Articles | Volume 10, issue 6
https://doi.org/10.5194/wes-10-1007-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-1007-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jun 2025
Research article |
| 02 Jun 2025
| 02 Jun 2025
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains
Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA
Lawrence Livermore National Laboratory, Livermore, California, USA
Robert S. Arthur
Lawrence Livermore National Laboratory, Livermore, California, USA
Aliza Abraham
National Renewable Energy Laboratory, Golden, Colorado, USA
Sonia Wharton
Lawrence Livermore National Laboratory, Livermore, California, USA
Raghavendra Krishnamurthy
Pacific Northwest National Laboratory, Richland, Washington, USA
Rob Newsom
Pacific Northwest National Laboratory, Richland, Washington, USA
Brian Hirth
National Wind Institute, Texas Tech University, Lubbock, Texas, USA
John Schroeder
Department of Geosciences, Texas Tech University, Lubbock, Texas, USA
Patrick Moriarty
National Renewable Energy Laboratory, Golden, Colorado, USA
Fotini K. Chow
Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA
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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
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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
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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.
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
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Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
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Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 6, 295–309, https://doi.org/10.5194/wes-6-295-2021, https://doi.org/10.5194/wes-6-295-2021, 2021
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Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
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A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 5, 959–975, https://doi.org/10.5194/wes-5-959-2020, https://doi.org/10.5194/wes-5-959-2020, 2020
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Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, https://doi.org/10.5194/wes-5-945-2020, 2020
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This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
James B. Duncan Jr., Brian D. Hirth, and John L. Schroeder
Wind Energ. Sci., 5, 469–488, https://doi.org/10.5194/wes-5-469-2020, https://doi.org/10.5194/wes-5-469-2020, 2020
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Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes on smaller timescales, (2) identifying reliable data sources and methods to quantify these control changes, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions.
Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Aditya Choukulkar, Larry K. Berg, Harindra J. S. Fernando, Eric P. Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, Mikhail Pekour, Yelena Pichugina, Mark T. Stoelinga, and David D. Turner
Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, https://doi.org/10.5194/gmd-12-4803-2019, 2019
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During the second Wind Forecast Improvement Project, improvements to the parameterizations were applied to the High Resolution Rapid Refresh model and its nested version. The impacts of the new parameterizations on the forecast of 80 m wind speeds and power are assessed, using sodars and profiling lidars observations for comparison. Improvements are evaluated as a function of the model’s initialization time, forecast horizon, time of the day, season, site elevation, and meteorological phenomena.
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
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Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, https://doi.org/10.5194/acp-19-4367-2019, 2019
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To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather prediction models, we have assessed its temporal and spatial variability at various scales in the Columbia River Gorge during the WFIP2 field experiment. The turbulence dissipation rate shows large spatial variability, even at the microscale, with larger values in sites located downwind of complex orographic structures or in wind farm wakes. Distinct diurnal and seasonal cycles in ε have also been found.
Bharat Rastogi, Max Berkelhammer, Sonia Wharton, Mary E. Whelan, Frederick C. Meinzer, David Noone, and Christopher J. Still
Biogeosciences, 15, 7127–7139, https://doi.org/10.5194/bg-15-7127-2018, https://doi.org/10.5194/bg-15-7127-2018, 2018
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Carbonyl sulfide (OCS) has gained prominence as an independent tracer for gross primary productivity, which is usually modelled by partitioning net CO2 fluxes. Here, we present a simple empirical model for estimating ecosystem-scale OCS fluxes for a temperate old-growth forest and find that OCS sink strength scales with independently estimated CO2 uptake and is sensitive to the the fraction of downwelling diffuse light. We also examine the response of OCS and CO2 fluxes to sequential heat waves.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
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Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
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Nicola Bodini, Julie K. Lundquist, and Rob K. Newsom
Atmos. Meas. Tech., 11, 4291–4308, https://doi.org/10.5194/amt-11-4291-2018, https://doi.org/10.5194/amt-11-4291-2018, 2018
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Turbulence within the atmospheric boundary layer is critically important to transfer heat, momentum, and moisture. Currently, improved turbulence parametrizations are crucially needed to refine the accuracy of model results at fine horizontal scales. In this study, we calculate turbulence dissipation rate from sonic anemometers and discuss a novel approach to derive turbulence dissipation from profiling lidar measurements.
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, https://doi.org/10.5194/wes-3-243-2018, 2018
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This paper investigates the role of flow structures in wind farm control through yaw misalignment. A pair of counter-rotating vortices is shown to be important in deforming the shape of the wake. Further, we demonstrate that the vortex structures created in wake steering can enable a greater change power generation than currently modeled in control-oriented models. We propose that wind farm controllers can be made more effective if designed to take advantage of these effects.
Rob K. Newsom, W. Alan Brewer, James M. Wilczak, Daniel E. Wolfe, Steven P. Oncley, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, https://doi.org/10.5194/amt-10-1229-2017, 2017
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Doppler lidars are remote sensing instruments that use infrared light to measure wind velocity in the lowest 2 to 3 km of the atmosphere. Quantifying the uncertainty in these measurements is crucial for applications ranging from wind resource assessment to model data assimilation. In this study, we evaluate three methods for estimating the random uncertainty by comparing the lidar wind measurements with nearly collocated in situ wind measurements at multiple levels on a tall tower.
