberson, and M. L. Black, 2006: Hurricane Isabel (2003): New insights into the physics of intense storms. Part I: Mean vortex structure and maximum intensity estimates. Bull. Amer. Meteor. Soc., 87, 1335–1347, https://doi.org/10.1175/BAMS-87-10-1335.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., J. Zhang, and R. K. Smith, 2014: An analysis of the observed low-level structure of rapidly intensifying and mature Hurricane Earl (2010). Quart. J. Roy. Meteor. Soc., 140, 2132–2146, https://doi.org/10.1002/qj.2283.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moon, Y., and D. S. Nolan, 2015: Spiral rainbands in a numerical simulation of Hurricane Bill (2009). Part II: Propagation of inner rainbands. J. Atmos. Sci., 72, 191–215, https://doi.org/10.1175/JAS-D-14-0056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Murillo, S. T., W.-C. Lee, M. M. Bell, F. D. Marks Jr., P. P. Dodge, and G. M. Barnes, 2011: Intercomparison of ground-based velocity track display (GBVTD)-retrieved circulation centers and structures of Hurricane Danny (1997) from two coastal WSR-88Ds. Mon. Wea. Rev., 139, 153–174, https://doi.org/10.1175/2010MWR3036.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nolan, D. S., 2013: On the use of Doppler radar-derived wind fields to diagnose the secondary circulations of tornadoes. J. Atmos. Sci., 70, 1160–1171, https://doi.org/10.1175/JAS-D-12-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nolan, D. S., Y. Moon, and D. P. Stern, 2007: Tropical cyclone intensification from asymmetric convection: Energetics and efficiency. J. Atmos. Sci., 64, 3377–3405, https://doi.org/10.1175/JAS3988.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ooyama, K. V., 1969: Numerical simulation of the life cycle of tropical cyclones. J. Atmos. Sci., 26, 3–40, https://doi.org/10.1175/1520-0469(1969)026<0003:NSOTLC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Pendergrass, A. G., and H. E. Willoughby, 2009: Diabatically induced secondary flows in tropical cyclones. Part I: Quasi-steady forcing. Mon. Wea. Rev., 137, 805–821, https://doi.org/10.1175/2008MWR2657.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Persing, J., M. T. Montgomery, J. C. McWilliams, and R. K. Smith, 2013: Asymmetric and axisymmetric tropical cyclone dynamics. Atmos. Chem. Phys., 13, 12 299–12 341, https://doi.org/10.5194/acp-13-12299-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Reasor, P. D., M. D. Eastin, and J. F. Gamache, 2009: Rapidly intensifying Hurricane Guillermo (1997). Part I: Low-wavenumber structure and evolution. Mon. Wea. Rev., 137, 603–631, https://doi.org/10.1175/2008MWR2487.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Reasor, P. D., R. Rogers, and S. Lorsolo, 2013: Environmental flow impacts on tropical cyclone structure diagnosed from airborne Doppler radar composites. Mon. Wea. Rev., 141, 2949–2969, https://doi.org/10.1175/MWR-D-12-00334.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., S. Lorsolo, P. D. Reasor, J. Gamache, and F. D. Marks Jr., 2012: Multiscale analysis of tropical cyclone kinematic structure from airborne Doppler radar composites. Mon. Wea. Rev., 140, 77–99, https://doi.org/10.1175/MWR-D-10-05075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., P. Reasor, and S. Lorsolo, 2013: Airborne Doppler observations of the inner-core structural differences between intensifying and steady-state tropical cyclones. Mon. Wea. Rev., 141, 2970–2991, https://doi.org/10.1175/MWR-D-12-00357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., P. Reasor, and J. Zhang, 2015: Multiscale structure and evolution of Hurricane Earl (2010) during rapid intensification. Mon. Wea. Rev., 143, 536–562, https://doi.org/10.1175/MWR-D-14-00175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., J. Zhang, J. Zawislak, H. Jiang, G. Alvey, E. Zipser, and S. Stevenson, 2016: Observations of the structure and evolution of Hurricane Edouard (2014) during intensity change. Part II: Kinematic structure and the distribution of deep convection. Mon. Wea. Rev., 144, 3355–3376, https://doi.org/10.1175/MWR-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rozoff, C. M., D. S. Nolan, J. P. Kossin, F. Zhang, and J. Fang, 2012: The roles of an expanding wind field and inertial stability in tropical cyclone secondary eyewall formation. J. Atmos. Sci., 69, 2621–2643, https://doi.org/10.1175/JAS-D-11-0326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sanger, N. T., M. T. Montgomery, R. K. Smith, and M. M. Bell, 2014: An observational study of tropical cyclone spinup in Supertyphoon Jangmi (2008) from 24 to 27 September. Mon. Wea. Rev., 142, 3–28, https://doi.org/10.1175/MWR-D-12-00306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shapiro, L., and H. E. Willoughby, 1982: The response of balanced hurricanes to local sources of heat and momentum. J. Atmos. Sci., 39, 378–394, https://doi.org/10.1175/1520-0469(1982)039<0378:TROBHT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shimada, U., M. Sawada, and H. Yamada, 2016: Evaluation of the accuracy and utility of tropical cyclone intensity estimation using single ground-based Doppler radar observations. Mon. Wea. Rev., 144, 1823–1840, https://doi.org/10.1175/MWR-D-15-0254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shimada, U., K. Aonashi, and Y. Miyamoto, 2017: Tropical cyclone intensity change and axisymmetricity deduced from GSMaP. Mon. Wea. Rev., 145, 1003–1017, https://doi.org/10.1175/MWR-D-16-0244.