Articles | Volume 6, issue 6
https://doi.org/10.5194/wes-6-1363-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-1363-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Nov 2021
Research article |
| 03 Nov 2021
| 03 Nov 2021
Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble
National Renewable Energy Laboratory, Golden, Colorado, USA
Weiming Hu
Department of Geography, Pennsylvania State University, University Park, PA, 16802, USA
Mike Optis
National Renewable Energy Laboratory, Golden, Colorado, USA
Guido Cervone
Department of Geography, Pennsylvania State University, University Park, PA, 16802, USA
National Center for Atmospheric Research, Boulder, CO, USA
Stefano Alessandrini
National Center for Atmospheric Research, Boulder, CO, USA
Related authors
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth N. Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, William Radünz, and Patrick Moriarty
Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, https://doi.org/10.5194/wes-10-1681-2025, 2025
Short summary
Short summary
This study is the first to use real-world atmospheric measurements to show that large wind plants can increase the height of the planetary boundary layer, the part of the atmosphere near the surface where life takes place. The planetary boundary layer height governs processes like pollutant transport and cloud formation and is a key parameter for modeling the atmosphere. The results of this study provide important insights into interactions between wind plants and their local environment.
Yelena L. Pichugina, Alan W. Brewer, Sunil Baidar, Robert Banta, Edward Strobach, Brandi McCarty, Brian Carroll, Nicola Bodini, Stefano Letizia, Richard Marchbanks, Michael Zucker, Maxwell Holloway, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-79, https://doi.org/10.5194/wes-2025-79, 2025
Preprint under review for WES
Short summary
Short summary
The truck-based Doppler lidar system was used during the American Wake Experiment (AWAKEN) to obtain the high-frequency, simultaneous measurements of the horizontal wind speed, direction, and vertical-velocity from a moving platform. The paper presents the unique capability of the novel lidar system to characterize the temporal, vertical, and spatial variability of winds at various distances from operating turbines and obtain quantitative estimates of wind speed reduction in the waked flow.
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, Ethan Young, and Rao Kotamarthi
Wind Energ. Sci., 10, 1551–1574, https://doi.org/10.5194/wes-10-1551-2025, https://doi.org/10.5194/wes-10-1551-2025, 2025
Short summary
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.
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
Short summary
Short summary
Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
Short summary
Short summary
This study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.
David Rosencrans, Julie K. Lundquist, Mike Optis, and Nicola Bodini
Wind Energ. Sci., 10, 59–81, https://doi.org/10.5194/wes-10-59-2025, https://doi.org/10.5194/wes-10-59-2025, 2025
Short summary
Short summary
The US offshore wind industry is growing rapidly. Expansion into cold climates will subject turbines and personnel to hazardous icing. We analyze the 21-year icing risk for US east coast wind areas based on numerical weather prediction simulations and further assess impacts from wind farm wakes over one winter season. Sea spray icing at 10 m can occur up to 67 h per month. However, turbine–atmosphere interactions reduce icing hours within wind plant areas.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
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
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Nicola Bodini, Simon Castagneri, and Mike Optis
Wind Energ. Sci., 8, 607–620, https://doi.org/10.5194/wes-8-607-2023, https://doi.org/10.5194/wes-8-607-2023, 2023
Short summary
Short summary
The National Renewable Energy Laboratory (NREL) has published updated maps of the wind resource along all US coasts. Given the upcoming offshore wind development, it is essential to quantify the uncertainty that comes with the modeled wind resource data set. The paper proposes a novel approach to quantify this numerical uncertainty by leveraging available observations along the US East Coast.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
Short summary
Short summary
Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
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
Short summary
Short summary
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
Preprint withdrawn
Short summary
Short summary
In this paper, we assess whether hub-height turbulence can easily be quantified from either other hub-height variables or ground-level measurements in complex terrain. We find a large variability across the three considered locations when trying to model hub-height turbulence intensity and turbulence kinetic energy. Our results highlight the nonlinear and complex nature of atmospheric turbulence, so that more powerful techniques should instead be recommended to model hub-height turbulence.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
Short summary
Short summary
While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth N. Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, William Radünz, and Patrick Moriarty
Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, https://doi.org/10.5194/wes-10-1681-2025, 2025
Short summary
Short summary
This study is the first to use real-world atmospheric measurements to show that large wind plants can increase the height of the planetary boundary layer, the part of the atmosphere near the surface where life takes place. The planetary boundary layer height governs processes like pollutant transport and cloud formation and is a key parameter for modeling the atmosphere. The results of this study provide important insights into interactions between wind plants and their local environment.
