Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1251-2023
© Author(s) 2023. 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-8-1251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Aug 2023
Research article |
| 16 Aug 2023
| 16 Aug 2023
Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy
National Center for Atmospheric Research, Boulder, CO 80301, USA
Branko Kosović
National Center for Atmospheric Research, Boulder, CO 80301, USA
Larry K. Berg
Pacific Northwest National Laboratory, Richland, WA 99354, USA
Colleen M. Kaul
Pacific Northwest National Laboratory, Richland, WA 99354, USA
Matthew Churchfield
National Renewable Energy Laboratory, Golden, CO 80401, USA
Jeffrey Mirocha
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Dries Allaerts
Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Thomas Brummet
National Center for Atmospheric Research, Boulder, CO 80301, USA
Shannon Davis
Wind Energy Technology Office, U.S. Department of Energy, Washington, DC 20585, USA
Amy DeCastro
National Center for Atmospheric Research, Boulder, CO 80301, USA
Susan Dettling
National Center for Atmospheric Research, Boulder, CO 80301, USA
Caroline Draxl
National Renewable Energy Laboratory, Golden, CO 80401, USA
David John Gagne
National Center for Atmospheric Research, Boulder, CO 80301, USA
Patrick Hawbecker
National Center for Atmospheric Research, Boulder, CO 80301, USA
Pankaj Jha
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Timothy Juliano
National Center for Atmospheric Research, Boulder, CO 80301, USA
William Lassman
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Eliot Quon
National Renewable Energy Laboratory, Golden, CO 80401, USA
Raj K. Rai
Pacific Northwest National Laboratory, Richland, WA 99354, USA
Michael Robinson
Wind Energy Technology Office, U.S. Department of Energy, Washington, DC 20585, USA
William Shaw
Pacific Northwest National Laboratory, Richland, WA 99354, USA
Regis Thedin
National Renewable Energy Laboratory, Golden, CO 80401, USA
Related authors
Mari R. Tye, Ming Ge, Jadwiga H. Richter, Ethan D. Gutmann, Allyson Rugg, Cindy L. Bruyère, Sue Ellen Haupt, Flavio Lehner, Rachel McCrary, Andrew J. Newman, and Andrew Wood
EGUsphere, https://doi.org/10.5194/egusphere-2023-2326, https://doi.org/10.5194/egusphere-2023-2326, 2023
Short summary
Short summary
There is a perceived mismatch between the spatial scales that global climate models can produce data and that needed for water management decisions. However, poor communication of specific metrics relevant to local decisions is also a problem. We identified a potential set of water use decision metrics to assess their credibility in the Community Earth System Model v2 (CESM2). CESM2 can reliably reproduce many of these metrics and shows potential to support long-range water resource decisions.
Martine G. de Vos, Wilco Hazeleger, Driss Bari, Jörg Behrens, Sofiane Bendoukha, Irene Garcia-Marti, Ronald van Haren, Sue Ellen Haupt, Rolf Hut, Fredrik Jansson, Andreas Mueller, Peter Neilley, Gijs van den Oord, Inti Pelupessy, Paolo Ruti, Martin G. Schultz, and Jeremy Walton
Geosci. Commun., 3, 191–201, https://doi.org/10.5194/gc-3-191-2020, https://doi.org/10.5194/gc-3-191-2020, 2020
Short summary
Short summary
At the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Open science offers manifold opportunities but goes beyond sharing code and data. Besides domain-specific technical challenges, we observed that the main challenges are non-technical and impact the system of science as a whole.
Tyler C. McCandless and Sue Ellen Haupt
Wind Energ. Sci., 4, 343–353, https://doi.org/10.5194/wes-4-343-2019, https://doi.org/10.5194/wes-4-343-2019, 2019
Short summary
Short summary
Often in wind power forecasting the mean wind speed is forecasted at a plant, converted to power, and multiplied by the number of turbines to predict the plant's generating capacity. This methodology ignores the variability among turbines caused by localized weather, terrain, and array orientation. We show that the wind farm mean wind speed approach for power conversion is impacted by Jensen's inequality, quantify the differences, and show machine learning can overcome these differences.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
Short summary
Short summary
This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-137, https://doi.org/10.5194/wes-2024-137, 2024
Preprint under review for WES
Short summary
Short summary
This paper evaluates a new model configuration for wind energy forecasting in complex terrain. We compare model results to observations in the Altamont Pass (California, USA), where wind channeling through a mountain pass leads to increased energy production. We show evidence of improved wind speed and turbulence predictions compared to a more established modeling approach. Our work helps to ensure the robustness of the new model configuration for future wind energy applications.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
Short summary
Short summary
Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
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, and Ethan Young
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-115, https://doi.org/10.5194/wes-2024-115, 2024
Preprint under review for WES
Short summary
Short summary
Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and inter-annual trends in the wind resource to support customers interested in small and midsize wind energy.
Lucas Carmo, Jason Jonkman, and Regis Thedin
Wind Energ. Sci., 9, 1827–1847, https://doi.org/10.5194/wes-9-1827-2024, https://doi.org/10.5194/wes-9-1827-2024, 2024
Short summary
Short summary
As floating wind turbines progress to arrays with multiple units, it becomes important to understand how the wake of a floating turbine affects the performance of other units in the array. Due to the compliance of the floating substructure, the wake of a floating wind turbine may behave differently from that of a fixed turbine. In this work, we present an investigation of the mutual interaction between the motions of floating wind turbines and wakes.
Pedro Angel Jimenez y Munoz, Maria Frediani, Masih Eghdami, Daniel Rosen, Michael Kavulich, and Timothy W. Juliano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-124, https://doi.org/10.5194/gmd-2024-124, 2024
Preprint under review for GMD
Short summary
Short summary
We present the Community Fire Behavior model (CFBM) a fire behavior model designed to facilitate coupling to atmospheric models. We describe its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak ire allowed us to verify our implementation. Our vision is to foster collaborative development in fire behavior modeling with the ultimate goal of increasing our fundamental understanding of fire science and minimizing the adverse impacts of wildland fires.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, https://doi.org/10.5194/wes-9-1647-2024, 2024
Short summary
Short summary
We introduce a novel way to model the impact of atmospheric gravity waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the interaction of wind farms with the free atmosphere, ultimately facilitating cost-effective research.
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
Short summary
Short summary
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.
Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-76, https://doi.org/10.5194/wes-2024-76, 2024
Revised manuscript under review for WES
Short summary
Short summary
Our study reveals how different weather patterns influence wind conditions off the U.S. 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.
Kelsey Shaler, Eliot Quon, Hristo Ivanov, and Jason Jonkman
Wind Energ. Sci., 9, 1451–1463, https://doi.org/10.5194/wes-9-1451-2024, https://doi.org/10.5194/wes-9-1451-2024, 2024
Short summary
Short summary
This paper presents a three-way verification and validation between an engineering-fidelity model, a high-fidelity model, and measured data for the wind farm structural response and wake dynamics during an evolving stable boundary layer of a small wind farm, generally with good agreement.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1123–1152, https://doi.org/10.5194/wes-9-1123-2024, https://doi.org/10.5194/wes-9-1123-2024, 2024
Short summary
Short summary
This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at modeling turbine–wake and wind farm–gravity wave interactions, as well as local and global blockage effects. Comparisons against large eddy simulations show that the MSC model offers a valid contribution towards advancing our understanding of the coupled wind farm–atmosphere interaction, helping refining power estimation methodologies for existing and future wind farm sites.