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sitkowski, M., J. Kossin, and C. M. Rozoff, 2011: Intensity and structure changes during hurricane eyewall replacement cycles. Mon. Wea. Rev., 139, 3829–3847, https://doi.org/10.1175/MWR-D-11-00034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, R. K., and M. T. Montgomery, 2016: The efficiency of diabatic heating and tropical cyclone intensification. Quart. J. Roy. Meteor. Soc., 142, 2081–2086, https://doi.org/10.1002/qj.2804.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, R. K., M. T. Montgomery, and V. S. Nguyen, 2009: Tropical cyclone spin-up revisited. Quart. J. Roy. Meteor. Soc., 135, 1321–1335, https://doi.org/10.1002/qj.428.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stern, D. P., J. R. Brisbois, and D. S. Nolan, 2014: An expanded dataset of hurricane eyewall sizes and slopes. J. Atmos. Sci., 71, 2747–2762, https://doi.org/10.1175/JAS-D-13-0302.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stern, D. P., J. L. Vigh, D. S. Nolan, and F. Zhang, 2015: Revisiting the relationship between eyewall contraction and intensification. J. Atmos. Sci., 72, 1283–1306, https://doi.org/10.1175/JAS-D-14-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stevenson, S. N., K. L. Corbosiero, and J. Molinari, 2014: The convective evolution and rapid intensification of Hurricane Earl (2010). Mon. Wea. Rev., 142, 4364–4380, https://doi.org/10.1175/MWR-D-14-00078.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sun, Y. Q., Y. Jiang, B. Tan, and F. Zhang, 2013: The governing dynamics of the secondary eyewall formation of Typhoon Sinlaku (2008). J. Atmos. Sci., 70, 3818–3837, https://doi.org/10.1175/JAS-D-13-044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Susca-Lopata, G., J. Zawislak, E. J. Zipser, and R. F. Rogers, 2015: The role of observed environmental conditions and precipitation evolution in the rapid intensification of Hurricane Earl (2010). Mon. Wea. Rev., 143, 2207–2223, https://doi.org/10.1175/MWR-D-14-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tao, C., and H. Jiang, 2015: Distributions of shallow to very deep precipitation–convection in rapidly intensifying tropical cyclones. J. Climate, 28, 8791–8824, https://doi.org/10.1175/JCLI-D-14-00448.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Vigh, J. L., and W. H. Schubert, 2009: Rapid development of the tropical cyclone warm core. J. Atmos. Sci., 66, 3335–3350, https://doi.org/10.1175/2009JAS3092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, H., and Y. Wang, 2014: A numerical study of Typhoon Megi (2010): Part I: Rapid intensification. Mon. Wea. Rev., 142, 29–48, https://doi.org/10.1175/MWR-D-13-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, Y., 2002: Vortex Rossby waves in a numerically simulated tropical cyclone. Part I: Overall structure, potential vorticity, and kinetic energy budgets. J. Atmos. Sci., 59, 1213–1238, https://doi.org/10.1175/1520-0469(2002)059<1213:VRWIAN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, C.-C., Y.-H. Huang, and G.-Y. Lien, 2012: Concentric eyewall formation in Typhoon Sinlaku (2008). Part I: Assimilation of T-PARC data based on the ensemble Kalman filter (EnKF). Mon. Wea. Rev., 140, 506–527, https://doi.org/10.1175/MWR-D-11-00057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zagrodnik, J. P., and H. Jiang, 2014: Rainfall, convection, and latent heating distributions in rapidly intensifying tropical cyclones. J. Atmos. Sci., 71, 2789–2809, https://doi.org/10.1175/JAS-D-13-0314.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zawislak, J., H. Jiang, G. Alvey, E. Zipser, R. Rogers, J. Zhang, and S. Stevenson, 2016: Observations of the structure and evolution of Hurricane Edouard (2014) during intensity change. Part I: Relationship between the thermodynamic structure and precipitation. Mon. Wea. Rev., 144, 3333–3354, https://doi.org/10.1175/MWR-D-16-0018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, D.-L., Y. Liu, and M. K. Yau, 2001: A multiscale numerical study of Hurricane Andrew (1992). Part IV: Unbalanced flows. Mon. Wea. Rev., 129, 92–107, https://doi.org/10.1175/1520-0493(2001)129<0092:AMNSOH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, J. A., R. F. Rogers, D. S. Nolan, and F. D. Marks Jr., 2011: On the characteristic height scales of the hurricane boundary layer. Mon. Wea. Rev., 139, 2523–2535, https://doi.org/10.1175/MWR-D-10-05017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, J. A., R. F. Rogers, and V. Tallapragada, 2017: Impact of parameterized boundary layer structure on tropical cyclone rapid intensification forecasts in HWRF. Mon. Wea. Rev., 145, 1413–1426, https://doi.org/10.1175/MWR-D-16-0129.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhao, K., W.-C. Lee, and B. J.-D. Jou, 2008: Single Doppler radar observation of the concentric eyewall in Typhoon Saomai, 2006, near landfall. Geophys. Res. Lett., 35, L07807, https://doi.org/10.1029/2007GL032773.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhao, K., M. Xue, and W.-C. Lee, 2012: Assimilation of GBVTD-retrieved winds from single-Doppler radar for short-term forecasting of Super Typhoon Saomai (0608) at landfall. Quart. J. Roy. Meteor. Soc., 138, 1055–1071, https://doi.org/10.1002/qj.975.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhao, K., Q. Lin, W.-C. Lee, Q. Sun, and F. Zhang, 2016: Doppler radar analysis of triple eyewalls in Typhoon Usagi (2013). Bull. Amer. Meteor. Soc., 97, 25–30, https://doi.org/10.1175/BAMS-D-15-00029.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
Save
Cover Journal of the Atmospheric Sciences
Journal of the Atmospheric Sciences

