Observed sub-hectometer-scale low level jets in surface-layer velocity profiles of landfalling typhoons

Journal of Wind Engineering & Industrial Aerodynamics 190 (2019) 151–165

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Observed sub-hectometer-scale low level jets in surface-layer velocity profiles of landfalling typhoons Lixiao Li a, Ahsan Kareem b, *, Julian Hunt c, Feng Xing a, Pakwai Chan d, Yiqing Xiao e, Chao Li e a

Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China NatHaz Modeling Laboratory, University of Notre Dame, Notre Dame, USA Dept of Earth Sciences, University College London, London, UK d Hong Kong Observatory, Hong Kong, China e Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China b c

A B S T R A C T

This paper extends previous studies concerning the structure of wind in tropical cyclones (TCs) approaching land mass over coastal waters. New sets of field measurements from wind observation towers and Doppler SODARS at levels between the land surface and 100 m height are presented. The measured mean (over 10-min periods) of the horizontal velocity U(r) shows the usual eye, eyewall and outer vortex structure. But very significantly they also exhibit that the mean vertical profile, U(z), does not increase monotonically as in a usual surface boundary layer in the backside eyewall regions. Rather, it is in the form of a jet, with a maximum velocity, ULLJ , at a height zLLJ , lying between 40 and 60 m. The data also show that the mean velocity gradient, defined by the ratio ULLJ =Uð10Þ, is typically smaller in the front side of a tropical cyclone in the direction of its motion as compared to the backside. This asymmetric distribution does not vary significantly with the radius. The mechanisms that result in the asymmetric distribution of the low-level jet are complex. One possible source is the downward transportation of convective turbulence. In the backside of a TC, the downflow is dominated, and it transports high velocity flow downward and enhances the mean momentum of wind flow at lower heights. Another factor may be the influence of the swell translation in different azimuth of a TC structure. These mechanisms need further investigation based on high resolution observations through a collaborative multiplatform measurement involving, e.g., Doppler SODAR, conventional sensors and numerical simulations.

1. Introduction Tropical cyclones impart major structural damage, especially to lowrise buildings and other built infrastructure, with attendant economic impact and loss of lives around the world (Kareem, 1985; Tamura, 2009; Gurley et al., 2011; Kopp et al., 2011; Munich, 2014; Li et al., 2015a,b). Damage surveys after landfalling typhoon/hurricane have shown interesting damage patterns of scattered debris and trees. They exhibit significant periodicity in damage with scale near 500 m and even as small as 10–200 m (Fujita, 1992; Wakimoto et al., 1994; Powell et al., 1996; Wurman et al., 1998). Generally, these periodical damages have been attributed to small-scale tornados or low-level jets (LLJs). The structure of tropical cyclones can be characterized by the asymmetrical helical flow field, which leads to changes in the wind speed and direction continuously along the radial and the vertical axes in the typhoon wind field, which is critical for estimating wind loads on buildings and infrastructures (Li et al., 2015a). Besides the influence of the flow structure, the effects of deep convection above the surface layer, downdraft and the role of blocking of large eddies by variable mean shear near ground also modulate the surface wind and turbulence dynamics in tropical cyclones,

although they are known to be transient, sporadic, and spatially patchy (Smedman et al., 2004). Other jets related events in extratropical storms have been noted in rapidly deepening areas of low pressure in which coherent strong wind descends towards the ground and is known as sting jet (Martinez-Alvarado et al., 2018; Clark and Gray, 2018). For structural design these features are of major concern and are described by the wind velocity profile and power spectral density of wind fluctuations. The velocity profile is used to model the variation of mean wind velocity in the vertical direction, while the power spectra density characterizes the nature and distribution of energy in the wind fluctuations. Delineating the mean flow structure and turbulence characteristics in tropical cyclone winds is essential for reducing the structural damage and for calibration of the design code. This approach also enhances wind tunnel and numerical modeling of winds in the cyclone-prone regions (Li et al., 2010). To investigate the characteristics of wind velocity profile in tropical cyclones, many field measurements have been conducted by using boundary layer wind observation towers equipped with meteorological instruments, and by using of remote sensing techniques. Previous studies on the tropical cyclone wind velocity profile revealed that the wind

* Corresponding author. E-mail address: [email protected] (A. Kareem). https://doi.org/10.1016/j.jweia.2019.04.016 Received 9 May 2018; Received in revised form 15 April 2019; Accepted 16 April 2019 Available online 12 May 2019 0167-6105/© 2019 Elsevier Ltd. All rights reserved.

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Journal of Wind Engineering & Industrial Aerodynamics 190 (2019) 151–165

(a) Exposure of the ZT

(b) Exposure of the GIT

Fig. 1. Exposure of the observation towers.

In a neutral boundary layer, the influence of the stability on the wind profile could be neglected and the preceding expression is reduced to the logarithmic law profile

velocity profile has a jet-like structure with an increase in wind speed to a maximum of around 500 m in the eye-wall regions, and the variations of the wind speed lower than the wind maximum height followed the loglaw (Franklin et al., 2003; Powell et al., 2003; Giammanco et al., 2012; He et al., 2013; Tse et al., 2013). The mechanisms of generating the jet-like structure of winds are in part attributed to the downward transport of higher momentum flow from aloft via convective cells (Wurman et al., 1998), and in part ascribed to the radial import of higher angular momentum, cooler air and the advection (Kepert, 2001; Kepert et al., 2001; Mashiko, 2008; Kitabatake et al., 2009). Moreover, as the typhoon moves over the ocean, the spatial variation in heat flux changes the local stability and affects the turbulent structures, including convective turbulence and the mean momentum in the flow (Owinoh et al., 2005). This also contributes to the formation of LLJs in the land-sea transition regions. Typically, the characteristic length scale of these LLJs observed physically or numerically simulated are found to be around 500 m or even lower (Powell et al., 1991, 1998). Thus overall, the examination of lower-level jets in the boundary layer of tropical cyclones is critical for better characterization of the structure of tropical cyclones, and also for determining wind forces on buildings and other structures.