Mithu Debnath, Giacomo Valerio Iungo, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Scott Gunter, Julie K. Lundquist, John L. Schroeder, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 1215–1227, https://doi.org/10.5194/amt-10-1215-2017, https://doi.org/10.5194/amt-10-1215-2017, 2017
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The XPIA experiment was conducted in 2015 at the Boulder Atmospheric Observatory to estimate capabilities of various remote-sensing techniques for the characterization of complex atmospheric flows. Among different tests, XPIA provided the unique opportunity to perform simultaneous virtual towers with Ka-band radars and scanning Doppler wind lidars. Wind speed and wind direction were assessed against lidar profilers and sonic anemometer data, highlighting a good accuracy of the data retrieved.
Timothy A. Bonin, Jennifer F. Newman, Petra M. Klein, Phillip B. Chilson, and Sonia Wharton
Atmos. Meas. Tech., 9, 5833–5852, https://doi.org/10.5194/amt-9-5833-2016, https://doi.org/10.5194/amt-9-5833-2016, 2016
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Turbulence measurements are important to boundary layer meteorology and related fields. Doppler lidars are capable of providing continuous profiles of turbulence statistics. Herein, the most direct turbulence measurement, vertical velocity variance, is validated with those from sonic anemometers. Spectra are also compared. A method of calculating velocity variance using the autocovariance is shown to improve the accuracy of the measurement by mitigating effects of noise and averaging.
K. K. Shukla, K. Niranjan Kumar, D. V. Phanikumar, R. K. Newsom, V. R. Kotamarthi, T. B. M. J. Ouarda, and M. V. Ratnam
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-162, https://doi.org/10.5194/amt-2016-162, 2016
Revised manuscript not accepted
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Estimation of Cloud base height was carried out by using various ground based instruments (Doppler Lidar and Ceilometer) and satellite datasets (MODIS) over central Himalayan region for the first time. The present study demonstrates the potential of Doppler Lidar in precise estimation of cloud base height and updraft velocities. More such deployments will be invaluable inputs for regional weather prediction models over complex Himalayan terrains.
Jennifer F. Newman, Petra M. Klein, Sonia Wharton, Ameya Sathe, Timothy A. Bonin, Phillip B. Chilson, and Andreas Muschinski
Atmos. Meas. Tech., 9, 1993–2013, https://doi.org/10.5194/amt-9-1993-2016, https://doi.org/10.5194/amt-9-1993-2016, 2016
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Remote sensing devices known as lidars are often used to take measurements at potential wind farm sites. These instruments are however not optimized for measuring turbulence, small-scale changes in wind speed. In this manuscript, the impact of lidar configurations and atmospheric conditions on turbulence accuracy is explored. A new method was developed to correct lidar turbulence measurements and is described in detail such that other lidar users can apply it to their own instruments.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Analyzing the performance of vertical wind profilers in rain events
Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast
The impact of far-reaching offshore cluster wakes on wind turbine fatigue loads
Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms
Brief communication: A note on the variance of wind speed and turbulence intensity
Investigating the Relationship between Simulation Parameters and Flow Variables in Simulating Atmospheric Gravity Waves for Wind Energy Applications
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
Quantifying Tropical Cyclone-Generated Waves in Extreme-Value-Derived Design for Offshore Wind
Modelling Frontal Low-Level Jets and Associated Extreme Wind Power Ramps over the North Sea
Mesoscale weather systems and associated potential wind power variations in a midlatitude sea strait (Kattegat)
A large-eddy simulation (LES) model for wind-farm-induced atmospheric gravity wave effects inside conventionally neutral boundary layers
Simulating low-frequency wind fluctuations
Estimating Long-Term Annual Energy Production of a Large Offshore Wind Farm from Large-Eddy Simulations: Methods and Validation with a 10-Year Simulation
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment
Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production
Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions
Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy
Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers
Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
Lifetime prediction of turbine blades using global precipitation products from satellites
Evaluation of low-level jets in the southern Baltic Sea: a comparison between ship-based lidar observational data and numerical models
Predicting power ramps from joint distributions of future wind speeds
Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
Research challenges and needs for the deployment of wind energy in hilly and mountainous regions
Observer-based power forecast of individual and aggregated offshore wind turbines
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
Adriel J. Carvalho, Francisco L. Albuquerque Neto, and Denisson Q. Oliveira
Wind Energ. Sci., 10, 971–986, https://doi.org/10.5194/wes-10-971-2025, https://doi.org/10.5194/wes-10-971-2025, 2025
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Wind profilers are important to the wind power industry since they capture wind velocity and direction at higher altitudes than meteorological masts. Although some studies have investigated their performance in different scenarios, this paper covers a gap in knowledge by investigating and comparing their performance under rain events. This investigation is important since the data collected support strategic decisions in the wind power industry, where high data availability in all situations is critical.
Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee
Wind Energ. Sci., 10, 483–495, https://doi.org/10.5194/wes-10-483-2025, https://doi.org/10.5194/wes-10-483-2025, 2025
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Our study reveals how different weather patterns influence wind conditions off the US West Coast. We identified key weather patterns affecting wind speeds at potential wind farm sites using advanced machine learning. This research helps improve weather prediction models, making wind energy production more reliable and efficient.