  • Abarca, S. F., and M. T. Montgomery, 2013: Essential dynamics of secondary eyewall formation. J. Atmos. Sci., 70, 3216–3230, https://doi.org/10.1175/JAS-D-12-0318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Abarca, S. F., and M. T. Montgomery, 2014: Departures from axisymmetric balance dynamics during secondary eyewall formation. J. Atmos. Sci., 71, 3723–3738, https://doi.org/10.1175/JAS-D-14-0018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Abarca, S. F., and M. T. Montgomery, 2015: Are eyewall replacement cycles governed largely by axisymmetric balance dynamics? J. Atmos. Sci., 72, 82–87, https://doi.org/10.1175/JAS-D-14-0151.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Abarca, S. F., M. T. Montgomery, S. Braun, and J. Dunion, 2016: On the secondary eyewall formation of Hurricane Edouard (2014). Mon. Wea. Rev., 144, 3321–3331, https://doi.org/10.1175/MWR-D-15-0421.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Aberson, S. D., M. T. Montgomery, M. Bell, and M. Black, 2006: Hurricane Isabel (2003): New insights into the physics of intense storms. Part II: Extreme localized wind. Bull. Amer. Meteor. Soc., 87, 1349–1354, https://doi.org/10.1175/BAMS-87-10-1349.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bell, M. M., and M. T. Montgomery, 2008: Observed structure, evolution, and potential intensity of category 5 Hurricane Isabel (2003) from 12 to 14 September. Mon. Wea. Rev., 136, 2023–2046, https://doi.org/10.1175/2007MWR1858.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bell, M. M., and W. Lee, 2012: Objective tropical cyclone center tracking using single-Doppler radar. J. Appl. Meteor. Climatol., 51, 878–896, https://doi.org/10.1175/JAMC-D-11-0167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bell, M. M., M. T. Montgomery, and W.-C. Lee, 2012: An axisymmetric view of concentric eyewall evolution in Hurricane Rita (2005). J. Atmos. Sci., 69, 2414–2432, https://doi.org/10.1175/JAS-D-11-0167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Braun, S. A., M. T. Montgomery, and Z. Pu, 2006: High-resolution simulation of Hurricane Bonnie (1998). Part I: The organization of eyewall vertical motion. J. Atmos. Sci., 63, 19–42, https://doi.org/10.1175/JAS3598.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chen, X., K. Zhao, W.-C. Lee, B. J.-D. Jou, M. Xue, and P. R. Harasti, 2013: The improvement to the environmental wind and tropical cyclone circulation retrievals with modified GBVTD (MGBVTD) technique. J. Appl. Meteor. Climatol., 52, 2493–2508, https://doi.org/10.1175/JAMC-D-13-031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dunion, J. P., C. D. Thorncroft, and C. S. Velden, 2014: The tropical cyclone diurnal cycle of mature hurricanes. Mon. Wea. Rev., 142, 3900–3919, https://doi.org/10.1175/MWR-D-13-00191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Emanuel, K. A., 1989: The finite-amplitude nature of tropical cyclogenesis. J. Atmos. Sci., 46, 3431–3456, https://doi.org/10.1175/1520-0469(1989)046<3431:TFANOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Franklin, J. L., M. L. Black, and K. Valde, 2003: GPS dropwindsonde wind profiles in hurricanes and their operational implications. Wea. Forecasting, 18, 32–44, https://doi.org/10.1175/1520-0434(2003)018<0032:GDWPIH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Frisius, T., and M. Lee, 2016: The impact of gradient wind imbalance on tropical cyclone intensification within Ooyama’s three-layer model. J. Atmos. Sci., 73, 3659–3679, https://doi.org/10.1175/JAS-D-15-0336.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Guimond, S. R., G. M. Heymsfield, P. D. Reasor, and A. C. Didlake, 2016: The rapid intensification of Hurricane Karl (2010): New remote sensing observations of convective bursts from the Global Hawk platform. J. Atmos. Sci., 73, 3617–3639, https://doi.org/10.1175/JAS-D-16-0026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hack, J. J., and W. H. Schubert, 1986: Nonlinear response of atmospheric vortices to heating by organized cumulus convection. J. Atmos. Sci., 43, 1559–1573, https://doi.org/10.1175/1520-0469(1986)043<1559:NROAVT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Harasti, P. R., C. J. McAdie, P. P. Dodge, W.-C. Lee, J. Tuttle, S. T. Murillo, and F. D. Marks, 2004: Real-time implementation of single-Doppler radar analysis methods for tropical cyclones: Algorithm improvements and use with WSR-88D display data. Wea. Forecasting, 19, 219–239, https://doi.org/10.1175/1520-0434(2004)019<0219:RIOSRA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hazelton, A. T., R. Rogers, and R. E. Hart, 2015: Shear-relative asymmetries in tropical cyclone eyewall slope. Mon. Wea. Rev., 143, 883–903, https://doi.org/10.1175/MWR-D-14-00122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hendricks, E. A., M. S. Peng, B. Fu, and T. Li, 2010: Quantifying environmental control on tropical cyclone intensity change. Mon. Wea. Rev., 138, 3243–3271, https://doi.org/10.1175/2010MWR3185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Houze, R. A., Jr., S. S. Chen, B. F. Smull, W.-C. Lee, and M. M. Bell, 2007: Hurricane intensity and eyewall replacement. Science, 315, 1235–1239, https://doi.org/10.1126/science.1135650.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Huang, Y.-H., M. T. Montgomery, and C.-C. Wu, 2012: Concentric eyewall formation in Typhoon Sinlaku (2008). Part II: Axisymmetric dynamical processes. J. Atmos. Sci., 69, 662–674, https://doi.org/10.1175/JAS-D-11-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jiang, H., and E. M. Ramirez, 2013: Necessary conditions for tropical cyclone rapid intensification as derived from 11 years of TRMM data. J. Climate, 26, 6459–6470, https://doi.org/10.1175/JCLI-D-12-00432.