Articles | Volume 10, issue 8
https://doi.org/10.5194/wes-10-1551-2025
© 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-10-1551-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Aug 2025
Research article |
| 04 Aug 2025
| 04 Aug 2025
Performance of wind assessment datasets in United States coastal areas
Lindsay M. Sheridan
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Richland, Washington 99354, USA
Jiali Wang
Argonne National Laboratory, Lemont, Illinois 60439, USA
Caroline Draxl
Electric Power Research Institute, Washington, DC 20005, USA
Nicola Bodini
National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Caleb Phillips
National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Dmitry Duplyakin
National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Heidi Tinnesand
One Power Company, Findlay, Ohio 45840, USA
Raj K. Rai
Pacific Northwest National Laboratory, Richland, Washington 99354, USA
Julia E. Flaherty
Pacific Northwest National Laboratory, Richland, Washington 99354, USA
Larry K. Berg
Pacific Northwest National Laboratory, Richland, Washington 99354, USA
Chunyong Jung
Argonne National Laboratory, Lemont, Illinois 60439, USA
Ethan Young
National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Rao Kotamarthi
Argonne National Laboratory, Lemont, Illinois 60439, USA
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Revised manuscript accepted for WES
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Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
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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.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
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Nicola Bodini, Simon Castagneri, and Mike Optis
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Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
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.
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022, https://doi.org/10.5194/ascmo-8-205-2022, 2022
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We study wind conditions and their potential future changes across the U.S. via a statistical conditional framework. We conclude that changes between historical and future wind directions are small, but wind speeds are generally weakened in the projected period, with some locations being intensified. Moreover, winter wind speeds are projected to decrease in the northwest, Colorado, and the northern Great Plains (GP), while summer wind speeds over the southern GP slightly increase in the future.
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.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
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
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
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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.
Chuxuan Li, Alexander L. Handwerger, Jiali Wang, Wei Yu, Xiang Li, Noah J. Finnegan, Yingying Xie, Giuseppe Buscarnera, and Daniel E. Horton
Nat. Hazards Earth Syst. Sci., 22, 2317–2345, https://doi.org/10.5194/nhess-22-2317-2022, https://doi.org/10.5194/nhess-22-2317-2022, 2022
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In January 2021 a storm triggered numerous debris flows in a wildfire burn scar in California. We use a hydrologic model to assess debris flow susceptibility in pre-fire and postfire scenarios. Compared to pre-fire conditions, postfire conditions yield dramatic increases in peak water discharge, substantially increasing debris flow susceptibility. Our work highlights the hydrologic model's utility in investigating and potentially forecasting postfire debris flows at regional scales.
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.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
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In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
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.
Geng Xia, Caroline Draxl, Michael Optis, and Stephanie Redfern
Wind Energ. Sci., 7, 815–829, https://doi.org/10.5194/wes-7-815-2022, https://doi.org/10.5194/wes-7-815-2022, 2022
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In this study, we propose a new method to detect sea breeze events from the Weather Research and Forecasting simulation. Our results suggest that the method can identify the three different types of sea breezes in the model simulation. In addition, the coastal impact, seasonal distribution and offshore wind potential associated with each type of sea breeze differ significantly, highlighting the importance of identifying the correct type of sea breeze in numerical weather/wind energy forecasting.
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.
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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In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Ethan Young, Jeffery Allen, John Jasa, Garrett Barter, and Ryan King
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-7, https://doi.org/10.5194/wes-2022-7, 2022
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In this study, we present ways to measure the phenomenon of wind plant blockage, or the the velocity slowdown upstream from a farm, and carry out turbine layout optimizations to reduce this effect. We find that farm-wide measurements provide a better characterization of blockage compared to more localized measurements and that, in the absence of any constraint on total power output, layouts which minimize the effect of blockage are frequently characterized by streamwise alignment of turbines.
Ye Liu, Yun Qian, and Larry K. Berg
Wind Energ. Sci., 7, 37–51, https://doi.org/10.5194/wes-7-37-2022, https://doi.org/10.5194/wes-7-37-2022, 2022
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Uncertainties in initial conditions (ICs) decrease the accuracy of wind speed forecasts. We find that IC uncertainties can alter wind speed by modulating the weather system. IC uncertainties in local thermal gradient and large-scale circulation jointly contribute to wind speed forecast uncertainties. Wind forecast accuracy in the Columbia River Basin is confined by initial uncertainties in a few specific regions, providing useful information for more intense measurement and modeling studies.
Nicola Bodini, Weiming Hu, Mike Optis, Guido Cervone, and Stefano Alessandrini
Wind Energ. Sci., 6, 1363–1377, https://doi.org/10.5194/wes-6-1363-2021, https://doi.org/10.5194/wes-6-1363-2021, 2021
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We develop two machine-learning-based approaches to temporally extrapolate uncertainty in hub-height wind speed modeled by a numerical weather prediction model. We test our approaches in the California Outer Continental Shelf, where a significant offshore wind energy development is currently being planned, and we find that both provide accurate results.
Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 6355–6372, https://doi.org/10.5194/gmd-14-6355-2021, https://doi.org/10.5194/gmd-14-6355-2021, 2021
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Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, https://doi.org/10.5194/wes-6-1043-2021, 2021
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As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. We leverage a year of data of two floating lidars to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. We find that almost 100 independent long events occur throughout the year.
Hannah Livingston, Nicola Bodini, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-68, https://doi.org/10.5194/wes-2021-68, 2021
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In this paper, we assess whether hub-height turbulence can easily be quantified from either other hub-height variables or ground-level measurements in complex terrain. We find a large variability across the three considered locations when trying to model hub-height turbulence intensity and turbulence kinetic energy. Our results highlight the nonlinear and complex nature of atmospheric turbulence, so that more powerful techniques should instead be recommended to model hub-height turbulence.
Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa
Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, https://doi.org/10.5194/wes-6-935-2021, 2021
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Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine-learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
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 %.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
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Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Jaydeep Singh, Narendra Singh, Narendra Ojha, Amit Sharma, Andrea Pozzer, Nadimpally Kiran Kumar, Kunjukrishnapillai Rajeev, Sachin S. Gunthe, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 1427–1443, https://doi.org/10.5194/gmd-14-1427-2021, https://doi.org/10.5194/gmd-14-1427-2021, 2021
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Atmospheric models often have limitations in simulating the geographically complex and climatically important central Himalayan region. In this direction, we have performed regional modeling at high resolutions to improve the simulation of meteorology and dynamics through a better representation of the topography. The study has implications for further model applications to investigate the effects of anthropogenic pressure over the Himalaya.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
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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.
Nicola Bodini and Mike Optis
Wind Energ. Sci., 5, 1435–1448, https://doi.org/10.5194/wes-5-1435-2020, https://doi.org/10.5194/wes-5-1435-2020, 2020
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Calculations of annual energy production (AEP) and its uncertainty are critical for wind farm financial transactions. Standard industry practice assumes that different uncertainty categories within an AEP calculation are uncorrelated and can therefore be combined through a sum of squares approach. In this project, we show the limits of this assumption by performing operational AEP estimates for over 470 wind farms in the United States and propose a more accurate way to combine uncertainties.
Tobias Ahsbahs, Galen Maclaurin, Caroline Draxl, Christopher R. Jackson, Frank Monaldo, and Merete Badger
Wind Energ. Sci., 5, 1191–1210, https://doi.org/10.5194/wes-5-1191-2020, https://doi.org/10.5194/wes-5-1191-2020, 2020
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Before constructing wind farms we need to know how much energy they will produce. This requires knowledge of long-term wind conditions from either measurements or models. At the US East Coast there are few wind measurements and little experience with offshore wind farms. Therefore, we created a satellite-based high-resolution wind resource map to quantify spatial variations in the wind conditions over potential sites for wind farms and found larger variation than modelling suggested.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
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While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Cited articles
Bao, S., Pietrafesa, L., Gayes, P., Noble, S., Viner, B., Qian, J-H., Werth, D., Mitchell, G., and Burdette, S.: Mapping the Spatial Footprint of Sea Breeze Winds in the Southeastern United States, J. Geophys. Res.-Atmos., 128, e2022JD037524, https://doi.org/10.1029/2022JD037524, 2023.
Barthelmie, R. J., Grisogono, B., and Pryor, S. C.: Observations and simulations of diurnal cycles of near-surface wind speeds over land and sea, J. Geophys. Res., 101, 21327–21337, https://doi.org/10.1029/96JD01520, 1996.
Bodini, N., Optis, M., Redfern, S., Rosencrans, D., Rybchuk, A., Lundquist, J. K., Pronk, V., Castagneri, S., Purkayastha, A., Draxl, C., Krishnamurthy, R., Young, E., Roberts, B., Rosenlieb, E., and Musial, W.: The 2023 National Offshore Wind data set (NOW-23), Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, 2024.
BPA: BPA Meteorological Information, Bonneville Power Administration [data set], https://transmission.bpa.gov/Business/Operations/Wind/MetData/default.aspx (last access: 30 July 2024), 2024.
Brindley Beach: Wind Turbines to Return to Jennette's Pier this Fall, BrindleyBeach.com, https://www.brindleybeach.com/obx-blog/wind-turbines-return-jennettes-pier-fall (last access: 7 August 2022), 2022.
Carrasco-Díaz, M., Roque-Rodríguez, A., Sánchez-Montante, O., and Rivas, D.: Local Breeze Effects on the Wind Energy Generation in the Northern Coast of Cuba, Wind Engineering, 35, 635–648, https://doi.org/10.1260/0309-524X.35.6.635, 2011.
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, 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.
DOE: Energy Transitions Initiative Partnership Project, U. S. Department of Energy, https://www.energy.gov/eere/energy-transitions-initiative-partnership-project (last access: 29 July 2024), 2024a.
DOE: Atmosphere to Electrons [data set], U. S. Department of Energy, https://a2e.energy.gov (last access: 30 July 2024), 2024b.