Arjun Anantharaman, Jörge Schneemann, Frauke Theuer, Laurent Beaudet, Valentin Bernard, Paul Deglaire, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-20, https://doi.org/10.5194/wes-2025-20, 2025
Revised manuscript accepted for WES
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The offshore wind farm sector is expanding rapidly, and the interactions between wind farms are important to analyse for both existing and planned wind farms. We developed a new methodology to quantify how much the reductions in wind speed behind a farm can affect the loads on turbines which are tens of kilometers downstream. We found a 2.5 % increase in the turbine loads and discuss how further measurements could add to the design standards of future wind farms.
Tsvetelina Ivanova, Sara Porchetta, Sophia Buckingham, Gertjan Glabeke, Jeroen van Beeck, and Wim Munters
Wind Energ. Sci., 10, 245–268, https://doi.org/10.5194/wes-10-245-2025, https://doi.org/10.5194/wes-10-245-2025, 2025
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This study explores how wind and power predictions can be improved by introducing local forcing of measurement data in a numerical weather model while taking into account the presence of neighboring wind farms. Practical implications for the wind energy industry include insights for informed offshore wind farm planning and decision-making strategies using open-source models, even under adverse weather conditions.
Cristina Lozej Archer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-159, https://doi.org/10.5194/wes-2024-159, 2024
Revised manuscript accepted for WES
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Two approximate analytical expressions are derived, one for the variance of wind speed and the other for turbulence intensity, based on one simple assumption: that the turbulent fluctuations of the wind are small with respect to the mean. The formulations perform well when applied to the observations from the VERTEX field campaign conducted in 2016.
Mehtab Ahmed Khan, Dries Allaerts, Simon J. Watson, and Matthew J. Churchfield
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-138, https://doi.org/10.5194/wes-2024-138, 2024
Revised manuscript accepted for WES
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To guide realistic atmospheric gravity wave simulations, we conduct an LES study of flow over a 2D hill and through a wind farm canopy, examining optimal domain size and Rayleigh damping layer setup. Wave properties based on a Froude number determine optimal domain and damping parameters. Reasonably accurate solutions require the domain length exceed the effective horizontal wavelength, height and damping thickness equal a vertical wavelength, and normalized-damping coefficient between 1–10.
Jonathan Minz, Axel Kleidon, and Nsilulu T. Mbungu
Wind Energ. Sci., 9, 2147–2169, https://doi.org/10.5194/wes-9-2147-2024, https://doi.org/10.5194/wes-9-2147-2024, 2024
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Estimates of power output from regional wind turbine deployments in energy scenarios assume that the impact of the atmospheric feedback on them is minimal. But numerical models show that the impact is large at the proposed scales of future deployment. We show that this impact can be captured by accounting only for the kinetic energy removed by turbines from the atmosphere. This can be easily applied to energy scenarios and leads to more physically representative estimates.
Sarah McElman, Amrit Verma, and Andrew Goupee
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-129, https://doi.org/10.5194/wes-2024-129, 2024
Revised manuscript accepted for WES
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This paper investigates how tropical cyclones are represented in metocean models and statistics applied to offshore wind design. It provides recommendations for ensuring the accurate representation of extreme waves for design and operation of offshore projects on the Atlantic coast of the USA.
Harish Baki, Sukanta Basu, and George Lavidas
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-99, https://doi.org/10.5194/wes-2024-99, 2024
Revised manuscript accepted for WES
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Our study explores how frontal low-level jets (FLLJs) impact wind power production by causing ramp-down events. Using the Weather Research and Forecasting model, we analyzed various modeling configurations and found that initial and boundary conditions, domain configuration, and wind farm parameterization significantly influence simulations. Our findings show such extreme events can be forecasted one day in advance, helping manage wind power more efficiently for a stable, reliable energy supply.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 1695–1711, https://doi.org/10.5194/wes-9-1695-2024, https://doi.org/10.5194/wes-9-1695-2024, 2024
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In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, https://doi.org/10.5194/wes-9-1647-2024, 2024
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We introduce a novel way to model the impact of atmospheric gravity waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the interaction of wind farms with the free atmosphere, ultimately facilitating cost-effective research.
Abdul Haseeb Syed and Jakob Mann
Wind Energ. Sci., 9, 1381–1391, https://doi.org/10.5194/wes-9-1381-2024, https://doi.org/10.5194/wes-9-1381-2024, 2024
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Wind flow consists of swirling patterns of air called eddies, some as big as many kilometers across, while others are as small as just a few meters. This paper introduces a method to simulate these large swirling patterns on a flat grid. Using these simulations we can better figure out how these large eddies affect big wind turbines in terms of loads and forces.
Bernard Postema, Remco Verzijlbergh, Pim van Dorp, Peter Baas, and Harm Jonker
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-54, https://doi.org/10.5194/wes-2024-54, 2024
Revised manuscript accepted for WES
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Atmospheric large-eddy simulation is a technique that simulates weather conditions high detail, and is used to plan new wind farms. This research presents ways to estimate the long-term (10-year) power production of a wind farm, without having to simulate 10 years of weather, but much shorter (one year or less). The results show that the methods reduce the uncertainty in power production estimates by an order of magnitude, and that wind observations can be included as well to add more insight.