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jordan, C. L., 1958: Mean soundings for the West Indies area. J. Meteor., 15, 91–97, https://doi.org/10.1175/1520-0469(1958)015<0091:MSFTWI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jou, B. J.-D., W. C. Lee, S. P. Liu, and Y. C. Kao, 2008: Generalized VTD retrieval of atmospheric vortex kinematic structure. Part I: Formulation and error analysis. Mon. Wea. Rev., 136, 995–1012, https://doi.org/10.1175/2007MWR2116.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part I: Linear theory. J. Atmos. Sci., 58, 2469–2484, https://doi.org/10.1175/1520-0469(2001)058<2469:TDOBLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., 2006a: Observed boundary layer wind structure and balance in the hurricane core. Part I: Hurricane Georges. J. Atmos. Sci., 63, 2169–2193, https://doi.org/10.1175/JAS3745.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., 2006b: Observed boundary layer wind structure and balance in the Hurricane core. Part II: Hurricane Mitch. J. Atmos. Sci., 63, 2194–2211, https://doi.org/10.1175/JAS3746.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., 2013: How does the boundary layer contribute to eyewall replacement cycles in axisymmetric tropical cyclones? J. Atmos. Sci., 70, 2808–2830, https://doi.org/10.1175/JAS-D-13-046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., and Y. Wang, 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part II: Nonlinear enhancement. J. Atmos. Sci., 58, 2485–2501, https://doi.org/10.1175/1520-0469(2001)058<2485:TDOBLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kepert, J., and D. S. Nolan, 2014: Reply to “Comments on ‘How does the boundary layer contribute to eyewall replacement cycles in axisymmetric tropical cyclones?’”. J. Atmos. Sci., 71, 4692–4704, https://doi.org/10.1175/JAS-D-14-0014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kieu, C., V. Tallapragada, and W. Hogsett, 2014: Vertical structure of tropical cyclones at onset of the rapid intensification in the HWRF model. Geophys. Res. Lett., 41, 3298–3306, https://doi.org/10.1002/2014GL059584.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kilroy, G., R. K. Smith, and M. T. Montgomery, 2016: Why do model tropical cyclones grow progressively in size and decay in intensity after reaching maturity? J. Atmos. Sci., 73, 487–503, https://doi.org/10.1175/JAS-D-15-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kobayashi, S., and Coauthors, 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kossin, J. P., and W. H. Schubert, 2004: Mesovortices in Hurricane Isabel. Bull. Amer. Meteor. Soc., 85, 151–153, https://doi.org/10.1175/BAMS-85-2-151.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., and F. D. Marks Jr., 2000: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part II: The GBVTD-simplex center finding algorithm. Mon. Wea. Rev., 128, 1925–1936, https://doi.org/10.1175/1520-0493(2000)128<1925:TCKSRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., and M. M. Bell, 2007: Rapid intensification, eyewall contraction, and breakdown of Hurricane Charley (2004) near landfall. Geophys. Res. Lett., 34, L02802, https://doi.org/10.1029/2006GL027889.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., B. J.-D. Jou, P.-L. Chang, and S.-M. Deng, 1999: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part I: Interpretation of Doppler velocity patterns and the GBVTD technique. Mon. Wea. Rev., 127, 2419–2439, https://doi.org/10.1175/1520-0493(1999)127<2419:TCKSRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., B. J.-D. Jou, P.-L. Chang, and F. D. Marks Jr., 2000: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part III: Evolution and structure of Typhoon Alex (1987). Mon. Wea. Rev., 128, 3982–4001, https://doi.org/10.1175/1520-0493(2000)129<3982:TCKSRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, W.-C., P. R. Harasti, M. Bell, B. J.-D. Jou, and M.-H. Chang, 2006: Doppler velocity signatures of idealized elliptical vortices. Terr. Atmos. Oceanic Sci., 17, 429–446.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, Y., D. Zhang, and M. Yau, 1999: A multiscale numerical study of Hurricane Andrew (1992). Part II: Kinematics and inner-core structures. Mon. Wea. Rev., 127, 2597–2616, https://doi.org/10.1175/1520-0493(1999)127<2597:AMNSOH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Marks, F. D., P. G. Black, M. T. Montgomery, and R. W. Burpee, 2008: Structure of the eye and eyewall of Hurricane Hugo (1989). Mon. Wea. Rev., 136, 1237–1259, https://doi.org/10.1175/2007MWR2073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Miyamoto, Y., and T. Takemi, 2013: A transition mechanism for the axisymmetric spontaneous intensification of tropical cyclones. J. Atmos. Sci., 70, 112–129, https://doi.org/10.1175/JAS-D-11-0285.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Miyamoto, Y., and T. Takemi, 2015: A triggering mechanism for rapid intensification of tropical cyclones. J. Atmos. Sci., 72, 2666–2681, https://doi.org/10.1175/JAS-D-14-0193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., and R. J. Kallenbach, 1997: A theory for vortex Rossby waves and its application to spiral bands and intensity changes in hurricanes. Quart. J. Roy. Meteor. Soc., 123, 435–465, https://doi.org/10.1002/qj.49712353810.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., and R. K. Smith, 2014: Paradigms for tropical cyclone intensification. Aust. Meteor. Oceanogr. J., 64, 37–66, https://doi.org/10.22499/2.6401.005.