Articles | Volume 11, issue 6
https://doi.org/10.5194/wes-11-2257-2026
© Author(s) 2026. 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-11-2257-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jun 2026
Research article |
| 29 Jun 2026
| 29 Jun 2026
Performance of reanalysis and mesoscale models off the coast of Hawai'i
Lindsay M. Sheridan
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Richland, Washington, United States
Raghavendra Krishnamurthy
Pacific Northwest National Laboratory, Richland, Washington, United States
Tien Manh Nguyen
University of Hawai'i, Honolulu, Hawai'i, United States
Yi-Leng Chen
University of Hawai'i, Honolulu, Hawai'i, United States
William I. Gustafson Jr.
Pacific Northwest National Laboratory, Richland, Washington, United States
Pacific Northwest National Laboratory, Richland, Washington, United States
Feng Hsiao
University of Hawai'i, Honolulu, Hawai'i, United States
Rob K. Newsom
Pacific Northwest National Laboratory, Richland, Washington, United States
Preston Spicer
Pacific Northwest National Laboratory, Richland, Washington, United States
Evgueni Kassianov
Pacific Northwest National Laboratory, Richland, Washington, United States
Mikhail Pekour
Pacific Northwest National Laboratory, Richland, Washington, United States
Nicola Bodini
National Laboratory of the Rockies, Golden, Colorado, United States
Mark Severy
Pacific Northwest National Laboratory, Richland, Washington, United States
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-55, https://doi.org/10.5194/wes-2025-55, 2025
Revised manuscript not accepted
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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.
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
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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
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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.
Jerome D. Fast, Adam C. Varble, Fan Mei, Mikhail Pekour, Jason Tomlinson, Alla Zelenyuk, Art J. Sedlacek III, Maria Zawadowicz, and Louisa Emmons
Atmos. Chem. Phys., 24, 13477–13502, https://doi.org/10.5194/acp-24-13477-2024, https://doi.org/10.5194/acp-24-13477-2024, 2024
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Aerosol property measurements recently collected on the ground and by a research aircraft in central Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign exhibit large spatial and temporal variability. These measurements coupled with coincident meteorological information provide a valuable data set needed to evaluate and improve model predictions of aerosols in a traditionally data-sparse region of South America.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
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Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Evgueni Kassianov, Connor J. Flynn, James C. Barnard, Brian D. Ermold, and Jennifer M. Comstock
Atmos. Meas. Tech., 17, 4997–5013, https://doi.org/10.5194/amt-17-4997-2024, https://doi.org/10.5194/amt-17-4997-2024, 2024
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Conventional ground-based radiometers commonly measure solar radiation at a few wavelengths within a narrow spectral range. These limitations prevent improved retrievals of aerosol, cloud, and surface characteristics. To address these limitations, an advanced ground-based radiometer with expanded spectral coverage and hyperspectral capability is introduced. Its good performance is demonstrated using reference data collected over three coastal regions with diverse types of aerosols and clouds.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
Atmos. Chem. Phys., 24, 8165–8181, https://doi.org/10.5194/acp-24-8165-2024, https://doi.org/10.5194/acp-24-8165-2024, 2024
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Deep convection under various large-scale meteorological patterns (LSMPs) shows distinct precipitation features. In southeastern Texas, mesoscale convective systems (MCSs) contribute significantly to precipitation year-round, while isolated deep convection (IDC) is prominent in summer and fall. Self-organizing maps (SOMs) reveal convection can occur without large-scale lifting or moisture convergence. MCSs and IDC events have distinct life cycles influenced by specific LSMPs.
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
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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
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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
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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
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
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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.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
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To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
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
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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.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
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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.
