Articles | Volume 10, issue 7
https://doi.org/10.5194/wes-10-1451-2025
© 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-10-1451-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jul 2025
Research article |
| 22 Jul 2025
| 22 Jul 2025
Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment
Lindsay M. Sheridan
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Richland, Washington, United States
Dmitry Duplyakin
National Renewable Energy Laboratory, Golden, Colorado, United States
Caleb Phillips
National Renewable Energy Laboratory, Golden, Colorado, United States
Heidi Tinnesand
National Renewable Energy Laboratory, Golden, Colorado, United States
One Power Company, Findlay, Ohio, United States
Raj K. Rai
Pacific Northwest National Laboratory, Richland, Washington, United States
Julia E. Flaherty
Pacific Northwest National Laboratory, Richland, Washington, United States
Larry K. Berg
Pacific Northwest National Laboratory, Richland, Washington, United States
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Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
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The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.
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Kyle Peco, Jiali Wang, Chunyong Jung, Gökhan Sever, Lindsay Sheridan, Jeremy Feinstein, Rao Kotamarthi, Caroline Draxl, Ethan Young, Avi Purkayastha, and Andrew Kumler
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Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
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Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Caleb Phillips, Lindsay M. Sheridan, Patrick Conry, Dimitrios K. Fytanidis, Dmitry Duplyakin, Sagi Zisman, Nicolas Duboc, Matt Nelson, Rao Kotamarthi, Rod Linn, Marc Broersma, Timo Spijkerboer, and Heidi Tinnesand
Wind Energ. Sci., 7, 1153–1169, https://doi.org/10.5194/wes-7-1153-2022, https://doi.org/10.5194/wes-7-1153-2022, 2022
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Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Lindsay M. Sheridan, Caleb Phillips, Alice C. Orrell, Larry K. Berg, Heidi Tinnesand, Raj K. Rai, Sagi Zisman, Dmitry Duplyakin, and Julia E. Flaherty
Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, https://doi.org/10.5194/wes-7-659-2022, 2022
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The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.
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Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
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Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
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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
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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.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
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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
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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.
Mike Optis, Jordan Perr-Sauer, Caleb Philips, Anna E. Craig, Joseph C. Y. Lee, Travis Kemper, Shuangwen Sheng, Eric Simley, Lindy Williams, Monte Lunacek, John Meissner, and M. Jason Fields
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-12, https://doi.org/10.5194/wes-2019-12, 2019
Preprint withdrawn
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As global wind capacity continues to grow, the need for accurate operational analyses of a rapidly growing fleet of wind power plants has increased in proportion. To address this need, the National Renewable Energy Laboratory has released OpenOA, an open-source codebase for operational analysis of wind farms. It is envisioned that OpenOA will evolve into a widely used codebase supported by a large group of global wind energy experts. This paper provides a summary of OpenOA.
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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This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Louis Marelle, Jean-Christophe Raut, Kathy S. Law, Larry K. Berg, Jerome D. Fast, Richard C. Easter, Manish Shrivastava, and Jennie L. Thomas
Geosci. Model Dev., 10, 3661–3677, https://doi.org/10.5194/gmd-10-3661-2017, https://doi.org/10.5194/gmd-10-3661-2017, 2017
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We develop the WRF-Chem 3.5.1 model to improve simulations of aerosols and ozone in the Arctic. Both species are important air pollutants and climate forcers, but models often struggle to reproduce observations in the Arctic. Our developments concern pollutant emissions, mixing, chemistry, and removal, including processes related to snow and sea ice. The effect of these changes are quantitatively validated against observations, showing significant improvements compared to the original model.
Jean-Christophe Raut, Louis Marelle, Jerome D. Fast, Jennie L. Thomas, Bernadett Weinzierl, Katharine S. Law, Larry K. Berg, Anke Roiger, Richard C. Easter, Katharina Heimerl, Tatsuo Onishi, Julien Delanoë, and Hans Schlager
Atmos. Chem. Phys., 17, 10969–10995, https://doi.org/10.5194/acp-17-10969-2017, https://doi.org/10.5194/acp-17-10969-2017, 2017
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We study the cross-polar transport of plumes from Siberian fires to the Arctic in summer, both in terms of transport pathways and efficiency of deposition processes. Those plumes containing soot may originate from anthropogenic and biomass burning sources in mid-latitude regions and may impact the Arctic climate by depositing on snow and ice surfaces. We evaluate the role of the respective source contributions, investigate the transport of plumes and treat pathway-dependent removal of particles.