UðzÞ ¼

UðzÞ ¼

  u* zd ln Ψ z0 k

To better understand detailed structure of typhoon winds and the mechanism of typhoon-induced damage, a number of wind observation towers varying between ten and hundreds of meters were installed along the coastline of South China, a region frequently hit each year by typhoons. Moreover, a wind profile radar (Beijing Airda Electronic Equipment Co. Ltd.) was also commissioned to capture the variation of wind speed and temperature at higher levels. In Hong Kong, the Hong Kong Observatory (HKO) also setup several automated stations equipped with the Doppler SODAR to measure the variation in wind speed and direction in the lower levels of passing typhoons over the island. In this study, datasets in three typhoons (Hagupit, Koppu and Nuri) were analysed. The data in typhoon Hagupit was measured at a 100-m meteorological observation tower (Zhizai tower, ZT) located at the top of Zhizai Island, which is a very small island, with 120 m in length, 50 m in width, and the highest spot above the sea level is about 11 m. The Zhizai tower is equipped with seven levels of sensors at the following elevations: 8 m, 10 m, 20 m, 40 m, 60 m, 80 m and 100 m. Wind profile data were acquired by six cup-anemometers (NRG #40C, NRG systems, Inc.) installed at 10 m, 20 m, 40 m, 60 m, 80 m and 100 m. The outputs from the cup-anemometers are the 10-min mean wind speeds recorded at 10-min intervals. The exposure of ZT is shown in Fig. 1a. More detailed information about the exposure of observation stations and sensors installed in the observation tower can be seen in Li et al. (2012). The data in typhoon Koppu was measured on Gaolan Island tower

(2)

where u* is the friction velocity, k is the von Karman constant, d is the zero plane displacement, z0 is the aerodynamic roughness length, and Ψ is a stability dependent parameter, which is a function of stability parameter z=L. The stability parameter z=L, can be estimated by
    g θ w' θ ' z 0  ¼ L u3* kz

(5)

2. Data source

(1)

where Uðz1 Þ and Uðz2 Þ are the mean wind speeds at height z1 and z2 , and α is the exponential index which depends on the surface roughness. The exponential index in tropical cyclones is generally greater than it in extratropical winds, i.e., the profile is flatter (Song et al., 2012). In micrometeorology, the Monin-Obukhov based logarithmic law is widely used to relate the underlying features of the surface boundary layer (Panofsky et al., 1984; Kaimal et al., 1994). It is expressed as UðzÞ ¼


 z n  u* zd ln a * z0 H k

where a and n are model parameter, which are given by 0.4 and 2.0, respective in Vickery et al. (2009); H* is the tropical cyclone boundary height parameter.

In engineering applications, wind velocity profile is generally described by the power law (Davenport, 1960), i.e.
α z1 z2

(4)

In the outer-vortex regions of a tropical cyclone, the logarithmic law adequately models the variation of wind speeds in the tropical cyclone boundary layer (Powell et al., 2003; Choi et al., 2009; Giammanco et al., 2012, 2013). However, in the eye-wall regions of a tropical cyclone, the spatial variation of surface heat flux changes the atmospheric stratification, distorts the structure of turbulence and adds convective turbulence to the mean momentum of the flow. This results in reorganization of the boundary-layer profile. Accordingly, in these regions, the LLJs may form and vary in space and time. Therefore, the Monin-Obukhov based logarithmic law profile may break down in these regions, Vickery et al. (2009). proposed the following modified logarithmic law to model the vertical wind variation,

1.1. Wind velocity profile theory

Uðz1 Þ ¼ Uðz2 Þ

u* z  d ln z0 k

(3)

where g is the acceleration due to gravity, the term g=θ is referred to as the buoyancy parameter, ðw' θ' Þ0 denotes the temperature flux at the surface. For neutral atmosphere, z=L tends to approach 0, for stable z=L > 0, and for unstable condition z=L < 0. 152

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360

40 35

10-min mean wind speed (m/s)

WE

10-min mean wind direction (o )

BOV

FOV

270

30 25

FEW

180

20 BEW

15

90

10 5 00:00

03:00

06:00

09:00

12:00 15:00 Time (hh:mm)

18:00

21:00

10-min mean wind direction (o)

10-min mean wind speed (m/s)

45

0 24:00

(a) Typhoon Hagupit 360 10-min mean wind speed (m/s) 10-min mean wind direction ( o)

35

270

FOV

BOV

25

180 FEW

15

90 BEW

5 00:00

06:00

12:00

18:00

00:00 06:00 Time (hh:mm)

12:00

18:00

10-min mean wind direction ( o )

10-min mean wind speed (m/s)

45

0 00:00

(b) Typhoon Koppu 360

FOV

15

o

10-min mean wind speed (m/s)

BEW

10-min mean wind direction ( o)

WE

270

10

180

5

90 FEW BOV

0 00:00

06:00

12:00

18:00

00:00 06:00 Time (hh:mm)

12:00

18:00

10-min mean wind direction ( )

10-min mean wind speed (m/s)

20

0 00:00

(c) Typhoon Nuri Fig. 2. Time histories of mean wind speed and direction in typhoons.

