The five main influencing factors on lidar errors in complex terrain

Abstract

Abstract. Light detection and ranging (notably Doppler lidar), has become a valuable technology to assess the wind resource at hub height of modern wind turbines. However, because of their measurement principle, common wind profile Doppler lidars suffer from errors at complex terrain sites. This study analyses the impact of the five main influencing factors at lidar measurement errors in complex terrain, i.e. orographic complexity, measurement height, surface roughness and forest, atmospheric stability and half-cone opening angle, in a non-dimensional, model-based parameter study. In a novel approach, the lidar error ε is split up into a part εc, caused by flow curvature at the measurement points of the lidar and a part εs, caused by the local speed-up effects between the measurement points. This approach, e.g., allows for a systematic and complete interpretation of the influence of the half-cone opening angle φ of the lidar. It also provides information about the uncertainty of simple lidar error estimations that are based on inflow and outflow angles at the measurement points. The model-based parameter study is limited to two-dimensional Gaussian hills with hill height H and hill half-width L. H/L and z/L, with z being the measurement height, are identified as the main scaling factors for the lidar error. Three flow models of different complexity are used to estimate the lidar errors. The outcome of the study provides manifold findings that enable an assessment of the applicability of these flow models. The study clearly shows that orographic complexity, roughness and forest characteristics, as well as atmospheric stability, have a significant influence on lidar error estimation. Based on the error separation approach it furthermore allows for an in-depth analysis of the influence of reduced half-cone opening angles. The choice and parameterization of flow models and the design of methods for lidar error estimation are found to be essential to achieve accurate results. The use of a RANS CFD model in conjunction with an appropriate forest model is highly recommended for lidar error estimations in complex terrain. If atmospheric stability variation at a measurement site plays a vital role, it should also be considered in the modelling. When planning a measurement campaign, an accurate estimation of the prospective lidar error should be carried out in advance to decrease measurement uncertainties and maximize the value.

Behavior and Mechanisms of Doppler Wind Lidar Error in Varying Stability Regimes

“…The character of the error in the reconstructed wind vector components is driven by the form of the turbulence, so that the lidar accuracy is dependent on the flow regime and vertical structure of the boundary layer. This derivation explains findings from other sensitivity studies (Klaas and Emeis, 2021;Rahlves et al, 2021) in that unstable conditions are prone to larger error than stable conditions. The error variances were connected to weighted, spatially filtered turbulent variances in the horizontal and vertical velocities.…”

Research challenges and needs for the deployment of wind energy in atmospherically complex locations

“…This can mean that they measure in inhomogeneous flows (Figure 4), which in turn may introduce errors in the windfield reconstruction process if not accounted for (see e.g. Klaas and Emeis, 2021). Complex flow conditions thus may introduce additional uncertainties to measurements with remote sensing devices.…”

“…), but it is clear that future wind energy developments may move in terrain or flow conditions that are outside of these existing guidelines. Research is therefore ongoing into the use of vertical-profiling wind lidar in more complex locations (see e.g., Clifton et al, 2015;Klaas and Emeis, 2021). And, scanning lidar have been seen to be vary useful for measuring wind conditions in complex terrain in research projects (Vasiljević et al, 2017;Menke et al, 2020).…”

A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines

“…DBS-based assessments of wake flow deficits in the near wake could be relied on by three rotor diameters downwind, but interpretations of field observations should consider the uncertainties caused by flow inhomogeneity. Klaas et al [40] conducted a non-dimensional, model-based parameter study to investigate the influence of different factors on LiDAR measurement errors in complex terrains. They separated the LiDAR errors into two components: flow curvature at the measurement points and local speed-up effects.…”