Characterization of Aircraft Dynamic Wake Vortices and

Atmospheric Turbulence by Coherent Doppler Lidar

Songhua Wu (a, b)*, Xiaochun Zhai (a), Bingyi Liu (a, b)

(a) Ocean Remote Sensing Institute, College of Information Science and Engineering,

Ocean University of China, Qingdao 266100, China.

(b) Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National

Laboratory for Marine Science and Technology, Qingdao 266100, China.

*Email: wush@ouc.edu.cn

1. Introduction

Wake vortex are large rolling air masses generated by aircraft as a consequence of lift. The behavior

of that wake vortex is a major issue in aeronautical research [1,2]. They are strongest and most

hazardous on congested airfields during take-off and landing, i.e. in close proximity to the ground.

It is held that serious interactions between wake vortex and the ground can significantly affect the

evolution of wake vortex, thus making them difficult to predict [3-4]. In order to investigate the real

approach to mitigate wake vortex using 1.55μm coherent Doppler lidar and to study the wake vortex

near ground effect (NGE), the wake vortex observation project was carried out during Jan-Mar 2017

at Beijing Capital International Airport (BCIA) [5]. This paper focuses on the NGE analyze based

on 2017 BCIA experiment (2017BCIAE) observation.

2. Setup and Method

A scanning Doppler lidar and a wind profiler lidar are used during 2017BCIAE. The main

specifications of these two setups are listed in Table 1. Figure 1 (a) shows the sketch map of lidar

location at BCIA from 20 Jan 2017 to 20 Mar 2017. Figure 1 (b) shows the field experiment at

BCIA. Since the direction of landing depends on the wind direction and the prevailing wind

direction at BCIA in winter is northerly wind, the meteorological station in the south of 01L/36R

runway was selected to deploy the CDL for wake vortex measurement.

Table 1. The specifications of the 3D scanning lidar and wind profile lidar

Qualification 3D scanning lidar wind profile lidar

Wavelength 1.55 μm 1.55 μm

Data update rate 4 Hz (fastest) 1 Hz

Measurement range 40 m - 4000 m 40 ~ 240 m

Radial velocity measurement range 37 5. 1ms 37 5. 1ms

Radial range resolution 15 m - 60 m configurable

Scanning features VAD 5- DBS

Weight ~75 kg 45 kg

Power consumption <300 W 90 W

Th5

Figure 1. (a) The second outfield experiment of wake vortex NGE observation at BCIA during 20 Jan to 23 March

2017 (b) A: wind profiler lidar, B: Scanning PCDL.

Generally, the spectral width is firstly used to judge whether the wake vortex exists or not in one

scanning measurement. If the maximal spectral width is less than the pre-set threshold (~10.5), it is

regarded as the background wind field which will be used in the later wake vortex analysis. On the

contrary, if the maximal spectral width is larger than the threshold, the wake vortex feature

extraction is then processed based on specificity of wake vortex ground effect.

Let the array of Lidar estimates of the radial velocity '( , ; )k mV R n contain the information about

aircraft wake vortex starting from the scan number '

0 1n n and up to '

0n n N . To avoid the

influence of the background wind, we obtain the following data array:

0 0( , ; ) ( , ; ) ( , ; )k m k m k mV R n V R n n V R n

Figure 2 (a) exemplifies the distribution of ( , ;2)k mV R obtained from the data measured by CDL

at the BCIA, as an A333 aircraft is scanned by CDL PPI mode at a height of 30 m. We can see that

at distance from 250 m to 380 m from the CDL, the pair of wake vortex affects the radial velocity.

Figure 2 (b) shows the distribution of spectral width ( , ;2)k mS R , and the spectral broadening due

to wake vortex is obvious and thus can be an available feature to determine the existence of wake.

To estimate coordinates of axes of the left and right wake vortex from the data analogous to those

shown in Figure 2 (a) and (b), we introduce the function ( , ;2)k mS R , ( ; )s kD R n , max ( ; )kR n and

min ( ; )kR n determined by the following algorithm:

maxmax { ( , ; )} ( , ; )k m kV R n V R n (1)

minmin { ( , ; )} ( , ; )k m kV R n V R n (2)

max min( ; ) | ( , ; ) | | ( , ; ) |v k k kD R n V R n V R n (3)

( ; ) max{ ( , ; )}s k k mD R n S R n (4)

where the maxima and minimums fall within the angle range m for every distance kR . Figure 2

(c) show ( ; )v kD R n and ( ; )s kD R n in green and blue lines, respectively. It can be seen that both

( ; )v kD R n and ( ; )s kD R n have two pronounced peaks. To better estimate the wake vortex

coordinates, we take ( ; ) /10s ka D R n as a weight factor, and introduce the function ( ; )kD R n

determined by the following equation:

( ; ) ( ; )k v kD R n a D R n (5)

which can be seen in Figure 2 (d)(e) described using black lines. So far the range bin of wake vortex

can be determined exactly. Figure 2 (e) show the distribution of 1max ( ; )cR n and

2min ( ; )cR n in

red and blue lines, respectively. Then the average of 2max ( ; )cR n and

2min ( ; )cR n can be regarded

as the elevation of the vortex in the polar coordinate system. In Figure 2 (f), the red and black

squares show the coordinates of the axes of the left and right aircraft vortex as obtained using above

method.