Doppler SODAR are the 5-min mean wind speed and direction, which are the averages of the 100 raw 3-s wind measurements in the 5-min interval. The limiting height of reliable observations of the Doppler SODAR is 100 m with a resolution of 5 m. Tropical cyclone footprints spread hundreds of kilometers in the horizontal plane. In the surface layer of tropical cyclones, the mean and turbulent wind fields have characteristic structures which are observed to vary significantly as the radius r increases from near the eye-wall radius. Therefore, the structure in the horizontal plane is divided into the following main segments: eye, eye-wall, rain bands and outer vortex region. In the eye-wall region, the mean flow in the lower part of a tropical cyclone is significantly different from that of unidirectional atmospheric boundary layers over flat surface. However, the wind

(GIT), which is a 60 m high tower located at the top of Changzui Mountain on Gaolan Island, Zhuhai City, China. The geographical coordinates of the GIT are N 21º540 300 latitude and E 113º160 9.900 longitude. The elevation of the base of the GIT is about 309 m above the mean sea level. Three NRG #40 anemometers were installed at 10 m, 40 m and 60 m height. The output of the three NRG #40 anemometer is 10-min mean wind speed and direction. The expose of the GIT is shown in Fig. 1b. Datasets in typhoon Nuri were measured by the Doppler SODAR at the Siu Ho Wan (SHW) station, which is about 250 m from the shore and 22 m above the mean sea level. The SHW station is located on the northern coast of Lantau Island. The detailed information about the SHW station has been reported in Choi et al. (2009). The outputs of the

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100

100

60

Field Measurements Mean Profile

80

Power Law Fitting =0.0267

Height (m)

Height (m)

80

40

60

20 10 0.9

0.95

1

1.05

1.1

40

Field Measurements Mean Profile

20

Power Law Fitting =0.0287

10 0.95

1.15

1

-1

(a)

Front-side outer-vortex region (FOV)

(b)

100

1.1

1.15

Front-side eyewall region (FEW)

Field Measurements Mean Profile

80 Height (m)

1.05 Wind Speed (m s-1)

Wind Speed (m s )

Power Law Fitting =0.0106

60 40 20 10 0.9

0.95

1

1.05

1.1

-1

Wind Speed (m s )

(c)

Wind eye (WE) 100

100

80 Field Measurements Mean Profile

60

Height (m)

Height (m)

80

Power Law Fitting =0.0148

40

40 20

0.95

1

1.05

1.1

1.15

1.2

10 0.9

-1

0.95

1

1.05

1.1

-1

Wind Speed (m s )

(d)

Field Measurements Mean Profile Power Law Fitting =0.0362

20 10 0.9

60

Wind Speed (m s )

Back-side eyewall region (BEW)

(e)

Back-side outer-vortex region (BOV)

Fig. 3. 10-min mean wind profiles in different structural locations in typhoon Hagupit.

following the expression in Simiu et al. (1996). From Fig. 2c, it can be noted that the eye of typhoon Nuri passed over SHW station as the time history of the 10-min mean wind speeds shows two peaks. In order to investigate differences in the wind profile in different sections of a tropical cyclone, the datasets in each typhoon were divided into different groups, named as front-side outer-vortex region (FOV), front-side eyewall region (FEW), wind eye (WE), back-side eyewall region (BEW) and back-side outer-vortex region (BOV), as shown in Fig. 2 for later studying. In previous analysis of the atmospheric stratification in typhoon Hagupit based on the datasets measured at 60 m height on Zhizai tower by Gill WindMaster Pro anemometer, it was noted that the atmospheric stratification was neutral in the front outer vortex region and front-side eyewall region, weakly stable in back-side eyewall region, and stable in the further back-side of the outer vortex (Li et al., 2012). In this study, the analysed datasets (FOV, FEW, WE, BEW and BOV) were basically located in the neutral and weakly stable regions. Thus in the later analysis, the influence of the atmospheric stratification on the wind profiles were not investigated.

characteristics and turbulence mechanism tend to be similar to extratropical winds in the outer vortex region (Owinoh et al., 2005). Fig. 2(a) and (b) show time histories of the 10-min mean wind speeds and directions at 10 m height on the Zhizai tower in typhoon Hagupit and on Gaolan Island tower in typhoon Koppu, respectively. It is noted that the eye of typhoon Hagupit passed through ZT as the time history of wind speeds reached two peaks. The maximum of the 10-min mean wind speeds was 44.1 m/s. In typhoon Koppu, the eyewall region passed through the GIT and the time history of the 10-min mean wind speeds shows only one peak and the variation of the 10-min mean wind directions before and after the passage of the eyewall region was about 180 . Fig. 2c shows the time histories of the 10-min mean wind speeds and directions at 20-m height in typhoon Nuri. As the outputs of the Doppler SODAR provides a 5-min mean wind velocity, a 10-min mean wind speed and direction which is averages of two adjacent 5-min wind records. In the Doppler SODAR data, once one record in one of the two adjacent 5-min wind records was detected to be suspect, the corresponding 10-min wind speeds were calculated through adjusting the remaining 5-min record by dividing by a conversion factor of 1.0169

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Height (m)

Height (m)

60

40 Field Measurements Mean Profile

40 Field Measurements Mean Profile

Power Law Fitting =0.1058

10 1

1.1

1.2

1.3

Power Law Fitting =0.0789

10

1.4

1

1.5

1.05

1.1

(a) Front-side outer-vortex region (FOV)

(b)

Height (m)

Height (m)

40 Field Measurements Mean Profile Power Law Fitting =0.1007

1.1

40 Field Measurements Mean Profile Power Law Fitting =0.1506

10

1.2

1

1.3

1.1

1.2

1.3

1.4

-1

-1

Wind Speed (m s )

Wind Speed (m s )

(c)

1.25

Front-side eyewall region (FEW)

60

1

1.2

Wind Speed (m s )

60

10

1.15 -1

Wind Speed (m s-1)

Back-side eyewall region (BEW)

(d)

Backside outer-vortex region (BOV)

100

100

90

90

80

80

Height (m)

Height (m)

Fig. 4. 10-min mean wind profiles in different structural locations in typhoon Koppu.

