Articles | Volume 3, issue 1
Wind Energ. Sci., 3, 353–370, 2018
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.
Wind Energ. Sci., 3, 353–370, 2018
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
From lidar scans to roughness maps for wind resource modelling in forested areas
Rogier Floors et al.
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14 citations as recorded by crossref.
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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. 10.3390/en13164258
- On the socio-technical potential for onshore wind in Europe: A response to critics P. Enevoldsen et al. 10.1016/j.enpol.2021.112147
- Wind energy resource assessment for Mukdahan, Thailand S. Polnumtiang & K. Tangchaichit 10.1080/15435075.2021.1941039
- Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR K. Trepekli & T. Friborg 10.3390/rs13173538
- Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling R. Floors et al. 10.5194/wes-6-1379-2021
- Socio-technical constraints in German wind power planning: An example of the failed interdisciplinary challenge for academia F. Permien & P. Enevoldsen 10.1016/j.erss.2019.04.021
- Estimating wind conditions in forests using roughness lengths: A matter of data input P. Enevoldsen 10.1177/0309524X19849849
- Estimating Air Density Using Observations and Re-Analysis Outputs for Wind Energy Purposes R. Floors & M. Nielsen 10.3390/en12112038
- Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend M. Nazir et al. 10.3390/su12093778
- The making of the New European Wind Atlas – Part 1: Model sensitivity A. Hahmann et al. 10.5194/gmd-13-5053-2020
- A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea H. Li et al. 10.1016/j.gsf.2021.101229
- Østerild: A natural laboratory for atmospheric turbulence A. Peña 10.1063/1.5121486
- New European Wind Atlas: The Østerild balconies experiment I. Karagali et al. 10.1088/1742-6596/1037/5/052029
13 citations as recorded by crossref.
- Characterizing wind gusts in complex terrain F. Letson et al. 10.5194/acp-19-3797-2019
- Single Column Model Simulations of Icing Conditions in Northern Sweden: Sensitivity to Surface Model Land Use Representation E. Janzon et al. 10.3390/en13164258
- On the socio-technical potential for onshore wind in Europe: A response to critics P. Enevoldsen et al. 10.1016/j.enpol.2021.112147
- Wind energy resource assessment for Mukdahan, Thailand S. Polnumtiang & K. Tangchaichit 10.1080/15435075.2021.1941039
- Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR K. Trepekli & T. Friborg 10.3390/rs13173538
- Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling R. Floors et al. 10.5194/wes-6-1379-2021
- Socio-technical constraints in German wind power planning: An example of the failed interdisciplinary challenge for academia F. Permien & P. Enevoldsen 10.1016/j.erss.2019.04.021
- Estimating wind conditions in forests using roughness lengths: A matter of data input P. Enevoldsen 10.1177/0309524X19849849
- Estimating Air Density Using Observations and Re-Analysis Outputs for Wind Energy Purposes R. Floors & M. Nielsen 10.3390/en12112038
- Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend M. Nazir et al. 10.3390/su12093778
- The making of the New European Wind Atlas – Part 1: Model sensitivity A. Hahmann et al. 10.5194/gmd-13-5053-2020
- A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea H. Li et al. 10.1016/j.gsf.2021.101229
- Østerild: A natural laboratory for atmospheric turbulence A. Peña 10.1063/1.5121486
1 citations as recorded by crossref.
Latest update: 01 Jul 2022
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...