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
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Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before landfall in a mesoscale model. The simulated wind speeds and storm structure are sensitive to the boundary parametrization. However, independent of the boundary layer parametrization, the median change in wind speed and wind direction with height is small relative to wind turbine design standards. Strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1123–1152, https://doi.org/10.5194/wes-9-1123-2024, https://doi.org/10.5194/wes-9-1123-2024, 2024
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This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at modeling turbine–wake and wind farm–gravity wave interactions, as well as local and global blockage effects. Comparisons against large eddy simulations show that the MSC model offers a valid contribution towards advancing our understanding of the coupled wind farm–atmosphere interaction, helping refining power estimation methodologies for existing and future wind farm sites.
Lindsay M. Sheridan, Dmitry Duplyakin, Caleb Phillips, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, and Larry K. Berg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-37, https://doi.org/10.5194/wes-2024-37, 2024
Revised manuscript accepted for WES
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Twelve months of onsite wind measurement is standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, however, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities for distributed wind projects. This study aims to answer the question of how low can you go in terms of the observational time period needed to make impactful improvements to long-term wind speed estimates.
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
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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.
Mohammad Golam Mostafa Khan and Mohammed Rafiuddin Ahmed
Wind Energ. Sci., 8, 1277–1298, https://doi.org/10.5194/wes-8-1277-2023, https://doi.org/10.5194/wes-8-1277-2023, 2023
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A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the tropical region from 1° N to 21° S revealed that the three-parameter (3-p) Weibull distribution with a non-zero shift parameter is a better fit for wind data that have a higher percentage of low wind speeds. Wind data with higher wind speeds are a special case of the 3-p distribution. This approach gives accurate results.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
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The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
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
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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.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
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As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
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When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
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A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
Wind Energ. Sci., 7, 2393–2405, https://doi.org/10.5194/wes-7-2393-2022, https://doi.org/10.5194/wes-7-2393-2022, 2022
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The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Andrew Clifton, Sarah Barber, Alexander Stökl, Helmut Frank, and Timo Karlsson
Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, https://doi.org/10.5194/wes-7-2231-2022, 2022
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The transition to low-carbon sources of energy means that wind turbines will need to be built in hilly or mountainous regions or in places affected by icing. These locations are called
complexand are hard to develop. This paper sets out the research and development (R&D) needed to make it easier and cheaper to harness wind energy there. This includes collaborative R&D facilities, improved wind and weather models, frameworks for sharing data, and a clear definition of site complexity.
Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
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Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
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The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
Cited articles
Aitken, M. L., Kosović, B., Mirocha, J. D., and Lundquist, J. K.: Large eddy simulation of wind turbine wake dynamics in the stable boundary layer using the Weather Research and Forecasting Model, J. Renew. Sustain. Ener., 6, 033137, https://doi.org/10.1063/1.4885111, 2014. a
Allaerts, D. and Meyers, J.: Gravity Waves and Wind-Farm Efficiency in Neutral and Stable Conditions, Bound.-Lay. Meteorol., 166, 269–299, https://doi.org/10.1007/s10546-017-0307-5, 2018. a