Yelena L. Pichugina, Alan W. Brewer, Sunil Baidar, Robert Banta, Edward Strobach, Brandi McCarty, Brian Carroll, Nicola Bodini, Stefano Letizia, Richard Marchbanks, Michael Zucker, Maxwell Holloway, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-79, https://doi.org/10.5194/wes-2025-79, 2025
Preprint under review for WES
Short summary
Short summary
The truck-based Doppler lidar system was used during the American Wake Experiment (AWAKEN) to obtain the high-frequency, simultaneous measurements of the horizontal wind speed, direction, and vertical-velocity from a moving platform. The paper presents the unique capability of the novel lidar system to characterize the temporal, vertical, and spatial variability of winds at various distances from operating turbines and obtain quantitative estimates of wind speed reduction in the waked flow.
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, Ethan Young, and Rao Kotamarthi
Wind Energ. Sci., 10, 1551–1574, https://doi.org/10.5194/wes-10-1551-2025, https://doi.org/10.5194/wes-10-1551-2025, 2025
Short summary
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.
Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
Short summary
Short summary
Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
Rajesh Kumar, Piyush Bhardwaj, Cenlin He, Jennifer Boehnert, Forrest Lacey, Stefano Alessandrini, Kevin Sampson, Matthew Casali, Scott Swerdlin, Olga Wilhelmi, Gabriele G. Pfister, Benjamin Gaubert, and Helen Worden
Earth Syst. Sci. Data, 17, 1807–1834, https://doi.org/10.5194/essd-17-1807-2025, https://doi.org/10.5194/essd-17-1807-2025, 2025
Short summary
Short summary
We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS). The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county-level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
Short summary
Short summary
This study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.
David Rosencrans, Julie K. Lundquist, Mike Optis, and Nicola Bodini
Wind Energ. Sci., 10, 59–81, https://doi.org/10.5194/wes-10-59-2025, https://doi.org/10.5194/wes-10-59-2025, 2025
Short summary
Short summary
The US offshore wind industry is growing rapidly. Expansion into cold climates will subject turbines and personnel to hazardous icing. We analyze the 21-year icing risk for US east coast wind areas based on numerical weather prediction simulations and further assess impacts from wind farm wakes over one winter season. Sea spray icing at 10 m can occur up to 67 h per month. However, turbine–atmosphere interactions reduce icing hours within wind plant areas.
Enrico Ferrero, Bianca Tenti, and Stefano Alessandrini
Adv. Sci. Res., 21, 19–25, https://doi.org/10.5194/asr-21-19-2024, https://doi.org/10.5194/asr-21-19-2024, 2024
Short summary
Short summary
A new plume rise scheme based on heat transport by particles was presented: the entrainment is simulated by the mixing of two fluids (air and plume particles) with different temperatures and the resulting temperature is given by Richmann's law. The new algorithm is compared with the one that is currently included in SPRAY-WEB by Alessandrini et al. (2013). The new scheme seems to behave better when the ambient wind speeds are higher, but the asymptotic behavior is correct even with lower speeds.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
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
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Marco Tedesco, Paolo Colosio, Xavier Fettweis, and Guido Cervone
The Cryosphere, 17, 5061–5074, https://doi.org/10.5194/tc-17-5061-2023, https://doi.org/10.5194/tc-17-5061-2023, 2023
Short summary
Short summary
We developed a technique to improve the outputs of a model that calculates the gain and loss of Greenland and consequently its contribution to sea level rise. Our technique generates “sharper” images of the maps generated by the model to better understand and quantify where losses occur. This has implications for improving models, understanding what drives the contributions of Greenland to sea level rise, and more.