Siegfried Schobesberger, Emma L. D'Ambro, Lejish Vettikkat, Ben H. Lee, Qiaoyun Peng, David M. Bell, John E. Shilling, Manish Shrivastava, Mikhail Pekour, Jerome Fast, and Joel A. Thornton
Atmos. Meas. Tech., 16, 247–271, https://doi.org/10.5194/amt-16-247-2023, https://doi.org/10.5194/amt-16-247-2023, 2023
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We present a new, highly sensitive technique for measuring atmospheric ammonia, an important trace gas that is emitted mainly by agriculture. We deployed the instrument on an aircraft during research flights over rural Oklahoma. Due to its fast response, we could analyze correlations with turbulent winds and calculate ammonia emissions from nearby areas at 1 to 2 km resolution. We observed high spatial variability and point sources that are not resolved in the US National Emissions Inventory.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
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Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2022-1111, https://doi.org/10.5194/egusphere-2022-1111, 2022
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A process-based plant Carbon (C)-Nitrogen (N) interface coupling framework has been developed, which mainly focuses on the plant resistance and N limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem-biogeochemical model and testing results show a general improvement in simulating plant properties with this framework.
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.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
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This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Suleiman Mostamandi, Paul A. Kucera, Duncan Axisa, William I. Gustafson Jr., and Yannian Zhu
Atmos. Chem. Phys., 22, 8659–8682, https://doi.org/10.5194/acp-22-8659-2022, https://doi.org/10.5194/acp-22-8659-2022, 2022
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Rainfall affects the distribution of surface- and groundwater resources, which are constantly declining over the Middle East and North Africa (MENA) due to overexploitation. Here, we explored the effects of dust on rainfall using WRF-Chem model simulations. Although dust is considered a nuisance from an air quality perspective, our results highlight the positive fundamental role of dust particles in modulating rainfall formation and distribution, which has implications for cloud seeding.
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.
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.
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.
Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin
Geosci. Model Dev., 14, 7639–7657, https://doi.org/10.5194/gmd-14-7639-2021, https://doi.org/10.5194/gmd-14-7639-2021, 2021
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This study applies a fire-coupled dynamic vegetation model to quantify fire impact at monthly to annual scales. We find fire reduces grass cover by 4–8 % annually for widespread areas in south African savanna and reduces tree cover by 1 % at the periphery of tropical Congolese rainforest. The grass cover reduction peaks at the beginning of the rainy season, which quickly diminishes before the next fire season. In contrast, the reduction of tree cover is irreversible within one growing season.
Fan Mei, Steven Spielman, Susanne Hering, Jian Wang, Mikhail S. Pekour, Gregory Lewis, Beat Schmid, Jason Tomlinson, and Maynard Havlicek
Atmos. Meas. Tech., 14, 7329–7340, https://doi.org/10.5194/amt-14-7329-2021, https://doi.org/10.5194/amt-14-7329-2021, 2021
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This study focuses on understanding a versatile water-based condensation particle counter (vWCPC 3789) performance under various ambient pressure conditions (500–1000 hPa). A vWCPC has the advantage of avoiding health and safety concerns. However, its performance characterization under low pressure is rare but crucial for ensuring successful airborne deployment. This paper provides advanced knowledge of operating a vWCPC 3789 to capture the spatial variations of atmospheric aerosols.
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.
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.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Cited articles
Argüeso, D. and Businger, S.: Wind power characteristics off O'ahu, Hawai'i, Renew. Energ., 128, 324–336, https://doi.org/10.1016/j.renene.2018.05.080, 2018.
AXYS Technologies Inc: TRIAXYSTM Directional Wave Buoy User's Manual, Version 12, Sydney, British Columbia, Canada, 2012.
Bianco, L., Mendeke, R., Lindblom, J., Djalalova, I. V., Turner, D. D., and Wilczak, J. M.: 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, Wind Energ. Sci., 10, 2117–2136, https://doi.org/10.5194/wes-10-2117-2025, 2025.
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, 2024a.
Bodini, N., Optis, M., Liu, M. Gaudet, B., Krishnamurthy, R., Kumler, A., Rosencrans, D., Rybchuk, A., Tai, S.-L., Berg, L., Musial, W., Lundquist, J. K., Purkayastha, A., Young, A., and Draxl, C.: Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf, National Renewable Energy Laboratory (NREL), Golden, Colorado, United States, NREL/TP-5000-88215, https://docs.nlr.gov/docs/fy24osti/88215.pdf (last access: 9 September 2025), 2024b.
Bucci, L.: Hurricane Dora, National Hurricane Center Tropical Cyclone Report, EP052023, https://www.nhc.noaa.gov/data/tcr/EP052023_Dora.pdf (last access: 5 March 2024).
