Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2095-2021
https://doi.org/10.5194/amt-14-2095-2021
Research article
 | 
16 Mar 2021
Research article |  | 16 Mar 2021

LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 2: Applications to lidar measurements of wind turbine wakes

Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo

Related authors

Observations of wind farm wake recovery at an operating wind farm
Raghavendra Krishnamurthy, Rob Newsom, Colleen Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna M. Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-29,https://doi.org/10.5194/wes-2024-29, 2024
Revised manuscript under review for WES
Short summary
Design, steady performance and wake characterization of a scaled wind turbine with pitch, torque and yaw actuation
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022,https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2065–2093, https://doi.org/10.5194/amt-14-2065-2021,https://doi.org/10.5194/amt-14-2065-2021, 2021
Short summary
Optimal tuning of engineering wake models through lidar measurements
Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo
Wind Energ. Sci., 5, 1601–1622, https://doi.org/10.5194/wes-5-1601-2020,https://doi.org/10.5194/wes-5-1601-2020, 2020
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
An improved geolocation methodology for spaceborne radar and lidar systems
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024,https://doi.org/10.5194/amt-17-6301-2024, 2024
Short summary
Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024,https://doi.org/10.5194/amt-17-6223-2024, 2024
Short summary
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024,https://doi.org/10.5194/amt-17-6025-2024, 2024
Short summary
Global-scale gravity wave analysis methodology for the ESA Earth Explorer 11 candidate CAIRT
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
Atmos. Meas. Tech., 17, 5785–5819, https://doi.org/10.5194/amt-17-5785-2024,https://doi.org/10.5194/amt-17-5785-2024, 2024
Short summary
Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS)
Suyoung Sim, Sungwon Choi, Daeseong Jung, Jongho Woo, Nayeon Kim, Sungwoo Park, Honghee Kim, Ukkyo Jeong, Hyunkee​​​​​​​ Hong, and Kyung-Soo Han
Atmos. Meas. Tech., 17, 5601–5618, https://doi.org/10.5194/amt-17-5601-2024,https://doi.org/10.5194/amt-17-5601-2024, 2024
Short summary

Cited articles

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. a
Argyle, P., Watson, S., Montavon, C., Jones, I., and Smith, M.: Modelling turbulence intensity within a large offshore wind farm, Wind Energy, 21, 1329–1343, https://doi.org/10.1002/we.2257, 2018. a
Ashton, R., Iungo, G. V., Viola, F., Gallaire, F., and Camarri, S.: Hub vortex instability within wind turbine wakes: Effects of wind turbulence, loading conditions and blade aerodynamics, Phys. Rev. Fluids, 1, 073603, https://doi.org/10.1103/PhysRevFluids.1.073603, 2016. a
Aubrun, S., Torres Garcia, E., Boquet, M., Coupiac, O., and Girard, N.: Wind turbine wake tracking and its correlations with wind turbine monitoring sensors. Preliminary results, J. Phys. Conf. Ser., 753, 032003, https://doi.org/10.1088/1742-6596/753/3/032003, 2016. a, b
Barnes, S. L.: A Technique for Maximizing Details in Numerical Weather Map Analysis, J. Appl. Meteorol., 3, 396–409, https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2, 1964. a
Short summary
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.