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- Author or Editor: Jakob Mann x
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Abstract
This study demonstrates that a pulsed wind lidar is a reliable instrument for measuring angles between horizontal vectors of significance in the atmospheric boundary layer. Three different angles are considered: the wind turning, the angle between the stress vector and the mean wind direction, and the angle between the stress vector and the vertical gradient of the mean velocity vector. The latter is assumed to be zero by the often applied turbulent-viscosity hypothesis, so that the stress vector can be described through the vertical gradient of velocity. In the atmospheric surface layer, where the Coriolis force is negligible, this is supposedly a good approximation. High-resolution large-eddy simulation data show that this is indeed the case even beyond the surface layer. In contrast, through analysis of WindCube lidar measurements supported by sonic measurements, the study shows that it is only valid very close to the surface. The deviation may be significant even at 100 m. This behavior is attributed to mesoscale effects.
Abstract
This study demonstrates that a pulsed wind lidar is a reliable instrument for measuring angles between horizontal vectors of significance in the atmospheric boundary layer. Three different angles are considered: the wind turning, the angle between the stress vector and the mean wind direction, and the angle between the stress vector and the vertical gradient of the mean velocity vector. The latter is assumed to be zero by the often applied turbulent-viscosity hypothesis, so that the stress vector can be described through the vertical gradient of velocity. In the atmospheric surface layer, where the Coriolis force is negligible, this is supposedly a good approximation. High-resolution large-eddy simulation data show that this is indeed the case even beyond the surface layer. In contrast, through analysis of WindCube lidar measurements supported by sonic measurements, the study shows that it is only valid very close to the surface. The deviation may be significant even at 100 m. This behavior is attributed to mesoscale effects.
Abstract
Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system, different rotor sizes, and wind evolution. The method is compared to real measurement data with promising results. In addition, examples depict how this model can be used to design an optimal controller and how the configuration of a lidar system is optimized for a given turbine to improve the correlation.
Abstract
Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system, different rotor sizes, and wind evolution. The method is compared to real measurement data with promising results. In addition, examples depict how this model can be used to design an optimal controller and how the configuration of a lidar system is optimized for a given turbine to improve the correlation.
Abstract
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Abstract
The wind speed profile for the neutral boundary layer is derived for a number of mixing-length parameterizations, which account for the height of the boundary layer. The wind speed profiles show good agreement with the reanalysis of the Leipzig wind profile (950 m high) and with combined cup–sonic anemometer and lidar measurements (300 m high) performed over flat and homogeneous terrain at Høvsøre, Denmark. In the surface layer, the mixing-length parameterizations agree well with the traditional surface-layer theory, but the wind speed profile is underestimated when the surface-layer scaling is extended to the entire boundary layer, demonstrating the importance of the boundary layer height as a scaling parameter. The turbulence measurements, performed up to 160-m height only at the Høvsøre site, provide the opportunity to derive the spectral-length scales from two spectral models. Good agreement is found between the behaviors of the mixing- and spectral-length scales.
Abstract
The wind speed profile for the neutral boundary layer is derived for a number of mixing-length parameterizations, which account for the height of the boundary layer. The wind speed profiles show good agreement with the reanalysis of the Leipzig wind profile (950 m high) and with combined cup–sonic anemometer and lidar measurements (300 m high) performed over flat and homogeneous terrain at Høvsøre, Denmark. In the surface layer, the mixing-length parameterizations agree well with the traditional surface-layer theory, but the wind speed profile is underestimated when the surface-layer scaling is extended to the entire boundary layer, demonstrating the importance of the boundary layer height as a scaling parameter. The turbulence measurements, performed up to 160-m height only at the Høvsøre site, provide the opportunity to derive the spectral-length scales from two spectral models. Good agreement is found between the behaviors of the mixing- and spectral-length scales.