70 60 50 Field Measurements Mean Profile

40

20 1

1.5

2

Power Law Fitting =0.4330

60 50 40 30

Power Law Fitting =0.5506

30

70

Field Measurements Mean Profile

2.5

20 0.5

3

1

1.5

2

2.5

Wind Speed (m s-1)

-1

Wind Speed (m s )

(a) Front-side outer-vortex region (FOV)

(b)

Front-side eyewall region (FEW)

100 90

Height (m)

80 Field Measurements Mean Profile

70 60

Power Law Fitting =0.2507

50 40 30 20 0

0.5

1

1.5

2

2.5

3

Wind Speed (m s-1)

Wind eye (WE)

Height (m)

Height (m)

(c) 100 90 Field Measurements 80 Mean profile 70 Power Law Fitting 60 =0.3633 50 40 30 20 0.5 1

1.5

2

100 90Field Measurements 80Mean profile Power Law Fitting 70 =0.4978 60 50 40 30 20 0 1

Wind Speed (m s-1)

(d)

2

Wind Speed (m s-1)

Back-side eyewall region (BEW)

(e)

Backside outer-vortex region (BOV)

Fig. 5. 10-min mean wind profile in different location of typhoon structure in typhoon Nuri. 155

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20 10 0.8

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Field Measuremnts Mean Profile Power Law Fitting

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10 0.8

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Wind Speed (m s-1)

(a)

0

1

1.1

1.2

-1

Wind Speed (m s )

0.0002 m

(b)

0

> 0.0002 m

Fig. 6. Wind profiles in different roughness length regime in typhoon Hagupit.

3. Results

10-min mean wind profiles, the mean profiles were only given at lower height in those mean wind profiles where there was no reported error. It is noted that in the FOV and FEW regions, the 10-min mean wind profiles increase from 20 m to 100 m height; in the WE region, the 10-min mean wind profile remain almost constant from 20 m to 100 m height; in the BEW region, the 10-min mean wind speed profiles show a trend of first increasing with height from 20 m to about 30 m and then remain almost constant to 100 m; however in the BOV region, the 10-min mean wind speed profiles show a trend of increasing monotonically from 20 m to 100 m height. Based on the analysis of the 10-min mean wind profiles of the three typhoons, it is concluded that in the FOV and FEW regions wind speeds increase monotonically with increase in height to about 100 m height, while in the WE region the mean wind profiles remain almost constant from 10 m to about 100 m height. However in the BEW region, there is a local maximum at the height lower than 100 m, and the 10-min mean wind speeds show a trend of first increasing with height and then remaining constant or decreasing with height. In the BOV region, the 10min mean wind speed profiles show a complex trend which depends both on the distance between the observation station and typhoon centre and the strength of the typhoon. In strong wind speed events combined with a closer proximity of the observation location and the typhoon centre, the 10-min wind speed profiles exhibit a trend of increasing first with height and then remaining constant or decreasing at relatively lower levels; however, in weak winds and over longer distance between observation point and the storm centre, the 10-min mean wind speed increases with increase in height.

3.1. Characteristics of mean wind profiles in different sections of a tropical cyclone In a boundary layer of a tropical cyclone (typhoon and hurricane), the mean and turbulent wind fields have characteristic flow structures that are driven by buoyancy and shear and observed to vary significantly as the radius r increase from near the eyewall radius (i.e. r  Rew  20km) to the outer-vortex regions (i.e. r  Rov  400km) of a tropical cyclone. In the eyewall regions, the effects of the sharp gradient of turbulence (i.e. dσ 2V =dr > 0), leads to outward mean radial velocity (Vr > 0) near the surface in the lower part of the boundary layer, driven by the Reynolds stresses (Townsend, 1976). In addition, the interaction between the convective turbulence and the shear induced turbulence leads to a vertical gradient in shear stress (i.e. dτ=dz > 0, where τ is the shear stress) that increases the mean wind speed (Owinoh et al., 2005). Thus, the mean wind velocity profile may vary at different locations in a tropical cyclone. Fig. 3 presents 10-min mean wind velocity profiles in different sections in typhoon Hagupit (blue lines). The mean profiles of all selected datasets (red lines) and the least square fitted power law profiles (black lines) are also given in Fig. 3 for the five regions. It is interesting to noted that basically the mean wind speed increases with height in FOV and FEW regions. However, in the WE region, the 10-min mean wind profiles show a complex trend, which departs from the logarithmic law or the power law. In BEW region, the 10-min mean wind speed increases from 10 m to 40 m height, then decreases with height to 60 m and then increases to 100 m height. In the BOV region, the 10-min mean wind speed increases to a maximum at about 40 m height and then decreases from 40 m to 100 m height. It is noted that in the back-side sections of typhoon Hagupit, there is a local maximum at about 40 m height in the 10-min mean wind speed profiles. Like the analysis of typhoon Hagupit, Fig. 4 shows the 10-min mean wind speed profiles in different sections in typhoon Koppu. The data in the wind eye region were not captured in typhoon Koppu. It is noted that the 10-min mean wind speed increases with height from 10 m to 40 m height and then remains almost constant from 40 m to 60 m height in the FOV; however, in the FEW region, the 10-min mean wind speed increases monotonically from 10 m to 60 m height. In the BEW and BOV regions, the 10-min wind speed profiles show the trend of first increasing and then decreasing, which is similar to the variation of wind speed with height as noted in typhoon Hagupit. Fig. 5 shows the variations of the 10-min mean wind speed with height in typhoon Nuri at the SHW station. Where the centre of typhoon Nuri passed through the SHW station, the datasets were selected in five locations in typhoon Nuri's structure as in typhoon Hagupit. Due to the Doppler SODAR reported error in data at higher elevations in some of the

3.2. Influence of upwind surface on the surface layer wind profiles The boundary layer structure is primarily determined by the underlying surface friction and vertical gradient of temperature in surface layer (Kaimal et al., 1994). From the analysis in section 4.1, it is noted that the 10-min mean wind speed profiles show different trends in FEW, WE and BEW regions. In a typical tropical cyclone, the wind direction changes by about 180 before and after the passage of the cyclone centre. Accordingly, the upwind fetch may be different in the FEW region and BEW region over a nonhomogeneous terrain. To investigate the influence of upwind fetch on the wind profiles in different sections of a tropical cyclone, field measured wind speed profiles were re-analysed based on the underlying surface roughness. In typhoon Hagupit, besides the six cup-anemometers (NRG #40C, NRG systems, Inc.), a 3-D ultrasonic anemometer (WindMaster™ Pro 3D, Gill Instruments Limited) was available at 60-m height on the ZT to measure 3-D wind speeds at the sampling frequency of 10 Hz. The underlying surface roughness lengths were estimated based on the measurements of the 3-D ultrasonic anemometer. ZT was surrounded by sea surface, thus the underlying surface roughness lengths were calculated using the 156

