Articles | Volume 15, issue 15
https://doi.org/10.5194/amt-15-4585-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-4585-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Aug 2022
Research article |
| 15 Aug 2022
| 15 Aug 2022
Behavior and mechanisms of Doppler wind lidar error in varying stability regimes
Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, USA
Julie K. Lundquist
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
National Renewable Energy Laboratory, Golden, Colorado, USA
Related authors
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
Short summary
Short summary
Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Preprint under review for WES
Short summary
Short summary
This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Anna Voss, Konrad B. Bärfuss, Beatriz Cañadillas, Maik Angermann, Mark Bitter, Matthias Cremer, Thomas Feuerle, Jonas Spoor, Julie K. Lundquist, Patrick Moriarty, and Astrid Lampert
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-113, https://doi.org/10.5194/wes-2025-113, 2025
Preprint under review for WES
Short summary
Short summary
This study analyses onshore wind farm wakes in a semi-complex terrain with data conducted with the research aircraft of TU Braunschweig during the AWAKEN project. Vertical profiles of temperature, humidity and wind give insights into the stratification of the atmospheric boundary layer, while horizontal profiles downwind of wind farms reveal an amplification of the reduction in wind speed in a semi-complex terrain in particular in a distance of 10 km.
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
Short summary
Short summary
Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci., 10, 1187–1209, https://doi.org/10.5194/wes-10-1187-2025, https://doi.org/10.5194/wes-10-1187-2025, 2025
Short summary
Short summary
This paper evaluates a new model configuration for wind energy forecasting in complex terrain. We compare model results to observations in the Altamont Pass (California, USA), where wind channeling through a mountain gap leads to increased energy production. We demonstrate that the new model configuration performs similarly to a more established approach, with some evidence of improved wind speed predictions, and provide guidance for future model testing.
Nathan J. Agarwal, Julie K. Lundquist, Timothy W. Juliano, and Alex Rybchuk
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-16, https://doi.org/10.5194/wes-2025-16, 2025
Preprint under review for WES
Short summary
Short summary
Models of wind behavior inform offshore wind farm site investment decisions. Here we compare a newly-developed model to another, historically-used, model based on how these models represent winds and turbulence at two North Sea sites. The best model depends on the site. While the older model performs best at the site above a wind farm, the newer model performs best at the site that is at the same altitude as the wind farm. We support using the new model to represent winds at the turbine level.
Daphne Quint, Julie K. Lundquist, and David Rosencrans
Wind Energ. Sci., 10, 117–142, https://doi.org/10.5194/wes-10-117-2025, https://doi.org/10.5194/wes-10-117-2025, 2025
Short summary
Short summary
Offshore wind farms will be built along the East Coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs and compare 1 year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.
David Rosencrans, Julie K. Lundquist, Mike Optis, and Nicola Bodini
Wind Energ. Sci., 10, 59–81, https://doi.org/10.5194/wes-10-59-2025, https://doi.org/10.5194/wes-10-59-2025, 2025
Short summary
Short summary
The US offshore wind industry is growing rapidly. Expansion into cold climates will subject turbines and personnel to hazardous icing. We analyze the 21-year icing risk for US east coast wind areas based on numerical weather prediction simulations and further assess impacts from wind farm wakes over one winter season. Sea spray icing at 10 m can occur up to 67 h per month. However, turbine–atmosphere interactions reduce icing hours within wind plant areas.
William Radünz, Bruno Carmo, Julie K. Lundquist, Stefano Letizia, Aliza Abraham, Adam S. Wise, Miguel Sanchez Gomez, Nicholas Hamilton, Raj K. Rai, and Pedro S. Peixoto
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-166, https://doi.org/10.5194/wes-2024-166, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This study investigates how simple terrain can cause significant variations in wind speed, especially during specific atmospheric conditions like low-level jets. By combining simulations and observations from a real wind farm, we found that downstream turbines generate more power than upstream ones, despite wake effects only impacting the upstream turbines. We highlight the crucial role of the strong vertical wind speed gradient in low-level jets in driving this effect.