Figure 2. The CDL RHI scanning measurement (a) radial velocity (b) spectral width when an A333 crossed

the scanning plane during 02:10:12 Jan 23 2017 at BCIA. (c) Corresponding function ( ; )v kD R n (green curve),

( ; )s kD R n (blue line) and ( ; )kD R n (black line) and (d) determined wake vortex position: red and black square

for left and right vortex, respectively.

The circulation is defined as the mean of radius 5~15 m values. When the core position height is

less than 15 m, that means the circulation in larger radius is unavailable. In this case, the continuation

property is used based on Burnham-Hallock model. Figure 3 shows the distribution of wake vortex

circulation with radius and corresponding B-H fitting. The fitting results match the measurement

well and reasonable for circulation correction.

Figure 3. BH model fit using CDL measured data (a) without NGE, (b) with NGE.

100 200 300 400 500 6000

50

100

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Distance (m)

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(m)

264-20170125

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20 40 60 80 100 120 140 160 180 200 2200

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Distance from Lidar,m

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100 200 300 400 500 6000

5

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Distance from Lidar,m

Ele

vation,d

eg

3. Results

Figure. 4 shows the example of wake vortex NGE observation. It is typical for aircraft wake vortex

evolution in ground proximity where the asymmetrical rebound is driven by crosswind. The results of

CDL measurements of the normalized distance between the cores of the aircraft vortex, nadir angle,

normalized vortex core altitude and horizontal distance, normalized vertical and horizontal component

of the vortex core movement speed when A332 overflight during 17:46:17–17:49:52, 25 Jan 2017 at

BCIA are shown in Figure 5 as a function of normalized time, respectively.

Figure 4. (a) Trajectories of left (red squares) and right (black squares) wake vortex axes and (b) evolution of

wake vortex circulation generated by A333 over the BCIA at 17:46:17–17:49:52, 25 Jan 2017.

Figure 5. Measurement results of (a) the normalized distance between the cores of the wake vortex (b) the

nadir angles of a pair of vortex (c) the normalized core altitude (d) normalized vertical component of the vortex

core movement speed (e) the normalized transverse distance (f) normalized transverse component of the vortex

core movement speed generated by A332 aircraft over the BCIA on 17:46:17 – 17:49:52 25 Jan 2017.

4. Discussion

The field experiment of wake vortex observation based on CDL quick-scanning mode in 2017 at

BCIA is summarized. Different from the previous algorithm for the velocity envelope processing, a

new algorithm based on the radial velocity and spectral width distribution has been developed to

capture wake vortex evolution process under NEG. The circulation of wake vortex is also corrected

and calculated using Burnham-Hallock model. Case studies shows the accuracy and reasonability

of BH correction. A lot of representative cases under NGE have been retrieved using the batching

processes based on this long-time period experimental observation. In the further study, the

turbulence parameters from RHI structure function and the background wind profile from V300

wind profile measurement will be used to analyze the atmospheric condition effect on wake vortex

evolution under NGE, which is fewer studied now, providing further knowledge and observational

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validation from simulation models.

References:

1. Köpp, Friedrich, Stephan Rahm, and Igor Smalikho. "Characterization of Aircraft Wake Vortices by

2-μ m Pulsed Doppler Lidar." Journal of Atmospheric and Oceanic Technology21.2 (2004): 194-

206.

2. Smalikho, I. N., and Sh Rahm. "Lidar investigations of the effects of wind and atmospheric turbulence

on an aircraft wake vortex." Atmospheric and Oceanic Optics 23.2 (2010): 137-146.

3. Holzapfel, Frank, and Meiko Steen. "Aircraft wake-vortex evolution in ground proximity: analysis

and parameterization." AIAA journal 45.1 (2007): 218.

4. Robins, Robert E., and DONALD P. Delisi. "Potential hazard of aircraft wake vortices in ground effect

with crosswind." Journal of Aircraft 30.2 (1993).

5. Wu, Songhua, et al. "Characterization of aircraft dynamic wake vortices and atmospheric turbulence

by coherent doppler lidar." EPJ Web of Conferences. Vol. 176. EDP Sciences, 2018.