Arthur, R. S., Mirocha, J. D., Marjanovic, N., Hirth, B. D., Schroeder, J. L., Wharton, S., and Chow, F. K.: Multi-Scale Simulation of Wind Farm Performance during a Frontal Passage, Atmosphere, 11, 245, https://doi.org/10.3390/atmos11030245, 2020. a, b, c
Blaylock, B. K., Horel, J. D., and Crosman, E. T.: Impact of Lake Breezes on Summer Ozone Concentrations in the Salt Lake Valley, J. Appl. Meteorol. Climatol., 56, 353–370, https://doi.org/10.1175/JAMC-D-16-0216.1, 2017. a
Carbajo Fuertes, F., Markfort, C. D., and Porté-Agel, F.: Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation, Remote Sens., 10, 668, https://doi.org/10.3390/rs10050668, 2018. a
Chen, B., Thompson, T., and Chow, F. K.: Hyper-local source strength retrieval and apportionment of black carbon in an urban area, Atmos. Environ. X, 22, 100252, https://doi.org/10.1016/j.aeaoa.2024.100252, 2024. a
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, 2001. a
Chow, F., Schär, C., Ban, N., Lundquist, K., Schlemmer, L., and Shi, X.: Crossing Multiple Gray Zones in the Transition from Mesoscale to Microscale Simulation over Complex Terrain, Atmosphere, 10, 274, https://doi.org/10.3390/atmos10050274, 2019. a
Chow, F. K.: Subfilter-scale turbulence modeling for large-eddy simulation of the atmospheric boundary layer over complex terrain, Ph.D. dissertation, Stanford University, 2004. a
Chow, F. K., Street, R. L., Xue, M., and Ferziger, J. H.: Explicit Filtering and Reconstruction Turbulence Modeling for Large-Eddy Simulation of Neutral Boundary Layer Flow, J. Atmos. Sci., 62, 2058–2077, https://doi.org/10.1175/JAS3456.1, 2005. a, b
Connolly, A., van Veen, L., Neher, J., Geurts, B. J., Mirocha, J., and Chow, F. K.: Efficacy of the Cell Perturbation Method in Large-Eddy Simulations of Boundary Layer Flow over Complex Terrain, Atmosphere, 12, 55, https://doi.org/10.3390/atmos12010055, 2021. a, b
Cook, D. R.: Eddy Correlation Flux Measurement System (ECOR) Instrument Handbook, https://doi.org/10.2172/1467448, 2018. a
Debnath, M., Scholbrock, A. K., Zalkind, D., Moriarty, P., Simley, E., Hamilton, N., Ivanov, C., Arthur, R. S., Barthelmie, R., Bodini, N., Brewer, A., Goldberger, L., Herges, T., Hirth, B., Valerio Iungo, G., Jager, D., Kaul, C., Klein, P., Krishnamurthy, R., Letizia, S., Lundquist, J. K., Maniaci, D., Newsom, R., Pekour, M., Pryor, S. C., Ritsche, M. T., Roadman, J., Schroeder, J., Shaw, W. J., Van Dam, J., and Wharton, S.: Design of the American Wake Experiment (AWAKEN) field campaign, J. Phys. Conf. Ser., 2265, 022058, https://doi.org/10.1088/1742-6596/2265/2/022058, 2022. a, b
Debnath, M., Moriarty, P., Krishnamurthy, R., Bodini, N., Newsom, R., Quon, E., Lundquist, J. K., Letizia, S., Iungo, G. V., and Klein, P.: Characterization of wind speed and directional shear at the AWAKEN field campaign site, J. Renew. Sustain. Ener., 15, 033308, https://doi.org/10.1063/5.0139737, 2023. a
Dowell, D. C., Alexander, C. R., James, E. P., Weygandt, S. S., Benjamin, S. G., Manikin, G. S., Blake, B. T., Brown, J. M., Olson, J. B., Hu, M., Smirnova, T. G., Ladwig, T., Kenyon, J. S., Ahmadov, R., Turner, D. D., Duda, J. D., and Alcott, T. I.: The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description, Weather Forecast., 37, 1371–1395, https://doi.org/10.1175/WAF-D-21-0151.1, 2022. a, b
Dudhia, J.: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077–3107, 1989. a
Durran, D., Weyn, J. A., and Menchaca, M. Q.: Practical considerations for computing dimensional spectra from gridded data, Mon. Weather Rev., 145, 3901–3910, 2017. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar topography mission, Rev. Geophys., 45, https://doi.org/10.1029/2005RG000183, 2007. a
Feng, Z., Houze, R. A., Leung, L. R., Song, F., Hardin, J. C., Wang, J., Gustafson, W. I., and Homeyer, C. R.: Spatiotemporal Characteristics and Large-Scale Environments of Mesoscale Convective Systems East of the Rocky Mountains, J. Climate, 32, 7303–7328, https://doi.org/10.1175/JCLI-D-19-0137.1, 2019. a
Geerts, B., Parsons, D., Ziegler, C. L., Weckwerth, T. M., Biggerstaff, M. I., Clark, R. D., Coniglio, M. C., Demoz, B. B., Ferrare, R. A., Gallus, W. A., Haghi, K., Hanesiak, J. M., Klein, P. M., Knupp, K. R., Kosiba, K., McFarquhar, G. M., Moore, J. A., Nehrir, A. R., Parker, M. D., Pinto, J. O., Rauber, R. M., Schumacher, R. S., Turner, D. D., Wang, Q., Wang, X., Wang, Z., and Wurman, J.: The 2015 Plains Elevated Convection at Night Field Project, B. Am. Meteorol. Soc., 98, 767–786, https://doi.org/10.1175/BAMS-D-15-00257.1, 2017. a, b, c
Germano, M., Piomelli, U., Moin, P., and Cabot, W. H.: A dynamic subgrid‐scale eddy viscosity model, Phys. Fluids A, 3, 1760–1765, https://doi.org/10.1063/1.857955, 1991. a
Haghi, K. R. and Durran, D. R.: On the Dynamics of Atmospheric Bores, J. Atmos. Sci., 78, 313–327, https://doi.org/10.1175/JAS-D-20-0181.1, 2021. a, b, c
