Qichao Wang 19th Coherent Laser Radar Conference

The Wind Observation over Sea Surface by UAV-borne

Coherent Doppler Lidar Qichao Wang (a), Songhua Wu (a, b)*, Jintao Liu (a), Kailin Zhang (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

Abstract: A compact UAV-borne Coherent Doppler Lidar (UCDL) was developed by

the Ocean University of China (OUC) for the observations of wind profiles and

boundary layer structures in Marine Atmospheric Boundary Layer (MABL). The first

flight campaign was conducted at Hailing Island (111.99°E, 21.62°N) from November

to December 2016. The motion-correction and wind retrieval methodology of the

UCDL are expounded in detail. Additionally, the backscatter signals of sea surface are

used to retrieve and calibrate the wind field. During the first flight campaign, the wind

observations from the UCDL are intercompared with the results from a collocated

ground-based coherent Doppler lidar (GCDL). The characteristics of the wind field are

also discussed in this paper.

Keywords: UAV-borne Coherent Doppler Lidar, Sea Surface Wind, Data Retrieval, Correction and

Validation.

1. Introduction

Accurate and rapid wide-range observation of sea surface wind is important for the research of ocean

dynamic prediction model, offshore wind resource assessment, air-sea interaction and flux. The

coherent Doppler wind lidar integrated at an air-based platform provides an approach to conduct such

observations [1-4]. Hence, an UAV-borne Coherent Doppler Lidar (UCDL) was developed by the

Ocean University of China (OUC), it is a compact wind detection system with a light weight of 23.2

kg. The operated eye-safety laser in the UCDL generates a light pulse at the wavelength of 1.55 μm

with the energy of 100 μJ. The full width at half maximum (FWHM) of the pulse width is 200 ns, which

indicates a radial spatial resolution of 30 m [5].

Figure 1. Shipborne UAV ocean observation system and flight routes of the first experiment

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Qichao Wang 19th Coherent Laser Radar Conference

The UCDL is carried at a shipborne UAV ocean observation system (Figure 1). The flight campaign

was conducted at Hailing Island (111.99°E, 21.62°N), Guangdong Province from November to

December 2016. During this campaign, more than ten flights at different heights (e.g. 800m, 1100m,

1200m, 1500m) and different measurement time (e.g. morning, noon and nightfall) were completed.

The airspace and flight routes are shown in Figure 1. During several flights, for purpose of

intercomparison, a ground-based coherent Doppler lidar (GCDL) was deployed simultaneously at the

seashore (red star in Figure 1).

2. ALGORITHM

A modified Velocity Azimuth Display (VAD) method is applied to determine the wind speed and the

direction [6]. In this method, the real radial velocities 𝑣𝑟 obtained from the FFT spectra at different

ranges are necessary. Differs from the GCDL, the mobile UCDL system measures the wind field under

the condition of high speed movement over the sea surface during the observation [7], which induces

the attitude and velocity of UAV into the FFT data and the recorded redial velocity 𝑣𝑟𝑒. Hence, it is

necessary to correct the effect caused by the movement of UAV [8].

The attitude information of the UAV is recoded by the GPS and the IRS (25Hz, 0.05°) those are carried

at the UAV. The pointing angle 𝜃𝑟 (azimuth and pitch angle) of each laser radial is calculated by

combining the roll angle α , pitch angle β , yaw angle γ of UAV and the recorded point angle 𝜃𝑟𝑒

of each laser radial [9]. Then, the range gate 𝑅𝑛 at which the laser reaches the sea surface in each radial

is determined by using the 𝜃𝑟 and the height information of UAV and UCDL. Afterwards, the speed

𝑣𝑒 caused by the movement of UAV in each radial is obtained by calculating the FFT data at range

gate 𝑅𝑛. The real radial speed 𝑣𝑟 caused by wind in every range gate can be obtained by subtracting

the 𝑣𝑒 from recorded radial velocities. Finally, the wind profile can be determined by taking 𝜃𝑟 and

𝑣𝑟 into VAD method.

Figure 2. The attitude information of UAV and the effect of data processing

Figure 2 (1) shows the attitude information of UAV, which includes the (a) roll angle α, (b) pitch angle

β and (c) yaw angle γ. Figure 2 (2, 3) shows the difference between the recorded point angle (black line)

and the corrected point angle (red line) of laser radial, (2) is the azimuth angle and (3) is the pitch angle.

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0Attitude information of UAV

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Qichao Wang 19th Coherent Laser Radar Conference

In Figure2 (4), (a) is the radial velocity 𝑣𝑟𝒆 recorded by UCDL system, (b) is 𝑣𝑒, (c) is 𝑣𝑟, and (d) is

real radial velocity 𝑣𝑟ℎ in horizontal plane, which could be calculated by using 𝑣𝑟 and 𝜃𝑟 .

3. Results

During this campaign, more than ten flights were carried out in about 30 days. The THI (Time-Height-

Intensity) plot of the wind profiles over the sea surface observed at several flights (20th November, 17th

~19th December) are shown in Figure 3.