Nicola Bodini, Simon Castagneri, and Mike Optis
Wind Energ. Sci., 8, 607–620, https://doi.org/10.5194/wes-8-607-2023, https://doi.org/10.5194/wes-8-607-2023, 2023
Short summary
Short summary
The National Renewable Energy Laboratory (NREL) has published updated maps of the wind resource along all US coasts. Given the upcoming offshore wind development, it is essential to quantify the uncertainty that comes with the modeled wind resource data set. The paper proposes a novel approach to quantify this numerical uncertainty by leveraging available observations along the US East Coast.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
Short summary
Short summary
Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
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
Short summary
Short summary
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
Preprint withdrawn
Short summary
Short summary
In this paper, we assess whether hub-height turbulence can easily be quantified from either other hub-height variables or ground-level measurements in complex terrain. We find a large variability across the three considered locations when trying to model hub-height turbulence intensity and turbulence kinetic energy. Our results highlight the nonlinear and complex nature of atmospheric turbulence, so that more powerful techniques should instead be recommended to model hub-height turbulence.
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
Short summary
Short summary
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.
Alex Rybchuk, Mike Optis, Julie K. Lundquist, Michael Rossol, and Walt Musial
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-50, https://doi.org/10.5194/gmd-2021-50, 2021
Preprint withdrawn
Short summary
Short summary
We characterize the wind resource off the coast of California by conducting simulations with the Weather Research and Forecasting (WRF) model between 2000 and 2019. We compare newly simulated winds to those from the WIND Toolkit. The newly simulated winds are substantially stronger, particularly in the late summer. We also conduct a refined analysis at three areas that are being considered for commercial development, finding that stronger winds translates to substantially more power here.
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
Short summary
Short summary
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.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
Short summary
Short summary
While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Cited articles
Alessandrini, S., Sperati, S., and Pinson, P.: A comparison between the ECMWF
and COSMO Ensemble Prediction Systems applied to short-term wind power
forecasting on real data, Appl. Energ., 107, 271–280,
https://doi.org/10.1016/j.apenergy.2013.02.041, 2013. a, b
Alessandrini, S., Delle Monache, L., Sperati, S., and Cervone, G.: An analog
ensemble for short-term probabilistic solar power forecast, Appl. Energ.,
157, 95–110, https://doi.org/10.1016/j.apenergy.2015.08.011,
2015a. a
Alessandrini, S., Delle Monache, L., Sperati, S., and Nissen, J.: A novel
application of an analog ensemble for short-term wind power forecasting,
Renew. Energ., 76, 768–781,
https://doi.org/10.1016/j.renene.2014.11.061, 2015b. a
Alessandrini, S., Sperati, S., and Delle Monache, L.: Improving the analog
ensemble wind speed forecasts for rare events, Mon. Weather Rev., 147,
2677–2692, https://doi.org/10.1175/MWR-D-19-0006.1, 2019. a, b, c
Archer, C. L., Colle, B. A., Delle Monache, L., Dvorak, M. J., Lundquist, J.,
Bailey, B. H., Beaucage, P., Churchfield, M. J., Fitch, A. C., Kosovic, B.,
Lee, S., Moriarty, P. J., Simao, H., Stevens, R. J. A. M., Veron, D., and Zack, J.: Meteorology for coastal/offshore wind energy in the United States:
Recommendations and research needs for the next 10 years, B.