Ivan Ortega, Sean Coburn, Larry K. Berg, Kathy Lantz, Joseph Michalsky, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, and Rainer Volkamer
Atmos. Meas. Tech., 9, 3893–3910, https://doi.org/10.5194/amt-9-3893-2016, https://doi.org/10.5194/amt-9-3893-2016, 2016
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We present an inherently calibrated retrieval to measure aerosol optical depth (AOD) and the aerosol phase function parameter, g, based on measurements of azimuth distributions of the Raman scattering probability (RSP), the near-absolute rotational Raman scattering (RRS) intensity by the University of Colorado two-dimensional (2-D) MAX-DOAS. The retrievals are maximally sensitive at low AOD and do not require absolute radiance calibration. We compare results with data from independent sensors.
Chun Zhao, Maoyi Huang, Jerome D. Fast, Larry K. Berg, Yun Qian, Alex Guenther, Dasa Gu, Manish Shrivastava, Ying Liu, Stacy Walters, Gabriele Pfister, Jiming Jin, John E. Shilling, and Carsten Warneke
Geosci. Model Dev., 9, 1959–1976, https://doi.org/10.5194/gmd-9-1959-2016, https://doi.org/10.5194/gmd-9-1959-2016, 2016
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In this study, the latest version of MEGAN is coupled within CLM4 in WRF-Chem. In this implementation, MEGAN shares a consistent vegetation map with CLM4. This improved modeling framework is used to investigate the impact of two land surface schemes on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models.
L. K. Berg, M. Shrivastava, R. C. Easter, J. D. Fast, E. G. Chapman, Y. Liu, and R. A. Ferrare
Geosci. Model Dev., 8, 409–429, https://doi.org/10.5194/gmd-8-409-2015, https://doi.org/10.5194/gmd-8-409-2015, 2015
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This work presents a new methodology for representing regional-scale impacts of cloud processing on both aerosol and trace gases in sub-grid-scale convective clouds. Using the new methodology, we can better simulate the aerosol lifecycle over large areas. The results presented in this work highlight the potential change in column-integrated amounts of black carbon, organic aerosol, and sulfate aerosol, which were found to range from -50% for black carbon to +40% for sulfate.
D. Müller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid
Atmos. Meas. Tech., 7, 3487–3496, https://doi.org/10.5194/amt-7-3487-2014, https://doi.org/10.5194/amt-7-3487-2014, 2014
E. Kassianov, J. Barnard, M. Pekour, L. K. Berg, J. Shilling, C. Flynn, F. Mei, and A. Jefferson
Atmos. Meas. Tech., 7, 3247–3261, https://doi.org/10.5194/amt-7-3247-2014, https://doi.org/10.5194/amt-7-3247-2014, 2014
A. J. Scarino, M. D. Obland, J. D. Fast, S. P. Burton, R. A. Ferrare, C. A. Hostetler, L. K. Berg, B. Lefer, C. Haman, J. W. Hair, R. R. Rogers, C. Butler, A. L. Cook, and D. B. Harper
Atmos. Chem. Phys., 14, 5547–5560, https://doi.org/10.5194/acp-14-5547-2014, https://doi.org/10.5194/acp-14-5547-2014, 2014
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Modeling frontal low-level jets and associated extreme wind power ramps over the North Sea
Quantifying tropical-cyclone-generated waves in extreme-value-derived design for offshore wind
Estimating long-term annual energy production from shorter-time-series data: methods and verification with a 10-year large-eddy simulation of a large offshore wind farm
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
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains
Analyzing the performance of vertical wind profilers in rain events
How do convective cold pools influence the boundary-layer atmosphere near two wind turbines in northern Germany?
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
Evaluating the ability of the operational High Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) to forecast wind ramp events in the US Great Plains
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
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
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
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)
Harish Baki, Sukanta Basu, and George Lavidas
Wind Energ. Sci., 10, 1575–1609, https://doi.org/10.5194/wes-10-1575-2025, https://doi.org/10.5194/wes-10-1575-2025, 2025
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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 1 d in advance, helping manage wind power more efficiently for a stable, reliable energy supply.
