Articles | Volume 13, issue 7
https://doi.org/10.5194/essd-13-3539-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-3539-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
22 Jul 2021
Data description paper |
| 22 Jul 2021
| 22 Jul 2021
Turbulence dissipation rate estimated from lidar observations during the LAPSE-RATE field campaign
Miguel Sanchez Gomez
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, University of Colorado
Boulder, Boulder, CO 80303, United States
Julie K. Lundquist
Department of Atmospheric and Oceanic Sciences, University of Colorado
Boulder, Boulder, CO 80303, United States
National Renewable Energy Laboratory, Golden, CO 80401, United States
Petra M. Klein
University of Oklahoma School of Meteorology, Norman, OK 73072, United
States
University of Oklahoma Center for Autonomous Sensing and Sampling,
Norman, OK 73072, United States
Tyler M. Bell
University of Oklahoma School of Meteorology, Norman, OK 73072, United
States
University of Oklahoma Center for Autonomous Sensing and Sampling,
Norman, OK 73072, United States
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At night, the wind direction often changes with height, and this veer affects structures near the surface like wind turbines. Wind turbines usually rotate clockwise, but this rotational direction interacts with veer to impact the flow field behind a wind turbine. If another turbine is located downwind, the direction of the upwind turbine's rotation will affect the downwind turbine.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
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While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, https://doi.org/10.5194/wes-5-1169-2020, 2020
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We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
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Short summary
In July 2018, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week to demonstrate unmanned aircraft systems' capabilities in sampling the atmospheric boundary layer. Three Doppler lidars were deployed during this week-long experiment. We use data from these lidars to estimate turbulence dissipation rate. We observe large temporal variability and significant differences in dissipation for lidars with different sampling techniques.
In July 2018, the International Society for Atmospheric Research using Remotely-piloted Aircraft...
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