Lidar arc scan uncertainty reduction through scanning geometry optimization

Abstract

Abstract. Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.

Spatial and temporal variability of turbulence dissipation rate in complex terrain

“…The Second Wind Forecast Improvement Project (WFIP2) , which involved a field campaign (Wilczak et al, 2019) in the US Pacific Northwest between October 2015 and March 2017, was designed to improve numerical weather prediction model forecasts in complex terrain for wind energy applications. A large number of instruments was deployed in the Columbia River Gorge and basin in a region over 500 km wide.…”

“…Multiple sonic anemometers were located on several meteorological towers at the Physics Site (Wilczak et al, 2019), which represented the finest array of instruments at WFIP2, aimed at having multiple measurements in an area similar in size to a grid cell of a high-resolution numerical weather prediction model. The site, covered by crop fields, is characterized by a moderately complex topography, with terrain elevation spanning from 405 m to 459 m a.s.l.…”

“…Turbulence dissipation can be calculated from sonic anemometers on meteorological towers (Champagne et al, 1977;Oncley et al, 1996) and super-high-frequency hot-wire anemometers suspended on tethered lifting systems (Frehlich et al, 2006;Lundquist and Bariteau, 2015) or flown on aircraft (Fairall et al, 1980) or UAVs (Lawrence and Balsley, 2013). Remote sensing instruments can provide additional insights into our understanding of turbulence dissipation by combining measurements at greater altitudes with their ease of deployment in complex terrain, despite their potential drawbacks of limited temporal frequency and inherent volume averaging (Frehlich and Cornman, 2002;Wang et al, 2016). Wind profiling radars (Shaw and LeMone, 2003;McCaffrey et al, 2017a), profiling lidars, and scanning lidars have all been successfully used to obtain turbulence measurements.…”

U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

“…Various techniques to retrieve turbulence dissipation rates from sonic anemometers (Champagne et al, ; Oncley et al, ), high‐frequency hot‐wire anemometers suspended on tethered lifting systems (Frehlich et al, ; Lundquist & Bariteau, ), or flown on aircrafts (Fairall et al, ) or unmanned aerial vehicles (Lawrence & Balsley, ) have been developed. Despite the potential drawback of their inherent volume averaging (Frehlich & Cornman, ; Wang et al, ), the ease of deployment and extended measurement range of remote sensing instruments have fueled research to derive methods to retrieve ϵ from lidars (Banakh et al, ; Frehlich, ; O'Connor et al, ; Wulfmeyer et al, ) and radars (McCaffrey et al, ; Shaw & LeMone, ). The extension and application of these techniques have led to a systematic assessment of the variability of ϵ in both flat (Bodini et al, ) and complex terrain (Bodini et al, ).…”

An LES-based airborne Doppler lidar simulator for investigation of wind profiling in inhomogeneous flow conditions

“…Consequently, although the RMSE increases further for higher wind speeds, the results suggest that the REL approaches a threshold level. The different background wind cases thereby also represent varying turbulence intensity conditions, which have been shown to be of importance for Doppler lidar retrieval quality before (Banakh et al, 1995;Wang et al, 2016).…”