Sarah McElman, Amrit Shankar Verma, and Andrew Goupee
Wind Energ. Sci., 10, 1529–1550, https://doi.org/10.5194/wes-10-1529-2025, https://doi.org/10.5194/wes-10-1529-2025, 2025
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This paper investigates the treatment of tropical-cyclone-generated waves in metocean models and in statistical evaluation for offshore wind design. It provides recommendations for ensuring the accurate representation of extreme waves for the design and operation of offshore projects on the Atlantic coast of the United States.
Bernard Postema, Remco A. Verzijlbergh, Pim van Dorp, Peter Baas, and Harm J. J. Jonker
Wind Energ. Sci., 10, 1471–1484, https://doi.org/10.5194/wes-10-1471-2025, https://doi.org/10.5194/wes-10-1471-2025, 2025
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Atmospheric large-eddy simulation is a technique that simulates weather conditions in 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 and instead simulating much less (1 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.
Cristina Lozej Archer
Wind Energ. Sci., 10, 1433–1438, https://doi.org/10.5194/wes-10-1433-2025, https://doi.org/10.5194/wes-10-1433-2025, 2025
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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 in wind are small with respect to the mean. The formulations perform well when applied to the observations from the American WAKE experimeNt (AWAKEN) field campaign conducted in 2023.
Mehtab Ahmed Khan, Dries Allaerts, Simon J. Watson, and Matthew J. Churchfield
Wind Energ. Sci., 10, 1167–1185, https://doi.org/10.5194/wes-10-1167-2025, https://doi.org/10.5194/wes-10-1167-2025, 2025
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To guide realistic atmospheric gravity wave simulations, we study flow over a two-dimensional hill and through a wind farm canopy, examining optimal domain size and damping layer setup. Wave properties based on non-dimensional numbers determine the optimal domain and damping parameters. Accurate solutions require the domain length to exceed the effective horizontal wavelength, height, and damping thickness to equal the vertical wavelength and non-dimensional damping strength between 1 and 10.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
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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.
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.
Jeffrey D. Thayer, Gerard Kilroy, and Norman Wildmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-38, https://doi.org/10.5194/wes-2025-38, 2025
Revised manuscript under review for WES
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With increasing wind energy in the German energy grid, it is crucial to better understand how different types of weather (including thunderstorms) can impact wind turbines and the surrounding atmosphere. We find rapid wind changes associated with the leading edge of thunderstorm outflows within the height range of wind turbines that would quickly increase wind power output, with longer-lasting changes in the near-surface atmosphere that would affect subsequent wind turbine operations.
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.
Laura Bianco, Reagan Mendeke, Jake Lindblom, Irina V. Djalalova, David D. Turner, and James M. Wilczak
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-133, https://doi.org/10.5194/wes-2024-133, 2024
Revised manuscript accepted for WES
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Including more renewable energy into the electric grid is a critical part of the strategy to mitigate climate change. Reliable numerical weather prediction (NWP) models need to be able to predict the intrinsic nature of weather-dependent resources, such as wind ramp events, as wind energy could quickly be available in abundance or temporarily cease to exist. We assess the ability of the operational High Resolution Rapid Refresh NWP model to forecast wind ramp events in two most recent versions.
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.
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.
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.
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
Argonne National Laboratory (ANL): Argonne 60 m Meteorological Tower, Argonne National Laboratory [data set], https://www.atmos.anl.gov/ANLMET/, last access: 14 February 2022.
ArcVera: Mesoscale and Microscale Modeling for Wind Energy Applications: Next-Generation Numerical Weather Prediction for Wind Flow Modeling, https://arcvera.com/mesoscale-and-microscale-modeling-for-wind-energy-applications-next-generation-numerical-weather-prediction-for-wind-flow-modeling/, last access: 24 August 2023.
Atmospheric Radiation Measurement (ARM): Atmospheric Radiation Measurement User Facility, Southern Great Plains Site, ARM [data set], https://www.arm.gov/capabilities/instruments?location[0]=Southern Great Plains, last access: 7 June 2021.
B2SHARE: The Tall Tower Dataset, B2SHARE [data set], https://b2share.eudat.eu/records/159158152f4d4be79559e2f3f6b1a410, last access: 9 March 2020.
Basse, A., Callies, D., Grötzner, A., and Pauscher, L.: Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data, Wind Energ. Sci., 6, 1473–1490, https://doi.org/10.5194/wes-6-1473-2021, 2021.
Brookhaven National Laboratory (BNL): Current Observations, Brookhaven National Laboratory [data set], https://wx1.bnl.gov/, last access: 14 April 2020.
