Articles | Volume 12, issue 6
https://doi.org/10.5194/amt-12-3463-2019
https://doi.org/10.5194/amt-12-3463-2019
Research article
 | 
28 Jun 2019
Research article |  | 28 Jun 2019

Automated wind turbine wake characterization in complex terrain

Rebecca J. Barthelmie and Sara C. Pryor

Related authors

Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024,https://doi.org/10.5194/wes-9-821-2024, 2024
Short summary
Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States
Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024,https://doi.org/10.5194/wes-9-263-2024, 2024
Short summary
Brief communication: On the definition of the low-level jet
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023,https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
How well are hazards associated with derechos reproduced in regional climate simulations?
Tristan J. Shepherd, Frederick L. Letson, Rebecca J. Barthelmie, and Sara C. Pryor
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-373,https://doi.org/10.5194/nhess-2021-373, 2021
Revised manuscript under review for NHESS
Short summary
WRF-simulated low-level jets over Iowa: characterization and sensitivity studies
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, and Sara C. Pryor
Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021,https://doi.org/10.5194/wes-6-1015-2021, 2021
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models
Laura Bianco, Bianca Adler, Ludovic Bariteau, Irina V. Djalalova, Timothy Myers, Sergio Pezoa, David D. Turner, and James M. Wilczak
Atmos. Meas. Tech., 17, 3933–3948, https://doi.org/10.5194/amt-17-3933-2024,https://doi.org/10.5194/amt-17-3933-2024, 2024
Short summary
Scale separation for gravity wave analysis from 3D temperature observations in the mesosphere and lower thermosphere (MLT) region
Björn Linder, Peter Preusse, Qiuyu Chen, Ole Martin Christensen, Lukas Krasauskas, Linda Megner, Manfred Ern, and Jörg Gumbel
Atmos. Meas. Tech., 17, 3829–3841, https://doi.org/10.5194/amt-17-3829-2024,https://doi.org/10.5194/amt-17-3829-2024, 2024
Short summary
Estimating the refractivity bias of FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) radio occultation in the deep troposphere
Gia Huan Pham, Shu-Chih Yang, Chih-Chien Chang, Shu-Ya Chen, and Cheng Yung Huang
Atmos. Meas. Tech., 17, 3605–3623, https://doi.org/10.5194/amt-17-3605-2024,https://doi.org/10.5194/amt-17-3605-2024, 2024
Short summary
High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation
Sanja Dmitrovic, Johnathan W. Hair, Brian L. Collister, Ewan Crosbie, Marta A. Fenn, Richard A. Ferrare, David B. Harper, Chris A. Hostetler, Yongxiang Hu, John A. Reagan, Claire E. Robinson, Shane T. Seaman, Taylor J. Shingler, Kenneth L. Thornhill, Holger Vömel, Xubin Zeng, and Armin Sorooshian
Atmos. Meas. Tech., 17, 3515–3532, https://doi.org/10.5194/amt-17-3515-2024,https://doi.org/10.5194/amt-17-3515-2024, 2024
Short summary
Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024,https://doi.org/10.5194/amt-17-3377-2024, 2024
Short summary

Cited articles

Abkar, M. and Porte-Agel, F.: Influence of the Coriolis force on the structure and evolution of wind turbine wakes, Phys. Rev. Fluids, 1, 063701, https://doi.org/10.1103/PhysRevFluids.1.063701, 2016. 
Ainslie, J. F.: Calculating the flow field in the wake of wind turbines, J. Wind. Eng. Ind. Aerod., 27, 213–224, 1988. 
Aitken, M. L. and Lundquist, J. K.: Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar, J. Atmos. Ocean. Tech., 31, 1529–1539, https://doi.org/10.1175/jtech-d-13-00218.1, 2014. 
Aitken, M. L., Banta, R. M., Pichugina, Y. L., and Lundquist, J. K.: Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data, J. Atmos. Ocean. Tech., 31, 765–787, https://doi.org/10.1175/jtech-d-13-00104.1, 2014. 
Banta, R. M., Pichugina, Y. L., Brewer, W. A., Lundquist, J. K., Kelley, N. D., Sandberg, S. P., Alvarez, R. J., Hardesty, R. M., and Weickmann, A. M.: 3D Volumetric Analysis of Wind Turbine Wake Properties in the Atmosphere Using High-Resolution Doppler Lidar, J. Atmos. Ocean. Tech., 32, 904–914, https://doi.org/10.1175/jtech-d-14-00078.1, 2015. 
Download
Short summary
Wakes are volumes of air with low wind speed that form downwind of wind turbines. Their properties and behaviour determine optimal turbine spacing in wind farms. We use scanning Doppler lidar to accurately and precisely measure wake characteristics at a complex terrain site in Portugal. We develop and apply an automatic processing algorithm to detect wakes and quantify their characteristics. In higher wind speeds, the wake centres are lower. Wake centres are also lower in convective conditions.