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
Short summary
Short summary
Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
Short summary
Short summary
The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
Short summary
Short summary
Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
Short summary
Short summary
Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
Hannah Livingston, Nicola Bodini, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-68, https://doi.org/10.5194/wes-2021-68, 2021
Preprint withdrawn
Short summary
Short summary
In this paper, we assess whether hub-height turbulence can easily be quantified from either other hub-height variables or ground-level measurements in complex terrain. We find a large variability across the three considered locations when trying to model hub-height turbulence intensity and turbulence kinetic energy. Our results highlight the nonlinear and complex nature of atmospheric turbulence, so that more powerful techniques should instead be recommended to model hub-height turbulence.
Miguel Sanchez Gomez, Julie K. Lundquist, Petra M. Klein, and Tyler M. Bell
Earth Syst. Sci. Data, 13, 3539–3549, https://doi.org/10.5194/essd-13-3539-2021, https://doi.org/10.5194/essd-13-3539-2021, 2021
Short summary
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.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, Robert S. Arthur, and Domingo Muñoz-Esparza
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-57, https://doi.org/10.5194/wes-2021-57, 2021
Revised manuscript not accepted
Short summary
Short summary
Winds decelerate upstream of a wind plant as turbines obstruct and extract energy from the flow. This effect is known as wind plant blockage. We assess how atmospheric stability modifies the upstream wind plant blockage. We find stronger stability amplifies this effect. We also explore different approaches to quantifying blockage from field-like observations. We find different methodologies may induce errors of the same order of magnitude as the blockage-induced velocity deficits.
Alex Rybchuk, Mike Optis, Julie K. Lundquist, Michael Rossol, and Walt Musial
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-50, https://doi.org/10.5194/gmd-2021-50, 2021
Preprint withdrawn
Short summary
Short summary
We characterize the wind resource off the coast of California by conducting simulations with the Weather Research and Forecasting (WRF) model between 2000 and 2019. We compare newly simulated winds to those from the WIND Toolkit. The newly simulated winds are substantially stronger, particularly in the late summer. We also conduct a refined analysis at three areas that are being considered for commercial development, finding that stronger winds translates to substantially more power here.
Tyler M. Bell, Petra M. Klein, Julie K. Lundquist, and Sean Waugh
Earth Syst. Sci. Data, 13, 1041–1051, https://doi.org/10.5194/essd-13-1041-2021, https://doi.org/10.5194/essd-13-1041-2021, 2021
Short summary
Short summary
In July 2018, numerous weather sensing remotely piloted aircraft systems (RPASs) were flown in a flight week called Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). As part of LAPSE-RATE, ground-based remote and in situ systems were also deployed to supplement and enhance observations from the RPASs. These instruments include multiple Doppler lidars, thermodynamic profilers, and radiosondes. This paper describes data from these systems.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
Short summary
Short summary
Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.
Jessica M. Tomaszewski and Julie K. Lundquist
Wind Energ. Sci., 6, 1–13, https://doi.org/10.5194/wes-6-1-2021, https://doi.org/10.5194/wes-6-1-2021, 2021
Short summary
Short summary
We use a mesoscale numerical weather prediction model to conduct a case study of a thunderstorm outflow passing over and interacting with a wind farm. These simulations and observations from a nearby radar and surface station confirm that interactions with the wind farm cause the outflow to reduce its speed by over 20 km h−1, with brief but significant impacts on the local meteorology, including temperature, moisture, and winds. Precipitation accumulation across the region was unaffected.
Gijs de Boer, Adam Houston, Jamey Jacob, Phillip B. Chilson, Suzanne W. Smith, Brian Argrow, Dale Lawrence, Jack Elston, David Brus, Osku Kemppinen, Petra Klein, Julie K. Lundquist, Sean Waugh, Sean C. C. Bailey, Amy Frazier, Michael P. Sama, Christopher Crick, David Schmale III, James Pinto, Elizabeth A. Pillar-Little, Victoria Natalie, and Anders Jensen
Earth Syst. Sci. Data, 12, 3357–3366, https://doi.org/10.5194/essd-12-3357-2020, https://doi.org/10.5194/essd-12-3357-2020, 2020
Short summary
Short summary
This paper provides an overview of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field campaign, held from 14 to 20 July 2018. This field campaign spanned a 1-week deployment to Colorado's San Luis Valley, involving over 100 students, scientists, engineers, pilots, and outreach coordinators. This overview paper provides insight into the campaign for a special issue focused on the datasets collected during LAPSE-RATE.