Lindsay M. Sheridan, Dmitry Duplyakin, Caleb Phillips, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, and Larry K. Berg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-37, https://doi.org/10.5194/wes-2024-37, 2024
Preprint under review for WES
Short summary
Short summary
Twelve months of onsite wind measurement is standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, however, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities for distributed wind projects. This study aims to answer the question of how low can you go in terms of the observational time period needed to make impactful improvements to long-term wind speed estimates.
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.
Raghavendra Krishnamurthy, Rob Newsom, Colleen Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna M. Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-29, https://doi.org/10.5194/wes-2024-29, 2024
Revised manuscript accepted for WES
Short summary
Short summary
The growth of wind farms in the central United States in the last decade has been staggering. This study looked at how wind farms affect the recovery of wind wakes – the disturbed air behind wind turbines. In places like the US Great Plains, phenomena such as low-level jets can form, changing how wind farms work. We studied how wind wakes recover under different weather conditions using real-world data, which is important for making wind energy more efficient and reliable.
Eliot Quon
Wind Energ. Sci., 9, 495–518, https://doi.org/10.5194/wes-9-495-2024, https://doi.org/10.5194/wes-9-495-2024, 2024
Short summary
Short summary
Engineering models used to design wind farms generally do not account for realistic atmospheric conditions that can rapidly evolve from minute to minute. This paper uses a first-principles simulation technique to predict the performance of five wind turbines during a wind farm control experiment. Challenges included limited observations and atypical conditions. The simulation accurately predicts the aerodynamics of a turbine when it is situated partially within the wake of an upstream turbine.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Regis Thedin, Garrett Barter, Jason Jonkman, Rafael Mudafort, Christopher J. Bay, Kelsey Shaler, and Jasper Kreeft
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-6, https://doi.org/10.5194/wes-2024-6, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This work investigates asymmetries in terms of power performance and fatigue loading on a 5-turbine wind farm subject to wake steering strategies. Both the yaw misalignment angle and the wind direction were varied from negative to positive. We highlight conditions in which fatigue loading is lower while still maintenance good power gains and show that partial wake is the source of the asymmetries observed. We provide recommendations in terms of yaw misalignment angles for a given wind direction.
Timothy W. Juliano, Fernando Szasdi-Bardales, Neil P. Lareau, Kasra Shamsaei, Branko Kosović, Negar Elhami-Khorasani, Eric P. James, and Hamed Ebrahimian
Nat. Hazards Earth Syst. Sci., 24, 47–52, https://doi.org/10.5194/nhess-24-47-2024, https://doi.org/10.5194/nhess-24-47-2024, 2024
Short summary
Short summary
Following the destructive Lahaina Fire in Hawaii, our team has modeled the wind and fire spread processes to understand the drivers of this devastating event. The simulation results show that extreme winds with high variability, a fire ignition close to the community, and construction characteristics led to continued fire spread in multiple directions. Our results suggest that available modeling capabilities can provide vital information to guide decision-making during wildfire events.
Tasnim Zaman, Timothy Juliano, Pat Hawbecker, and Marina Astitha
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-148, https://doi.org/10.5194/wes-2023-148, 2024
Preprint withdrawn
Short summary
Short summary
We suggest a model configuration to predict offshore wind speed and wind power density in the Northeast US. We focused on wind droughts, long periods of low wind speed that affect the reliability of wind power generation. We show that wind prediction depends primarily on the initial and boundary conditions, and that it is important to evaluate the connection of wind speed to wind power generation, to select the best model configuration.
Mari R. Tye, Ming Ge, Jadwiga H. Richter, Ethan D. Gutmann, Allyson Rugg, Cindy L. Bruyère, Sue Ellen Haupt, Flavio Lehner, Rachel McCrary, Andrew J. Newman, and Andrew Wood
EGUsphere, https://doi.org/10.5194/egusphere-2023-2326, https://doi.org/10.5194/egusphere-2023-2326, 2023
Short summary
Short summary
There is a perceived mismatch between the spatial scales that global climate models can produce data and that needed for water management decisions. However, poor communication of specific metrics relevant to local decisions is also a problem. We identified a potential set of water use decision metrics to assess their credibility in the Community Earth System Model v2 (CESM2). CESM2 can reliably reproduce many of these metrics and shows potential to support long-range water resource decisions.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
Short summary
Short summary
The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
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
Short summary
Short summary
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.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
Short summary
Short summary
This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
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
Short summary
Short summary
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.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
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
Short summary
Short summary
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.
Marcus Becker, Bastian Ritter, Bart Doekemeijer, Daan van der Hoek, Ulrich Konigorski, Dries Allaerts, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, https://doi.org/10.5194/wes-7-2163-2022, 2022
Short summary
Short summary
In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
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.
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
Short summary
Short summary
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.
John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, and David John Gagne
Atmos. Meas. Tech., 15, 5793–5819, https://doi.org/10.5194/amt-15-5793-2022, https://doi.org/10.5194/amt-15-5793-2022, 2022
Short summary
Short summary
We show promising results for a new machine-learning based paradigm for processing field-acquired cloud droplet holograms. The approach is fast, scalable, and leverages GPUs and other heterogeneous computing platforms. It combines applications of transfer and active learning by using synthetic data for training and a small set of hand-labeled data for refinement and validation. Artificial noise applied during synthetic training enables optimized models for real-world situations.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
Short summary
Short summary
Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
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
Short summary
Short summary
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.
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.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, Robert S. Arthur, and Domingo Muñoz-Esparza
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-57, https://doi.org/10.5194/wes-2021-57, 2021
Revised manuscript not accepted
Short summary
Short summary
Winds decelerate upstream of a wind plant as turbines obstruct and extract energy from the flow. This effect is known as wind plant blockage. We assess how atmospheric stability modifies the upstream wind plant blockage. We find stronger stability amplifies this effect. We also explore different approaches to quantifying blockage from field-like observations. We find different methodologies may induce errors of the same order of magnitude as the blockage-induced velocity deficits.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Alfredo Peña, Branko Kosović, and Jeffrey D. Mirocha
Wind Energ. Sci., 6, 645–661, https://doi.org/10.5194/wes-6-645-2021, https://doi.org/10.5194/wes-6-645-2021, 2021
Short summary
Short summary
We investigate the ability of a community-open weather model to simulate the turbulent atmosphere by comparison with measurements from a 250 m mast at a flat site in Denmark. We found that within three main atmospheric stability regimes, idealized simulations reproduce closely the characteristics of the observations with regards to the mean wind, direction, turbulent fluxes, and turbulence spectra. Our work provides foundation for the use of the weather model in multiscale real-time simulations.
Luis A. Martínez-Tossas, Jennifer King, Eliot Quon, Christopher J. Bay, Rafael Mudafort, Nicholas Hamilton, Michael F. Howland, and Paul A. Fleming
Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, https://doi.org/10.5194/wes-6-555-2021, 2021
Short summary
Short summary
In this paper a three-dimensional steady-state solver for flow through a wind farm is developed and validated. The computational cost of the solver is on the order of seconds for large wind farms. The model is validated using high-fidelity simulations and SCADA.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Martine G. de Vos, Wilco Hazeleger, Driss Bari, Jörg Behrens, Sofiane Bendoukha, Irene Garcia-Marti, Ronald van Haren, Sue Ellen Haupt, Rolf Hut, Fredrik Jansson, Andreas Mueller, Peter Neilley, Gijs van den Oord, Inti Pelupessy, Paolo Ruti, Martin G. Schultz, and Jeremy Walton
Geosci. Commun., 3, 191–201, https://doi.org/10.5194/gc-3-191-2020, https://doi.org/10.5194/gc-3-191-2020, 2020
Short summary
Short summary
At the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Open science offers manifold opportunities but goes beyond sharing code and data. Besides domain-specific technical challenges, we observed that the main challenges are non-technical and impact the system of science as a whole.
