Articles | Volume 10, issue 2
https://doi.org/10.5194/wes-10-483-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-483-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Feb 2025
Research article |
| 24 Feb 2025
| 24 Feb 2025
Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Timothy W. Juliano
US National Science Foundation National Center for Atmospheric Research, Boulder, CO 80307, USA
Raghavendra Krishnamurthy
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Brian J. Gaudet
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Jungmin Lee
Lawrence Livermore National Laboratory, Livermore, CA 94551, USA
Related authors
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Sheng-Lun Tai, Zhao Yang, Brian Gaudet, Koichi Sakaguchi, Larry Berg, Colleen Kaul, Yun Qian, Ye Liu, and Jerome Fast
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-599, https://doi.org/10.5194/essd-2024-599, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Our study created a high-resolution soil moisture dataset for the eastern U.S. by integrating satellite data with a land surface model and advanced algorithms, achieving 1-km scale analyses. Validated against multiple networks and datasets, it demonstrated superior accuracy. This dataset is vital for understanding soil moisture dynamics, especially during droughts, and highlights the need for improved modeling of clay soils to refine future predictions.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
Atmos. Chem. Phys., 24, 8165–8181, https://doi.org/10.5194/acp-24-8165-2024, https://doi.org/10.5194/acp-24-8165-2024, 2024
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.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
Short summary
Short summary
Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2022-1111, https://doi.org/10.5194/egusphere-2022-1111, 2022
Preprint archived
Short summary
Short summary
A process-based plant Carbon (C)-Nitrogen (N) interface coupling framework has been developed, which mainly focuses on the plant resistance and N limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem-biogeochemical model and testing results show a general improvement in simulating plant properties with this framework.
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.
Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin
Geosci. Model Dev., 14, 7639–7657, https://doi.org/10.5194/gmd-14-7639-2021, https://doi.org/10.5194/gmd-14-7639-2021, 2021
Short summary
Short summary
This study applies a fire-coupled dynamic vegetation model to quantify fire impact at monthly to annual scales. We find fire reduces grass cover by 4–8 % annually for widespread areas in south African savanna and reduces tree cover by 1 % at the periphery of tropical Congolese rainforest. The grass cover reduction peaks at the beginning of the rainy season, which quickly diminishes before the next fire season. In contrast, the reduction of tree cover is irreversible within one growing season.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Huilin Huang, Yongkang Xue, Fang Li, and Ye Liu
Geosci. Model Dev., 13, 6029–6050, https://doi.org/10.5194/gmd-13-6029-2020, https://doi.org/10.5194/gmd-13-6029-2020, 2020
Short summary
Short summary
We developed a fire-coupled dynamic vegetation model that captures the spatial distribution, temporal variability, and especially the seasonal variability of fire regimes. The fire model is applied to assess the long-term fire impact on ecosystems and surface energy. We find that fire is an important determinant of the structure and function of the tropical savanna. By changing the vegetation composition and ecosystem characteristics, fire significantly alters surface energy balance.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth N. Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, William Radünz, and Patrick Moriarty
Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, https://doi.org/10.5194/wes-10-1681-2025, 2025
Short summary
Short summary
This study is the first to use real-world atmospheric measurements to show that large wind plants can increase the height of the planetary boundary layer, the part of the atmosphere near the surface where life takes place. The planetary boundary layer height governs processes like pollutant transport and cloud formation and is a key parameter for modeling the atmosphere. The results of this study provide important insights into interactions between wind plants and their local environment.
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
Atmos. Meas. Tech., 18, 3453–3475, https://doi.org/10.5194/amt-18-3453-2025, https://doi.org/10.5194/amt-18-3453-2025, 2025
Short summary
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci., 10, 1187–1209, https://doi.org/10.5194/wes-10-1187-2025, https://doi.org/10.5194/wes-10-1187-2025, 2025
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 gap leads to increased energy production. We demonstrate that the new model configuration performs similarly to a more established approach, with some evidence of improved wind speed predictions, and provide guidance for future model testing.
Jungmin Lee, Virendra P. Ghate, Arka Mitra, Lee M. Miller, Raghavendra Krishnamurthy, and Ulrike Egerer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-108, https://doi.org/10.5194/wes-2025-108, 2025
Preprint under review for WES
Short summary
Short summary
This study compares weather model predictions to real-world measurements of wind and clouds off California's coast, where offshore wind farms are planned. It finds the model often underestimates wind speeds in cloudy conditions and shows larger errors in clear skies. These results highlight when and where the model is most accurate, helping improve wind forecasts and support better planning for offshore wind energy projects.
Macy Frost Chang, Raghavendra Krishnamurthy, and Fotini Katopodes Chow
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-85, https://doi.org/10.5194/wes-2025-85, 2025
Preprint under review for WES
Short summary
Short summary
The development of offshore wind energy sites hinges on accurate prediction of hub-height wind speeds. This paper compares three machine learning (ML) algorithms to a standard log-law wind extrapolation at two offshore buoy sites. The ML methods demonstrate new capabilities for providing accurate and adaptable predictions of offshore wind characteristics in comparison to conventional approaches. These ML techniques can help inform the development of offshore wind energy projects.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
Short summary
Short summary
Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Arka Mitra, Virendra Ghate, and Raghavendra Krishnamurthy
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-55, https://doi.org/10.5194/wes-2025-55, 2025
Revised manuscript under review for WES
Short summary
Short summary
This study introduces a new metric to quantify the spatiotemporal variability of wind resources and a novel numerical technique to locate the optimal wind resource within a large wind farm. The new metric and the novel optimization technique are applied to assist in the pre-construction wind resource assessments of two Californian offshore wind energy areas. This optimization is stable for a diverse choice of wind turbines and is easily scalable and adaptable to any other offshore location.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Nathan J. Agarwal, Julie K. Lundquist, Timothy W. Juliano, and Alex Rybchuk
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-16, https://doi.org/10.5194/wes-2025-16, 2025
Revised manuscript under review for WES
Short summary
Short summary
Models of wind behavior inform offshore wind farm site investment decisions. Here we compare a newly-developed model to another, historically-used, model based on how these models represent winds and turbulence at two North Sea sites. The best model depends on the site. While the older model performs best at the site above a wind farm, the newer model performs best at the site that is at the same altitude as the wind farm. We support using the new model to represent winds at the turbine level.