Buster, G., Pinchuk, P. Lavin, L., Benton, B., and Bodini, N.: Bias Correcting NOAA's High-Resolution Rapid Refresh (HRRR) Wind Resource Data for Grid Integration Applications, https://docs.nlr.gov/docs/fy25osti/91749.pdf (last access: 6 September 2025), 2024.
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.
CDS (Climate Data Store): Complete ERA5 global atmospheric reanalysis [data set], https://cds.climate.copernicus.eu/datasets/reanalysis-era5-complete (last access: 7 January 2025), 2025a.
CDS (Climate Data Store): ERA5 hourly data on single levels from 1940 to present [data set], https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels (last access: 26 November 2025), 2025b.
Cowin, E., Wang, C., and Walsh, S. D. C.: Assessing predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets, Energies, 16, 3404, https://doi.org/10.3390/en16083404, 2023.
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.
DOE (U.S. Department of Energy): 10 min Lidar Winds/Derived Data, Wind Data Hub [data set], https://wdh.energy.gov/ds/buoy/lidar.z07.c0 (last access: 6 January 2025), 2025a.
DOE (U.S. Department of Energy): Hawai'i – Wind Sentinel (120), Oahu, Hawai'i/Reviewed Data, Wind Data Hub [data set], https://wdh.energy.gov/ds/buoy/buoy.z07.b0 (last access: 6 January 2025), 2025b.
DOE (U.S. Department of Energy): 1 Hz Lidar Winds/Reviewed Data, Wind Data Hub [data set], https://wdh.energy.gov/ds/buoy/lidar.z07.b0 (last access: 24 November 2025), 2025c.
Edson, J. B., Jampana, V., Weller, R. A., Bigorre, S. P., Plueddemann, A. J., Fairall, C. W., Miller, S. D., Mahrt, L., Vickers, D., and Hersbach, H.: On the Exchange of Momentum over the Open Ocean, J. Phys. Oceanogr., 43, 1589–1610, https://doi.org/10.1175/JPO-D-12-0173.1, 2013.
Fragano, C. G. and Colle, B. A.: Validation of Offshore Winds in the ERA5 Reanalysis and NREL NOW-23 WRF Analysis Using Two Floating Lidars in the New York Bight, Weather Forecast., 1307–1323, https://doi.org/10.1175/WAF-D-24-0155.1, 2025.
Gandoin, R. and Garza, J.: Underestimation of strong wind speeds offshore in ERA5: evidence, discussion and correction, Wind Energ. Sci., 9, 1727–1745, https://doi.org/10.5194/wes-9-1727-2024, 2024.
Gaudet, B. J., García Medina, G., Krishnamurthy, R., Shaw, W. J., Sheridan, L. M., Yang, Z., Newsom, R. K., and Pekour, M.: Evaluation of Coupled Wind-Wave Model Simulations of Offshore Winds in the Mid-Atlantic Bight Using Lidar-Equipped Buoys, Mon. Weather Rev., 150, 1377–1395, https://doi.org/10.1175/MWR-D-21-0166.1, 2022.
Gaudet, B. J., García Medina, G., Krishnamurthy, R., Sheridan, L. M., Yang, Z., Newsom, R. K., Pekour, M., Gustafson Jr., W. I., and Liu, J.: Assessing Impacts of Waves on Hub-Height Winds off the U.S. West Coast Using Lidar Buoys and Coupled Modeling Approaches, Pacific Northwest National Laboratory, Richland, WA, United States, PNNL-35856, https://doi.org/10.2172/2337529, 2024.
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., Putnam, 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.
Gorton, A. M. and Shaw, W. J.: Advancing offshore wind resource characterization using buoy-based observations, Mar. Technol. Soc. J., 54, 37–43, https://doi.org/10.4031/MTSJ.54.6.5, 2020.
Hansen, K. S., Barthelmie, R. J., Jensen, L. E., and Sommer, A.: The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm, Wind Energy, 15, 183–196, https://doi.org/10.1002/we.512, 2011.
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.
Hsiao, F., Chen, Y.-L., and Hitzl, D. E.: Heavy rainfall events over central O'ahu under weak wind conditions during seasonal transitions, Mon. Weather Rev., 148, 4117–4141, https://doi.org/10.1175/MWR-D-19-0358.1, 2020.
