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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Light detection and ranging (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 lidars suffer from errors at orographically complex, i.e. hilly or mountainious sites. This study analyses the impact of the five main influencing factors in a non-dimensional, model-based parameter study.
Preprints
https://doi.org/10.5194/wes-2021-26
https://doi.org/10.5194/wes-2021-26

  29 Apr 2021

29 Apr 2021

Review status: this preprint is currently under review for the journal WES.

The five main influencing factors on lidar errors in complex terrain

Tobias Klaas1 and Stefan Emeis2,3 Tobias Klaas and Stefan Emeis
  • 1Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Kassel, 34419, Germany
  • 2Institute of Geophysics and Meteorology, University of Cologne, Germany
  • 3Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Germany

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.

Tobias Klaas and Stefan Emeis

Status: open (until 16 Jun 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Tobias Klaas and Stefan Emeis

Tobias Klaas and Stefan Emeis

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Short summary
Light detection and ranging (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 lidars suffer from errors at orographically complex, i.e. hilly or mountainious sites. This study analyses the impact of the five main influencing factors in a non-dimensional, model-based parameter study.
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