Am. Meteorol. Soc., 95, 515–519,
https://doi.org/10.1175/BAMS-D-13-00108.1, 2014. a
Archer, C. L., Colle, B. A., Veron, D. L., Veron, F., and Sienkiewicz, M. J.:
On the predominance of unstable atmospheric conditions in the marine boundary
layer offshore of the US northeastern coast, J. Geophys. Res.-Atmos., 121, 8869–8885, https://doi.org/10.1002/2016JD024896,
2016. a
Arcos Jiménez, A., Gómez Muñoz, C., and García Márquez,
F.: Machine learning for wind turbine blades maintenance management,
Energies, 11, 13, https://doi.org/10.3390/en11010013, 2018. a
Arrillaga, J. A., Yagüe, C., Sastre, M., and Román-Cascón, C.: A
characterisation of sea-breeze events in the eastern Cantabrian coast (Spain)
from observational data and WRF simulations, Atmos. Res., 181,
265–280, https://doi.org/10.1016/j.atmosres.2016.06.021, 2016. a
Bodini, N., Optis, M., Rossol, M., and Rybchuk, A.: US Offshore Wind Resource data for 2000–2019, OpenEI [data set], https://doi.org/10.25984/1821404, 2021a. a
Bodini, N.: nbodini/ML_UQ_offshore: ML_UQ_offshore (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.5618470, 2021b. a
Bodini, N. and Optis, M.: The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds, Wind Energ. Sci., 5, 489–501, https://doi.org/10.5194/wes-5-489-2020, 2020. a
Bodini, N., Lundquist, J. K., and Optis, M.: Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?, Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, 2020. a
Brandily, T.: Levelized Cost of Electricity 1H 2020: Renewable Chase
Plunging Comodity Prices, Tech. rep., Bloomberg New Energy Finance Limited, available at:
https://www.bnef.com/core/lcoe?tab=Forecast LCOE (last access: 27 October 2021), 2020. a
Brower, M.: Wind Resource Assessment: A Practical Guide to Developing a Wind
Project, John Wiley & Sons, Hoboken, New Jersey,
https://doi.org/10.1002/9781118249864, 2012. a
Buizza, R., Leutbecher, M., and Isaksen, L.: Potential use of an ensemble of
analyses in the ECMWF Ensemble Prediction System, Q. J.
Roy. Meteor. Soc., 134, 2051–2066,
https://doi.org/10.1002/qj.346, 2008. a, b
Bureau of Ocean Energy Management: Outer Continental Shelf Renewable Energy
Leases Map Book, Tech. rep., Bureau of Ocean Energy Management, available at: https://www.boem.gov/sites/default/files/renewable-energy-program/Mapping-and-Data/Renewable_Energy_Leases_Map_Book_March_2019.pdf (last access: 27 October 2021),
2018. a
Carbon Trust Offshore Wind Accelerator: Carbon Trust Offshore Wind
Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR
Technology, Tech. rep., Carbon Trust, available at:
https://prod-drupal-files.storage.googleapis.com/documents/resource/public/Roadmap for Commercial Acceptance of Floating LiDAR REPORT.pdf (last access: 27 October 2021),
2018. a
Carvalho, D., Rocha, A., Gómez-Gesteira, M., and Santos, C. S.: Sensitivity
of the WRF model wind simulation and wind energy production estimates to
planetary boundary layer parameterizations for onshore and offshore areas in
the Iberian Peninsula, Appl. Energ., 135, 234–246,
https://doi.org/10.1016/j.apenergy.2014.08.082, 2014a. a
Carvalho, D., Rocha, A., Gómez-Gesteira, M., and Silva Santos, C.: WRF
wind simulation and wind energy production estimates forced by different
reanalyses: Comparison with observed data for Portugal, Appl. Energ.,
117, 116–126, https://doi.org/10.1016/j.apenergy.2013.12.001, 2014b. a
Cervone, G., Clemente-Harding, L., Alessandrini, S., and Delle Monache, L.:
Short-term photovoltaic power forecasting using Artificial Neural Networks
and an Analog Ensemble, Renew. Energ., 108, 274–286,
https://doi.org/10.1016/j.renene.2017.02.052, 2017. a
Clifton, A., Kilcher, L., Lundquist, J., and Fleming, P.: Using machine
learning to predict wind turbine power output, Environ. Res.