Hsiao, F., Chen, Y.-L., Nguyen, H. P., Hitzl, D. E., and Ballard, R.: Effects of Trade Wind Strength on Airflow and Cloudiness over O'ahu, Mon. Weather Rev., 149, 3037–3062, https://doi.org/10.1175/MWR-D-20-0399.1, 2021.
Kalverla, P. C., Duncan Jr., J. B., Steeneveld, G.-J., and Holtslag, A. A. M.: Low-level jets over the North Sea based on ERA5 and observations: together they do better, Wind Energ. Sci., 4, 193–209, https://doi.org/10.5194/wes-4-193-2019, 2019.
Kalverla, P. C., Holtslag, A. A. M., Ronda, R. J., and Steeneveld, G.-J.: Quality of wind characteristics in recent wind atlases over the North Sea, Q. J. Roy. Meteor. Soc., 146, 1498–1515, https://doi.org/10.1002/qj.3748, 2020.
Krishnamurthy, R., García Medina, G., Gaudet, B., Gustafson Jr., W. I., Kassianov, E. I., Liu, J., Newsom, R. K., Sheridan, L. M., and Mahon, A. M.: Year-long buoy-based observations of the air–sea transition zone off the US west coast, Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, 2023.
Lee, J., Ghate, V. P., Mitra, A., Miller, L. M., Krishnamurthy, R., and Egerer, U.: Characterization of HRRR-simulated rotor layer wind speeds and clouds along the coast of California, Wind Energ. Sci., 10, 2755–2769, https://doi.org/10.5194/wes-10-2755-2025, 2025.
Longman, R. J., Elison Timm, O., Giambelluca, T. W., and Kaiser, L.: A 20-Year Analysis of Disturbance-Driven Rainfall on O`ahu, Hawai`i, Mon. Weather Rev., 149, 1767–1783, https://doi.org/10.1175/MWR-D-20-0287.1, 2021.
Lu, B.-Y., Chu, P.-S., Kim, S.-H., and Karamperidou, C.: Hawaiian Regional Climate Variability during Two Types of El Niño, J. Climate, 33, 9929–9943, https://doi.org/10.1175/JCLI-D-19-0985.1, 2020.
Mass, C. and Ovens, D.: The Meteorology of the August 2023 Maui Wildfire, Weather Forecast., 39, 1097–1115, https://doi.org/10.1175/WAF-D-23-0210.1, 2024.
McCoy, A., Musial, W., Hammond, R., Mulas Hernando, D., Duffy, P., Beiter, P., Pérez, P., Baranowski, R., Reber, G., and Spitsen, P.: Offshore Wind Market Report: 2024 Edition, National Renewable Energy Laboratory, Golden, CO, United States, NREL/TP-5000-90525, https://www.nlr.gov/docs/fy24osti/90525.pdf (last access: 25 May 2025), 2024.
Morrison, I. and Businger, S.: Synoptic Structure and Evolution of a Kona Low, Weather Forecast., 16, 81–98, https://doi.org/10.1175/1520-0434(2001)016<0081:SSAEOA>2.0.CO;2, 2001.
Müller, P., Garrett, C., and Osborne, A.: Rogue waves, Oceanogr., 18, 66–75, https://doi.org/10.5670/oceanog.2005.30, 2005.
Musial, W., Beiter, P., Nunemaker, J., Heimiller, D., Ahmann, J., and Busch, J.: Oregon Offshore Wind Site Feasibility and Cost Study, National Renewable Energy Laboratory, Golden, CO, United States, NREL/TP-5000-74597, https://doi.org/10.2172/1570430, 2019.
NDBC (National Data Buoy Center): Nondirectional and Directional Wave Data Analysis Procedures, NDBC Technical Document 96–01, Stennis Space Center, Slidell, Louisiana, USA, https://www.ndbc.noaa.gov/wavemeas.pdf (last access: 12 March 2024), 1996.
NOAA (National Oceanic and Atmospheric Administration): Kona Lows Produce Two Rounds of Flooding – February 2023, https://www.weather.gov/hfo/KonaLowFeb2023 (last access: 18 November 2025), 2023a.