DTU: Global Wind Atlas, Technical University of Denmark [data set], https://globalwindatlas.info/en/ (last access: 30 June 2024), 2024.
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.
Duplyakin, D., Zisman, S., Phillips, C., and Tinnesand, H.: Bias characterization, vertical interpolation, and horizontal interpolation for distributed wind siting using mesoscale wind resource estimates, National Renewable Energy Laboratory, Golden, CO (United States), NREL/TP-2C00-78412, https://doi.org/10.2172/1760659, 2021.
ECMWF: IFS Documentation CY41R2 – Part IV: Physical Processes, ECMWF, https://doi.org/10.21957/tr5rv27xu, 2016.
ECMWF: ERA5, ECMWF [data set], https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download (last access: 10 January 2024), 2024.
Gille, S., Llewellyn Smith, S., Statom, N.: Global observations of the land breeze, Geophys. Res. Lett., 32, L05605, https://doi.org/10.1029/2004GL022139, 2005.
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. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hong, S. Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
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%3C0927:TSMECM%3E2.0.CO;2, 1994.
McCabe, E. and Freedman, J.: Development of an Objective Methodology for Identifying the Sea-Breeze Circulation and Associated Low-Level Jet in the New York Bight, Weather Forecast., 38, 571–589, https://doi.org/10.1175/WAF-D-22-0119.1, 2023.
McCabe, K., Prasanna, A., Lockshin, J., Bhaskar, P., Bowen, T., Baranowski, R., Sigrin, B., and Lantz, E.: Distributed Wind Energy Futures Study, National Renewable Energy Laboratory, Golden, CO (United States), NREL/TP-7A40-82519, https://doi.org/10.2172/1868329, 2022.
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.
Nakanishi, N. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn. Ser. II, 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
NDBC: National Data Buoy Center [data set], https://www.ndbc.noaa.gov/ (last access: 13 January 2024), 2024.
NREL: WindWatts [data set], NREL, https://windwatts.nrel.gov/ (last access: 1 August 2024), 2024a.
NREL: Wind Resource Database [data set], NREL, https://wrdb.nrel.gov/data-viewer (last access: 30 July 2024), 2024b.
Olauson, J.: ERA5: The new champion of wind power modelling?, Renew. Energ., 126, 322–331, https://doi.org/10.1016/j.renene.2018.03.056, 2018.
Parish, T. R.: Forcing of the Summertime Low-Level Jet along the California Coast, J. Appl. Meteorol. Clim., 39, 2421–2433, https://doi.org/10.1175/1520-0450(2000)039<2421:FOTSLL>2.0.CO;2, 2000.
Potapov, P., Hansen, M. C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., and Harris, N.: The global 2000–2020 land cover and land use change dataset derived from the Landsat archive: first results, Front. Remote Sens., 3, 856903, https://doi.org/10.3389/frsen.2022.856903, 2022.
Ramon, J., Lledó, L., Torralba, V., Soret, A., and Doblas-Reyes, F. J.: Which global reanalysis best represents near-surface winds?, Q. J. Roy. Meteor. Soc., 145, 3236–3251, https://doi.org/10.1002/qj.3616, 2019.
Sheridan, L. M., Krishnamurthy, R., García Medina, G., Gaudet, B. J., Gustafson Jr., W. I., Mahon, A. M., Shaw, W. J., Newsom, R. K., Pekour, M., and Yang, Z.: Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast, Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, 2022a.
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, 2022b.
Sheridan, L., Kazimierczuk, K., Garbe, J., and Preziuso, D.: Distributed Wind Market Report: 2024 Edition, Pacific Northwest National Laboratory, Richland, WA (United States), PNNL-36057, https://doi.org/10.2172/2428926, 2024.
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, https://doi.org/10.1088/1748-9326/abb10d, 2020.
Storm, B., Dudhia, J., Basu, S., Swift, A., and Giammanco, I.: Evaluation of the Weather Research and Forecasting model on forecasting low-level jets: implications for wind energy, Wind Energy, 12, 81–90, https://doi.org/10.1002/we.288, 2008.
Troen, I. and Petersen, E. L.: European Wind Atlas, Risø National Laboratory Report, DTU, https://backend.orbit.dtu.dk/ws/portalfiles/portal/112135732/European_Wind_Atlas.pdf (last access: 10 August 2024), 1989.
Viner, B., Noble, S., Qian, J-H., Werth, D., Gayes, P., Pietrafesa, L., and Bao, S.: Frequency and Characteristics of Inland Advecting Sea Breezes in the Southeast United States, Atmosphere, 12, 950, https://doi.org/10.3390/atmos12080950, 2021.
Wieringa, J.: Shapes of annual frequency distributions of wind speed observed on high meteorological masts, Bound. Lay. Meteorol., 47, 85–110, https://doi.org/10.1007/BF00122324, 1989.
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.
Short summary
Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and interannual trends in the wind resource in coastal locations to support customers interested in small and midsize wind energy.
Three recent wind resource datasets are assessed for their skills in representing annual average...
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