Timothy W. Juliano and Zachary J. Lebo
Atmos. Chem. Phys., 20, 7125–7138, https://doi.org/10.5194/acp-20-7125-2020, https://doi.org/10.5194/acp-20-7125-2020, 2020
Short summary
Short summary
In this study, we use a machine learning method to examine the relationship between synoptic-scale changes in the North Pacific High structure and maritime cloud properties. Our novel approach suggests that there is a wide range (>30 W m−2, ~20 % of magnitude) of possible shortwave cloud radiative effect that is a clear function of the circulation pattern. We hope that this work will help improve fundamental understanding of the sensitivity of the climate system to various warm-cloud regimes.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
Short summary
Short summary
Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Aditya Choukulkar, Larry K. Berg, Harindra J. S. Fernando, Eric P. Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, Mikhail Pekour, Yelena Pichugina, Mark T. Stoelinga, and David D. Turner
Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, https://doi.org/10.5194/gmd-12-4803-2019, 2019
Short summary
Short summary
During the second Wind Forecast Improvement Project, improvements to the parameterizations were applied to the High Resolution Rapid Refresh model and its nested version. The impacts of the new parameterizations on the forecast of 80 m wind speeds and power are assessed, using sodars and profiling lidars observations for comparison. Improvements are evaluated as a function of the model’s initialization time, forecast horizon, time of the day, season, site elevation, and meteorological phenomena.
Jennifer Annoni, Christopher Bay, Kathryn Johnson, Emiliano Dall'Anese, Eliot Quon, Travis Kemper, and Paul Fleming
Wind Energ. Sci., 4, 355–368, https://doi.org/10.5194/wes-4-355-2019, https://doi.org/10.5194/wes-4-355-2019, 2019
Short summary
Short summary
Typically, turbines do not share information with nearby turbines in a wind farm. Relying on a single turbine sensor on the back of a turbine nacelle can lead to large errors in yaw misalignment or excessive yawing due to noisy sensor measurements. The wind farm consensus control approach in this paper shows the benefits of sharing information between nearby turbines by computing a robust estimate of the wind direction using noisy sensor information from these neighboring turbines.
Tyler C. McCandless and Sue Ellen Haupt
Wind Energ. Sci., 4, 343–353, https://doi.org/10.5194/wes-4-343-2019, https://doi.org/10.5194/wes-4-343-2019, 2019
Short summary
Short summary
Often in wind power forecasting the mean wind speed is forecasted at a plant, converted to power, and multiplied by the number of turbines to predict the plant's generating capacity. This methodology ignores the variability among turbines caused by localized weather, terrain, and array orientation. We show that the wind farm mean wind speed approach for power conversion is impacted by Jensen's inequality, quantify the differences, and show machine learning can overcome these differences.
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, https://doi.org/10.5194/acp-19-4367-2019, 2019
Short summary
Short summary
To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather prediction models, we have assessed its temporal and spatial variability at various scales in the Columbia River Gorge during the WFIP2 field experiment. The turbulence dissipation rate shows large spatial variability, even at the microscale, with larger values in sites located downwind of complex orographic structures or in wind farm wakes. Distinct diurnal and seasonal cycles in ε have also been found.
Luis A. Martínez-Tossas, Jennifer Annoni, Paul A. Fleming, and Matthew J. Churchfield
Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, https://doi.org/10.5194/wes-4-127-2019, 2019
Short summary
Short summary
A new control-oriented model is developed to compute the wake of a wind turbine under yaw. The model uses a simplified version of the Navier–Stokes equation with assumptions. Good agreement is found between the model-proposed and large eddy simulations of a wind turbine in yaw.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
Short summary
Short summary
Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
Short summary
Short summary
This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, https://doi.org/10.5194/wes-3-243-2018, 2018
Short summary
Short summary
This paper investigates the role of flow structures in wind farm control through yaw misalignment. A pair of counter-rotating vortices is shown to be important in deforming the shape of the wake. Further, we demonstrate that the vortex structures created in wake steering can enable a greater change power generation than currently modeled in control-oriented models. We propose that wind farm controllers can be made more effective if designed to take advantage of these effects.
Louis Marelle, Jean-Christophe Raut, Kathy S. Law, Larry K. Berg, Jerome D. Fast, Richard C. Easter, Manish Shrivastava, and Jennie L. Thomas
Geosci. Model Dev., 10, 3661–3677, https://doi.org/10.5194/gmd-10-3661-2017, https://doi.org/10.5194/gmd-10-3661-2017, 2017
Short summary
Short summary
We develop the WRF-Chem 3.5.1 model to improve simulations of aerosols and ozone in the Arctic. Both species are important air pollutants and climate forcers, but models often struggle to reproduce observations in the Arctic. Our developments concern pollutant emissions, mixing, chemistry, and removal, including processes related to snow and sea ice. The effect of these changes are quantitatively validated against observations, showing significant improvements compared to the original model.
Jean-Christophe Raut, Louis Marelle, Jerome D. Fast, Jennie L. Thomas, Bernadett Weinzierl, Katharine S. Law, Larry K. Berg, Anke Roiger, Richard C. Easter, Katharina Heimerl, Tatsuo Onishi, Julien Delanoë, and Hans Schlager
Atmos. Chem. Phys., 17, 10969–10995, https://doi.org/10.5194/acp-17-10969-2017, https://doi.org/10.5194/acp-17-10969-2017, 2017
Short summary
Short summary
We study the cross-polar transport of plumes from Siberian fires to the Arctic in summer, both in terms of transport pathways and efficiency of deposition processes. Those plumes containing soot may originate from anthropogenic and biomass burning sources in mid-latitude regions and may impact the Arctic climate by depositing on snow and ice surfaces. We evaluate the role of the respective source contributions, investigate the transport of plumes and treat pathway-dependent removal of particles.
Javier Sanz Rodrigo, Matthew Churchfield, and Branko Kosovic
Wind Energ. Sci., 2, 35–54, https://doi.org/10.5194/wes-2-35-2017, https://doi.org/10.5194/wes-2-35-2017, 2017
Short summary
Short summary
The series of GABLS model intercomparison benchmarks is revisited in the context of wind energy atmospheric boundary layer (ABL) models. GABLS 1 and 2 are used for verification purposes. Then GABLS 3 is used to develop a methodology for using realistic mesoscale forcing for microscale ABL models. The method also uses profile nudging to dynamically reduce the bias. Different data assimilation strategies are discussed based on typical instrumentation setups of wind energy campaigns.
Mithu Debnath, G. Valerio Iungo, Ryan Ashton, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Julie K. Lundquist, William J. Shaw, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, https://doi.org/10.5194/amt-10-431-2017, 2017
Short summary
Short summary
Triple RHI scans were performed with three simultaneous scanning Doppler wind lidars and assessed with lidar profiler and sonic anemometer data. This test is part of the XPIA experiment. The scan strategy consists in two lidars performing co-planar RHI scans, while a third lidar measures the transversal velocity component. The results show that horizontal velocity and wind direction are measured with good accuracy, while the vertical velocity is typically measured with a significant error.