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Table 1 Power-law index for fitting the 10-min mean wind profiles. Typhoon events

Roughness regimes

Power-law index α

Hagupit

z0  0:0002 z0 > 0:0002 z0  0:0049 0:0050  z0  0:0199 0:0200  z0  0:0499 0:0900  z0  0:1899 S1 (Sea) S2 (Land) S3 (Land) S4 (Land) S5 (Land) S6 (Land) S7 (Sea) S8 (Sea)

0.0066 0.0094 0.0879 0.1506 0.1691 0.2174 0.2416 0.6016 0.2556 0.0364 0.6069 0.3345 0.3720 0.6263

Koppu

Nuri

where U10 is the mean wind speed at 10 m height; u*25 is the friction velocity at a scalar wind speed of 25 m/s; δ and γ are two tuneable parameters with value of 1.0 and 0.6, respectively (Zeng et al., 2010). This model can be effective for wind speeds up to 65 m/s and can capture the decrease of drag coefficient as wind speed exceeds a threshold value (Kareem, 1985; Powell et al., 2003; Vickery et al., 2010; Takagaki et al., 2012, 2014). Fig. 6 shows the 10-min mean wind profiles in different roughness length regimes of typhoon Hagupit. The roughness lengths calculated from Eq. (6) were classed into two groups named as “Sea (z0  0:0002 m)” and “Smooth (0:0002 < z0  0:005 m)” following Wieringa (1992). The maximum roughness length was 0.0046 m. In Fig. 6, each blue thin line denotes a 10-min mean wind profile, the red thick line is the average profile of all 10-min mean wind speeds at each height, and the black thick line is the power law fitted profile of the mean wind speed profile. It can be noted that in the “Sea” regime, the mean wind increases with height to its maximum at about 40 m height, and then it decreases with height to 100 m height. In the “Smooth” regime, the mean wind speed slightly increases with height. Table 1 lists the power law indices based on the least square fitted mean wind profiles. Due to a decrease in wind speed from 40 m to 100 m in “Sea” regime, the power law index results in a negative value. In typhoon Koppu, as there is no anemometer installed on the GIT to document the fluctuating wind speeds, the roughness lengths were calculated by the wind profile method (Wieringa, 1993),

revised Charnock model (Charnock, 1955; Zeng et al., 2010), z0 ¼ αs

u2* 0:11ν þ g u*

(6)

where ν is the molecular viscosity of air; g is the gravitational acceleration; αs is the Charnock constant. Based on various levels of wave profile maturity and wind speeds, a more advanced description of the Charnock constant was proposed in Zeng et al. (2010): 0:11


 U2 lnðz1 Þ  U1 lnðz2 Þ z0 ¼ exp U2  U1

for U10  10

0:11þ0:000875ðU10 10Þ

for 10 < U10  18

0:018 for 18 < U10  25 > ( ) > > > > 0:018 > > for U10 > 25 : max 2:0103 ; 1þδðu* u*25 Þ2 γðu* u*25 Þ1:6

60

60

Height (m)

Height (m)

(8)

where U1 and U2 are the mean wind speed at height of z1 and z2 , respectively. In typhoon Koppu, the mean wind speeds at 10 m and 60 m heights were used to estimate the roughness length. Based on Eq. (8), the

(7)

40

Field Measurements Mean Profile Power Law Fitting 1

1.05

1.1

1.15

1.2

1.25

Field Measurements Mean Profile Power Law Fitting 40

10

10

1

1.3

1.05

0

1.15

1.2

1.25

1.3

1.35

Wind Speed (m s )

Wind Speed (m s )

(a)

1.1

-1

-1

0.0049

0.0050

(b)

60

0

0.0199

60

Height (m)

Field Measurements Mean Profile Power Law Fitting

Height (m)

αs ¼

8 > > > > > > > <

40

Field Measurements Mean Profile Power Law Fitting 40

10

10 1

1.1

1.2

1.3

1.4

1

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(0:0200  z0  0:0499)” and “Rough (0:0900  z0  0:1899)”). Similar to the analysis in typhoon Hagupit, the 10-min mean wind profiles (blue thin lines), the average profile (red thick lines) and the power law fitted profile (black thick lines) of each regime were included in Fig. 7. It can be noted that in the “Sea” regime, the mean wind speeds increase from 10 m

roughness lengths were categorized by referring to the previous classification method (Wieringa, 1992; Schroeder et al., 2009; Wang et al., 2011). There is no data located in the “Roughly open” regime in typhoon Koppu. Fig. 7a–d shows the wind profiles in different regimes (“Sea (z0  0:0049)”, “Smooth (0:0050  z0  0:0199)”, “Open 158

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upwind exposure influences the mean wind speed profiles, however, it is not the decisive in affecting the low-level maximum in the wind profiles in typhoons. In typhoon Hagupit, the observation station is surrounded by the sea surface, the wind profiles show different trends in different sea surfaces. Datasets in typhoon Nuri were measured at the same station (SHW station) by Doppler SODAR, the wind profiles showed different trend and even opposite in the same sub-segment.