Lett., 8, 024009, https://doi.org/10.1088/1748-9326/8/2/024009,
2013. a
Deepwater Wind: Block Island Wind Farm, available at: https://dwwind.com/project/block-island-wind-farm (last access: 27 October 2021), 2016. a
Delle Monache, L., Eckel, F. A., Rife, D. L., Nagarajan, B., and Searight, K.:
Probabilistic weather prediction with an analog ensemble, Mon. Weather
Rev., 141, 3498–3516, https://doi.org/10.1175/MWR-D-12-00281.1,
2013. a, b
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The operational sea surface temperature and sea ice analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
Fabre, S., Stickland, M., Scanlon, T., Oldroyd, A., Kindler, D., and Quail, F.:
Measurement and simulation of the flow field around the FINO 3 triangular
lattice meteorological mast, J. Wind Eng. Ind.
Aerod., 130, 99–107,
https://doi.org/10.1016/j.jweia.2014.04.002, 2014. a
Friedman, J. H.: Stochastic gradient boosting, Comput. Stat. Data
An., 38, 367–378, https://doi.org/10.1016/S0167-9473(01)00065-2,
2002. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The
modern-era retrospective analysis for research and applications, version 2
(MERRA-2), J. Climate, 30, 5419–5454,
https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
machine learning break the convection parameterization deadlock?, Geophys.
Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202,
2018. a
Gorton, A.: Atmosphere to Electrons (A2e), buoy/lidar.z06.00, Maintained
by A2e Data Archive and Portal for U.S. Department of Energy, Office of
Energy Efficiency and Renew. Energ., Tech. rep., Pacific Northwest
National Lab (PNNL), Richland, WA, United States, https://doi.org/10.21947/1669352,
2020. a
Hahmann, A. N., Vincent, C. L., Peña, A., Lange, J., and Hasager, C. B.: Wind
climate estimation using WRF model output: method and model sensitivities
over the sea, Int. J. Climatol., 35, 3422–3439,
https://doi.org/10.1002/joc.4217, 2015. a, b, c
Hahmann, A. N., Sīle, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T., and Söderberg, S.: The making of the New European Wind Atlas – Part 1: Model sensitivity, Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, 2020. 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
Holstag, E.: Improved Bankability, The Ecofys position on Lidar Use, Ecofys
report, 2013. a
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. a
Hu, W.: Parallel Analog Ensemble, GitHub [code], available at: https://weiming-hu.github.io/AnalogsEnsemble,
last access: 27 October 2021. a
Hu, W., Cervone, G., Clemente-Harding, L., and Calovi, M.: Parallel Analog
Ensemble – The Power Of Weather Analogs, NCAR Technical Notes NCAR/TN-564+
PROC, p. 1, https://doi.org/10.5065/P2JJ-9878, 2021a. a
Hu, W., Cervone, G., Young, G., and Monache, L. D.: Weather Analogs with a
Machine Learning Similarity Metric for Renewable Resource Forecasting, arXiv [preprint], arXiv:2103.04530, 9 March 2021b. a
JCGM 100:2008: Evaluation of measurement data – Guide to the expression of
uncertainty in measurement, Joint Committee for Guides in Metrology, 2008. a
Johnson, C., White, E., and Jones, S.: Summary of Actual vs. Predicted Wind
Farm Performance: Recap of WINDPOWER 2008, in: AWEA Wind Resource and Project
Energy Assessment Workshop, available at:
http://www.enecafe.com/interdomain/idlidar/paper/2008/AWEA workshop 2008 Johnson_Clint.pdf (last access: 27 October 2021),
2008. a
Junk, C., Delle Monache, L., Alessandrini, S., Cervone, G., and Von Bremen, L.:
Predictor-weighting strategies for probabilistic wind power forecasting with
an analog ensemble, Meteorol. Z., 24, 361–379,
https://doi.org/10.1127/metz/2015/0659, 2015. a, b
Kirincich, A.: A Metocean Reference Station for offshore wind Energy research
in the US, J. Phys. Conf. Ser., 1452, 012028,
https://doi.org/10.1088/1742-6596/1452/1/012028, 2020. a
Levene, H.: Robust tests for equality of variances, Contributions to
probability and statistics, Essays in honor of Harold Hotelling,
279–292, https://doi.org/10.2307/2285659, Stanford University Press, Palo Alto, 1961. a
Mattar, C. and Borvarán, D.: Offshore wind power simulation by using WRF in
the central coast of Chile, Renew. Energ., 94, 22–31,
https://doi.org/10.1016/j.renene.2016.03.005, 2016. a
Murphy, J. M., Sexton, D. M., Barnett, D. N., Jones, G. S., Webb, M. J.,
Collins, M., and Stainforth, D. A.: Quantification of modelling uncertainties
in a large ensemble of climate change simulations, Nature, 430, 768–772,
https://doi.org/10.1038/nature02771, 2004. a
Musial, W., Heimiller, D., Beiter, P., Scott, G., and Draxl, C.: Offshore wind
energy resource assessment for the United States, Tech. rep., National
Renewable Energy Laboratory (NREL), Golden, CO, United States, available at: https://www.nrel.gov/docs/fy16osti/66599.pdf (last access: 27 October 2021), 2016. a
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model with
condensation physics: Its design and verification, Bound.-Lay.