Ivan Ortega, Sean Coburn, Larry K. Berg, Kathy Lantz, Joseph Michalsky, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, and Rainer Volkamer
Atmos. Meas. Tech., 9, 3893–3910, https://doi.org/10.5194/amt-9-3893-2016, https://doi.org/10.5194/amt-9-3893-2016, 2016
Short summary
Short summary
We present an inherently calibrated retrieval to measure aerosol optical depth (AOD) and the aerosol phase function parameter, g, based on measurements of azimuth distributions of the Raman scattering probability (RSP), the near-absolute rotational Raman scattering (RRS) intensity by the University of Colorado two-dimensional (2-D) MAX-DOAS. The retrievals are maximally sensitive at low AOD and do not require absolute radiance calibration. We compare results with data from independent sensors.
Chun Zhao, Maoyi Huang, Jerome D. Fast, Larry K. Berg, Yun Qian, Alex Guenther, Dasa Gu, Manish Shrivastava, Ying Liu, Stacy Walters, Gabriele Pfister, Jiming Jin, John E. Shilling, and Carsten Warneke
Geosci. Model Dev., 9, 1959–1976, https://doi.org/10.5194/gmd-9-1959-2016, https://doi.org/10.5194/gmd-9-1959-2016, 2016
Short summary
Short summary
In this study, the latest version of MEGAN is coupled within CLM4 in WRF-Chem. In this implementation, MEGAN shares a consistent vegetation map with CLM4. This improved modeling framework is used to investigate the impact of two land surface schemes on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models.
L. K. Berg, M. Shrivastava, R. C. Easter, J. D. Fast, E. G. Chapman, Y. Liu, and R. A. Ferrare
Geosci. Model Dev., 8, 409–429, https://doi.org/10.5194/gmd-8-409-2015, https://doi.org/10.5194/gmd-8-409-2015, 2015
Short summary
Short summary
This work presents a new methodology for representing regional-scale impacts of cloud processing on both aerosol and trace gases in sub-grid-scale convective clouds. Using the new methodology, we can better simulate the aerosol lifecycle over large areas. The results presented in this work highlight the potential change in column-integrated amounts of black carbon, organic aerosol, and sulfate aerosol, which were found to range from -50% for black carbon to +40% for sulfate.
J. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton
Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, https://doi.org/10.5194/amt-8-907-2015, 2015
Short summary
Short summary
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications like wind energy, but their use often relies on assuming homogeneity in the wind. Using numerical simulations of stable flow past a wind turbine, we quantify the error expected because of the inhomogeneity of the flow. Large errors (30%) in winds are found near the wind turbine, but by three rotor diameters downwind, errors in the horizontal components have decreased to 15% of the inflow.
D. Müller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid
Atmos. Meas. Tech., 7, 3487–3496, https://doi.org/10.5194/amt-7-3487-2014, https://doi.org/10.5194/amt-7-3487-2014, 2014
E. Kassianov, J. Barnard, M. Pekour, L. K. Berg, J. Shilling, C. Flynn, F. Mei, and A. Jefferson
Atmos. Meas. Tech., 7, 3247–3261, https://doi.org/10.5194/amt-7-3247-2014, https://doi.org/10.5194/amt-7-3247-2014, 2014
A. J. Scarino, M. D. Obland, J. D. Fast, S. P. Burton, R. A. Ferrare, C. A. Hostetler, L. K. Berg, B. Lefer, C. Haman, J. W. Hair, R. R. Rogers, C. Butler, A. L. Cook, and D. B. Harper
Atmos. Chem. Phys., 14, 5547–5560, https://doi.org/10.5194/acp-14-5547-2014, https://doi.org/10.5194/acp-14-5547-2014, 2014
G. G. Palancar, B. L. Lefer, S. R. Hall, W. J. Shaw, C. A. Corr, S. C. Herndon, J. R. Slusser, and S. Madronich
Atmos. Chem. Phys., 13, 1011–1022, https://doi.org/10.5194/acp-13-1011-2013, https://doi.org/10.5194/acp-13-1011-2013, 2013
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
Mesoscale weather systems and associated potential wind power variations in a midlatitude sea strait (Kattegat)
A large-eddy simulation (LES) model for wind-farm-induced atmospheric gravity wave effects inside conventionally neutral boundary layers
Simulating low-frequency wind fluctuations
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production
Improving Wind and Power Predictions via Four-Dimensional Data Assimilation in the WRF Model: Case Study of Storms in February 2022 at Belgian Offshore Wind Farms
Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions
Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers
Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
Lifetime prediction of turbine blades using global precipitation products from satellites
Evaluation of low-level jets in the southern Baltic Sea: a comparison between ship-based lidar observational data and numerical models
Predicting power ramps from joint distributions of future wind speeds
Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
Research challenges and needs for the deployment of wind energy in hilly and mountainous regions
Observer-based power forecast of individual and aggregated offshore wind turbines
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
Jonathan Minz, Axel Kleidon, and Nsilulu T. Mbungu
Wind Energ. Sci., 9, 2147–2169, https://doi.org/10.5194/wes-9-2147-2024, https://doi.org/10.5194/wes-9-2147-2024, 2024
Short summary
Short summary
Estimates of power output from regional wind turbine deployments in energy scenarios assume that the impact of the atmospheric feedback on them is minimal. But numerical models show that the impact is large at the proposed scales of future deployment. We show that this impact can be captured by accounting only for the kinetic energy removed by turbines from the atmosphere. This can be easily applied to energy scenarios and leads to more physically representative estimates.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 1695–1711, https://doi.org/10.5194/wes-9-1695-2024, https://doi.org/10.5194/wes-9-1695-2024, 2024
Short summary
Short summary
In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, https://doi.org/10.5194/wes-9-1647-2024, 2024
Short summary
Short summary
We introduce a novel way to model the impact of atmospheric gravity waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the interaction of wind farms with the free atmosphere, ultimately facilitating cost-effective research.
Abdul Haseeb Syed and Jakob Mann
Wind Energ. Sci., 9, 1381–1391, https://doi.org/10.5194/wes-9-1381-2024, https://doi.org/10.5194/wes-9-1381-2024, 2024
Short summary
Short summary
Wind flow consists of swirling patterns of air called eddies, some as big as many kilometers across, while others are as small as just a few meters. This paper introduces a method to simulate these large swirling patterns on a flat grid. Using these simulations we can better figure out how these large eddies affect big wind turbines in terms of loads and forces.
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
Short summary
Short summary
Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before landfall in a mesoscale model. The simulated wind speeds and storm structure are sensitive to the boundary parametrization. However, independent of the boundary layer parametrization, the median change in wind speed and wind direction with height is small relative to wind turbine design standards. Strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1123–1152, https://doi.org/10.5194/wes-9-1123-2024, https://doi.org/10.5194/wes-9-1123-2024, 2024
Short summary
Short summary
This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at modeling turbine–wake and wind farm–gravity wave interactions, as well as local and global blockage effects. Comparisons against large eddy simulations show that the MSC model offers a valid contribution towards advancing our understanding of the coupled wind farm–atmosphere interaction, helping refining power estimation methodologies for existing and future wind farm sites.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
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.
Tsvetelina Ivanova, Sara Porchetta, Sophia Buckingham, Jeroen van Beeck, and Wim Munters
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-177, https://doi.org/10.5194/wes-2023-177, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This study explores how wind and power predictions can be improved by introducing local forcing of measurement data in a numerical weather model, while taking into account the presence of neighboring wind farms. Practical implications for the wind energy industry include insights for informed offshore wind farm planning and decision-making strategies using open-source models, even under adverse weather conditions.
