Mean wind vector estimation using the velocity–azimuth display (VAD) method: an explicit algebraic solution

Teschke, Gerd; Lehmann, Volker

doi:10.5194/amt-10-3265-2017

Cited by 21 publications

(20 citation statements)

References 21 publications

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“…This aspect has been shown by the determination of the second statistical moment by lidar data using an analytical model [23]. The optimum angle of a four point VAD method was estimated with an analytical solution to φ = 54.7356 • [30]. This correlates very well with the result in this study.…”

Section: Discussionsupporting

confidence: 84%

“…The results are visualized using the frequency of In this study of 3820 data points, the CLS method resulted in a lower error in 91.1% of the measurement periods. One reason for this is, that the additional measuring point of the CLS method reduces the reconstruction error compared to the PRESS algorithm; the reconstruction error for the wind components is proportional to 1/ √ N where N as number of laser beams [30]. This is also surprising as it was hoped initially that the PRESS method would be more robust than the CLS method because of the rejection of outliers.…”

Section: Discussionmentioning

confidence: 99%

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Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach

2018

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In complex terrain, ground-based lidar wind speed measurements sometimes show noticeable differences compared to measurements made with in-situ sensors mounted on meteorological masts. These differences are mostly caused by the inhomogeneities of the flow field and the applied reconstruction methods. This study investigates three different methods to optimize the reconstruction algorithm in order to improve the agreement between lidar measurements and data from sensors on meteorological masts. The methods include a typical velocity azimuth display (VAD) method, a leave-one-out cross-validation method, and a linear model which takes into account the gradients of the wind velocity components. In addition, further aspects such as the influence of the half opening angle of the scanning cone and the scan duration are considered. The measurements were carried out with two different lidar systems, that measured simultaneously. The reference was a 100 m high meteorological mast. The measurements took place in complex terrain characterized by a 150 m high escarpment. The results from the individual methods are quantitatively compared with the measurements of the cup anemometer mounted on the meteorological mast by means of the three parameters of a linear regression (slope, offset, R 2 ) and the width of the 5th–95th quantile. The results show that expanding the half angle of the scanning cone from 20 ∘ to 55 ∘ reduces the offset by a factor of 14.9, but reducing the scan duration does not have an observable benefit. The linear method has the lowest uncertainty and the best agreement with the reference data (i.e., lowest offset and scatter) of all of the methods that were investigated.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach

2018

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“…As shown in Figure 2, the beam orientation is defined in terms of the azimuth angle θ and the elevation angle φ. In the Vertical Azimuth Display (VAD) scanning technique, the measurement is made in at least three linearly independent directions, and when the wind fields are not uniform, the measurement errors will increase [40,41]. When more than three directions are used, extended tests under uniform and stable conditions can be carried out [21].…”

Section: Scanning Modementioning

confidence: 99%

Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar

Zhao

Zhang

et al. 2021

Remote Sensing

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Real-time measurement of atmospheric wind field parameters plays an important role in weather analysis and forecasting, including improving the efficiency of wind energy, particle tracking, boundary layer measurements, and airport security. In this study, a wind profile coherent wind Light Detection and Ranging (Lidar) measurement with a wavelength of 1.55 µm was developed and demonstrated based on the principle of eight-beam velocimetry. The wind speed information was retrieved, and vertical and horizontal profiles were calculated via power spectrum estimation of sampled echo signals through the measurement of the atmospheric wind field in Hefei for several consecutive days. The experimental results show that the wind profiles produced using different techniques are quite consistent and the standard error is less than 0.42 m/s compared with three-beam and five-beam wind measurements.

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“…So, we chose the radar data under comparatively stable air condition for velocity verification, where v b z is approximately equal to zero. With the Doppler velocity detected by radar, the vertical velocity component of the raindrops can be obtained with VAD (velocity-azimuth display) method if the background wind obeys a linear distribution model [50]. For a certain radar range bin at azimuth angle θ and elevation angle φ, the Doppler velocity can be written as [11]…”

Section: B Verification Of the Velocity Of The Characteristic Raindropsmentioning

confidence: 99%

Retrieval of the Characteristic Size of Raindrops for Wind Sensing Based on Dual-Polarization Radar

Peng

Yin

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Wind velocity is of great importance for weather monitoring, aviation hazard alerting, wind energy exploring, and etc. Doppler radar is widely used to measure wind under rainy condition by sensing the raindrops entrained by the background wind. However, the Doppler velocity, which is a reflection of the raindrops' velocity in radial direction, is not coincident with the background wind because of the strong inertia of raindrops. Efforts should be made to distinguish the difference between the raindrops' velocity and the background wind velocity. In this paper, we try to establish a relationship between the background wind velocity and the raindrops' velocity by introducing a definition of the raindrops' characteristic size, which is related to the velocity characterized by the strongest Doppler spectral component. It is found that the fusion of differential reflectivity and the depolarization ratio can serve as a good proxy for the estimation of the characteristic size. Simulation results for S/C/X band radars and radar measurements verify the good performance of the proposed retrieval model for the characteristic size, which lays a solid foundation for the retrieval of the background wind velocity.

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Mean wind vector estimation using the velocity–azimuth display (VAD) method: an explicit algebraic solution

Cited by 21 publications

References 21 publications

Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach

Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach

Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar

Retrieval of the Characteristic Size of Raindrops for Wind Sensing Based on Dual-Polarization Radar

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