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., M. M. Bell, S. D. Aberson, and M. L. Black, 2006: Hurricane Isabel (2003): New insights into the physics of intense storms. Part I: Mean vortex structure and maximum intensity estimates. Bull. Amer. Meteor. Soc., 87, 1335–1347, https://doi.org/10.1175/BAMS-87-10-1335.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., J. Zhang, and R. K. Smith, 2014: An analysis of the observed low-level structure of rapidly intensifying and mature Hurricane Earl (2010). Quart. J. Roy. Meteor. Soc., 140, 2132–2146, https://doi.org/10.1002/qj.2283.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moon, Y., and D. S. Nolan, 2015: Spiral rainbands in a numerical simulation of Hurricane Bill (2009). Part II: Propagation of inner rainbands. J. Atmos. Sci., 72, 191–215, https://doi.org/10.1175/JAS-D-14-0056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Murillo, S. T., W.-C. Lee, M. M. Bell, F. D. Marks Jr., P. P. Dodge, and G. M. Barnes, 2011: Intercomparison of ground-based velocity track display (GBVTD)-retrieved circulation centers and structures of Hurricane Danny (1997) from two coastal WSR-88Ds. Mon. Wea. Rev., 139, 153–174, https://doi.org/10.1175/2010MWR3036.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nolan, D. S., 2013: On the use of Doppler radar-derived wind fields to diagnose the secondary circulations of tornadoes. J. Atmos. Sci., 70, 1160–1171, https://doi.org/10.1175/JAS-D-12-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nolan, D. S., Y. Moon, and D. P. Stern, 2007: Tropical cyclone intensification from asymmetric convection: Energetics and efficiency. J. Atmos. Sci., 64, 3377–3405, https://doi.org/10.1175/JAS3988.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ooyama, K. V., 1969: Numerical simulation of the life cycle of tropical cyclones. J. Atmos. Sci., 26, 3–40, https://doi.org/10.1175/1520-0469(1969)026<0003:NSOTLC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Pendergrass, A. G., and H. E. Willoughby, 2009: Diabatically induced secondary flows in tropical cyclones. Part I: Quasi-steady forcing. Mon. Wea. Rev., 137, 805–821, https://doi.org/10.1175/2008MWR2657.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Persing, J., M. T. Montgomery, J. C. McWilliams, and R. K. Smith, 2013: Asymmetric and axisymmetric tropical cyclone dynamics. Atmos. Chem. Phys., 13, 12 299–12 341, https://doi.org/10.5194/acp-13-12299-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Reasor, P. D., M. D. Eastin, and J. F. Gamache, 2009: Rapidly intensifying Hurricane Guillermo (1997). Part I: Low-wavenumber structure and evolution. Mon. Wea. Rev., 137, 603–631, https://doi.org/10.1175/2008MWR2487.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Reasor, P. D., R. Rogers, and S. Lorsolo, 2013: Environmental flow impacts on tropical cyclone structure diagnosed from airborne Doppler radar composites. Mon. Wea. Rev., 141, 2949–2969, https://doi.org/10.1175/MWR-D-12-00334.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., S. Lorsolo, P. D. Reasor, J. Gamache, and F. D. Marks Jr., 2012: Multiscale analysis of tropical cyclone kinematic structure from airborne Doppler radar composites. Mon. Wea. Rev., 140, 77–99, https://doi.org/10.1175/MWR-D-10-05075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., P. Reasor, and S. Lorsolo, 2013: Airborne Doppler observations of the inner-core structural differences between intensifying and steady-state tropical cyclones. Mon. Wea. Rev., 141, 2970–2991, https://doi.org/10.1175/MWR-D-12-00357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., P. Reasor, and J. Zhang, 2015: Multiscale structure and evolution of Hurricane Earl (2010) during rapid intensification. Mon. Wea. Rev., 143, 536–562, https://doi.org/10.1175/MWR-D-14-00175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., J. Zhang, J. Zawislak, H. Jiang, G. Alvey, E. Zipser, and S. Stevenson, 2016: Observations of the structure and evolution of Hurricane Edouard (2014) during intensity change. Part II: Kinematic structure and the distribution of deep convection. Mon. Wea. Rev., 144, 3355–3376, https://doi.org/10.1175/MWR-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rozoff, C. M., D. S. Nolan, J. P. Kossin, F. Zhang, and J. Fang, 2012: The roles of an expanding wind field and inertial stability in tropical cyclone secondary eyewall formation. J. Atmos. Sci., 69, 2621–2643, https://doi.org/10.1175/JAS-D-11-0326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sanger, N. T., M. T. Montgomery, R. K. Smith, and M. M. Bell, 2014: An observational study of tropical cyclone spinup in Supertyphoon Jangmi (2008) from 24 to 27 September. Mon. Wea. Rev., 142, 3–28, https://doi.org/10.1175/MWR-D-12-00306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shapiro, L., and H. E. Willoughby, 1982: The response of balanced hurricanes to local sources of heat and momentum. J. Atmos. Sci., 39, 378–394, https://doi.org/10.1175/1520-0469(1982)039<0378:TROBHT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shimada, U., M. Sawada, and H. Yamada, 2016: Evaluation of the accuracy and utility of tropical cyclone intensity estimation using single ground-based Doppler radar observations. Mon. Wea. Rev., 144, 1823–1840, https://doi.org/10.1175/MWR-D-15-0254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shimada, U., K. Aonashi, and Y. Miyamoto, 2017: Tropical cyclone intensity change and axisymmetricity deduced from GSMaP. Mon. Wea. Rev., 145, 1003–1017, https://doi.org/10.1175/MWR-D-16-0244.