Can Wind Lidars Measure Turbulence?

Wind-turbine-wake evolution during the evening transition introduces variability to wind-farm power production at a time of day typically characterized by high electricity demand. During the evening transition, the atmosphere evolves from an unstable to a stable regime, and vertical stratification of the wind profile develops as the residual planetary boundary layer decouples from the surface layer. The evolution of wind-turbine wakes during the evening transition is examined from two perspectives: wake observations from single turbines, and simulations of multiple turbine wakes using the mesoscale Weather Research and Forecasting (WRF) model. Throughout the evening transition, the wake's wind-speed deficit and turbulence enhancement are confined within the rotor layer when the atmospheric stability changes from unstable to stable. The height variations of maximum upwind-downwind differences of wind speed and turbulence intensity gradually decrease during the evening transition. After verifying the WRF-model-simulated upwind wind speed, wind direction and turbulent kinetic energy profiles with observations, the wind-farm-scale wake evolution during the evening transition is investigated using the WRF-model wind-farm parametrization scheme. As the evening progresses, due to the presence of the wind farm, the modelled hub-height wind-speed deficit monotonically increases, the relative turbulence enhancement at hub height grows by 50%, and the downwind surface sensible heat flux increases, reducing surface cooling. Overall, the intensifying wakes from upwind turbines respond to the evolving atmospheric boundary layer during the evening transition, and undermine the power production of downwind turbines in the evening.

Section: Wake Observationsmentioning

confidence: 97%

Observing and Simulating Wind-Turbine Wakes During the Evening Transition

Lee

Lundquist

2017

“…Fortunately, such parameters tend to be internally consistent, at least for the weakly stable NBL (LeMone et al 2014). The other thing to note is relatively large measurement errors may be introduced under very stable conditions (Sathe et al 2011), and so it is imperative to understand and correctly categorize the NBL regimes before adopting a method for estimating the NBL depth.…”

Section: Nbl Features and Classificationmentioning

confidence: 99%

Estimate of Boundary-Layer Depth Over Beijing, China, Using Doppler Lidar Data During SURF-2015

Huang

Gao

Miao

et al. 2016

Planetary boundary-layer (PBL) structure was investigated using observations from a Doppler lidar and the 325-m Institute of Atmospheric Physics (IAP) meteorological tower in the centre of Beijing during the summer 2015 Study of Urban-impacts on Rainfall and Fog/haze (SURF-2015) field campaign. Using six fair-weather days of lidar and tower data under clear to cloudy skies, we evaluate the ability of the Doppler lidar to probe the urban boundary-layer structure, and then propose a composite method for estimating the diurnal cycle of the PBL depth using the Doppler lidar. For the convective boundary layer (CBL), a threshold method using vertical velocity variance (σ 2 w > 0.1 m 2 s −2 ) is used, since it provides more reliable CBL depths than a conventional maximum wind-shear method. The nocturnal boundary-layer (NBL) depth is defined as the height at which σ 2 w decreases to 10 % of its near-surface maximum minus a background variance. The PBL depths determined by combining these methods have average values ranging from ≈270 to ≈1500 m for the six days, with the greatest maximum depths associated with clear skies. Release of stored and anthropogenic heat contributes to the maintenance of turbulence until late evening, keeping the NBL near-neutral and deeper at night than would be expected over a natural surface. The NBL typically becomes more shallow with time, but grows in the presence of low-level nocturnal jets. While current results are promising, data over a broader range of conditions are needed to fully develop our PBL-depth algorithms.

“…Model evaluation was performed using sonic measurements, and the scatter plots showed linear regressions in the range of 1.00 to 1.07. Berg et al (2011) measured the stress vectors and the vertical gradient of the mean velocity vector using lidars and sonics and found significant deviations between the two vectors even at 100 m. Sathe et al (2011) modelled the lidar turbulence for all components of the Reynolds stress tensor using the velocity azimuth display (VAD) method. Here, two effects causing incorrect estimations of turbulence with lidars were identified: (1) spatial averaging, and (2) cross-contamination by the different wind-field components.…”

Section: Traditional Anemometry-based Studiesmentioning

confidence: 99%

Ten Years of Boundary-Layer and Wind-Power Meteorology at Høvsøre, Denmark

Peña

Floors

Sathe

et al. 2015

100

103

Operational since 2004, the National Centre for Wind Turbines at Høvsøre, Denmark has become a reference research site for wind-power meteorology. In this study, we review the site, its instrumentation, observations, and main research programs. The programs comprise activities on, inter alia, remote sensing, where measurements from lidars have been compared extensively with those from traditional instrumentation on masts. In addition, with regard to wind-power meteorology, wind-resource methodologies for wind climate extrapolation have been evaluated and improved. Further, special attention has been given to research on boundary-layer flow, where parametrizations of the length scale and wind profile have been developed and evaluated. Atmospheric turbulence studies are continuously conducted at Høvsøre, where spectral tensor models have been evaluated and extended to account for atmospheric stability, and experiments using microscale and mesoscale numerical modelling.