Mohammad Golam Mostafa Khan and Mohammed Rafiuddin Ahmed
Wind Energ. Sci., 8, 1277–1298, https://doi.org/10.5194/wes-8-1277-2023, https://doi.org/10.5194/wes-8-1277-2023, 2023
Short summary
Short summary
A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the tropical region from 1° N to 21° S revealed that the three-parameter (3-p) Weibull distribution with a non-zero shift parameter is a better fit for wind data that have a higher percentage of low wind speeds. Wind data with higher wind speeds are a special case of the 3-p distribution. This approach gives accurate results.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
Short summary
Short summary
The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
Short summary
Short summary
As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
Short summary
Short summary
When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
Short summary
Short summary
A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
Wind Energ. Sci., 7, 2393–2405, https://doi.org/10.5194/wes-7-2393-2022, https://doi.org/10.5194/wes-7-2393-2022, 2022
Short summary
Short summary
The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
Short summary
Short summary
This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Andrew Clifton, Sarah Barber, Alexander Stökl, Helmut Frank, and Timo Karlsson
Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, https://doi.org/10.5194/wes-7-2231-2022, 2022
Short summary
Short summary
The transition to low-carbon sources of energy means that wind turbines will need to be built in hilly or mountainous regions or in places affected by icing. These locations are called
complexand are hard to develop. This paper sets out the research and development (R&D) needed to make it easier and cheaper to harness wind energy there. This includes collaborative R&D facilities, improved wind and weather models, frameworks for sharing data, and a clear definition of site complexity.
Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
Short summary
Short summary
Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
Short summary
Short summary
The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
Cited articles
Adler, B., Wilczak, J. W., Bianco, L., Djalalova, I., Duncan Jr., J. B., and
Turner, D.: Observational case study of a persistent cold pool and gap flow
in the Columbia River basin, J. Appl. Meteorol. Clim., 60, 1071–1090,
https://doi.org/10.1175/JAMC-D-21-0013.1, 2021.
Allaerts, D., Quon, E., Draxl, C., and Churchfield, M.: Development of a
Time-Height Profile Assimilation Technique for Large-Eddy Simulation,
Bound.-Lay. Meteorol., 176, 329–348,
https://doi.org/10.1007/s10546-020-00538-5, 2020.
Allaerts, D., Quon, E., and Churchfield, M.: Using observational mean-flow
data to drive large-eddy simulations of a diurnal cycle at the SWiFT site,
Wind Energ., 126, 469–492, https://doi.org/10.1002/we.2811, 2023.
Arthur, R. S., Mirocha, J. D., Lundquist, K. A., and Street, R. L.: Using a
canopy model framework to improve large-eddy simulations of the atmospheric
boundary layer in the Weather Research and Forecasting model, Mon. Weather
Rev., 147, 31–52, https://doi.org/10.1175/MWR-D-18-0204.1, 2019.
Arthur, R. S., Mirocha, J. D., Marjanovic, N., Hirth, B. D, Schroeder, J.
L., Wharton, S., and Chow, F. K.: Multi-scale simulation of wind farm
performance during a frontal passage, Atmosphere, 11, 245,
https://doi.org/10.3390/atmos11030245, 2020.
Arthur, R. S., Juliano, T. W., Adler, B., Krishnamurthy, R., Lundquist, J.
K., Kosović, B., and Jiménez, P. A.: Improved representation of
horizontal variability and turbulence in mesoscale simulations of an
extended cold-air pool event,
J. Appl. Meteorol. Clim., 61, 685–707, https://doi.org/10.1175/JAMC-D-21-0138.1,
2022.
Berg, L. K., Liu, Y., Yang, B., Qian, Y., Olson, J., Ma, P.-L., and Hou, Z.:
Sensitivity of turbine-height wind speeds to parameters in the planetary
boundary-layer parametrization used in the Weather Research and Forecasting
model: Extension to wintertime conditions, Bound.-Lay. Meteorol., 170,
507–518, https://doi.org/10.1007/s10546-018-0406-y, 2019.
Berkooz, G., Holmes, P., and Lumley, J. L.: The proper orthogonal
decomposition in the analysis of turbulent flows, Annu. Rev. Fluid Mech.,
25, 539–575, https://doi.org/10.1146/annurev.fl.25.010193.002543, 1993.
Bosveld, F. C., Baas, P., van Meijgaard, E., de Bruijn, E. I.
F., Steeneveld, G.-J., and Holtslag, A. A. M.: The Third GABLS Intercomparison
Case for Evaluation Studies of Boundary-Layer Models. Part A: Case Selection
and Set-Up, Bound.-Lay. Meteorol., 152, 133–156,
https://doi.org/10.1007/s10546-014-9919-1, 2014.
Bou-Zeid, E., Meneveau, C., and Parlange, M. B.: A scale-dependent Lagrangian
dynamic model for large eddy simulation of complex turbulent flows, Phys.
Fluids, 17, 025105, https://doi.org/10.1063/1.1839152, 2005.
Brasseur, J. G. and Wie, T: Designing large-eddy simulation of the
turbulent boundary layer to capture law-of-the-wall scaling, Phys. Fluids,
22, 021303, https://doi.org/10.1063/1.3319073, 2010.
Brown, A. R., Hobson, J. M., and Wood, N.: Large-eddy simulation of neutral
turbulent flow over rough sinusoidal ridges, Bound.-Lay. Meteorol., 98,
411–441, https://doi.org/10.1023/A:1018703209408, 2001.
Canada Meteorological Center (CMC): GHRSST Level 4 CMC0.1deg Global Foundation Sea
Surface Temperature Analysis (GDS version 2), Canada Meteorological Center [data set],
https://doi.org/10.5067/GHCMC-4FM03, 2017.
Ching, J., Rotunno, R., LeMone, M., Martilli, A., Kosović, B.,
Jiménez, P. A., and Dudhia, J.: Convectively induced secondary
circulations in fine-grid mesoscale numerical weather prediction models,
Mon. Weather Rev., 142, 3284–3302, https://doi.org/10.1175/MWR-D-13-00318.1,
2014.
Chow, F. K., Street, R. L., Xue, M., and Ferziger, J. H.: Explicit filtering
and reconstruction turbulence modeling for large-eddy simulation of neutral
boundary layer flow, J. Atmos. Sci., 62, 2058–2077,
https://doi.org/10.1175/JAS3456.1, 2004.
Churchfield, M. J., Lee, S., Moriarty, P. J., Martínez, L. A., Leonardi, S., Vijayakumar, G., and Brasseur, J. G.: A large-eddy simulation of wind-plant aerodynamics, 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 9–12 January 2012,
Nashville, Tennessee, https://doi.org/10.2514/6.2012-537, 2012.
Debnath, M., Doubrawa, P., Optis, M., Hawbecker, P., and Bodini, N.: Extreme wind shear events in US offshore wind energy areas and the role of induced stratification, Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, 2021.
Dettling, S., Brummet, T., Gagne, D. J., Kosovic, B., and Haupt, S. E.:
Downscaling from Mesoscale to Microscale in Complex Terrain using a
Generative Adversarial Network, in preparation, 2023.
DOE: The Wind Data Hub, A2EDAP (Atmosphere to Electrons (A2e), Data Archive and Portal [data set], https://a2e.energy.gov/data#ProjectFilter=["wfip2"] (last access: 11 August 2023), 2023.