to 40 m, and almost remain constant from 40 m to 60 m; in the rougher regimes, however, the mean wind speed increases from 10 m to 40 m, and then decreases from 40 m to 60 m. The irregular variation in the 10-min mean wind speed with height in typhoon Nuri precludes the use of the wind profile method to estimate the roughness length. As the fluctuating wind data was not stored, methods based on the fluctuating wind data, i.e. turbulence intensity based method (Schroeder et al., 2009), gust factor based method (Wiernga, 1993), hybrid method (Yu, 2007), were not applicable. Thus the morphometric information based method was utilized (Lettau, 1969; Kondo et al., 1986; Grimmond et al., 1999). The surrounding of the SHW station were divided into 8 sub-segments (S1 (0-45 ), S2 (45-90 ), S3 (90-135 ), S4 (135-180 ), S5 (180-225 ), S6 (225-270 ), S7 (270-315 ) and S8 (315-360 )), as shown in Tse et al. (2014). In NNE-N–NW–W directions it is sea exposure. In E-SE-S–W directions, the SHW station is influenced by the Lantau Island and the flow may be distorted. In this analysis, the mean wind directions, the averages of the wind directions at each level from the Doppler SODAR data, were calculated and grouped into 8 sub-segments. Fig. 8 shows the 10-min mean wind profiles in each sub-segment in typhoon Nuri. In typhoon Nuri, when wind approaches from the sea exposure (S1, S7 and S8), the mean wind speed increases with height from 20 m to 100 m; when the wind approaches from the land exposure (S2, S3, S4, S5 and S6), the wind speed profiles show a rather complex trend. In sub-segments S2 and S3, it is interesting to note that the mean wind speed decreases with height first and then increases. In subsegments S4, S5 and S6, the 10-min mean wind speeds increase with height to its maximum and then remaining constant. The power law index based on the least square fitted mean wind profile are listed in Table 1. For relatively small mean wind speeds, the power law index estimated by the least square fitted were relatively large due to the scatter in the data. Based on the preceding analysis, it can be reaffirmed that indeed the

3.3. Influence of wind speeds on surface layer wind profiles Both the dynamic and thermodynamic effects are normally considered in the atmospheric boundary layer as they determine the mean flow and turbulence structures. The wind profiles are significantly influenced by the shear induced by the surface friction force, the temperature difference and evaporation driven buoyance. From the previous analysis, it is noted that the upwind exposure is not the decisive factor in influencing the low-level maximum in the wind profiles in typhoons. In this section, the influence of wind speeds on the surface layer wind profile are examined. Fig. 9 shows the 10-min mean wind profiles in different wind speed ranges in typhoon Hagupit. Analogous to the previous analysis, the mean wind profiles of all datasets at the same wind speed level are also included. It can be noted that at lower wind speed level (U  17:2 m s1 ), the 10-min mean wind profiles show a complex trend; with the increase in wind speeds, the 10-min mean wind speed first increases with height, and then decreases; at higher wind speed levels (U > 24:5 m s1 ), the local wind maximums are noted at 40 m height, and the mean wind speeds decrease with height from 40 m to 60 m, or remains constant or increases at high levels. Fig. 10 shows the 10-min mean wind profiles at different wind speed ranges in typhoon Koppu. It can be seen that the 10-min mean wind speed profiles do not show any significant change at different wind speeds. In all cases, the local wind maxima were noted to be present around 40 m height.

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topographic speed-up may have some influence on the wind profile. Based on Eq. (9), the speed-up factor η at 10 m and 20 m height are about 1.26 and 1.09, respectively. It is noted that the speed-up effect is insignificant when the measurement height is higher than 20 m. In this study, the maximum of the low-level jets were mainly noted at about 40 m height which is higher than the range (lower than 20 m) affected by the topographic speed-up. For the Gaolan Station, the observation tower is located at the top of Changzui mountain, and the base of the GIT is about 309 m above sea level. The speed-up of topography have some influence on the wind speed profile. According to the investigation of Ngo et al. (2009), the speed-up at the crest increases with slope until a slope of ~30% and appears constant above this to a slope of 100%. The maximum speed-up position was shifted to an elevation of about four times of the ridge height and shifted downwind of the crest. This was also verified by the third and fifth authors of this study (Stocker et al., 2016). At the Gaolan island station, the measured height (10 m, 40 m and 60 m height) should locate in the inner region of the separation flow and the speed-up effect would be at a similar level at the three observation heights. For the data in typhoon Nuri, the Doppler SODAR was located at the SHW station which is about 250 m from the coastline and 22 m above the sea level. The topographic speed-up can be neglected in this station. Its noteworthy the low level jets have also been noted in other observations over flat terrains. Amano et al. (1999) reported a low level jet in 9612 tropical cyclone observed by Dopplar sodar at campus of University of the Ryukyus in the central part of Okinawa main island, Japan. The reported local maximum value of velocity in the jet was observed at about 50 m height.

Fig. 11 shows the 10-min mean wind profiles in different wind speed ranges in typhoon Nuri. The mean wind profiles in typhoon Nuri show similar variation trend as in the wind profiles in typhoon Koppu. At low wind speed levels, the mean wind speeds increase with height; at higher wind speed levels (U > 15 m s1 ), the mean wind speeds decrease with height first, and then increase at higher heights; in the moderate wind level (10 m s1 < U  15 m s1 ), the local wind maxima were detected and the 10-min mean wind speeds increase with height from 20 m to 25 m and remain even constant up to 60 m height, finally these increase with height. From the above analysis, it can be concluded that the mean wind speed levels have significant influences on the mean wind speed profiles. At lower wind speeds, the wind profiles show the trend of how the mean wind speeds increase with height. However, at higher wind speeds, the trend of the wind profiles is that the mean wind speed increases with height first, and then remains constant or decreases with height. A probable reason for the influence of wind speed on wind profiles is that the datasets with higher wind speed were recorded in the regions closer to the typhoon centre. In these regions, the strong convective turbulence changes the vertical gradient of the turbulent shear stress (i.e. dτ=dz > 0, where τ is the shear stress) that amplifies the jet flow in the lower boundary layer (Owinoh et al., 2005). 4. Discussion From the analysis in Sec. 3, it is noted that the 10-min mean wind speed increases monotonically with height in the near surface layer in the FOV and FEW regions of a typhoon. The 10-min mean wind speed profiles follow the logarithmic law or power law in the lower 100 m height. In the WE region, the 10-min mean wind speed remains almost constant. In the BEW regions, the 10-min mean wind speed profiles show a complex trend and do not follow the power law or logarithmic law, since there is a low-level jet at depths less than 100 m. In the BOV regions, the variation of the 10-min mean wind speed with height was dependent on the distance from the typhoon centre, the intensity and spatial scale of a typhoon. Close to the typhoon centre, the 10-min mean wind profiles show a trend similar to that in the back-side eyewall region; however far from the typhoon centre, the 10-min mean wind profiles follow the logarithmic law or power law.