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sitkowski, M., J. Kossin, and C. M. Rozoff, 2011: Intensity and structure changes during hurricane eyewall replacement cycles. Mon. Wea. Rev., 139, 3829–3847, https://doi.org/10.1175/MWR-D-11-00034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, R. K., and M. T. Montgomery, 2016: The efficiency of diabatic heating and tropical cyclone intensification. Quart. J. Roy. Meteor. Soc., 142, 2081–2086, https://doi.org/10.1002/qj.2804.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, R. K., M. T. Montgomery, and V. S. Nguyen, 2009: Tropical cyclone spin-up revisited. Quart. J. Roy. Meteor. Soc., 135, 1321–1335, https://doi.org/10.1002/qj.428.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stern, D. P., J. R. Brisbois, and D. S. Nolan, 2014: An expanded dataset of hurricane eyewall sizes and slopes. J. Atmos. Sci., 71, 2747–2762, https://doi.org/10.1175/JAS-D-13-0302.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stern, D. P., J. L. Vigh, D. S. Nolan, and F. Zhang, 2015: Revisiting the relationship between eyewall contraction and intensification. J. Atmos. Sci., 72, 1283–1306, https://doi.org/10.1175/JAS-D-14-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stevenson, S. N., K. L. Corbosiero, and J. Molinari, 2014: The convective evolution and rapid intensification of Hurricane Earl (2010). Mon. Wea. Rev., 142, 4364–4380, https://doi.org/10.1175/MWR-D-14-00078.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sun, Y. Q., Y. Jiang, B. Tan, and F. Zhang, 2013: The governing dynamics of the secondary eyewall formation of Typhoon Sinlaku (2008). J. Atmos. Sci., 70, 3818–3837, https://doi.org/10.1175/JAS-D-13-044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Susca-Lopata, G., J. Zawislak, E. J. Zipser, and R. F. Rogers, 2015: The role of observed environmental conditions and precipitation evolution in the rapid intensification of Hurricane Earl (2010). Mon. Wea. Rev., 143, 2207–2223, https://doi.org/10.1175/MWR-D-14-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tao, C., and H. Jiang, 2015: Distributions of shallow to very deep precipitation–convection in rapidly intensifying tropical cyclones. J. Climate, 28, 8791–8824, https://doi.org/10.1175/JCLI-D-14-00448.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Vigh, J. L., and W. H. Schubert, 2009: Rapid development of the tropical cyclone warm core. J. Atmos. Sci., 66, 3335–3350, https://doi.org/10.1175/2009JAS3092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, H., and Y. Wang, 2014: A numerical study of Typhoon Megi (2010): Part I: Rapid intensification. Mon. Wea. Rev., 142, 29–48, https://doi.org/10.1175/MWR-D-13-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, Y., 2002: Vortex Rossby waves in a numerically simulated tropical cyclone. Part I: Overall structure, potential vorticity, and kinetic energy budgets. J. Atmos. Sci., 59, 1213–1238, https://doi.org/10.1175/1520-0469(2002)059<1213:VRWIAN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, C.-C., Y.-H. Huang, and G.-Y. Lien, 2012: Concentric eyewall formation in Typhoon Sinlaku (2008). Part I: Assimilation of T-PARC data based on the ensemble Kalman filter (EnKF). Mon. Wea. Rev., 140, 506–527, https://doi.org/10.1175/MWR-D-11-00057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zagrodnik, J. P., and H. Jiang, 2014: Rainfall, convection, and latent heating distributions in rapidly intensifying tropical cyclones. J. Atmos. Sci., 71, 2789–2809, https://doi.org/10.1175/JAS-D-13-0314.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zawislak, J., H. Jiang, G. Alvey, E. Zipser, R. Rogers, J. Zhang, and S. Stevenson, 2016: Observations of the structure and evolution of Hurricane Edouard (2014) during intensity change. Part I: Relationship between the thermodynamic structure and precipitation. Mon. Wea. Rev., 144, 3333–3354, https://doi.org/10.1175/MWR-D-16-0018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, D.-L., Y. Liu, and M. K. Yau, 2001: A multiscale numerical study of Hurricane Andrew (1992). Part IV: Unbalanced flows. Mon. Wea. Rev., 129, 92–107, https://doi.org/10.1175/1520-0493(2001)129<0092:AMNSOH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, J. A., R. F. Rogers, D. S. Nolan, and F. D. Marks Jr., 2011: On the characteristic height scales of the hurricane boundary layer. Mon. Wea. Rev., 139, 2523–2535, https://doi.org/10.1175/MWR-D-10-05017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, J. A., R. F. Rogers, and V. Tallapragada, 2017: Impact of parameterized boundary layer structure on tropical cyclone rapid intensification forecasts in HWRF. Mon. Wea. Rev., 145, 1413–1426, https://doi.org/10.1175/MWR-D-16-0129.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhao, K., W.-C. Lee, and B. J.-D. Jou, 2008: Single Doppler radar observation of the concentric eyewall in Typhoon Saomai, 2006, near landfall. Geophys. Res. Lett., 35, L07807, https://doi.org/10.1029/2007GL032773.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhao, K., M. Xue, and W.-C. Lee, 2012: Assimilation of GBVTD-retrieved winds from single-Doppler radar for short-term forecasting of Super Typhoon Saomai (0608) at landfall. Quart. J. Roy. Meteor. Soc., 138, 1055–1071, https://doi.org/10.1002/qj.975.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhao, K., Q. Lin, W.-C. Lee, Q. Sun, and F. Zhang, 2016: Doppler radar analysis of triple eyewalls in Typhoon Usagi (2013). Bull. Amer. Meteor. Soc., 97, 25–30, https://doi.org/10.1175/BAMS-D-15-00029.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • <sc>Fig</sc>. 1.
    View in gallery
    Fig. 1.