BPA: BPA Meteorological Information, Bonneville Power Administration [data set], https://transmission.bpa.gov/Business/Operations/Wind/MetData/default.aspx, last access: 17 March 2022.
Carta, J. A. and Velázquez, S.: A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site, Energy, 36, 2671–2685, https://doi.org/10.1016/j.energy.2011.02.008, 2011.
Carta, J. A., Velázquez, S., and Cabrera, P.: A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site, Renew. Sust. Energ. Rev., 27, 362–400, https://doi.org/10.1016/j.rser.2013.07.004, 2013.
Chen, D., Zhou, Z., and Yang, X.: A measure-correlate-predict model based on neural networks and frozen flow hypothesis for wind resource assessment, Phys. Fluids, 34, 045107, https://doi.org/10.1063/5.0086354, 2022.
Davis, N. N., Badger, J., Hahmann, A. N., Hansen, B. O., Mortensen, N. G., Kelly, M., Larsén, X. G., Olsen, B. T., Floors, R., Lizcano, G., Casso, P., Lacave, O., Bosch, A., Bauwens, I., Knight, O. J., Potter van Loon, A., Fox, R., Parvanyan, T., Krohn Hansen, S. B., Heathfield, D., Onninen, M., and Drummond, R.: The Global Wind Atlas: A High-Resolution Dataset of Climatologies and Associated Web-Based Application, B. Am. Meteorol. Soc., 104.8, E1507–E1525, https://doi.org/10.1175/BAMS-D-21-0075.1, 2023.
de Assis Tavares, L. F., Shadman, M., de Freitas Assad, L. P., and Estefen, S. F.: Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State, Energy, 240, 122767, https://doi.org/10.1016/j.energy.2021.122767, 2022.
Derrick, A.: Development of the measure-correlate-predict strategy for site assessment, in: Proceedings of the 14th British Wind Energy Association, Nottingham, UK, 25–27 March 1992, 1992.
Díaz, S. Carta, J. A., and Matías, J. M.: Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques, Appl. Energ., 209, 455–477, https://doi.org/10.1016/j.apenergy.2017.11.007, 2018.
Dinler, A.: A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting, Energy, 63, 152–160, https://doi.org/10.1016/j.energy.2013.10.007, 2013.
Draxl, C., Wang, J., Sheridan, L., Jung, C., Bodini, N., Buckhold, S., Aghili, C. T., Peco, K., Kotamarthi, R., Kumler, A., Phillips, C., Purkayastha, A., Young, E., Rosenlieb, E., and Tinnesand, H.: WTK-LED: The WIND Toolkit Long-term Ensemble Dataset, National Renewable Energy Laboratory, Golden, CO (United States), NREL/TP-5000-88457, https://doi.org/10.2172/2473210, 2024.
FEMC: University of Vermont, Forest Ecosystem Monitoring Cooperative, Raw Proctor Maple Research Center Meteorological Data, FEMC [data set], https://www.uvm.edu/femc/data/archive/project/proctor-maple-research-center-meteorological-monitoring/dataset/proctor-maple-research-center-air-quality-1/overview, last access: 15 April 2020.
Friedman, J. H.: Multivariate Adaptive Regression Splines (with discussion), Ann. Stat., 19, 1–67, https://doi.org/10.1214/aos/1176347963, 1991.
García-Rojo, R.: Algorithm for the estimation of the long-term wind climate at a meteorological mast using a joint probabilistic approach, Wind Engineering, 28, 213–224, https://doi.org/10.1260/0309524041211378, 2004.
Gualtieri, G.: Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers, Energies, 14, 4169, https://doi.org/10.3390/en14144169, 2021.
Hayes, L., Stocks, M., and Blakers, A.: Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis, Energy, 229, 120603, https://doi.org/10.1016/j.energy.2021.120603, 2021.
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. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
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, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994.
Jekabsons, G.: Adaptive Regressions Splines toolbox for Matlab/Octave, version 1.13.0, http://www.cs.rtu.lv/jekabsons/Files/ARESLab.pdf (last access: 13 January 2024), 2016.
Jimenez, T.: The Wind Powering America Anemometer Loan Program: A Retrospective, Tech. Rep., NREL/TP-7A30-57351, National Renewable Energy Laboratory, Golden, CO, USA, https://doi.org/10.2172/1081367, 2013.