Antonia Englberger, Julie K. Lundquist, and Andreas Dörnbrack
Wind Energ. Sci., 5, 1623–1644, https://doi.org/10.5194/wes-5-1623-2020, https://doi.org/10.5194/wes-5-1623-2020, 2020
Short summary
Short summary
Wind turbines rotate clockwise. The rotational direction of the rotor interacts with the nighttime veering wind, resulting in a rotational-direction impact on the wake. In the case of counterclockwise-rotating blades the streamwise velocity in the wake is larger in the Northern Hemisphere whereas it is smaller in the Southern Hemisphere.
Antonia Englberger, Andreas Dörnbrack, and Julie K. Lundquist
Wind Energ. Sci., 5, 1359–1374, https://doi.org/10.5194/wes-5-1359-2020, https://doi.org/10.5194/wes-5-1359-2020, 2020
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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. Technol., 31, 1529–1539,
https://doi.org/10.1175/JTECH-D-13-00218.1, 2014. a
Aitken, M. L., Rhodes, M. E., and Lundquist, J. K.: Performance of a
Wind-Profiling Lidar in the Region of Wind Turbine Rotor Disks,
J. Atmos. Ocean. Technol., 29, 347–355,
https://doi.org/10.1175/JTECH-D-11-00033.1, 2012. a, b
Banakh, V., Smalikho, I., Köpp, F., and Werner, C.: Turbulent Energy
Dissipation Rate Measurement Using Doppler Lidar, in: Advances in
Atmospheric Remote Sensing with Lidar, edited by: Ansmann, A., Neuber,
R., Rairoux, P., and Wandinger, U., 255–258, Springer, Berlin,
Heidelberg, https://doi.org/10.1007/978-3-642-60612-0_63, 1997. a
Barad, M. L.: Project Prairie Grass, a Field Program in Diffusion, Tech.
Rep. AFCRC-TR-58-235(I), Air Force Cambridge Research Center Geophysics
Research Directorate Geophysical Research Papers 59, 1958. a
Basu, S., Holtslag, B., Van de Wiel, B., Moene, A., and Steeneveld, G.-J.: An
Inconvenient ”Truth” about Using Sensible Heat Flux as a Surface Boundary
Condition in Models under Stably Stratified Regimes, Acta Geophys., 56,
88–99, https://doi.org/10.2478/s11600-007-0038-y, 2007. a
Bingöl, F., Mann, J., and Foussekis, D.: Conically scanning lidar error in
complex terrain, Meteorol. Z., 18, 189–195,
https://doi.org/10.1127/0941-2948/2009/0368, 2009. a, b, c
Bodini, N., Zardi, D., and Lundquist, J. K.: Three-dimensional structure of wind turbine wakes as measured by scanning lidar, Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, 2017. a
Bodini, N., Lundquist, J. K., Krishnamurthy, R., Pekour, M., Berg, L. K., and Choukulkar, A.: Spatial and temporal variability of turbulence dissipation rate in complex terrain, Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, 2019. a
Bonin, T. A., Newman, J. F., Klein, P. M., Chilson, P. B., and Wharton, S.: Improvement of vertical velocity statistics measured by a Doppler lidar through comparison with sonic anemometer observations, Atmos. Meas. Tech., 9, 5833–5852, https://doi.org/10.5194/amt-9-5833-2016, 2016. a
Boquet, M., Royer, P., Cariou, J.-P., Machta, M., and Valla, M.: Simulation of
Doppler Lidar Measurement Range and Data Availability, J.
