Improved wind speed estimation and rain quantification with continuous-wave wind lidar

Jin, Liqin; Angelou, Nikolas; Mann, Jakob; Larsen, Gunner Chr.

doi:10.1088/1742-6596/2265/2/022093

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Therefore, we placed the three lidars as close to the intended scanning positions as possible to decrease the measurement volume (Angelou et al, 2012) and minimize potential biases resulting from volume averaging (Clive, 2008;Sjöholm et al, 2009;Forsting et al, 2017). To improve the accuracy of the flow velocity retrieval, we discard spectra containing Doppler shifts caused by hard targets and outof-focus moving objects during post-processing (Jin et al, 2022a).…”

Section: The Windscanner Systemmentioning

confidence: 99%

Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

Jin,

Ghirardelli,

Mann

et al. 2024

Atmos. Meas. Tech.

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Abstract. Ultrasonic anemometers mounted on rotary-wing drones have the potential to provide a cost-efficient alternative to the classical meteorological mast-mounted counterpart for atmospheric boundary layer research. However, the propeller-induced flow may degrade the accuracy of free-stream wind velocity measurements by wind sensors mounted on drones – a fact that needs to be investigated for optimal sensor placement. Computational fluid dynamics (CFD) simulations are an alternative to experiments for studying characteristics of the propeller-induced flow but require validation. Therefore, we performed an experiment using three short-range continuous-wave Doppler lidars (light detection and ranging; DTU WindScanners) to measure the complex and turbulent three-dimensional wind field around a hovering drone at low ambient wind speeds. Good agreement is found between experimental results and those obtained using CFD simulations under similar conditions. Both methods conclude that the disturbance zone (defined as a relative deviation from the mean free-stream velocity by more than 1 %) on a horizontal plane located at 1 D (rotor diameter D of 0.71 m) below the drone extends about 2.8 D upstream from the drone center for the horizontal wind velocity and more than 7 D for the vertical wind velocity. By comparing wind velocities along horizontal lines in the upstream direction, we find that the velocity difference between the two methods is ≤ 0.1 m s−1 (less than a 4 % difference relative to the free-stream velocity) in most cases. Both the plane and line scan results validate the reliability of the simulations. Furthermore, simulations of flow patterns in a vertical plane at the ambient speed of 1.3 m s−1 indicate that it is difficult to accurately measure the vertical wind component with less than a 1 % distortion using drone-mounted sonic anemometers.

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Section: The Windscanner Systemmentioning

confidence: 99%

Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

Jin,

Ghirardelli,

Mann

et al. 2024

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

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Suppression of precipitation bias in wind velocities from continuous-wave Doppler lidars

Jin,

Mann,

Angelou

et al. 2023

Atmos. Meas. Tech.

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Abstract. In moderate to heavy precipitation, raindrops may deteriorate the accuracy of Doppler lidar measurements of the line-of-sight wind velocity because their projected velocity in the beam direction differs greatly from that of air. Therefore, we propose a method for effectively suppressing the adverse effects of rain on velocity estimation by sampling the Doppler spectra faster than the time taken for a raindrop to transit through the beam. By using a special averaging procedure, we can suppress the strong rain signal by sampling the spectrum at 3 kHz. A proof-of-concept field measurement campaign was performed on a moderately rainy day with a maximum rain intensity of 4 mm h−1 using three ground-based continuous-wave Doppler lidars at the Risø campus of the Technical University of Denmark. We demonstrate that the rain bias can effectively be removed by normalizing the noise-flattened 3 kHz sampled Doppler spectra with their peak values before they are averaged down to 50 Hz prior to the determination of the speed. In comparison to the sonic anemometer measurements acquired at the same location, the wind velocity bias at 50 Hz (20 ms) temporal resolution is reduced from up to −1.58 m s−1 for the original raw lidar data to −0.18 m s−1 for the normalized lidar data after suppressing strong rain signals. This reduction in the bias occurs during the minute with the highest amount of rain when the focus distance of the lidar is 103.9 m and the corresponding probe length is 9.8 m. With the smallest probe length, 1.2 m, the rain-induced bias is only present at the period with the highest rain intensity and is also effectively eliminated with the procedure. Thus, the proposed method for reducing the impact of rain on continuous-wave Doppler lidar measurements of air velocity is promising and does not require much computational effort.

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Improved wind speed estimation and rain quantification with continuous-wave wind lidar

Cited by 2 publications

References 15 publications

Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

Suppression of precipitation bias in wind velocities from continuous-wave Doppler lidars

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