Meteorol., 112, 1–31,
https://doi.org/10.1023/B:BOUN.0000020164.04146.98, 2004. a
Neumann, T., Nolopp, K., and Herklotz, K.: First operating experience with the
FINO1 research platform in the North Sea; Erste Betriebserfahrungen mit der
FINO1-Forschungsplattform in der Nordsee, DEWI-Magazin, Germany, 2004. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah
land surface model with multiparameterization options (Noah-MP): 1. Model
description and evaluation with local-scale measurements, J.
Geophys. Res.-Atmos., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011. a
OceanTech Services/DNV GL: NYSERDA Floating Lidar Buoy Data, OceanTech Services/DNV GL [data set], available at:
https://oswbuoysny.resourcepanorama.dnvgl.com (last access: 27 October 2021), 2020. a
Olsen, B. T., Hahmann, A. N., Sempreviva, A. M., Badger, J., and Jørgensen, H. E.: An intercomparison of mesoscale models at simple sites for wind energy applications, Wind Energ. Sci., 2, 211–228, https://doi.org/10.5194/wes-2-211-2017, 2017. a, b
Optis, M. and Perr-Sauer, J.: The importance of atmospheric turbulence and
stability in machine-learning models of wind farm power production, Renew. Sustain. Energ. Rev., 112, 27–41,
https://doi.org/10.1016/j.rser.2019.05.031, 2019. a
Optis, M., Rybchuk, O., Bodini, N., Rossol, M., and Musial, W.: 2020 Offshore
Wind Resource Assessment for the California Pacific Outer Continental Shelf,
Tech. rep., National Renewable Energy Laboratory (NREL), Golden, CO, United
States, available at: https://www.nrel.gov/docs/fy21osti/77642.pdf (last access: 27 October 2021), 2020. a, b, c
Optis, M., Bodini, N., Debnath, M., and Doubrawa, P.: New methods to improve the vertical extrapolation of near-surface offshore wind speeds, Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, 2021a. a
Optis, M., Kumler, A., Brodie, J., and Miles, T.: Quantifying sensitivity in
numerical weather prediction-modeled offshore wind speeds through an ensemble
modeling approach, Wind Energ., 24, 957–973, https://doi.org/10.1002/we.2611,
2021b. a, b
Papanastasiou, D., Melas, D., and Lissaridis, I.: Study of wind field under sea
breeze conditions; an application of WRF model, Atmos. Res., 98,
102–117, https://doi.org/10.1016/j.atmosres.2010.06.005, 2010. a
Parker, W. S.: Ensemble modeling, uncertainty and robust predictions, WIRES Clim. Change, 4, 213–223,
https://doi.org/10.1002/wcc.220, 2013. a
Peña, A., Floors, R., and Gryning, S.-E.: The Høvsøre tall
wind-profile experiment: a description of wind profile observations in the
atmospheric boundary layer, Bound.-Lay. Meteorol., 150, 69–89,
https://doi.org/10.1007/s10546-013-9856-4, 2014. a
Ruiz, J. J., Saulo, C., and Nogués-Paegle, J.: WRF Model Sensitivity to
Choice of Parameterization over South America: Validation against Surface
Variables, Mon. Weather Rev., 138, 3342–3355,
https://doi.org/10.1175/2010MWR3358.1, 2010. a
Rybchuk, A., Optis, M., Lundquist, J. K., Rossol, M., and Musial, W.: A Twenty-Year Analysis of Winds in California for Offshore Wind Energy Production Using WRF v4.1.2, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-50, in review, 2021. a
Salvação, N. and Soares, C. G.: Wind resource assessment offshore the
Atlantic Iberian coast with the WRF model, Energy, 145, 276–287,
https://doi.org/10.1016/j.energy.2017.12.101, 2018. a
Sidel, A., Hu, W., Cervone, G., and Calovi, M.: Heat Wave Identification Using
an Operational Weather Model and Analog Ensemble, in: 100th American
Meteorological Society Annual Meeting,
available at: https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/371727 (last access: 27 October 2021),
2020.