Draxl, C., Allaerts, D., Quon, E., and Churchfield, M.: Coupling Mesoscale
Budget Components to Large-Eddy Simulations for Wind-Energy Applications,
Bound.-Lay. Meteorol., 179, 73–98,
https://doi.org/10.1007/s10546-020-00584-z, 2021.
Eghdami, M., Barros, A. P., Jiménez, P. A., Juliano, T. W., and Kosovic,
B.: Diagnosis of Second-Order Turbulent Properties of the Surface Layer for
Three-Dimensional Flow Based on the Mellor–Yamada Model, Mon. Weather
Rev., 150, 1003–1021, https://doi.org/10.1175/MWR-D-21-0101.1, 2022.
Gill, D., Dudhia, J., Wang, W., Peckham, S., Bresch, J., Kavulich, M., Black, T., Carson, L., Zhang, X., Werner, K., Hawbecker, P., Huang, W., Manning, K., Duda, M., Walters, S., Zhiquan, J., Jha, P., Juliano, T. Guerrette, J. J., Jimenez, P., and Munoz-Esparza, D.: MMC-WRF, a2e-mmc/WRF: End of A2e MMC Project
(v4.3), Zenodo [code], https://doi.org/10.5281/zenodo.7765891, 2023.
Gopalan, H., Gundling, C., Brown, K., Roget, B. Sitaraman, J., Mirocha, J. D.,
and Miller, W. O.: A Coupled Mesoscale-Microscale Framework for Wind
Resource Estimation and Farm Aerodynamics, J. Wind Eng. Ind. Aerodyn., 132,
13–26, https://doi.org/10.1016/j.jweia.2014.06.001, 2014.
Haupt, S. E., Kosovic, B., Shaw, W., Berg, L., Churchfield, M., Cline, J., Draxl, C., Ennis, B., Koo, E., Kotamarthi, R., Mazzaro, L., Mirocha, J., Moriarty, P., Munoz-Esparza, D., Quon, E., Rai, R. K., Robinson, M., and Sever, G.: On
Bridging a Modeling Scale Gap: Mesoscale to Microscale Coupling for Wind
Energy, B. Am. Meteorol. Soc., 100,
2533–2549, https://doi.org/10.1175/BAMS-D-18-0033.1,
2019a.
Haupt, S. E., Allaerts, D., Berg, L., Churchfield, M., DeCastro, A., Draxl,
C., Gagne, D. J., Hawbecker, P., Jimenez, P., Jonko, A.,. Juliano, T., Kaul,
C., Kosovic, B., McCandless, T., Mirocha, J., Munoz-Esparza, D., Quon, E.,
Rai, R., Sauer, J., and Shaw, W.: FY19 Report of the Atmosphere to Electrons
Mesoscale to Microscale Coupling Project: Pacific Northwest Laboratory
Report PNNL-29603, 127 pp., https://doi.org/10.2172/1735568, 2019b.
Haupt, S. E., Arthur, R., Berg, L., Churchfield, M., DeCastro, A., Dettling,
S., Draxl, C., Gagne, D. J., Hawbecker, P., Jimenez, P., Jonko, A., Juliano,
T., Kaul, C., Kosovic, B., Lassman, Kumar, M., McCandless, T. C., Mirocha,
J., Quon, E., Rai, R., Shaw, W., and Thedin, R.: FY20 Report of the Atmosphere
to Electrons Land-Based Mesoscale to Microscale Coupling Project: Pacific
Northwest Laboratory Report PNNL-30841, 104 pp., https://www.osti.gov/servlets/purl/1762812 (last access: 6 August 2023), 2020.
Hawbecker, P. and Churchfield, M.: Evaluating Terrain as a Turbulence
Generation Method, Energies, 14, 6858,
https://doi.org/10.3390/en14216858, 2021.
Hawbecker, P., Lassman, W., Juliano, T. W., Kosivic, B., and Haupt, S. E.: Model
sensitivity across scales, in preparation, 2023a.
Hawbecker, P., Quon, E., Jha, P., Sauer, J., Rai, R., Juliano, T., and Lassman,
W.: WRF Setups, a2e-mmc/WRF-setups: End of A2e MMC Project (v1.0),
Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.7766133, 2023b.
Initiative for Energy Justice: https://iejusa.org/, last access: 30 November
2022.
Jayaraman, B., Quon, E., Li, J., and Chatterjee, T.: Structure of offshore
low-level jet turbulence and implications to mesoscale-to-microscale
coupling, Journal of Phyasics: Conference Series, The Scientce of Making
Torque from Wind (TORQUE 2022), 2265, 022064,
https://doi.org/10.1088/1742-6596/2265/2/022, 2022.
Jiménez, P. A. and Dudhia, J.: On the need to modify the sea surface
roughness formulation over shallow waters, J. Appl. Meteorol. Climatol., 57, 1101–1110, 2018.
Jonkman, B. J.: TurbSim user's guide, No. NREL/TP-500-39797, National
Renewable Energy Lab (NREL), Golden, CO (United States), 2006.
Juliano, T. W., Kosović, B., Jiménez, P. A., Eghdami, M., Haupt, S.
E., and Martilli, A.: Gray zone simulations using a three-dimensional
planetary boundary layer parameterization in the Weather Research and
Forecasting model, Mon. Weather Rev., 150, 1585–1619, https://doi.org/10.1175/MWR-D-21-0164.1, 2022.
Kaul, C. M., Ananthan, S., Churchfield, M. J., Mirocha, J. D., Berg, L. K., and Rai, R.: Large-eddy simulations of idealized atmospheric boundary layers using Nalu-Wind, J. Phys. Conf. Ser., 1452, 012078,
https://doi.org/10.1088/1742-6596/1452/1/012078, 2020.
Kaul, C. M., Hou, Z. J., Zhou, H., Rai, R. K., and Berg, L. K.:.
Sensitivity analysis of wind and turbulence predictions with
mesoscale-coupled large eddy simulations using ensemble machine learning,
J. Geophys. Res.-Atmos., 127, e2022JD037150,
https://doi.org/10.1029/2022JD037150, 2022.
Kelley, C. L. and Ennis, B. L.: SWiFT site atmospheric characterization (No.
SAND2016-0216), Sandia National Laboratories, Albuquerque, NM,
https://doi.org/10.2172/1237403, 2016.
Kelley, N. D.: Turbulence-Turbine Interaction: The Basis for the
Development of the TurbSim Stochastic Simulator, NREL/TP-5000-52353,
https://doi.org/10.2172/1031981, 2011.
Khani, S. and Porté-Agel, F.: A modulated-gradient parametrization for
the large eddy simulation of the atmospheric boundary layer using the
Weather Research and Forecasting model, Bound.-Lay. Meteorol., 165,
385–404, 2017.
Kirkil, G., Mirocha, J. D., Bou-Zeid, E., Chow, F. K., and Kosović, B.:
Implementation and Evaluation of Dynamic Subfilter-Scale Stress Models for
Large-Eddy Simulation using WRF, Mon. Weather Rev., 140, 266–284,
https://doi.org/10.1175/MWR-D-11-00037.1, 2012.
Kosović, B., Munoz, P. J., Juliano, T. W., Martilli, A., Eghdami, M.,
Barros, A. P., and Haupt, S. E.: Three-dimensional planetary boundary layer
parameterization for high-resolution mesoscale simulations, Journal of
Physics: Conference Series, IOP Publishing, 1452, 012080, https://doi.org/10.1088/1742-6596/1452/1/012080,
2020.
Kosović, B., Jimenez, P. A., Juliano, T. W., Eghdami, M., and Haupt, S.