4.2. Influence of internal boundary layer formation during transition from sea to land The internal boundary layer formation as wind field makes a transition from the sea may impact some of the findings of the study. This possibility was examined and for the Zhizai station in this study, the observation tower was very close to the sea shore and all cup anemometers (lowest is 10 m) captured the data with underlying exposure as sea surface. For the datasets measured at SHW by Doppler SODAR, the internal boundary layer were calculated based on the method proposed in ESDU (2002). The internal boundary height hi is calculated by the following expression,

4.1. Influence of topographic wind speed-up on the typhoon velocity profile

  Kx ðu =u1 Þlnz0  lnz01 hi ¼ exp Kx ðu =u1 Þ  1

It is likely that some of the measurements reported in this paper may be influenced by the possible topographic speed-up as the towers are located on island with the terrain sloping up from the shore line. In this regard there have been several reported studies on the topic and most major building codes and standards have some recommendations. In anticipation of our follow-up study, we offer some analysis of the potential impact of the topographic speed-up in our measurements sites and the associated observations. In the Chinese wind code (GB50009-2012), the topographic speed-up effect is estimated by the following expression, η ¼ 1 þ κ tan α 1  h

z i2 2:5H

(10)

where u and u1 are the friction velocity in the new terrain and upwind terrain, respectively. z0 and z01 are the roughness length in the new terrain and upwind terrain, respectively. Kx is estimated by the following formula, Kx ¼ 1 þ 0:67R0:85 fsr

(11)

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(9)

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where η is the topographic speed-up factor; κ equals to 2.2 for ridge and 1.4 for escarpment, respectively; α is the slope angle; z is the wind speed height over the ground surface, and H is the height of the crest of the ridge or escarpment. According to the study of Finnigan (1988), the topographic speed-up could be neglected with slope angle less than 17.5 . In the Japanese wind code (AIJ-RLB, 2004), the topographic speed-up effect isn't accounted when the slope ratio is less than 13.2%. For the Zhizai station, the Zhizai tower is located at the top of the Zhizai island. The highest spot of the Zhizai island is about 11 m, and the slope ratio is about 20%. The

jlnz0 =z01 j ½u =ðfz0 Þ0:23

fsr ¼ 0:1143X 2  1:372X þ 4:087 where X ¼ log10 x, and x is the distance from the observation point to the terrain roughness changing point. Taking z0 ¼ 0:01 and z01 ¼ 0:001 for open terrain and sea surface, and x ¼ 220 m for SHW station into Eq. (11), the internal height hi is about 16 m high. As the datasets lower than 20 m height measured by the Doppler SODAR were contaminated by the noisy ground reflections, these datasets were discarded. Only the datasets higher than 20 m were 161

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Fig. 12. Data intact rate versus mean wind speed measured by ultrasonic anemometers in typhoon Nesat.

4.4. Mechanisms to format the low-level jet

analysed in the manuscript, and they are higher than the height of the internal boundary layer.

Potential mechanisms of generating the low-level jet can be attributed to the following factors: (1) downward mixing of higher momentum air from aloft via convective downdrafts; (2) radial import of high angular momentum air; (3) horizontal advection; (4) topographic effects; and (5) boundary layer roll. The convective cells are known to be transient, sporadic and spatially patchy in the typhoon wind field (e.g., Li et al., 2012). The influence of the radial import of high angular momentum air and horizontal advection are significant in the left and front part of a typhoon structure due to the asymmetry inflow resulting from the interaction of the moving storm with the surface (Kepert, 2001; Kitabatake and Tanaka, 2009). Moreover, a typical scale of the low-level jet derived from the horizontal advection model is several hundreds of meters in the cyclone core and increase with radius (Kepert, 2001; Kepert et al., 2001). However, in the three typhoons studied here, the low-level jet is more significant in the rear part of typhoon footprint; the height of the low-level jets was observed to be lower than 100 m height. In hurricanes, the low-level jets were also observed in very high wind velocity over sea surface and the mechanism was attributed to the intermittent high air velocities above high wave crests and a near saturated, well-mixed thick foam layers (Holthuijsen et al., 2012). Fig. 13 presents a comparison of the time history of 5-min mean horizontal wind speed at 20 m height and 5-min mean vertical wind speed at 40 m height observed at SHW station by Doppler SODAR in typhoon Nuri. It can be noted that regardless of the location in the footprint of typhoon Nuri, as shown with the passage of time, the extreme excursions in the horizontal wind speeds tend to respond to a sudden change in the vertical wind speed, especially in the front-side regions e.g. the sudden reduction in vertical wind speed leads to a local maximum in the horizontal wind speed. In most of the front-side regions, the vertical

4.3. Role of data loss in ultrasonic anemometers on the shaping of the velocity Loss of data will underestimate the real mean wind speed if the length of the averaging datasets is fixed. In this study, the NRG #40C (NRG systems, Inc.) was adopted to measure the mean wind speed. It is a cup anemometer and is not sensitive to the heavy rain as ultrasonic anemometer. Each NRG #40C anemometer was individually calibrated prior to deployment. The maximum predicted deviation (error) of the NRG #40C anemometer is 0.168 m/s for Class A 4 m/s horizontal wind speed and the operational standard uncertainty is 0.14 m/s at 10 m/s for Class A. If the simple model (Hunter et al., 2003) indicating the over speeding error in percent were adopted, as follows E ¼ I 2 ð1:8d  1:4Þ

(12)

where E is the percentage error, I is the turbulence intensity and d is the distance constant for the anemometer. Then the over speeding error of the NRG #40C anemometer is only about 0.13%, which is acceptable for the estimation of the mean wind speed. Fig. 12a and (b) show the data intact rate versus mean wind speed measured by ultrasonic anemometers at two station in typhoon Nesat (1117). The horizontal axis is the time, the left ordinate is the data intact rate and the right ordinate is the mean wind speed. The green triangle is the data intact rate and the red circle is the mean wind speed. It is noted that in the strong wind section the data loss rate is about 10% or even more. It is because that in the eyewall region with strong wind normally accompanied by heavy rain. 20