Figure 3. Wind THI diagrams of several flights observed by UCDL

During these flights, the maximum flying height of the UAV is 1200 m and the blind range of UCDL is

60 m. The lack of data on the left and right sides in each flight is limited by the height of UAV during

the climb and landing phases. There is a distinct symmetry in the THI diagram of 18th December (Figure

3(3)). In this flight, the UAV choose the A-E-A route (red route), it is a reciprocating route from the

seashore to the offshore. The structure of atmosphere is stable and the wind profile show the same

tendency. The wind speed has slightly increased over time, which can also be concurrently observed by

GCDL.

The GCDL was deployed at the seashore and kept observing the wind profile during the flights. This

system offers the wind profiles, which can be used to validate the performance of the UCDL. The results

of comparison are shown in Figure 4.

Figure 4. Wind data observed by GCDL.

Figure 4 (1) is the THI plot observed by GCDL at the same time of Figure 3 (4), which observed by

4 6 8 10 12 140

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UCDL(m/s)

GCDL&UCDL（2016/11/27 09:50-10:00）

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GCDL&UCDL(m/s)

Qichao Wang 19th Coherent Laser Radar Conference

UCDL. Significant vertical stratification of wind speed is observed by both systems, and observations

of two systems match well. Figure 4 (2) shows the comparison result of ten-minute average wind profile.

The discrepancies between two systems are mainly caused by the difference locations and the non-

homogeneous wind field. The ten-minute average wind profile of UCDL and GCDL are compared. The

maximum deviation between two systems is 0.39m/s, the minimum deviation is less than 0.1m/s and

the average deviation in different height is 0.14m/s.

Figure 5. Observe range of UCDL and GCDL

During the flight on 19th December, The observe range of UCDL (15m~1140m above sea surface) is

wider than GCDL(113m~1000m above sea surface). Because of the low concentration of aerosol and

the long distance between the GCDL and particles in the upper layers, the GCDL can only provide the

wind field below 1000 m (B2 in Figure 5). Comparing with the GCDL, the UCDL measured the wind

profile from the sky, taking the advantage of the short distance between the UCDL and the upper layers,

the UCDL performs better in high altitude wind observations. In addition, the UCDL can avoid the

limitation of blind range in low altitude area, provides the wind profiles from 15m above the sea surface.

4. Conclusion

The UCDL system developed by OUC team has the ability to observe the wind over sea surface. We

already completed the first flight campaign at Hailing Island, the inversion algorithm has been tested

and the accuracy of UCDL has been validated by GCDL. Due to the operating position, it can lower the

blind range near to the sea surface. Comparing with GCDL, UCDL performs better in high altitude

wind measurement and it has the advantage of rapid wide-range observations. Further data processing

and analysis will be carried out.

5. References

[1] Baker, W. E., et al, “Lidar-Measured Wind Profiles: The Missing Link in the Global Observing System”,

Bull. Amer. Meteor. Soc., 95, 4, 543-564 (2014).

[2] Koch, G. J., Beyon, J. Y., Cowen, L. J., Kavaya, M. J., & Grant, M. S., “Three-dimensional wind profiling

of offshore wind energy areas with airborne Doppler lidar”. Journal of Applied Remote Sensing, 8(1), 083662.

(2014).

[3] Peña, A., Hasager, C. B., Gryning, S. E., Courtney, M., Antoniou, I., & Mikkelsen, T., “Offshore wind

profiling using light detection and ranging measurements”. Wind Energy, 12(2), 105-124 (2009).

[4] Hardesty, M., Tucker, S., Baidar, S., & Beubien, M., “Airborne tests of an OAWL Doppler lidar: Results

and potential for space deployment”. In EPJ Web of Conferences (Vol. 176, p. 02004, 2018). EDP Sciences.

Qichao Wang 19th Coherent Laser Radar Conference

[5] Wu, S., Wang, Q., Liu, B., Liu, J., Zhang, K., & Song, X., “UAV-borne coherent doppler lidar for marine

atmospheric boundary layer observations”. In EPJ Web of Conferences (Vol. 176, p. 02012, 2018). EDP Sciences.

[6] Browning K A., Wexler R., “The determination of kinetic properties of a wind field using Doppler radar”,

Journal of Applied Meteorology, (1968), 15, 1302-1306.

[7] J. Y. Beyon et al., “Airborne wind profiling algorithms for the pulsed 2-micron coherent Doppler lidar at

NASA Langley Research Center,” Proc. SPIE 8731, 87310K (2013).

[8] Werner, C., Leike, I., Streicher, J., Reitebuch, O., Cress, A., & Wergen, W., “Validation of doppler lidar

wind measurements with the local model of the german weather service”. In International Conference on Space

Optics (2017, November)—ICSO 2000 (Vol. 10569, p. 105690K). International Society for Optics and Photonics.

[9] Li Z. G., Liu Z. S., Zhu J. S., Liu B. Y., Reitebuch O., “Wind Retrieval Algorithms For The Wind Products

Of The Airborne Coherent Doppler Lidar”, Proceeding ‘Dragon 3 Mid-Term Results Symposium’, Chengdu,

China (ESA SP-724, November 2014).