E.: Analysis of Horizontal Shear and Mixing at Gray Zone Length Scales Using
Filtered Large-Eddy Simulation of a Flow over Complex Terrain, in: 101st
American Meteorological Society Annual Meeting, AMS, https://ui.adsabs.harvard.edu/abs/2020AGUFMGC0590002K/abstract (last access: 6 August 2023), 2021.
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A.,
Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W.: Photo-Realistic
Single Image Super-Resolution Using a Generative Adversarial Network, arXiv [preprint],
https://doi.org/10.48550/arXiv.1609.04802, 2017.
Liu, Y., Warner, T., Vincent, C. L., Wu, W., Mahoney, W., Swerdlin, S.,
Parks, K., and Boehnert, J.: Simultaneous nested modeling from the synoptic
scale to the LES scale for wind energy applications, J. Wind Eng. Ind.
Aerodyn., 99, 308–319, 2011.
Mann, J.: Wind field simulation, Probabilist. Eng. Mech., 13.4, 269–282, 1998.
Mason, P. J. and Thomson, D. J.,: Stochastic backscatter in large-eddy
simulations of boundary layers, J. Fluid Mech., 242, 51–78, 1992.
Mazzaro, L. J., Koo, E., Muñoz-Esparza, D., Lundquist, J. K., and Linn, R.
R.: Random Force Perturbations: A New Extension of the Cell Perturbation
Method for Turbulence Generation in Multiscale Atmospheric Boundary Layer
Simulations, J. Adv. Model. Earth Syst., 11, 2311–2329,
https://doi.org/10.1029/2019MS001608, 2019.
McCandless, T., Gagne, D. J., Kosović, B., Haupt, S. E., Yang, B.,
Becker, C., and Schreck, J.: Machine Learning for Improving
Surface-Layer-Flux Estimates, Bound.-Lay. Meteorol.,
https://doi.org/10.1007/s10546-022-00727-4, 2022.
Mellor, G. L.: Analytic prediction of the properties of stratified
planetary surface layers, J. Atmos. Sci., 30, 1061–1069, 1973.
Mellor, G. L. and Yamada, T.: A hierarchy of turbulence closure models for
planetary boundary layers, J. Atmos. Sci., 31, 1791–1806, 1974.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for
geophysical fluid problems, Rev. Geophys., 20, 851–875, 1982.
Mesoscale-to-Microscale Coupling: MMC Project code and workflow descriptions,
https://mmc.readthedocs.io/en/latest/, last access: 14 March 2023.
Mirocha, J. D., Lundquist, J. K., and Kosović, B.: Implementation of a
nonlinear subfilter turbulence stress model for large-eddy simulation in the
Advanced Research WRF Model, Mon. Weather Rev., 138, 4212–4228,
https://doi.org/10.1175/2010MWR3286.1, 2010.
Mirocha, J. D., Kirkil, G., Bou-Zeid, E., Chow, F. K., and Kosović, B.:
Transition and equilibration of neutral atmospheric boundary layer flow in
one-way nested large-eddy simulations using the Weather Research and
Forecasting model, Mon. Weather Rev., 141, 918–940,
https://doi.org/10.1175/MWR-D-11-00263.1, 2013.
Mirocha, J. D., Kosović, B., Aitken, M. L., and Lundquist, J. K.:
Implementation of a generalized actuator disk wind turbine model into the
weather research and forecasting model for large-eddy simulation
applications, J. Renew. Sustain. Energy, 6, 013104,
https://doi.org/10.1063/1.4861061, 2014a.
Mirocha, J. D., Kosović, B., and Kirkil, G.: Resolved turbulence
characteristics in large-eddy simulations nested within mesoscale
simulations using the Weather Research and Forecasting model, Mon. Weather
Rev., 142, 806–831, https://doi.org/10.1175/MWR-D-13-00064.1, 2014b.
Monin, A. S. and Obukhov, A. M. F.: Basic laws of turbulent mixing in the
surface layer of the atmosphere, Tr. Geofiz. Inst., Akad. Nauk SSSR, 24,
163–187, 1954.
Muñoz-Esparza, D. and Kosovic, B.: Generation of inflow turbulence in
large-eddy simulations of nonneutral atmospheric boundary layers with the
cell perturbation method, Mon. Weather Rev., 146, 1889–1909,
https://doi.org/10.1175/MWR-D-18-0077.1, 2018.
Muñoz-Esparza, D., Kosović, B., Mirocha, J. D., and van Beek, J.:
Bridging the transition from mesoscales to microscale turbulence in
atmospheric models, Bound.-Lay. Meteorol., 153, 409–440,
https://doi.org/10.1007/s10546-014-9956-9, 2014.
Muñoz-Esparza, D., Kosović, B., van Beek, J., and Mirocha, J. D.: A
stochastic perturbation method to generate inflow turbulence in large-eddy
simulation models: application to neutrally stratified atmospheric boundary
layers, Phys. Fluids, 27, 035102, https://doi.org/10.1063/1.4913572, 2015.
Muñoz-Esparza, D., Becker, C., Sauer, J. A., Gagne II, D. J., Schreck, J.,
and Kosović, B.: On the application of an observations-based machine
learning parameterization of surface layer fluxes within an atmospheric
large-eddy simulation model, J. Geophys. Res., 127, e2021JD036214,
https://doi.org/10.1029/2021jd036214, 2022.
Nakanishi, M. and Niino, H.: An improved mellor–yamada level 3 model: its
numerical stability and application to a regional prediction of advecting
fog, Bound. Lay. Meteor., 119, 397–407, 2006.
NASA Jet Propulsion Laboratory: GHRSST Level 4 MUR Global Foundation Sea
Surface Temperature Analysis (v4.1), NASA [data set], https://doi.org/10.5067/GHGMR-4FJ04,
2015.
NASA Jet Propulsion Laboratory: GHRSST Level 4 K10_SST Global
10 km Analyzed Sea Surface Temperature from Naval Oceanographic Office
(NAVO) in GDS2.0, NASA [data set], https://doi.org/10.5067/GHK10-L4N01, 2018.
NOAA/NESDIS/STAR: GHRSST NOAA/STAR GOES-16 ABI L3C America Region SST. Ver.
2.70, NOAA [data set], https://doi.org/10.5067/GHG16-3UO27, 2019.
OpenFAST: openfast, GitHub [code], https://github.com/OpenFAST/openfast (last access: 6 August 2023), 2022.
OSPO: GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis
(GDS version 2), OSPO [data set], https://doi.org/10.5067/GHGPB-4FO02, 2015.
Patton, E. G. and Finnigan, J. J.: Canopy turbulence, Handbook of
environmental fluid
706 dynamics, edited by: Fernando, H. J. S., Vol. 1, CRC Press, Chap. 24, 311–328,
2012.
Quon, E., Hawbecker, P., Sauer, J., Thedin, R., Lassman, W., Allaerts, D.,
and Churchfield, M.: Assessment tools, a2e-mmc/assessment: End of A2e MMC
Project (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7768670, 2023a.
Quon, E., Hawbecker, P., Sauer, J., Thedin, R., Lassman, W., Allaerts, D., and
DeCastro, A.: Python Utilities, a2e-mmc/mmctools: End of A2e MMC Project
(v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7768674, 2023b.
Quon, E., Thedin, R., and Allaerts, D.: SOWFA Setups, a2e-mmc/SOWFA-setups: End
of A2e MMC Project (v1.0.0), Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.7764348,
2023c.