NOAA (National Oceanic and Atmospheric Administration): Cold Front Brings Winds and Rain to Hawai'i – April 2023, https://www.weather.gov/hfo/coldfront_apr2023 (last access:18 November 2025), 2023b.
NOAA (National Oceanic and Atmospheric Administration): Climate Variability: Oceanic Niño Index, https://www.climate.gov/news-features/understanding-climate/climate-variability-oceanic-nino-index (last updated: 25 June 2025), 2025.
Nehzad, M., Neshat, M., Groppi, D., Marzialetti, P., Heydari, A., Sylaios, G., and Astiaso Garcia, D.: A primary offshore wind farm site assessment using reanalysis data: a case study for Samothraki island, Renew. Energ., 172, 667–679, https://doi.org/10.1016/j.renene.2021.03.045, 2021.
Nikolkina, I. and Didenkulova, I.: Rogue waves in 2006–2010, Nat. Hazards Earth Syst. Sci., 11, 2913–2924, https://doi.org/10.5194/nhess-11-2913-2011, 2011.
Nwogu, O.: Maximum entropy estimation of directional wave spectra from an array of wave probes, Appl. Ocean Res., 11, 176–182, 1989.
Pronk, V., Bodini, N., Optis, M., Lundquist, J. K., Moriarty, P., Draxl, C., Purkayastha, A., and Young, E.: Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?, Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, 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.
Severy, M., Gorton, A. M., Krishnamurthy, R., and Levin, M. S.: Lidar Buoy Data Dictionary: For the 2020–2021 California Deployments, Pacific Northwest National Laboratory (PNNL), Richland, WA, USA, PNNL-30947, https://doi.org/10.2172/1987710, 2021.
Sheridan, L. M., Krishnamurthy, R., Gorton, A. M., Shaw, W. J., and Newsom, R. K.: Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observation, Mar. Technol. Soc. J., 54, 44–61, https://doi.org/10.4031/MTSJ.54.6.13, 2020.
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, 2022.
Sheridan, L. M., Krishnamurthy, R., Gustafson Jr., W. I., Liu, Y., Gaudet, B. J., Bodini, N., Newsom, R. K., and Pekour, M.: Offshore low-level jet observations and model representation using lidar buoy data off the California coast, Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, 2024.
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.
Soukissian, T. H., Karathanaski, F. E., and Zaragkas, D. K.: Exploiting offshore wind and solar resources in the Mediterranean using ERA5 reanalysis data, Energ. Convers. Manage., 237, 114092, https://doi.org/10.1016/j.enconman.2021.114092, 2021.
Wagner, R., Cañadillas, B., Clifton, A., Feeney, S., Nygaard, N., Poodt, M., St. Martin, C. Tüxen, E., and Wagenaar, J. W.: Rotor equivalent wind speed for power curve measurement – comparative exercise for IEA Wind Annex 32, J. Phys.: Conf. Ser., 524, 012108, https://doi.org/10.1088/1742-6596/524/1/012108, 2014.
Wharton, S. and Lundquist, J. K.: Atmospheric stability affects wind turbine power collection, Environ. Res. Lett., 7, 014005, https://doi.org/10.1088/1748-9326/7/1/014005, 2012.
Wilczak, J. M., Akish, E., Capotondi, A., and Compo, G. P.: 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.
Zhang, Y., Chen, Y.-L., Hong, S.-Y., Juang, H.-M. H., and Kodama, K.: Validation of the coupled NCEP Meso-scale Spectral Model and an advanced land surface model over the Hawaiian Islands. Part I: Summer trade wind conditions and a heavy rainfall event, Weather Forecast., 20, 847–872, https://doi.org/10.1175/WAF891.1, 2005.
Short summary
Wind simulations can contain significant errors, which can lead to inaccurate estimates of wind energy generation. Using observations from a floating lidar off Hawai'i, we hypothesize and establish that distinct simulation datasets will exhibit diverse ranges of errors in this offshore environment. The most commonly used simulation dataset produces the largest wind speed biases due to underestimation of fast wind speeds and misrepresentation of how wind speed varies throughout the day and night.
Wind simulations can contain significant errors, which can lead to inaccurate estimates of wind...
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