Haghi, K. R., Parsons, D. B., and Shapiro, A.: Bores Observed during IHOP_2002: The Relationship of Bores to the Nocturnal Environment, Mon. Weather Rev., 145, 3929–3946, https://doi.org/10.1175/MWR-D-16-0415.1, 2017. a
Han, B., Fan, J., Varble, A., Morrison, H., Williams, C. R., Chen, B., Dong, X., Giangrande, S. E., Khain, A., Mansell, E., Milbrandt, J. A., Shpund, J., and Thompson, G.: Cloud-Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties, J. Geophys. Res.-Atmos., 124, 1090–1117, https://doi.org/10.1029/2018JD029596, 2019. a, b
Haupt, S. E., Kosović, 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
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hirth, B. D., Schroeder, J. L., and Guynes, J. G.: Diurnal evolution of wind structure and data availability measured by the DOE prototype radar system, J. Phys. Conf. Ser., 926, 012003, https://doi.org/10.1088/1742-6596/926/1/012003, 2017. a
Hirth, B. D., Schroeder, J. L., and Guynes, J. G.: An Onshore Deployment of Advanced Dual-Doppler Radar for Wind Energy Applications, J. Phys. Conf. Ser., 2745, 012013, https://doi.org/10.1088/1742-6596/2745/1/012013, 2024. a
Houze Jr., R. A.: Mesoscale convective systems, Rev. Geophys., 42, RG2004, https://doi.org/10.1029/2004RG000150, 2004. a
Janjić, Z. I.: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes, Mon. Weather Rev., 122, 927–945, 1994. a
Johnson, A. and Wang, X.: Design and Implementation of a GSI-Based Convection-Allowing Ensemble Data Assimilation and Forecast System for the PECAN Field Experiment. Part I: Optimal Configurations for Nocturnal Convection Prediction Using Retrospective Cases, Weather Forecast., 32, 289–315, https://doi.org/10.1175/WAF-D-16-0102.1, 2017. a, b, c
Johnson, A. and Wang, X.: Multicase Assessment of the Impacts of Horizontal and Vertical Grid Spacing, and Turbulence Closure Model, on Subkilometer-Scale Simulations of Atmospheric Bores during PECAN, Mon. Weather Rev., 147, 1533–1555, https://doi.org/10.1175/MWR-D-18-0322.1, 2019. a, b, c
Johnson, A., Wang, X., and Degelia, S.: Design and Implementation of a GSI-Based Convection-Allowing Ensemble-Based Data Assimilation and Forecast System for the PECAN Field Experiment. Part II: Overview and Evaluation of a Real-Time System, Weather Forecast., 32, 1227–1251, https://doi.org/10.1175/WAF-D-16-0201.1, 2017. a
Keane, A., Aguirre, P. E. O., Ferchland, H., Clive, P., and Gallacher, D.: An analytical model for a full wind turbine wake, J. Phys. Conf. Ser., 753, 032039, https://doi.org/10.1088/1742-6596/753/3/032039, 2016. a
Kirkil, G., Mirocha, J., Bou-Zeid, E., Chow, F. K., and Kosović, 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
Knupp, K.: Observational Analysis of a Gust Front to Bore to Solitary Wave Transition within an Evolving Nocturnal Boundary Layer, J. Atmos. Sci., 63, 2016–2035, https://doi.org/10.1175/JAS3731.1, 2006. a
Kolmogorov, A. N.: The Local Structure of Turbulence in Incompressible Viscous Fluid for Very Large Reynolds' Numbers, in: Dokl. Akad. Nauk SSSR, 30, 301–305, 1941. a
Krishnamurthy, R., Newsom, R. K., Chand, D., and Shaw, W. J.: Boundary Layer Climatology at ARM Southern Great Plains, U.S. Department of Energy, https://doi.org/10.2172/1779279, 2021. a
Krishnamurthy, R., Newsom, R. K., Kaul, C. M., Letizia, S., Pekour, M., Hamilton, N., Chand, D., Flynn, D., Bodini, N., and Moriarty, P.: Observations of wind farm wake recovery at an operating wind farm, Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, 2025. a
Lanzilao, L. and Meyers, J.: Effects of self-induced gravity waves on finite wind-farm operations using a large-eddy simulation framework, J. Phys. Conf. Ser., 2265, 022043, https://doi.org/10.1088/1742-6596/2265/2/022043, 2022. a
Lilly, D. K.: A proposed modification of the Germano subgrid‐scale closure method, Phys. Fluids A, 4, 633–635, https://doi.org/10.1063/1.858280, 1992. a
Lim, K.-S. S. and Hong, S.-Y.: Development of an Effective Double-Moment Cloud Microphysics Scheme with Prognostic Cloud Condensation Nuclei (CCN) for Weather and Climate Models, Mon. Weather Rev., 138, 1587–1612, https://doi.org/10.1175/2009MWR2968.1, 2010. a
Lundquist, J. K.: Intermittent and Elliptical Inertial Oscillations in the Atmospheric Boundary Layer, J. Atmos. Sci., 60, 2661–2673, https://doi.org/10.1175/1520-0469(2003)060<2661:IAEIOI>2.0.CO;2, 2003. a
Markowski, P. and Richardson, Y.: Mesoscale Convective Systems, Chap. 9, 245–272, John Wiley & Sons, Ltd, ISBN 9780470682104, https://doi.org/10.1002/9780470682104.ch9, 2010. a, b
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, J. Adv. Model. Earth Sy., 9, 1795–1810, https://doi.org/10.1002/2017MS000912, 2017. a
Mirocha, J. D., Kosović, B., Aitken, M. L., and Lundquist, J. K.: Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications, J. Renew. Sustain. Ener., 6, 013104, https://doi.org/10.1063/1.4861061, 2014. a, b, c
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, 1997. a
