Comparison of wind measurements between virtual tower and VAD methods with different elevation angles

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1 P32 Xiaoying Liu et al. Comparison of wind measurements between virtual tower and AD methods with different elevation angles Xiaoying Liu 1, Songhua Wu 1,2*, Hongwei Zhang 1, Qichao Wang 1, Xiaochun Zhai 1 1 Ocean Remote Sensing Institute, College of Information Science and Engineering, Ocean University of China, Qingdao , China. 2 Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao , China. * wush@ouc.edu.cn Abstract: This paper compares wind retrieval methods of T (irtual Tower) mode and AD (elocity-azimuth Display) with different scanning elevation angles. The field experiment was performed from September 23 to October at the campus of Ocean University of China under various weather conditions. A total of seven lidars were involved in the experiment. Three lidars carried out the staring mode to constitute the T. The other four lidars were concurrently operating PPI scanning at different elevation angles, corresponding to different spatial average volume. The results of wind speed and direction from T and AD shows good correlation when lidars are well synchronized. The influence of spatial homogeneity on wind retrieval is also described in this paper. Keywords: Lidar, irtual Tower, AD, olume Average 1. Introduction Accurate wind field information can be used to explore the subtle structure of complex wind flow, meanwhile, it can also provide the possibility to comprehend the underlying physical mechanisms and to optimize wind field models [1]. The single-lidar commonly uses the Doppler beam swinging (DBS) strategy and the velocity azimuth display (AD) technique to obtain high accuracy [2-4]. Both of these methods are based on spatial horizontal homogeneity assumption, which is often invalid in complex wind field [5-6]. In response to this question, two or more additional lidars are used to obtain a threedimensional wind field by spatiotemporal synchronization scanning, which can be called virtual tower (T) [7-8]. In order to demonstrate the feasibility and accuracy of T, the comparison between DBS mode and T are studied in [6, 8], and the T and sonic anemometer also show good agreement in [1, 9]. This paper introduces the results of T and AD mode with different scanning elevation angles. 2. Experiments The field experiment was performed from September 23 to October at the campus of Ocean University of China. As shown in Figure 1, a total of seven coherent Doppler lidars were involved in the experiment. Three lidars (T1, T2 and T3 in the Figure 1 and ) constituted the virtual tower by carrying out the staring mode. They were located on the north, east and south of the playground respectively to meet the requirements that three lidar beams should be intersecting and non-coplanar [10]. The other four lidars (as shown in Figure1 and, the AD1, AD2, AD3 and AD4) were concurrently operating PPI scanning at different elevation angles, corresponding to different spatial average volume. Four lidars on AD mode were all placed on the west side of the playground and each of two adjacent lidars is spaced one meter apart. During the experiment, three lidars of T simultaneously stared at the six points (50 m to 300 m with 50 m increments) above the AD2 (see in Figure 1). The distance of these three lidars to AD2 were all 98m, which were approximately the same. This ensured that they had the same elevation angle when measuring the same height. The three lidar beams are continuously observed for 1 min at each height at the sampling rate of 0.5Hz, which can capture the rapid atmospheric process while ensuring the high temporal resolution as much as possible. The other four lidars on AD mode scanned at the speed of 6 degrees per second for full-cycle PPI scan. Therefore, it took 1 minute to obtain a wind profile. The experimental parameters of seven lidars are shown in Table 1. CLRC 2018, June

2 Figure 1. Schematic of experimental configuration. Seven lidars location on the playground. The virtual tower stare scan and four full-cycle PPI scan with different elevation angles. Table 1. Experimental observation parameters of seven lidars Lidar Code Retrieval Elevation( ) Azimuth( ) Methods 50 m 100 m 150 m 200 m 250 m 300 m T T2 T T AD1 50 AD2 60 AD 0~360 AD AD Methodology Lidar measures the movement of aerosol particles in the atmosphere to obtain a line-of-sight (LOS) velocity (or radial velocity). The LOS velocity is a projection of the wind field in the pointing direction of the laser beam, so the horizontal and vertical velocity can be retrieved from the LOS velocity by Eq.(1). u sin cos v cos cos w sin (1) r where r is the LOS velocity, u, v, w are the zonal velocity, the meridional velocity, and the vertical velocity, respectively. And, are the azimuth and elevation angles, respectively. In order to retrieve the three components of the wind field, two additional equations are needed. Therefore, the three radial velocities from three lidars of the virtual tower can form a set of wind vector equations. In Eq. (2), the terms on the left hand side are the radial velocity measurements of the T1, T2 and T3, which can be represented by r. The right-hand side includes the geometrical information in the matrix M and the velocity vector in meteorological coordinates [10]. So the relationships among these three vector matrix can be written as Eq. (3). And the vector is our concerned actual wind information. CLRC 2018, June