Atmos. Ocean. Technol., 33, 977–987,
https://doi.org/10.1175/JTECH-D-15-0057.1, 2016. a
Cheynet, E., Jakobsen, J., Snæbjörnsson, J., Mann, J., Courtney, M., Lea, G.,
and Svardal, B.: Measurements of Surface-Layer Turbulence in a Wide
Norwegian Fjord Using Synchronized Long-Range Doppler Wind
Lidars, Remote Sens., 9, 977, https://doi.org/10.3390/rs9100977, 2017. a
Choukulkar, A., Brewer, W. A., Sandberg, S. P., Weickmann, A., Bonin, T. A., Hardesty, R. M., Lundquist, J. K., Delgado, R., Iungo, G. V., Ashton, R., Debnath, M., Bianco, L., Wilczak, J. M., Oncley, S., and Wolfe, D.: Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign, Atmos. Meas. Tech., 10, 247–264, https://doi.org/10.5194/amt-10-247-2017, 2017. a
Clements, C. B., Lareau, N. P., Kingsmill, D. E., Bowers, C. L., Camacho,
C. P., Bagley, R., and Davis, B.: The Rapid Deployments to Wildfires
Experiment (RaDFIRE): Observations from the Fire Zone, B. Am. Meteorol. Soc., 99, 2539–2559,
https://doi.org/10.1175/BAMS-D-17-0230.1, 2018. a
Clifton, A., Boquet, M., Burin Des Roziers, E., Westerhellweg, A., Hofsass, M.,
Klaas, T., Vogstad, K., Clive, P., Harris, M., Wylie, S., Osler, E., Banta,
B., Choukulkar, A., Lundquist, J., and Aitken, M.: Remote Sensing of
Complex Flows by Doppler Wind Lidar: Issues and Preliminary
Recommendations, Tech. Rep. NREL/TP–5000-64634, 1351595,
https://doi.org/10.2172/1351595, 2015. a
Clive, P. J. M.: Compensation of Vector and Volume Averaging Bias in Lidar Wind
Speed Measurements, IOP Conference Series: Earth and Environmental Science,
1, 012036, https://doi.org/10.1088/1755-1315/1/1/012036, 2008. a, b, c, d
Courtney, M., Wagner, R., and Lindelöw, P.: Testing and Comparison of
LIDARs for Profile and Turbulence Measurements in Wind Energy, IOP
Conference Series: Earth and Environmental Science, 1, 012021,
https://doi.org/10.1088/1755-1315/1/1/012021, 2008. a, b, c, d
Frehlich, R.: Effects of Wind Turbulence on Coherent Doppler Lidar
Performance, J. Atmos. Ocean. Technol., 14, 54–75,
https://doi.org/10.1175/1520-0426(1997)014<0054:EOWTOC>2.0.CO;2, 1997. a, b
Gasch, P., Wieser, A., Lundquist, J. K., and Kalthoff, N.: An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions, Atmos. Meas. Tech., 13, 1609–1631, https://doi.org/10.5194/amt-13-1609-2020, 2020. a, b, c
Gottschall, J. and Courtney, M.: Verification Test for Three WindCubeTM WLS7
LiDARs at the Høvsøre Test Site, Report 978-87-550-3819-6,
Danmarks Tekniske Universitet, RisøNationallaboratoriet for Bæredygtig Energi, Roskilde, ISBN 978-87-550-3819-6, 2010. a
Gryning, S.-E., Mikkelsen, T., Baehr, C., Dabas, A., O'Connor, E.,
Rottner-Peyrat, L., Sjöholm, M., Suomi, I., and Vasiljevic, N.:
Measurement Methodologies for Wind Energy Based on Ground-Level Remote
Sensing, in: Renewable Energy Forecasting: From Models to
Applications, pp. 29–56, https://doi.org/10.1016/B978-0-08-100504-0.00002-0, 2017. a, b
Haupt, S. E., Kosovic, B., Shaw, W., Berg, L. K., Churchfield, M., Cline, J.,
Draxl, C., Ennis, B., Koo, E., Kotamarthi, R., Mazzaro, L., Mirocha, J.,
Moriarty, P., Muñoz-Esparza, D., Quon, E., Rai, R. K., Robinson, M., and
Sever, G.: On Bridging A Modeling Scale Gap: Mesoscale to
Microscale Coupling for Wind Energy, B. Am.