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR, 151, e187, 1954. a
Moriarty, P., Hamilton, N., Debnath, M., Herges, T., Isom, B., Lundquist, J., Maniaci, D., Naughton, B., Pauly, R., Roadman, J., Shaw, W., Van Dam, J., and Wharton, S.: American WAKE experimeNt (AWAKEN), Tech. Rep. NREL/TP-5000-75789, 1659798, MainId:5894, https://doi.org/10.2172/1659798, 2020. a
Moriarty, P., Bodini, N., Letizia, S., Abraham, A., Ashley, T., Bärfuss, K. B., Barthelmie, R. J., Brewer, A., Brugger, P., Feuerle, T., Frère, A., Goldberger, L., Gottschall, J., Hamilton, N., Herges, T., Hirth, B., Hung, L.-Y., Iungo, G. V., Ivanov, H., Kaul, C., Kern, S., Klein, P.,Krishnamurthy, R., Lampert, A., Lundquist, J. K., Morris, V. R., Newsom, R., Pekour, M., Pichugina, Y., Porté-Angel, F., Pryor, S. C., Scholbrock, A., Schroeder, J., Shartzer, S., Simley, E., Vöhringer, L., Wharton, S., Zalkind, D.: Overview of preparation for the American WAKE ExperimeNt (AWAKEN), J. Renew. Sustain. Energ., 16, 053306, https://doi.org/10.1063/5.0141683, 2024. a, b
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W., Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A., Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van Diedenhoven, B., and Xue, L.: Confronting the Challenge of Modeling Cloud and Precipitation Microphysics, J. Adv. Model. Earth Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a
Muller, C. and Abramian, S.: The cloud dynamics of convective storm systems, Physics Today, 76, 28–28, https://doi.org/10.1063/PT.3.5234, 2023. a
Muñoz-Esparza, D., Kosović, B., Van Beeck, J., and Mirocha, J.: A stochastic perturbation method to generate inflow turbulence in large-eddy simulation models: Application to neutrally stratified atmospheric boundary layers, Phys. Fluids, 27, 035102, https://doi.org/10.1063/1.4913572, 2015. a, b
Muñoz-Esparza, D., Lundquist, J. K., Sauer, J. A., Kosović, B., and Linn, R. R.: Coupled mesoscale-LES modeling of a diurnal cycle during the CWEX-13 field campaign: From weather to boundary-layer eddies, J. Adv. Model. Earth Sy., 9, 1572–1594, https://doi.org/10.1002/2017MS000960, 2017. a
National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive, https://doi.org/10.5065/D65D8PWK, 2015. a
Newsom, R. and Krishnamurthy, R.: Doppler Lidar (DL) Instrument Handbook, U.S. Department of Energy [data set], https://doi.org/10.2172/1034640, 2022. a, b
Newsom, R. and Krishnamurthy, R.: Doppler Lidar (DLPPI2) Site A1 (S4) for AWAKEN, ARM Data Discovery [data set], https://doi.org/10.5439/1890922, 2024. a
Newsom, R. K. and Banta, R. M.: Shear-Flow Instability in the Stable Nocturnal Boundary Layer as Observed by Doppler Lidar during CASES-99, J. Atmos. Sci., 60, 16–33, https://doi.org/10.1175/1520-0469(2003)060<0016:SFIITS>2.0.CO;2, 2003. a
Olson, J. B., Kenyon, J. S., Angevine, W. A., Brown, J. M., Pagowski, M., and Sušelj, K.: A Description of the MYNN-EDMF Scheme and the Coupling to Other Components in WRF–ARW, NOAA Technical Memorandum OAR GSD, 61, https://doi.org/10.25923/n9wm-be49, 2019. a
Pandey, A., Lamraoui, F., Smith, J. B., Clapp, C. E., Sayres, D. S., and Kuang, Z.: Sensitivity of Deep Convection and Cross-Tropopause Water Transport to Microphysical Parameterizations in WRF, J. Geophys. Res.-Atmos., 128, e2022JD037053, https://doi.org/10.1029/2022JD037053, 2023. a, b
Quon, E.: NREL/openfast-turbine-models, GitHub [code], https://github.com/NREL/openfast-turbine-models/tree/main/IEA-scaled/NREL-2.8-127, 2024. a
Ralph, F. M., Neiman, P. J., and Keller, T. L.: Deep-Tropospheric Gravity Waves Created by Leeside Cold Fronts, J. Atmos. Sci., 56, 2986–3009, https://doi.org/10.1175/1520-0469(1999)056<2986:DTGWCB>2.0.CO;2, 1999. a
Rottman, J. W. and Simpson, J. E.: The formation of internal bores in the atmosphere: A laboratory model, Q. J. Roy. Meteor. Soc., 115, 941–963, https://doi.org/10.1002/qj.49711548809, 1989. a, b
Sanchez Gomez, M., Lundquist, J. K., Mirocha, J. D., Arthur, R. S., Muñoz-Esparza, D., and Robey, R.: Can lidars assess wind plant blockage in simple terrain? A WRF-LES study, J. Renew. Sustain. Ener., 14, 063303, https://doi.org/10.1063/5.0103668, 2022. a
Sanchez Gomez, M., Lundquist, J. K., Mirocha, J. D., and Arthur, R. S.: Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers, Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, 2023. a
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
Schreiber, J., Balbaa, A., and Bottasso, C. L.: Brief communication: A double-Gaussian wake model, Wind Energ. Sci., 5, 237–244, https://doi.org/10.5194/wes-5-237-2020, 2020. a
Simpson, J.: Gravity Currents in the Environment and the Laboratory, Cambridge University Press, ISBN-10 9780521561099, ISBN-13 978-0521561099, 1997. a
Skamarock, W. C.: Evaluating mesoscale NWP models using kinetic energy spectra, Mon. Weather Rev., 132, 3019–3032, 2004. 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, technical report, https://doi.org/10.5065/1dfh-6p97, 2021. a, b, c