Quon, E. W.: Measurement-Driven Large-Eddy Simulations of a Wind Turbine
Array during a Wake Steering Field Campaign, in preparation, 2023.
Rai, R. K., Berg, L. K., Kosovic, B., Mirocha, J. D., Pekour, M. S., and Shaw,
W. J.: Comparison of measured and numerically simulated turbulence
statistics in a convective boundary layer over complex terrain,
Bound.-Lay. Meteorol., 163, 69–98, 2017.
Rai, R. K., Berg, L. K., Kosovic, B., Haupt, S. E., Mirocha, J. D.,
Ennis, B., and Draxl, C.: Evaluation of the Impact of Horizontal Grid
Spacing in Terra Incognita on Coupled Mesoscale-microscale Simulations using
the WRF Framework, Mon. Weather Rev., 147, 1007–1027,
https://doi.org/10.1175/MWR-D-18-0282.1, 2019.
Rinker, J. M.: PyConTurb: an open-source constrained turbulence generator,
Journal of Physics: Conference Series, 1037, 06032,
https://doi.org/10.1088/1742-6596/1037/6/062032, 2018.
Rybchuk, A., Juliano, T. W., Lundquist, J. K., Rosencrans, D., Bodini, N., and Optis, M.: The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme, Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, 2022.
Sandia National Laboratories: Scaled Wind Farm Technology Facility (SWiFT), https://tours.sandia.gov/swift_info.html, last access: 24 July 2023.
Sanz Rodrigo, J., Churchfield, M., and Kosovic, B.: A methodology for the design and testing of atmospheric boundary layer models for wind energy applications, Wind Energ. Sci., 2, 35–54, https://doi.org/10.5194/wes-2-35-2017, 2017a.
Sanz Rodrigo, J., Allaerts, D., Avila, M., Barcons, J., Cavar, D., Chavez Arroyo, R. A., Churchfield, M., Kosovic, B., Lundquist, J. K., and Meyers, J.: Results of the GABLS3 diurnal-cycle benchmark
for wind energy applications, J. Phys.-Conf. Ser., 854, 012037, https://doi.org/10.1088/1742-6596/854/1/012037, 2017b.
Sanz Rodrigo, J.: Assessment of meso-micro offline coupling methodology based
on driving CFDWind single-column-model with WRF tendencies: the GABLS3
diurnal cycle case, Zenodo [code], https://doi.org/10.5281/zenodo.834355,
2017c.
Sanz Rodrigo, J., Santos, P., Chávez-Arroyo, R., Avila, M., Cavar, D.,
Lehmkuhl, O., Owen, H., Li, R., and Tromeur, E.: `The ALEX17 Diurnal Cycles
in Complex Terrain Benchmark, Journal of Physics Conference Series,
1934, 012002, https://doi.org/10.1088/1742-6596/1934/1/012002, 2021.
Shaw, R. H. and Patton, E. G.: Canopy element influences on resolved-and
subgrid-scale
716 energy within a large-eddy simulation, Agr. Forest Meteorol., 115, 5–17,
2003.
Shaw, W. J., Berg, L. K., Cline, J., Draxl, C., Djalalova, E., Grimit, E. P., Lundquist, J. K., Marquis, M., McCaa, J., Olson, J. B., Sivaraman, C., Sharp, J., and Wilczak, J. M.: The
second wind forecasting improvement project (WFIPs): General overview, B.
Am. Meteorol. Soc., 100, 1687–1699,
https://doi.org/10.1175/BAMS-D-18-0036.1, 2021.
Shaw, W. J., Berg, L. K., Debnath, M., Deskos, G., Draxl, C., Ghate, V. P., Hasager, C. B., Kotamarthi, R., Mirocha, J. D., Muradyan, P., Pringle, W. J., Turner, D. D., and Wilczak, J. M.: Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer, Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, 2022.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the Advanced Research
WRF Version 3 (No. NCAR/TN-475+STR), University Corporation for
Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008.
Smagorinsky, J.: General circulation experiments with the primitive
equations, I. The basic experiment, Mon. Weather Rev., 91, 99–164,
https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963.
Smagorinsky, J.: Some Historical Remarks on the Use of Non-linear
Viscosities in GeophysicalModels, Program Int. Workshop Large Eddy Simul., International Workshop,
19–21 December 1990, St. Petersburg, FL, USA, https://apps.dtic.mil/sti/tr/pdf/ADA230835.pdf (last access: 6 August 2023), 1990.
Thedin, R., Quon, E., Churchfield, M., and Veers, P.: Investigations of correlation and coherence in turbulence from a large-eddy simulation, Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, 2023.
UKMO: GHRSST Level 4 OSTIA Global Foundation Sea Surface Temperature
Analysis, UKMO [data set], https://doi.org/10.5067/GHOST-4FK01, 2005.
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Loy, C. C., Qian, Y., and
Tang, X.: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks,
Computer Vision and Pattern Recognition, arXiv [preprint],
https://doi.org/10.48550/arXiv.1809.00219,
2018.
White House: Justice40, a Whole of Government Initiative,
https://www.whitehouse.gov/environmentaljustice/justice40/, last access: 30
November 2022.
Wilczak, J. M., Stoelinga, M., Berg, L. K., Sharp, J., Draxl, C., McCaffrey,
K., Banta, R. M., Bianco, L., Djalalova, I., Lundquist, J. K., and Muradyan,
P.: The second wind forecast improvement project (WFIP2): Observational
field campaign, B. Am. Meteorol. Soc.,
100, 1701–1723, 2019.
Wyngaard, J. C.: Toward Numerical Modeling in the “Terra Incognita”,
J. Atmos. Sci.,
61, 1816–1826, 2004.
Yang, B., Qian, Y., Berg, L. K., Ma, P.-L., Wharton, S., Bulaevskaya, V., Yan, H., Hou, Z., and Shaw, W.: Sensitivity of turbine-height wind speeds to parameters in planetary
boundary-layer and surface-layer schemes in the Weather Research and
Forecasting model, Bound.-Lay. Meteorol., 162, 117–142, 2017.
Yang, B., Berg, L. K., Qian, Y., Wang, C., Hou, Z., Liu, Y., Shin, H. H., Hong, S., and Pekour, M.:
Parametric and structural sensitivities of turbine-height wind speeds in the
boundary layer parameterizations in the Weather Research and Forecasting
model, J. Geophys. Res.-Atmos., 124, 5951–5969, https://doi.org/10.1029/2018JD029691, 2019.
Zajaczkowski, F. J., Haupt, S. E., and Schmehl, K. J.: A Preliminary Study of
Assimilating Numerical Weather Prediction Data into Computational Fluid
Dynamics Models for Wind Prediction, J. Wind Eng. Ind. Aerodyn., 99, 320–329
https://doi.org/10.1016/j.jweia.2011.01.023, 2011.
Zuidema, P., Chang, P., Medeiros, B., Kirtman, B. P., Mechoso, R.,
Schneider, E. K., Toniazzo, T., Richter, I., Small, R. J., Bellomo, K., Brandt, P., de Szoeke, S., Farra, J. T., Jung, E., Kato, S., Li, M., Patricola, C., Wang, Z., Wood, R., and Xu, Z.: Challenges and prospects for reducing
coupled climate model SST biases in the eastern tropical Atlantic and
Pacific oceans: The US CLIVAR Eastern Tropical Oceans Synthesis Working
Group, B. Am. Meteorol. Soc., 97, 2305–2328,
2016.
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to...
Altmetrics
Final-revised paper
Preprint