Meteorol. Soc., 100, 2533–2550, https://doi.org/10.1175/BAMS-D-18-0033.1, 2019. a
Joanes, D. N. and Gill, C. A.: Comparing Measures of Sample Skewness
and Kurtosis, J. Roy. Stat. Soc. D (The
Statistician), 47, 183–189, 1998. a
Kendall, M. G., Stuart, A., Ord, J. K., and O'Hagan, A.: Kendall's Advanced
Theory of Statistics, vol. 1, Edward Arnold; Halsted Press, 6th Edn.,
1994. a
Kirkil, G., Mirocha, J., Bou-Zeid, E., Chow, F., and Kosovic, B.:
Implementation and Evaluation of Dynamic Subfilter-Scale Stress
Models for Large-Eddy Simulation Using WRF*, Mon. Weather Rev.,
140, 266–284, https://doi.org/10.1175/MWR-D-11-00037.1, 2012. a
Klaas, T., Pauscher, L., and Callies, D.: LiDAR-mast Deviations in Complex Terrain and Their Simulation Using CFD, Meteorol. Z., 24,
591–603, https://doi.org/10.1127/metz/2015/0637, 2015. a
Klaas-Witt, T. and Emeis, S.: The five main influencing factors for lidar errors in complex terrain, Wind Energ. Sci., 7, 413–431, https://doi.org/10.5194/wes-7-413-2022, 2022. a
Lindelöw, P., Courtney, M., Parmentier, R., and Cariou, J. P.: Wind Shear
Proportional Errors in the Horizontal Wind Speed Sensed by Focused, Range
Gated Lidars, IOP Conference Series: Earth and Environmental Science, 1,
012023, https://doi.org/10.1088/1755-1315/1/1/012023, 2008. a, b
Liu, Z., Barlow, J. F., Chan, P.-W., Fung, J. C. H., Li, Y., Ren, C., Mak, H.
W. L., and Ng, E.: A Review of Progress and Applications of
Pulsed Doppler Wind LiDARs, Remote Sens., 11, 2522,
https://doi.org/10.3390/rs11212522, 2019. a
Lumley, J. L. and Panofsky, H. A.: The Structure of Atmospheric Turbulence, no.
v. 12 in Interscience Monographs and Texts in Physics and Astronomy,
Interscience Publishers, 1964. a
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015. a
Mazzaro, L. J., Muñoz-Esparza, D., Lundquist, J. K., and Linn, R. R.: Nested
Mesoscale-to-LES modeling of the atmospheric boundary layer in the
presence of under-resolved convective structures, Journal of Advances in
Modeling Earth Systems, 9, 1795–1810,
https://doi.org/10.1002/2017MS000912, 2017. a
Menke, R., Vasiljević, N., Wagner, J., Oncley, S. P., and Mann, J.: Multi-lidar wind resource mapping in complex terrain, Wind Energ. Sci., 5, 1059–1073, https://doi.org/10.5194/wes-5-1059-2020, 2020. a, b
Mirocha, J. D., Lundquist, J. K., and Kosović, B.: Implementation of a
Nonlinear Subfilter Turbulence Stress Model for Large-Eddy Simulation
in the Advanced Research WRF Model, Mon. Weather Rev., 138,
4212–4228, https://doi.org/10.1175/2010MWR3286.1, 2010. a, b
Mirocha, J. D., Rajewski, D. A., Marjanovic, N., Lundquist, J. K., Kosović,
B., Draxl, C., and Churchfield, M. J.: Investigating Wind Turbine Impacts on
Near-Wake Flow Using Profiling Lidar Data and Large-Eddy Simulations with an
Actuator Disk Model, J. Renew. Sustain. Energy, 7,
043143, https://doi.org/10.1063/1.4928873, 2015. a
Muñoz-Esparza, D., Cañadillas, B., Neumann, T., and van Beeck, J.:
Turbulent Fluxes, Stability and Shear in the Offshore Environment:
Mesoscale Modelling and Field Observations at FINO1, J.