Abstract
A spectral tensor model is presented for turbulent fluctuations of wind velocity components and temperature, assuming uniform vertical gradients in mean temperature and mean wind speed. The model is built upon rapid distortion theory (RDT) following studies by Mann and by Hanazaki and Hunt, using the eddy lifetime parameterization of Mann to make the model stationary. The buoyant spectral tensor model is driven via five parameters: the viscous dissipation rate ε, length scale of energy-containing eddies L, a turbulence anisotropy parameter 
Abstract
A spectral tensor model is presented for turbulent fluctuations of wind velocity components and temperature, assuming uniform vertical gradients in mean temperature and mean wind speed. The model is built upon rapid distortion theory (RDT) following studies by Mann and by Hanazaki and Hunt, using the eddy lifetime parameterization of Mann to make the model stationary. The buoyant spectral tensor model is driven via five parameters: the viscous dissipation rate ε, length scale of energy-containing eddies L, a turbulence anisotropy parameter
Abstract
We investigate characteristics of large-scale coherent motions in the atmospheric boundary layer using field measurements made with two long-range scanning wind lidars. The joint scans provide quasi-instantaneous wind fields over a domain of ∼50 km2, at two heights above flat but partially forested terrain. Along with the two-dimensional wind fields, two-point statistics and spectra are used to identify and characterize the scales, shape, and anisotropy of coherent structures—as well as their influence on wind field homogeneity. For moderate to high wind speeds in near-neutral conditions, most of the observed structures correspond to narrow streaks of low streamwise momentum near the surface, extending several hundred meters in the streamwise direction; these are associated with positive vertical velocity ejections. For unstable conditions and moderate winds, these structures become large-scale rolls, with longitudinal extent exceeding the measuring domain (>∼5 km); they dominate the conventional surface-layer structures in terms of both physical scale and relative size of velocity-component variances, appearing as quasi-two-dimensional structures throughout the entire boundary layer. The observations shown here are consistent with numerical simulations of atmospheric flows, field observations, and laboratory experiments under similar conditions.
Significance Statement
Coherent structures have attracted the interest of researchers for decades, being viewed as the closest to “order” that we can find within the chaos of turbulence. In the turbulent atmospheric boundary layer, micro- and mesoscale coherent structures come in many shapes and sizes, such as convective cells, rolls, or streaks. In this study we used dual lidars (remote sensing measurements), developing analysis of their tandem usage to characterize in detail some of the large-scale coherent structures generated over flat terrain. This allowed us to better understand the mechanisms that generate such structures and describe their influence on the morphology of the turbulent atmospheric boundary layer across a good deal of its depth.
Abstract
We investigate characteristics of large-scale coherent motions in the atmospheric boundary layer using field measurements made with two long-range scanning wind lidars. The joint scans provide quasi-instantaneous wind fields over a domain of ∼50 km2, at two heights above flat but partially forested terrain. Along with the two-dimensional wind fields, two-point statistics and spectra are used to identify and characterize the scales, shape, and anisotropy of coherent structures—as well as their influence on wind field homogeneity. For moderate to high wind speeds in near-neutral conditions, most of the observed structures correspond to narrow streaks of low streamwise momentum near the surface, extending several hundred meters in the streamwise direction; these are associated with positive vertical velocity ejections. For unstable conditions and moderate winds, these structures become large-scale rolls, with longitudinal extent exceeding the measuring domain (>∼5 km); they dominate the conventional surface-layer structures in terms of both physical scale and relative size of velocity-component variances, appearing as quasi-two-dimensional structures throughout the entire boundary layer. The observations shown here are consistent with numerical simulations of atmospheric flows, field observations, and laboratory experiments under similar conditions.
Significance Statement
Coherent structures have attracted the interest of researchers for decades, being viewed as the closest to “order” that we can find within the chaos of turbulence. In the turbulent atmospheric boundary layer, micro- and mesoscale coherent structures come in many shapes and sizes, such as convective cells, rolls, or streaks. In this study we used dual lidars (remote sensing measurements), developing analysis of their tandem usage to characterize in detail some of the large-scale coherent structures generated over flat terrain. This allowed us to better understand the mechanisms that generate such structures and describe their influence on the morphology of the turbulent atmospheric boundary layer across a good deal of its depth.