    (a) Ocean temperatures at 50-m depth on 22 Aug 2015 and Goni’s track; small blue-colored circles show the track at 6-h intervals, and black circles show the track at 1-day intervals. The large white circle centered at Ishigaki Island denotes the range of the Ishigaki Doppler radar, ~200 km. The ocean temperature data were provided by the JMA. (b) Time evolution of Goni’s intensity (green line: maximum wind; blue line: central pressure). RSMC Tokyo’s best-track data do not include maximum wind speeds for a TC categorized as a tropical depression. The vertical orange bar indicates the period focused on in this study.

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

    Time evolution of the overall average of the RMSD between VD resampled from the GBVTD-retrieved winds at 2-km altitude and observed VD at 2-km altitude.

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

    Vertical wind shear (black line) and its heading direction (red line) between 200 and 850 hPa averaged within 500 km from the center. The shear was calculated by using JRA-55 data (Kobayashi et al. 2015). The shading indicates the radar observation period.

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

    (a) Time evolutions of estimated central pressures (blue, red, green, and purple lines), their 2-h running mean (black line), and the best-track central pressure (brown line) of Goni. The blue, red, green, and purple lines indicate central pressures derived from sea level pressure observations at Yonaguni, Iriomote, Ishigaki, and Miyako, respectively. (b) Time evolutions of maximum wind speed (blue line) and RMW (red line) at 2-km altitude of Goni. Periods I–IV, defined in section 4a and Table 1, are also shown at the bottom of each panel.

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

    Brightness temperatures from the Global Change Observation Mission 1st–Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2; at 89 GHz A-horn, vertically polarized wave) polar-orbiting microwave satellite at (a) 0529 and (b) 1739 UTC 22 Aug and (c) 0435 UTC 23 Aug.

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

    Infrared brightness temperatures (at 10.4 μm) from the Himawari-8 geostationary satellite: (a)–(g) 2200 UTC 22 Aug to 2105 UTC 23 Aug.

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

    As in Fig, 6, but for radar rainfall rate provided by the JMA. The red dot indicates the radar site. The black circle denotes the RMW.

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

    As in Fig, 6, but for retrieved wind speed at 2-km altitude. The black line indicates Goni’s track, which is composed of the centers (from 2200 UTC 22 Aug to 2200 UTC 23 Aug) used in the GBVTD analysis and axisymmetric analyses. The red dot indicates the radar site. The black circle denotes the RMW. There was no GBVTD-retrieved wind field at 1200 UTC 23 Aug.

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

    (a) Radius–time Hovmöller diagram of (color scale), (105 m2 s−1; purple lines), and RMW (thick black line) at 2-km altitude. The blank areas are where the GBVTD technique could not retrieve (i.e., during the TC’s passage near Ishigaki Island and in the eye region). (b) Radius–time Hovmöller diagram of at 1-km altitude (color scale), radius of maximum radar reflectivity at 2-km altitude (green line), and RMW at 2-km altitude (thick black line). The blank areas are where the GBVTD technique could not retrieve . (c) Radius–time Hovmöller diagram of azimuthal-mean radar reflectivity at 2-km altitude (color), radius of maximum radar reflectivity (red line), and RMW (thick black line). The black horizontal lines indicate the boundaries of the periods defined in Table 1.

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

    Radius–height plots of (a) time-averaged (m s−1; black contours), azimuthal-mean radar reflectivity (color scale), and (105 m2 s−1; red contours); (b) time-averaged (m s−1; black contours positive, white contours negative), azimuthal-mean radar reflectivity (color scale), and (105 m2 s−1; red contours); and (c) time-averaged (color scale), azimuthal-mean radar reflectivity (black contours but the zero contour is omitted), and (105 m2 s−1; green contours) during period I. The dashed line is the RMW, and the white line is the RMR. Contours were drawn in areas where there were observations averaged at least over 1 h in total. (d)–(f) As in (a)–(c), respectively, but for period II. (g)–(i) As in (a)–(c), respectively, but for period III. (j)–(l) As in (a)–(c), respectively, but for period IV.

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

    Radial profile of gradient wind (black) and GBVTD-retrieved (red) at 1-km altitude.

  • <sc>Fig</sc>. 12.
    View in gallery
    Fig. 12.

    (a) Hovmöller diagram of I (10−3 s−1; color scale), (105 m2 s−1; red lines), and RMW (thick black line) at 2-km altitude. The blank areas are where the GBVTD technique could not retrieve (i.e., during the TC’s passage near Ishigaki Island and in the eye region). The black horizontal lines indicate the boundaries of the periods defined in Table 1. (b),(c) Radius–height plots of time-averaged I (10−3 s−1; black contours) and azimuthal-mean radar reflectivity (color scale) during (b) period III and (c) period IV.

  • <sc>Fig</sc>. 13.
    View in gallery
    Fig. 13.

    Radius–height plots of time-averaged (a) local time tendency, (color scale), (b) axisymmetric radial advection (color scale), (c) axisymmetric vertical advection (color scale), (d) eddy radial advection (color scale), (e) eddy vertical advection (color scale), and (f) residual difference between the local time tendency and the sum from the first term to the forth term (color scale) during period II. The black contours are azimuthal-mean radar reflectivity (the zero contour is omitted), the dashed line is the RMW, and the green contours are (105 m2 s−1). In (b)–(f), the region inside the RMW and at radii > 80 km are masked out.