Renew. Sustain. Energy, 4, 063136, https://doi.org/10.1063/1.4769201, 2012. a
Muñoz-Esparza, D., Kosović, B., Mirocha, J., and van Beeck, J.:
Bridging the Transition from Mesoscale to Microscale Turbulence
in Numerical Weather Prediction Models, Bound.-Lay. Meteorol., 153,
409–440, https://doi.org/10.1007/s10546-014-9956-9, 2014. a
Muschinski, A., Sullivan, P. P., Wuertz, D. B., Hill, R. J., Cohn, S. A.,
Lenschow, D. H., and Doviak, R. J.: First Synthesis of Wind-Profiler Signals
on the Basis of Large-Eddy Simulation Data, Radio Sci., 34, 1437–1459,
https://doi.org/10.1029/1999RS900090, 1999. a, b
Newman, J. F., Klein, P. M., Wharton, S., Sathe, A., Bonin, T. A., Chilson, P. B., and Muschinski, A.: Evaluation of three lidar scanning strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993–2013, https://doi.org/10.5194/amt-9-1993-2016, 2016. a, b, c
Newsom, R., Calhoun, R., Ligon, D., and Allwine, J.: Linearly Organized
Turbulence Structures Observed Over a Suburban Area by Dual-Doppler
Lidar, Bound.-Lay. Meteorol., 127, 111–130,
https://doi.org/10.1007/s10546-007-9243-0, 2008. a
Newsom, R. K., Sivaraman, C., Shippert, T. R., and Riihimaki, L. D.: Doppler
Lidar Wind Value-Added Product, Tech. Rep. DOE/SC-ARM/TR-148, DOE ARM
Climate Research Facility, Pacific Northwest National Laboratory; Richland,
Washington, https://doi.org/10.2172/1238069, 2015. a, b
Peña, A., Kosović, B., and Mirocha, J. D.: Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mast, Wind Energ. Sci., 6, 645–661, https://doi.org/10.5194/wes-6-645-2021, 2021. a
Raasch, S. and Schröter, M.: PALM – A Large-Eddy Simulation Model Performing on Massively Parallel Computers, Meteorol. Z., 10,
363–372, https://doi.org/10.1127/0941-2948/2001/0010-0363, 2001. a
Robey, R.: Virtual Lidar Python, Gitlab [code], https://gitlab.com/raro0632/virtual-lidar, last access: 3 August 2022. a
Robey, R. and Lundquist, J. K.: Supporting virtual lidar and LES files for “Behavior and Mechanisms of Lidar Error in Varying Stability Regimes”, Zenodo [data set], https://doi.org/10.5281/zenodo.6112629, 2022. a
Rösner, B., Egli, S., Thies, B., Beyer, T., Callies, D., Pauscher, L., and
Bendix, J.: Fog and Low Stratus Obstruction of Wind Lidar
Observations in Germany – A Remote Sensing-Based Data Set for
Wind Energy Planning, Energies, 13, 3859, https://doi.org/10.3390/en13153859,
number: 15 Publisher: Multidisciplinary Digital Publishing Institute, 2020. a
Rybchuk, A., Alden, C. B., Lundquist, J. K., and Rieker, G. B.: A Statistical
Evaluation of WRF-LES Trace Gas Dispersion Using Project Prairie Grass
Measurements, Mon. Weather Rev., 149, 1619–1633,
https://doi.org/10.1175/MWR-D-20-0233.1, 2021. a
Salesky, S. T., Chamecki, M., and Bou-Zeid, E.: On the Nature of the
Transition Between Roll and Cellular Organization in the Convective
Boundary Layer, Bound.-Lay. Meteorol., 163, 41–68,
https://doi.org/10.1007/s10546-016-0220-3, 2017. a
Sanchez Gomez, M., Lundquist, J. K., Mirocha, J. D., Arthur, R. S., and Muñoz-Esparza, D.: Quantifying wind plant blockage under stable atmospheric conditions, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-57, 2021. a
Sathe, A. and Mann, J.: Measurement of Turbulence Spectra Using Scanning Pulsed Wind Lidars, J. Geophys. Res.-Atmos., 117, D01201,
https://doi.org/10.1029/2011JD016786, 2012. a
Sathe, A., Mann, J., Gottschall, J., and Courtney, M. S.: Can Wind Lidars
