Articles | Volume 9, issue 4
https://doi.org/10.5194/amt-9-1653-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-1653-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Apr 2016
Research article |
| 13 Apr 2016
Lidar arc scan uncertainty reduction through scanning geometry optimization
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA
Rebecca J. Barthelmie
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA
Sara C. Pryor
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York, USA
Gareth. Brown
SgurrEnergy Ltd, Vancouver, British Columbia, Canada
Viewed
Total article views: 3,674 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 07 Oct 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,126 | 1,338 | 210 | 3,674 | 239 | 256 |
- HTML: 2,126
- PDF: 1,338
- XML: 210
- Total: 3,674
- BibTeX: 239
- EndNote: 256
Total article views: 3,043 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Apr 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,853 | 993 | 197 | 3,043 | 226 | 243 |
- HTML: 1,853
- PDF: 993
- XML: 197
- Total: 3,043
- BibTeX: 226
- EndNote: 243
Total article views: 631 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 07 Oct 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 273 | 345 | 13 | 631 | 13 | 13 |
- HTML: 273
- PDF: 345
- XML: 13
- Total: 631
- BibTeX: 13
- EndNote: 13
Cited
13 citations as recorded by crossref.
- Spatial and temporal variability of turbulence dissipation rate in complex terrain N. Bodini et al. https://doi.org/10.5194/acp-19-4367-2019
- Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements P. Doubrawa et al. https://doi.org/10.3390/rs8110939
- A Closed-Form Statistical Expression for Evaluating Wind Speed and Direction Prediction Intervals from Doppler Lidar Arc Scans T. Tzadok et al. https://doi.org/10.3390/rs18172879
- Automated wind turbine wake characterization in complex terrain R. Barthelmie & S. Pryor https://doi.org/10.5194/amt-12-3463-2019
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. https://doi.org/10.1063/5.0141683
- Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain J. Aird et al. https://doi.org/10.3390/rs13214438
- Impact of local meteorology on wake characteristics at Perdigão R. Barthelmie & S. Pryor https://doi.org/10.1088/1742-6596/1256/1/012007
- Defining wake characteristics from scanning and vertical full- scale lidar measurements R. Barthelmie et al. https://doi.org/10.1088/1742-6596/753/3/032034
- Tilted lidar profiling: Development and testing of a novel scanning strategy for inhomogeneous flows S. Letizia et al. https://doi.org/10.1063/5.0209729
- Errors in radial velocity variance from Doppler wind lidar H. Wang et al. https://doi.org/10.5194/amt-9-4123-2016
- Modular wind profile retrieval software for heterogeneous Doppler lidar measurements (AtmoProKIT v1.1) A. Erdmann & P. Gasch https://doi.org/10.5194/gmd-19-2497-2026
- U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. https://doi.org/10.1029/2019GL082636
- High-fidelity retrieval from instantaneous line-of-sight returns of nacelle-mounted lidar including supervised machine learning K. Brown & T. Herges https://doi.org/10.5194/amt-15-7211-2022
13 citations as recorded by crossref.
- Spatial and temporal variability of turbulence dissipation rate in complex terrain N. Bodini et al. https://doi.org/10.5194/acp-19-4367-2019
- Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements P. Doubrawa et al. https://doi.org/10.3390/rs8110939
- A Closed-Form Statistical Expression for Evaluating Wind Speed and Direction Prediction Intervals from Doppler Lidar Arc Scans T. Tzadok et al. https://doi.org/10.3390/rs18172879
- Automated wind turbine wake characterization in complex terrain R. Barthelmie & S. Pryor https://doi.org/10.5194/amt-12-3463-2019
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. https://doi.org/10.1063/5.0141683
- Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain J. Aird et al. https://doi.org/10.3390/rs13214438
- Impact of local meteorology on wake characteristics at Perdigão R. Barthelmie & S. Pryor https://doi.org/10.1088/1742-6596/1256/1/012007
- Defining wake characteristics from scanning and vertical full- scale lidar measurements R. Barthelmie et al. https://doi.org/10.1088/1742-6596/753/3/032034
- Tilted lidar profiling: Development and testing of a novel scanning strategy for inhomogeneous flows S. Letizia et al. https://doi.org/10.1063/5.0209729
- Errors in radial velocity variance from Doppler wind lidar H. Wang et al. https://doi.org/10.5194/amt-9-4123-2016
- Modular wind profile retrieval software for heterogeneous Doppler lidar measurements (AtmoProKIT v1.1) A. Erdmann & P. Gasch https://doi.org/10.5194/gmd-19-2497-2026
- U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. https://doi.org/10.1029/2019GL082636
- High-fidelity retrieval from instantaneous line-of-sight returns of nacelle-mounted lidar including supervised machine learning K. Brown & T. Herges https://doi.org/10.5194/amt-15-7211-2022
Saved (final revised paper)
Latest update: 17 Oct 2026