3 4. Analysis and Results r1 sin1 cos1 cos1 cos1 sin1 U sin cos cos cos sin r r3 sin3 cos3 cos3 cos3 sin 3 W = M -1 r (3) The single lidar measurements need to consider the difference between scalar and vector average when compared to the point measurements. The analysis of averaged wind vector need to average these components over a period of time e.g.10 min by scalar averaging and vector averaging. The magnitude of the vector is different from the scalar, and the ratio between them and the standard deviation of the wind direction follow the law of the Bessel function as seen in Figure 2. The ratio of vector to scalar average can be written in Eq. (4) [11], where and are the vector average and the scalar vector scalar average respectively, and is standard deviation of the wind direction in the interval time. Ratio vector scalar sin 3 3 (2) (4) Figure 2. The Bessel function of the ratio of vector to scalar average with the standard deviation of the wind direction. The results of theoretical analysis. The results of experimental data with T and AD (The interval time is 10 min). Analysis of AD data (volume measurements) can be well fitted to Bessel function. T is not point measurements in the strict sense with a certain measurement volume and is sensitive to wind direction. It is also possible to calculate the vector and scale average in the interval time. And the results of T are also fitted to the function well. The following analysis are all based on the vector average of T and AD. The comparison of horizontal velocity and direction between T and AD are shown in Figure 3. The data came from the observations made on September 27 and 28, The scanning elevation angle of the AD used in the comparison is 60. As seen from the Figure 3, the results of wind speed and direction from T and AD shows good correlation. The correlation coefficient (R) are the 0.93 and 0.99 in the velocity and direction, respectively. The standard deviation of 0.78 m/s and 3.8 are all in the reasonable range. Figure 4 shows the time series of horizontal velocity of T and AD at four elevation angles. The absence of data in the plot is due to quality control based on time synchronization criteria. The wind speed of T and AD show a consistent changing trend over time. When the wind speed is lower, the wind magnitude of T and AD are roughly equivalent. As the wind speed continues to increase, the speed of T is significantly higher than that of AD and the fluctuations also increase. CLRC 2018, June

4 Figure 3. Comparison of the horizontal velocity and horizontal direction from the T technique with the measurements made by AD technique. (c) (d) Figure 4. Time series of horizontal velocity of T and AD with four elevation angles, as shown 35.3, 50, (c) 60 and (d) 70 at 250 m on September 30, The shaded portion in the figure represents the fluctuation of wind speed. Compared with the cup anemometer, lidar measures the wind with a volume average around a given height [11]. Lidar has an effective probe length corresponding to a finite height resolution and elevation angle. In this experiment, the radial resolution is 30m, and the elevation angles is different as seen in Table 1. Figure 5 shows the result of average volume, where the ratio is the wind speed of volume average around a given height to the speed of the given height. According to the figure, the larger elevation angle and the lower height have the smaller ratio, which means the more significant influence of volume average. CLRC 2018, June

5 Figure 5. The ratio of volume average wind speed around a given height to the speed of the given height. It is worth to noting that these ratios are all above From the perspective of theoretical analysis, the effect of the volume average on wind speed is not obvious at different elevation angles with the 30 m radial resolution. 5. Conclusions This paper introduces the synchronous observation experiments of seven lidars. The comparison between T and AD shows good agreement. The volume average effect is also analyzed theoretically. In the next work, it is also necessary to analyze the influence on the actual wind field with measurement data. The time series analysis indicates that T and AD have the same tendency. 6. Acknowledgement This work was supported by National Natural Science Foundation of China (No ) and the National Key Research and Development Program of China (016YFC ). The authors wish to thank Fanghan Wang for valuable discussion. 7. References [1] Aditya Choukulkar, Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign, Atmospheric Measurement Techniques 10, (2017). [2] Strauch, R. G., The Colorado Wind-Profiling Net-work, Atmos. Ocean. Tech., 1, (1984). [3] Browning, K. A. The determination of kinematic properties of a wind field using Doppler radar, Appl. Meteorol., 7, (1968). [4] Gottschall, J, Lidar profilers in the context of wind energy A verification procedure for traceable measurements, Wind Energy, 15, (2012). [5] Bingöl F, Conically scanning lidar error in complex terrain, Meteorologische Zeitschrift, 18, (2009). [6] Pauscher L, An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain, Remote Sensing, 8, 782 (2016). [7] Calhoun, R., irtual towers using coherent Doppler lidar during the Joint Urban 2003 dispersion experiment, Appl. Meteo-rol. Clim, 45, (2006). [8] Newman, Jennifer F., "Testing and validation of multi lidar scanning strategies for wind energy applications." Wind Energy (2016): [9] Mann, Jakob, "Comparison of 3D turbulence measurements using three staring wind lidars and a sonic anemometer." IOP Conference Series: Earth and Environmental Science.1, (2008). [10] Fernando Carbajo Fuertes, 3D Turbulence Measurements Using Three Synchronous Wind Lidars: alidation against Sonic Anemometry, Journal of Atmospheric and Oceanic Technology 31.7, (2014). [11] Clive, P. J. M. "Compensation of bias in Lidar wind resource assessment." Wind Engineering 32.5, (2008). CLRC 2018, June