Joenson, A., Landberg, L., and Madsen, H.: A new measure-correlate-predict approach for resource assessment, in: 1999 European Wind Energy Conference, London, UK, 1–5 March 1999, https://doi.org/10.4324/9781315074337, 1999.
Lackner, M. A., Rogers, A. L., and Manwell, J. F.: The round robin site assessment method: A new approach to wind energy site estimation, Renewable Energy, 33, 2019–2026, https://doi.org/10.1016/j.renene.2007.12.011, 2008.
Landberg, L. and Mortenson, N. G.: A comparison of physical and statistical methods for estimating the wind resource at a site, in: Wind Energy Conversion, 1993 Proceedings, 119–125, Mechanical Engineering Publications Limited, 1994.
LBNL: AmeriFlux, Lawrence Berkeley National Laboratory [data set], https://ameriflux.lbl.gov/sites/site-search/#filter-type=all&has-data=All&site_id=, last access: 16 April 2020.
Liléo, S., Berge, E. Undheim, O., Klinkert, R., and Bredesen, R. E.: Long-term correction of wind measurements, Tech. Rep., Elforsk Report 13:18, https://energiforskmedia.blob.core.windows.net/media/19814/long-term-correction-of-wind-measurements-elforskrapport-2013-18.pdf (last access: 3 August 2024), 2013.
MathWorks: fitrensemble, MATLAB [code], https://www.mathworks.com/help/stats/fitrensemble.html, last access: 13 December 2024.
Mifsud, M. D., Sant, T., and Farrugia, R. N.: A comparison of Measure-Correlate-Predict Methodologies using LiDAR as a candidate site measurement device for the Mediterranean Island of Malta, Renewable Energy, 127, 947–959, https://doi.org/10.1016/j.renene.2018.05.023, 2018.
Miguel, J. V. P., Fadigas, E. A., and Sauer, I. L.: The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment, Energies, 12, 3606, https://doi.org/10.3390/en12193606, 2019.
Milborrow, S.: Earth: Multivariate Adaptive Regression Spline Models [code], https://cran.r-project.org/web/packages/earth/index.html (last access: 3 August 2024), 2016.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Murcia, J. P., Koivisto, M. J., Luzia, G., Olsen, B. T., Hahmann, A. N., Sørensen, P. E., and Als, M.: Validation of European-scale simulated wind speed and wind generation time series, Appl. Energ., 305, 117794, https://doi.org/10.1016/j.apenergy.2021.117794, 2022.
National Data Buoy Center (NDBC): National Data Buoy Center [data set], https://www.ndbc.noaa.gov/, last access: 21 January 2024.
NREL: 2019COE_DW100_100kW_27.6, National Renewable Energy Laboratory [data set], https://nrel.github.io/turbine-models/2019COE_DW100_100kW_27.6.html (last access: 21 January 2024), 2019.
NREL: Flatirons Campus Data, NREL [data set], https://midcdmz.nrel.gov//apps/sitehome.pl?site=NWTC, last access: 16 February 2022.
NREL: WindWatts, NREL [data set], https://windwatts.nrel.gov/, last access: 13 December 2024.
NRG Systems: Wind Resource Assessment, https://www.nrgsystems.com/products/applications/wind-resource-assessment/, last access: 30 August 2023.
Olauson, J.: ERA5: The new champion of wind power modelling?, Renewable Energy, 126, 322–331, https://doi.org/10.1016/j.renene.2018.03.056, 2018.
Phillips, C., Sheridan, L. M., Conry, P., Fytanidis, D. K., Duplyakin, D., Zisman, S., Duboc, N., Nelson, M., Kotamarthi, R., Linn, R., Broersma, M., Spijkerboer, T., and Tinnesand, H.: Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands, Wind Energ. Sci., 7, 1153–1169, https://doi.org/10.5194/wes-7-1153-2022, 2022.
PNNL: Distributed Wind Project Database, PNNL [data set], https://www.pnnl.gov/distributed-wind/market-report/data, last access: 12 January 2024.
Ramon, J., Lledó, L., Torralba, V., Soret, A., and Doblas-Reyes, F. J.: Which global reanalysis best represents near-surface winds?, Q. J. Roy. Meteorol. Soc., 145, 3236–3251, https://doi.org/10.1002/qj.3616, 2019.