Stipa, S., Ahmed Khan, M., Allaerts, D., and Brinkerhoff, J.: A large-eddy simulation (LES) model for wind-farm-induced atmospheric gravity wave effects inside conventionally neutral boundary layers, Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, 2024. a
Sun, J., Burns, S. P., Lenschow, D. H., Banta, R., Newsom, R., Coulter, R., Frasier, S., Ince, T., Nappo, C., Cuxart, J., Blumen, W., Lee, X., and Hu, X.-Z.: Intermittent Turbulence Associated with a Density Current Passage in the Stable Boundary Layer, Bound.-Lay. Meteorol., 105, 199–219, https://doi.org/10.1023/A:1019969131774, 2002. a
Tatsuya Seiki, W. R. and Satoh, M.: Cloud Microphysics in Global Cloud Resolving Models, Atmosphere-Ocean, 60, 477–505, https://doi.org/10.1080/07055900.2022.2075310, 2022. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Tomaszewski, J. M. and Lundquist, J. K.: Observations and simulations of a wind farm modifying a thunderstorm outflow boundary, Wind Energ. Sci., 6, 1–13, https://doi.org/10.5194/wes-6-1-2021, 2021. a, b
Toms, B. A., Tomaszewski, J. M., Turner, D. D., and Koch, S. E.: Analysis of a Lower-Tropospheric Gravity Wave Train Using Direct and Remote Sensing Measurement Systems, Mon. Weather Rev., 145, 2791–2812, https://doi.org/10.1175/MWR-D-16-0216.1, 2017. a
U.S. Department of Energy: A2E Wind Data Hub, U.S. Department of Energy [data set], https://www.a2e.energy.gov, last access: 1 July 2024. a
U.S. Energy Information Administration: Oklahoma State Energy Profile, Tech. rep., Washington, D.C., https://www.eia.gov/state/print.php?sid=OK (last access: 1 July 2024), 2023. a
Vermeer, L., Sørensen, J., and Crespo, A.: Wind turbine wake aerodynamics, Prog. Aerospace Sci., 39, 467–510, https://doi.org/10.1016/S0376-0421(03)00078-2, 2003. a
Wang, J., Foley, S., Nanos, E. M., Yu, T., Campagnolo, F., Bottasso, C. L., Zanotti, A., and Croce, A.: Numerical and Experimental Study of Wake Redirection Techniques in a Boundary Layer Wind Tunnel, J. Phys. Conf. Ser., 854, 012048, https://doi.org/10.1088/1742-6596/854/1/012048, 2017. a
Weckwerth, T. M. and Romatschke, U.: Where, When, and Why Did It Rain during PECAN?, Mon. Weather Rev., 147, 3557–3573, https://doi.org/10.1175/MWR-D-18-0458.1, 2019. a, b
Weckwerth, T. M., Hanesiak, J., Wilson, J. W., Trier, S. B., Degelia, S. K., Gallus, W. A., Roberts, R. D., and Wang, X.: Nocturnal Convection Initiation during PECAN 2015, B. Am. Meteorol. Soc., 100, 2223–2239, https://doi.org/10.1175/BAMS-D-18-0299.1, 2019. a, b
Welch, P.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms, IEEE T. Acoust. Speech, 15, 70–73, https://doi.org/10.1109/TAU.1967.1161901, 1967. a
Wharton, S.: awaken/sa1.lidar.z01.00, Wind Data Hub for U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy [data set], https://doi.org/10.21947/1915018 (last access: 1 July 2024), 2023. a
Wiersema, D. J., Lundquist, K. A., Mirocha, J. D., and Chow, F. K.: Evaluation of Turbulence and Dispersion in Multiscale Atmospheric Simulations over Complex Urban Terrain during the Joint Urban 2003 Field Campaign, Mon. Weather Rev., 150, 3195–3209, https://doi.org/10.1175/MWR-D-22-0056.1, 2022. a
Wise, A. S.: Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the Southern Great Plains, Zenodo [video], https://doi.org/10.5281/zenodo.12551369, 2024b. a
Wise, A. S.: adamwise95/WRFv4.4-DRM_GAD: WRFv4.4-DRM_GAD (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.15492528, 2025. a
Wise, A. S., Neher, J. M. T., Arthur, R. S., Mirocha, J. D., Lundquist, J. K., and Chow, F. K.: Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain, Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, 2022. a, b, c
Wong, V. C. and Lilly, D. K.: A comparison of two dynamic subgrid closure methods for turbulent thermal convection, Phys. Fluids, 6, 1016–1023, https://doi.org/10.1063/1.868335, 1994. a
Wyngaard, J. C.: Toward Numerical Modeling in the “Terra Incognita”, J. Atmos. Sci., 61, 1816–1826, https://doi.org/10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2, 2004. a
Zhou, B. and Chow, F. K.: Large-Eddy Simulation of the Stable Boundary Layer with Explicit Filtering and Reconstruction Turbulence Modeling, J. Atmos. Sci., 68, 2142–2155, https://doi.org/10.1175/2011JAS3693.1, 2011. a, b, c, d
Zhou, B. and Chow, F. K.: Turbulence Modeling for the Stable Atmospheric Boundary Layer and Implications for Wind Energy, Flow, Turbulence and Combustion, 88, 255–277, https://doi.org/10.1007/s10494-011-9359-7, 2012. a, b
Zhou, B. and Chow, F. K.: Nested Large-Eddy Simulations of the Intermittently Turbulent Stable Atmospheric Boundary Layer over Real Terrain, J. Atmos. Sci., 71, 1021–1039, https://doi.org/10.1175/JAS-D-13-0168.1, 2014. a, b, c, d
Short summary
Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Wind farms can be subject to rapidly changing weather events. In the United States Great Plains,...
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