Measure Turbulence?, J. Atmos. Ocean. Technol., 28,
853–868, https://doi.org/10.1175/JTECH-D-10-05004.1, 2011. a, b
Sathe, A., Mann, J., Vasiljevic, N., and Lea, G.: A six-beam method to measure turbulence statistics using ground-based wind lidars, Atmos. Meas. Tech., 8, 729–740, https://doi.org/10.5194/amt-8-729-2015, 2015. a, b, c
Simley, E., Pao, L., Frehlich, R., Jonkman, B., and Kelley, N.: Analysis of Wind Speed Measurements Using Continuous Wave LIDAR for Wind Turbine Control, in: 49th AIAA Aerospace Sciences Meeting Including the
New Horizons Forum and Aerospace Exposition, Aerospace Sciences
Meetings, 4–7 January 2011, Orlando, Florida, USA, American Inst. Aero. Astro., https://doi.org/10.2514/6.2011-263, 2011. a
Simley, E., Fürst, H., Haizmann, F., and Schlipf, D.: Optimizing Lidars for
Wind Turbine Control Applications—Results from the IEA Wind
Task 32 Workshop, Remote Sens., 10, 863, https://doi.org/10.3390/rs10060863, 2018. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J.,
Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A
Description of the Advanced Research WRF Model Version 4, Tech. rep.,
UCAR/NCAR, https://doi.org/10.5065/1DFH-6P97, 2019.
a
Smith, E. N., Gebauer, J. G., Klein, P. M., Fedorovich, E., and Gibbs, J. A.:
The Great Plains Low-Level Jet during PECAN: Observed and
Simulated Characteristics, Mon. Weather Rev., 147, 1845–1869,
https://doi.org/10.1175/MWR-D-18-0293.1, 2019. a
Stawiarski, C., Träumner, K., Knigge, C., and Calhoun, R.: Scopes and
Challenges of Dual-Doppler Lidar Wind Measurements An
Error Analysis, J. Atmos. Ocean. Technol., 30,
2044–2062, https://doi.org/10.1175/JTECH-D-12-00244.1, 2013. a, b, c, d
Thobois, L., Krishnamurthy, R., Boquet, M., Cariou, J.-P., Santiago, A., and
Sas, L.: Coherent Pulsed Doppler LIDAR Metrological Performances and
Applications for Wind Eng., p. 13, 2015. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., SciPy 1.0 Contributors,
Vijaykumar, A., Bardelli, A. P., Rothberg, A., Hilboll, A., Kloeckner, A.,
Scopatz, A., Lee, A., Rokem, A., Woods, C. N., Fulton, C., Masson, C.,
Häggström, C., Fitzgerald, C., Nicholson, D. A., Hagen, D. R.,
Pasechnik, D. V., Olivetti, E., Martin, E., Wieser, E., Silva, F., Lenders,
F., Wilhelm, F., Young, G., Price, G. A., Ingold, G.-L., Allen, G. E., Lee,
G. R., Audren, H., Probst, I., Dietrich, J. P., Silterra, J., Webber, J. T.,
Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger,
J. L., de Miranda Cardoso, J. V., Reimer, J., Harrington, J.,
Rodríguez, J. L. C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K.,
Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak,
M., Smith, N. J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P. A.,
Lee, P., McGibbon, R. T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S.,
Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T. J.,
Robitaille, T. P., Spura, T., Jones, T. R., Cera, T., Leslie, T., Zito, T.,
Krauss, T., Upadhyay, U., Halchenko, Y. O., and Vázquez-Baeza, Y.:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wainwright, C. E., Stepanian, P. M., Chilson, P. B., Palmer, R. D., Fedorovich,
E., and Gibbs, J. A.: A Time Series Sodar Simulator Based on Large-Eddy
Simulation, J. Atmos. Ocean. Technol., 31, 876–889,
https://doi.org/10.1175/JTECH-D-13-00161.1, 2014. a, b, c
Short summary
Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to...
