Articles | Volume 3, issue 1
https://doi.org/10.5194/wes-3-353-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-353-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jun 2018
Research article |
| 08 Jun 2018
| 08 Jun 2018
From lidar scans to roughness maps for wind resource modelling in forested areas
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
Peter Enevoldsen
Center for Energy Technologies, Aarhus University, Aarhus, Denmark
Envision Energy, Silkeborg, Denmark
Neil Davis
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
Johan Arnqvist
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Ebba Dellwik
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
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- Single Column Model Simulations of Icing Conditions in Northern Sweden: Sensitivity to Surface Model Land Use Representation E. Janzon et al. https://doi.org/10.3390/en13164258
- On the socio-technical potential for onshore wind in Europe: A response to critics P. Enevoldsen et al. https://doi.org/10.1016/j.enpol.2021.112147
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- Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling R. Floors et al. https://doi.org/10.5194/wes-6-1379-2021
- Estimating wind conditions in forests using roughness lengths: A matter of data input P. Enevoldsen https://doi.org/10.1177/0309524X19849849
- Estimating Air Density Using Observations and Re-Analysis Outputs for Wind Energy Purposes R. Floors & M. Nielsen https://doi.org/10.3390/en12112038
- Østerild: A natural laboratory for atmospheric turbulence A. Peña https://doi.org/10.1063/1.5121486
- A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management L. Zhao et al. https://doi.org/10.1007/s11356-022-19902-8
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- Fully Automated Wind Site Assessment in Complex Terrain Using Satellite Data and Global Circulation Models A. Horvath et al. https://doi.org/10.3390/rs18091403
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- Bridging Scales: Explicit Forest Representation and Full-Physics LES for Improved Wind Resource Modelling J. Sturm et al. https://doi.org/10.1088/1742-6596/3232/1/012010
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- Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend M. Nazir et al. https://doi.org/10.3390/su12093778
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- A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea H. Li et al. https://doi.org/10.1016/j.gsf.2021.101229
25 citations as recorded by crossref.
- Characterizing wind gusts in complex terrain F. Letson et al. https://doi.org/10.5194/acp-19-3797-2019
- One-year-long turbulence measurements and modeling using large-eddy simulation domains in the Weather Research and Forecasting model A. Peña & J. Mirocha https://doi.org/10.1016/j.apenergy.2024.123069
- Single Column Model Simulations of Icing Conditions in Northern Sweden: Sensitivity to Surface Model Land Use Representation E. Janzon et al. https://doi.org/10.3390/en13164258
- On the socio-technical potential for onshore wind in Europe: A response to critics P. Enevoldsen et al. https://doi.org/10.1016/j.enpol.2021.112147
- Spatiotemporal variability of the potential wind erosion risk in Southern Africa between 2005 and 2019 F. Kestel et al. https://doi.org/10.1002/ldr.4659
- A novel calibration of global soil roughness effects for SMOS-IC soil moisture and L-VOD products P. Konkathi et al. https://doi.org/10.1016/j.rse.2025.114946
- Integrating multi-source remote sensing and numerical simulation approaches for enhanced flood hazard assessment E. Hamidi et al. https://doi.org/10.1016/j.jhydrol.2026.135933
- Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling R. Floors et al. https://doi.org/10.5194/wes-6-1379-2021
- Estimating wind conditions in forests using roughness lengths: A matter of data input P. Enevoldsen https://doi.org/10.1177/0309524X19849849
- Estimating Air Density Using Observations and Re-Analysis Outputs for Wind Energy Purposes R. Floors & M. Nielsen https://doi.org/10.3390/en12112038
- Østerild: A natural laboratory for atmospheric turbulence A. Peña https://doi.org/10.1063/1.5121486
- A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management L. Zhao et al. https://doi.org/10.1007/s11356-022-19902-8
- Land Cover Control on the Drivers of Evaporation and Sensible Heat Fluxes: An Observation‐Based Synthesis for the Netherlands F. Jansen et al. https://doi.org/10.1029/2022WR034361
- Fully Automated Wind Site Assessment in Complex Terrain Using Satellite Data and Global Circulation Models A. Horvath et al. https://doi.org/10.3390/rs18091403
- Wind energy resource assessment for Mukdahan, Thailand S. Polnumtiang & K. Tangchaichit https://doi.org/10.1080/15435075.2021.1941039
- Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR K. Trepekli & T. Friborg https://doi.org/10.3390/rs13173538
- Bridging Scales: Explicit Forest Representation and Full-Physics LES for Improved Wind Resource Modelling J. Sturm et al. https://doi.org/10.1088/1742-6596/3232/1/012010
- Socio-technical constraints in German wind power planning: An example of the failed interdisciplinary challenge for academia F. Permien & P. Enevoldsen https://doi.org/10.1016/j.erss.2019.04.021
- Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend M. Nazir et al. https://doi.org/10.3390/su12093778
- Variational Autoencoder to Obtain High Resolution Wind Fields from Reanalysis Data B. Rösch et al. https://doi.org/10.3390/wind6010013
- The making of the New European Wind Atlas – Part 1: Model sensitivity A. Hahmann et al. https://doi.org/10.5194/gmd-13-5053-2020
- Variation in Zero Plane Displacement and Roughness Length for Momentum Revisited A. Kunadi et al. https://doi.org/10.1007/s10546-024-00876-8
- A Complete CFD Methodology Based on Iterative Model Adjustment to Improve Wind Simulation Accuracy in Highly Dense Forest Area E. Leonard et al. https://doi.org/10.3390/en19092243
- Enhancing compound flood simulation accuracy and efficiency in urbanized coastal areas using hybrid meshes and modified digital elevation model E. Hamidi et al. https://doi.org/10.1016/j.scs.2025.106184
- A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea H. Li et al. https://doi.org/10.1016/j.gsf.2021.101229
Saved (final revised paper)
Latest update: 17 Jul 2026
Short summary
Applying erroneous boundary conditions (surface roughness) for wind flow modelling can have a large impact on the estimated performance of wind turbines, particularly in forested areas. Traditionally the estimation of the surface roughness is based on a subjective process that requires assigning a value to each land use class in the vicinity of the wind farm. Here we propose a new method which converts lidar scans from a plane into maps that can be used for wind flow modelling.
Applying erroneous boundary conditions (surface roughness) for wind flow modelling can have a...
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