Abstract
Extreme winds derived from simulations using mesoscale models are underestimated because of the effective spatial and temporal resolutions. This is reflected in the spectral domain as an energy deficit in the mesoscale range. The energy deficit implies smaller spectral moments and thus underestimation in the extreme winds. The authors have developed two approaches for correcting the smoothing effect resulting from the mesoscale model resolution that impacts the extreme wind estimation by taking into account the difference between the modeled and measured spectra in the high-frequency range. Both approaches give estimates of the smoothing effect that are in good agreement with measurements from several sites in Denmark and Germany.
Abstract
Extreme winds derived from simulations using mesoscale models are underestimated because of the effective spatial and temporal resolutions. This is reflected in the spectral domain as an energy deficit in the mesoscale range. The energy deficit implies smaller spectral moments and thus underestimation in the extreme winds. The authors have developed two approaches for correcting the smoothing effect resulting from the mesoscale model resolution that impacts the extreme wind estimation by taking into account the difference between the modeled and measured spectra in the high-frequency range. Both approaches give estimates of the smoothing effect that are in good agreement with measurements from several sites in Denmark and Germany.
Abstract
The capability of the NCAR 10.6-μm-wavelength CO2 Doppler lidar to measure radial air motion is validated by examining hard-target test data, comparing measurements with those from a two-axis propeller anemometer and a 915-MHz profiling radar, and analyzing power spectra and autocovariance functions of the lidar radial velocities in a daytime convective boundary layer. Results demonstrate that the lidar is capable of measuring radial velocity to less than 0.5 m s−1 precision from 20 laser pulse averages under high signal-to-noise ratio conditions. Hard-target test data and comparisons with other sensors show that the lidar data can be biased by as much as ±2 m s−1 when operating in the coherent oscillator mode and that correlated errors are negligible. Correlation coefficients are as large as 0.96 for 90-min comparisons of horizontal velocities averaged for 1 min from the lidar and anemometer, and 0.87 for 2.5-h comparisons between vertical velocities averaged for 30 s from the lidar and profiler. Comparisons of the lidar and profiler vertical velocities are particularly encouraging for the profiler since these results show that 915-MHz profilers are capable of making good vertical velocity measurements in strong convective boundary layers. The authors conclude that despite the commonplace systematic bias in lidar radial velocity, ground-based operation of the NCAR CO2 Doppler lidar can provide valuable velocity data for meso- and microscale meteorological studies. The lidar can also provide filtered velocity statistics that may be useful for boundary layer turbulence research.
Abstract
The capability of the NCAR 10.6-μm-wavelength CO2 Doppler lidar to measure radial air motion is validated by examining hard-target test data, comparing measurements with those from a two-axis propeller anemometer and a 915-MHz profiling radar, and analyzing power spectra and autocovariance functions of the lidar radial velocities in a daytime convective boundary layer. Results demonstrate that the lidar is capable of measuring radial velocity to less than 0.5 m s−1 precision from 20 laser pulse averages under high signal-to-noise ratio conditions. Hard-target test data and comparisons with other sensors show that the lidar data can be biased by as much as ±2 m s−1 when operating in the coherent oscillator mode and that correlated errors are negligible. Correlation coefficients are as large as 0.96 for 90-min comparisons of horizontal velocities averaged for 1 min from the lidar and anemometer, and 0.87 for 2.5-h comparisons between vertical velocities averaged for 30 s from the lidar and profiler. Comparisons of the lidar and profiler vertical velocities are particularly encouraging for the profiler since these results show that 915-MHz profilers are capable of making good vertical velocity measurements in strong convective boundary layers. The authors conclude that despite the commonplace systematic bias in lidar radial velocity, ground-based operation of the NCAR CO2 Doppler lidar can provide valuable velocity data for meso- and microscale meteorological studies. The lidar can also provide filtered velocity statistics that may be useful for boundary layer turbulence research.