  • <sc>Fig</sc>. 14.
    View in gallery
    Fig. 14.

    As in Fig. 13, but for period III.

Fig. 1.

(a) Ocean temperatures at 50-m depth on 22 Aug 2015 and Goni’s track; small blue-colored circles show the track at 6-h intervals, and black circles show the track at 1-day intervals. The large white circle centered at Ishigaki Island denotes the range of the Ishigaki Doppler radar, ~200 km. The ocean temperature data were provided by the JMA. (b) Time evolution of Goni’s intensity (green line: maximum wind; blue line: central pressure). RSMC Tokyo’s best-track data do not include maximum wind speeds for a TC categorized as a tropical depression. The vertical orange bar indicates the period focused on in this study.
Fig. 2.

Time evolution of the overall average of the RMSD between VD resampled from the GBVTD-retrieved winds at 2-km altitude and observed VD at 2-km altitude.
Fig. 3.

Vertical wind shear (black line) and its heading direction (red line) between 200 and 850 hPa averaged within 500 km from the center. The shear was calculated by using JRA-55 data (Kobayashi et al. 2015). The shading indicates the radar observation period.
Fig. 4.

(a) Time evolutions of estimated central pressures (blue, red, green, and purple lines), their 2-h running mean (black line), and the best-track central pressure (brown line) of Goni. The blue, red, green, and purple lines indicate central pressures derived from sea level pressure observations at Yonaguni, Iriomote, Ishigaki, and Miyako, respectively. (b) Time evolutions of maximum wind speed (blue line) and RMW (red line) at 2-km altitude of Goni. Periods I–IV, defined in section 4a and Table 1, are also shown at the bottom of each panel.
Fig. 5.

Brightness temperatures from the Global Change Observation Mission 1st–Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2; at 89 GHz A-horn, vertically polarized wave) polar-orbiting microwave satellite at (a) 0529 and (b) 1739 UTC 22 Aug and (c) 0435 UTC 23 Aug.
Fig. 6.

Infrared brightness temperatures (at 10.4 μm) from the Himawari-8 geostationary satellite: (a)–(g) 2200 UTC 22 Aug to 2105 UTC 23 Aug.
Fig. 7.

As in Fig, 6, but for radar rainfall rate provided by the JMA. The red dot indicates the radar site. The black circle denotes the RMW.
Fig. 8.

As in Fig, 6, but for retrieved wind speed at 2-km altitude. The black line indicates Goni’s track, which is composed of the centers (from 2200 UTC 22 Aug to 2200 UTC 23 Aug) used in the GBVTD analysis and axisymmetric analyses. The red dot indicates the radar site. The black circle denotes the RMW. There was no GBVTD-retrieved wind field at 1200 UTC 23 Aug.
Fig. 9.

(a) Radius–time Hovmöller diagram of (color scale), (105 m2 s−1; purple lines), and RMW (thick black line) at 2-km altitude. The blank areas are where the GBVTD technique could not retrieve (i.e., during the TC’s passage near Ishigaki Island and in the eye region). (b) Radius–time Hovmöller diagram of at 1-km altitude (color scale), radius of maximum radar reflectivity at 2-km altitude (green line), and RMW at 2-km altitude (thick black line). The blank areas are where the GBVTD technique could not retrieve . (c) Radius–time Hovmöller diagram of azimuthal-mean radar reflectivity at 2-km altitude (color), radius of maximum radar reflectivity (red line), and RMW (thick black line). The black horizontal lines indicate the boundaries of the periods defined in Table 1.
Fig. 10.

Radius–height plots of (a) time-averaged (m s−1; black contours), azimuthal-mean radar reflectivity (color scale), and (105 m2 s−1; red contours); (b) time-averaged (m s−1; black contours positive, white contours negative), azimuthal-mean radar reflectivity (color scale), and (105 m2 s−1; red contours); and (c) time-averaged (color scale), azimuthal-mean radar reflectivity (black contours but the zero contour is omitted), and (105 m2 s−1; green contours) during period I. The dashed line is the RMW, and the white line is the RMR. Contours were drawn in areas where there were observations averaged at least over 1 h in total. (d)–(f) As in (a)–(c), respectively, but for period II. (g)–(i) As in (a)–(c), respectively, but for period III. (j)–(l) As in (a)–(c), respectively, but for period IV.
Fig. 11.

Radial profile of gradient wind (black) and GBVTD-retrieved (red) at 1-km altitude.
Fig. 12.

(a) Hovmöller diagram of I (10−3 s−1; color scale), (105 m2 s−1; red lines), and RMW (thick black line) at 2-km altitude. The blank areas are where the GBVTD technique could not retrieve (i.e., during the TC’s passage near Ishigaki Island and in the eye region). The black horizontal lines indicate the boundaries of the periods defined in Table 1. (b),(c) Radius–height plots of time-averaged I (10−3 s−1; black contours) and azimuthal-mean radar reflectivity (color scale) during (b) period III and (c) period IV.
Fig. 13.

Radius–height plots of time-averaged (a) local time tendency, (color scale), (b) axisymmetric radial advection (color scale), (c) axisymmetric vertical advection (color scale), (d) eddy radial advection (color scale), (e) eddy vertical advection (color scale), and (f) residual difference between the local time tendency and the sum from the first term to the forth term (color scale) during period II. The black contours are azimuthal-mean radar reflectivity (the zero contour is omitted), the dashed line is the RMW, and the green contours are (105 m2 s−1). In (b)–(f), the region inside the RMW and at radii > 80 km are masked out.
Fig. 14.

As in Fig. 13, but for period III.
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1454 982 400
PDF Downloads 454 66 7