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wind eye region, the downward transport of the flow was significant from 100 m height to 25 m height, which enhances the surface layer wind and the horizontal wind speed profile to remain constant in the surface layer (Fig. 5c). In the BEW region, the downward transport of high momentum airflow is significant from 100 m to about 50 m height that leads to a maximum of the horizontal winds at about 50 m height. In the BOV region, the datasets were selected not quite far away from the eyewall, so the downward flow was still significant, which induced slowly increasing horizontal wind speed with height. Based on the previous analysis, a plausible mechanism that generates the sub-hectometre-scale low-level jets can be attributed to the downward transport of convective turbulence by the sub-hectometre boundary-layer roll, which enhances the mean momentum of the wind flow near low surface layer in the rear side of a typhoon, as sketched in Fig. 15. For a typhoon generated in the Northern hemisphere, it moves toward north or northwest ward, and the winds rotate CCW around the typhoon eye. With the moving of the typhoon centre from sea surface to land, the front side surface winds of the typhoon are heated as the temperature of the land is higher than the sea surface temperature. The surface wind will move upward and the perturbation of horizontal winds will increase from the surface. However, in the rear part of the typhoon, the land temperature is lower than the sea surface, the surface winds of the typhoon are cooled. A significant negative vertical velocity perturbation is generated closed to the surface, which induces the low-level jets in surface flow. This effect of suddenly applied surface fluxes on the atmospheric boundary layer flow were investigated by Owinoh et al. (2005) in a theoretical analysis, numerical simulation and field experiments. In addition, the effects of swell may also affect the mean wind profile in different regions of a typhoon. High waves propagate as swells to the northeast region of the eye of a typhoon as following swell, to the northwest region of the eye as cross swell and to the southern region of the eye as opposing swell, as shown in Fig. 16a (Holthuijsen et al., 2012). Different types of swells have different effects on the flow structures and turbulence. At relatively low wind speeds, cross swell induces some reduction of white capping, while at high wind speed, cross swell postpones the reduction of the wind drag, possibly by postponing the creation of a foam-spray slip layer. However, the following swell and the opposing swell increase the wave generation, and at high winds, the continuous breaking of wave and the generation of streaks of foam and droplets at the surface creates a foam-spray slip layer. Thus, in the right front and immediate rear of the eye the drag coefficient is very low as shown in

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wind speeds were positive which means that the upward draft is dominant in these regions; however, in the back-side regions, the downward draft was dominant. Fig. 14 presents 10-min vertical wind profiles in different sections in typhoon Nuri observed at SHW by Doppler SODAR. The datasets analysed in Fig. 14 were taken over the same time window as those in Fig. 5. The thick red line in each figure is the mean wind profile of all datasets in the same time interval. It can be noted that in the FOV region, the vertical wind profiles show the trend of increasing with height, which means that the upflow transports the surface wind upward. In the FEW region, both the positive and negative vertical wind profiles were noted, which may be attributed to the convective mixing in the front-eyewall region. In the

Fig. 15. Sketches of flow structure of a typhoon (Fig.1a in (Li et al., 2015a,b)). 163

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(a)

The swell types and location in a typical TC

(b) Disitribution of drag coefficient in a typical TC

Fig. 16. The swell types, location and distribution of drag coefficient in a typical TC in the Northern hemisphere (Holthuijsen et al., 2012).

Knowledge Innovation Project of Shenzhen (Grant No. JCYJ20170302143625006), New Teacher Program of Shenzhen University (Grant No. 2016066) and the US National Science Foundation (Grant Nos. CMMI 1562244 & CMMI1612843) is gratefully acknowledged.

Fig. 16b (Holthuijsen et al., 2012). Therefore, in the right and rear part of a typhoon at high wind speed, the white out is generated and the drag coefficient goes down, and the surface jet begins to develop in this region. In the observation of these three typhoons, the measured sites were located on the left sides of typhoons, consequently the observation station captured the cross swell in the front side and opposing swell in the back side of these typhoons, and the LLJs were observed apparently in the backside eyewall regions of the typhoons. This mechanism may need to be further investigated based on high resolution observations by Doppler sonde and surface layer observation systems.

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5. Concluding remarks The wind structure at height lower than 100 m based on observations in three landfalling typhoons was investigated. The data in strong typhoons Hagupit and Koppu was measured at wind observation towers equipped with several layers of wind sensors; and the data in typhoon Nuri was recorded by a Doppler SODAR at the observation heights between 5 m and 100 m. The analysis of 10-min horizontal mean wind speed profiles revealed local wind speed maxima at heights lower than 100 m (low-level jets). The formation mechanism and the spatial distribution of these low-level jets were examined. It was found that the development of the low-level jets was neither influenced by the topographic roughness nor apparently by the mean wind speed. These were generated predominantly in the back-side region of the typhoon flow structure, especially in the backside eyewall region. The existence of the low-level jets in the back-side outer-vortex regions depended on the distance from the typhoon centre, the strength and spatial size of a typhoon. In the front-side region of the footprint of a typhoon, the upflow was significant, whereas, in the back-side eyewall region the downflow dominated. The downward transport by sub-hectometre boundary-lay roll brings the high winds from about 100 m to around 40 m height and enhances the surface winds and results in a low-level jet. Additionally, the opposing swell may also have some contribution on the generation of low-level jets in backside of a typhoon. These influences need further investigation based on high resolution observations by a collaborative multiplatform measurement involving Doppler SODAR, conventional sensors and numerical simulation. The low-level jets may have significant influence on wind loads on low-rise buildings, transmission lines structure and trees as observed in post typhoon/hurricane reconnaissance studies. Acknowledgements The support for this research has been provided by the National Natural Science Foundation of China (Grant No. 51778373), the

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