a, b
Siuta, D., West, G., and Stull, R.: WRF Hub-Height Wind Forecast Sensitivity
to PBL Scheme, Grid Length, and Initial Condition Choice in Complex Terrain,
Weather Forecast., 32, 493–509, https://doi.org/10.1175/WAF-D-16-0120.1, 2017. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3, Tech. Rep. NCAR/TN-475+STR, Mesoscale and
Microscale Meteorology Division, National Center for Atmospheric Research,
Boulder, Colorado, USA, https://doi.org/10.5065/D68S4MVH, 2008. a
Steele, C. J., Dorling, S. R., von Glasow, R., and Bacon, J.: Idealized WRF model sensitivity simulations of sea breeze types and their effects on offshore windfields, Atmos. Chem. Phys., 13, 443–461, https://doi.org/10.5194/acp-13-443-2013, 2013. a
Stiesdal, H.: Midt i en disruptionstid, Ingeniøren,
available at: https://ing.dk/blog/midt-disruptionstid-190449 (last access: 27 October 2021), 2016. a
Truepower, A.: AWS Truepower Loss and Uncertainty Methods, Albany, NY, available at:
https://www.awstruepower.com/assets/AWS-Truepower-Loss-and-Uncertainty-Memorandum-5-Jun-2014.pdf (last access: 27 October 2021),
2014. a
Ulazia, A., Saenz, J., and Ibarra-Berastegui, G.: Sensitivity to the use of
3DVAR data assimilation in a mesoscale model for estimating offshore wind
energy potential. A case study of the Iberian northern coastline, Appl.
Energ., 180, 617–627, https://doi.org/10.1016/j.apenergy.2016.08.033, 2016. a
van Hoof, J.: Unlocking Europe's offshore wind potential, Tech. rep.,
PricewaterhouseCoopers B.V., available at:
https://www.pwc.nl/nl/assets/documents/pwc-unlocking-europes-offshore-wind-potential.pdf (last access: 27 October 2021),
2017. a
Vanvyve, E., Delle Monache, L., Monaghan, A. J., and Pinto, J. O.: Wind
resource estimates with an analog ensemble approach, Renew. Energ., 74,
761–773, https://doi.org/10.1016/j.renene.2014.08.060, 2015. a
White, E.: Continuing Work on Improving Plant Performance Estimates, in: AWEA
Wind Resource and Project Energy Assessment Workshop, 2008. a
Xingjian, S., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-C.:
Convolutional LSTM network: A machine learning approach for precipitation
nowcasting, in: Advances in Neural Information Processing Systems,
802–810, 2015. a
Zhu, Y.: Ensemble forecast: A new approach to uncertainty and predictability,
Adv. Atmos. Sci., 22, 781–788,
https://doi.org/10.1007/BF02918678, 2005. a
Short summary
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.
We develop two machine-learning-based approaches to temporally extrapolate uncertainty in...
Altmetrics
Final-revised paper
Preprint