Riedel, V. and Strack, M.: Robust approximation of functional relationships between meteorological data: alternative measure-correlate-predict algorithms, European Wind Energy Conference, Copenhagen, Denmark, 2–6 July 2001, 806–809, 2001.
Rogers, A. L., Rogers, J. W., and Manwell, J. F.: Comparison of the performance of four measure-correlate-predict algorithms, J. Wind Eng. Ind. Aerod., 93, 243–264, https://doi.org/10.1016/j.jweia.2004.12.002, 2005.
Seto, C.: Can wind power the North? Innovation, https://www.innovation.ca/projects-results/research-stories/can-wind-power-north, last access: 30 August 2022.
Sheppard, C. J. R.: Analysis of the measure-correlate-predict methodology for wind resource assessment, Thesis, California State Polytechnic University, https://scholarworks.calstate.edu/concern/theses/9593tx516 (last access: 20 December 2024), 2009.
Sheridan, L. M., Phillips, C., Orrell, A. C., Berg, L. K., Tinnesand, H., Rai, R. K., Zisman, S., Duplyakin, D., and Flaherty, J. E.: Validation of wind resource and energy production simulations for small wind turbines in the United States, Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, 2022.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G., Huang, X., Wang, W., and Powers, J.: A Description of the Advanced Research WRF Version 3, NCAR/TN-475+STR, University Corporation for Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008.
Soares, P. M. M., Lima, D. C. A., and Nogueira, M.: Global offshore wind energy resources using the new ERA-5 reanalysis, Environ. Res. Lett., 15, 1040a2, 10.1088/1748-9326/abb10d, 2020.
Tang, X-Y., Zhao, S., Fan, B., Peinke, J., and Stoevesandt, B.: Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts, Appl. Energ., 238, 806–815, https://doi.org/10.1016/j.apenergy.2019.01.129, 2019.
Taylor, M., Mackiewicz, P., Brower, M. C., and Markus, M.: An analysis of wind resource uncertainty in energy production estimates, Proceedings of the European Wind Energy Conference and Exhibition, London, UK, 22–25 November 2004, https://www.scribd.com/document/358271449/An-Analysis-of-Wind-Resource-Uncertainty-in-Energy-Production-Estimates (last access: 3 August 2024), 2004.
Technical University of Denmark (DTU): Global Wind Atlas, DTU [data set], https://globalwindatlas.info/en/, last access: 13 December 2024.
Troen, I. and Petersen, E. L.: European Wind Atlas, Risø National Laboratory Report, https://backend.orbit.dtu.dk/ws/portalfiles/portal/112135732/European_Wind_Atlas.pdf (last access: 20 December 2024), 1989.
U.S. Department of Energy (DOE): Atmosphere to Electrons, DOE [data set], https://a2e.energy.gov, last access: 13 February 2024.
U.S. Department of Energy (DOE): Energy Technology Innovation Partnership Project, https://www.energy.gov/eere/energy-technology-innovation-partnership-project (last access: 16 July 2025), 2025.
Vermeulen, P. E. J., Marijanyan, A., Abrahamyan, A., and den Boom J. H.: Application of matrix MCP analysis in mountainous Armenia, Wind Energy: Wind energy for the new millennium, 737–740, ISBN: 3936338094, 2001.
Weekes, S. M. and Tomlin, A. S.: Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy, Renewable Energy, 63, 162–171, https://doi.org/10.1016/j.renene.2013.08.033, 2014.
Wilczak, J. M., Akish, E., Capotondi, A., and Compo, G.: Evaluation and Bias Correction of the ERA5 Reanalysis over the United States for Wind and Solar Energy Applications, Energies, 17, 1667, https://doi.org/10.3390/en17071667, 2024.
Woods, J. C. and Watson, S. J.: A new matrix method of predicting long-term wind roses with MCP, J. Wind Eng. Ind. Aerodyn., 66, 85–94, https://doi.org/10.1016/S0167-6105(97)00009-3, 1997.
Zakaria, A., Früh, W. G., and Ismail, F. B.: Wind resource forecasting using enhanced measure correlate predict (MCP), in: 6th International Conference on Production, Energy, and Reliability, Kuala Lumpur, Malaysia, 13–14 August 2018, https://doi.org/10.1063/1.5075569, 2018.
Short summary
A total of 12 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 short you can go in terms of the observational time period needed to make impactful improvements to long-term wind speed estimates.
A total of 12 months of onsite wind measurement is standard for correcting model-based long-term...
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