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
Short summary
Short summary
This study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.
Sheng-Lun Tai, Zhao Yang, Brian Gaudet, Koichi Sakaguchi, Larry Berg, Colleen Kaul, Yun Qian, Ye Liu, and Jerome Fast
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-599, https://doi.org/10.5194/essd-2024-599, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Our study created a high-resolution soil moisture dataset for the eastern U.S. by integrating satellite data with a land surface model and advanced algorithms, achieving 1-km scale analyses. Validated against multiple networks and datasets, it demonstrated superior accuracy. This dataset is vital for understanding soil moisture dynamics, especially during droughts, and highlights the need for improved modeling of clay soils to refine future predictions.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
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
Revised manuscript 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.
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.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
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.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
Short summary
Short summary
Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
Short summary
Short summary
Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Da Gao, Bin Zhao, Shuxiao Wang, Yuan Wang, Brian Gaudet, Yun Zhu, Xiaochun Wang, Jiewen Shen, Shengyue Li, Yicong He, Dejia Yin, and Zhaoxin Dong
Atmos. Chem. Phys., 23, 14359–14373, https://doi.org/10.5194/acp-23-14359-2023, https://doi.org/10.5194/acp-23-14359-2023, 2023
Short summary
Short summary
Surface PM2.5 concentrations can be enhanced by aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs). In this study, we found PM2.5 enhancement induced by ACIs shows a significantly smaller decrease ratio than that induced by ARIs in China with anthropogenic emission reduction from 2013 to 2021, making ACIs more important for enhancing PM2.5 concentrations. ACI-induced PM2.5 enhancement needs to be emphatically considered to meet the national PM2.5 air quality standard.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
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.
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.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
Short summary
Short summary
Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2022-1111, https://doi.org/10.5194/egusphere-2022-1111, 2022
Preprint archived
Short summary
Short summary
A process-based plant Carbon (C)-Nitrogen (N) interface coupling framework has been developed, which mainly focuses on the plant resistance and N limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem-biogeochemical model and testing results show a general improvement in simulating plant properties with this framework.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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.
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.
Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin
Geosci. Model Dev., 14, 7639–7657, https://doi.org/10.5194/gmd-14-7639-2021, https://doi.org/10.5194/gmd-14-7639-2021, 2021
Short summary
Short summary
This study applies a fire-coupled dynamic vegetation model to quantify fire impact at monthly to annual scales. We find fire reduces grass cover by 4–8 % annually for widespread areas in south African savanna and reduces tree cover by 1 % at the periphery of tropical Congolese rainforest. The grass cover reduction peaks at the beginning of the rainy season, which quickly diminishes before the next fire season. In contrast, the reduction of tree cover is irreversible within one growing season.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
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 %.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 6, 295–309, https://doi.org/10.5194/wes-6-295-2021, https://doi.org/10.5194/wes-6-295-2021, 2021
Short summary
Short summary
Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
Huilin Huang, Yongkang Xue, Fang Li, and Ye Liu
Geosci. Model Dev., 13, 6029–6050, https://doi.org/10.5194/gmd-13-6029-2020, https://doi.org/10.5194/gmd-13-6029-2020, 2020
Short summary
Short summary
We developed a fire-coupled dynamic vegetation model that captures the spatial distribution, temporal variability, and especially the seasonal variability of fire regimes. The fire model is applied to assess the long-term fire impact on ecosystems and surface energy. We find that fire is an important determinant of the structure and function of the tropical savanna. By changing the vegetation composition and ecosystem characteristics, fire significantly alters surface energy balance.
Cited articles
Athanasiadis, P. J., Wallace, J. M., and Wettstein, J. J.: Patterns of Wintertime Jet Stream Variability and Their Relation to the Storm Tracks, J. Atmos. Sci., 67, 1361–1381, 2010.
Atmosphere to Electrons (A2e): buoy/lidar.z05.b0, Maintained by A2e Data Archive and Portal for U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy [data set], https://doi.org/10.21947/1783809 (last access: 20 June 2024), 2021a.
Atmosphere to Electrons (A2e): buoy/lidar.z06.b0, Maintained by A2e Data Archive and Portal for U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy [data set], https://doi.org/10.21947/1959721 (last access: 20 June2024), 2021b.
Banta, R. M., Pichugina, Y. L., Kelley, N. D., Hardesty, R. M., and Brewer, W. A.: Wind Energy Meteorology: Insight into Wind Properties in the Turbine-Rotor Layer of the Atmosphere from High-Resolution Doppler Lidar, B. Am. Meteorol. Soc., 94, 883–902, 2013.
Banta, R. M., Pichugina, Y. L., Brewer, W. A., James, E. P., Olson, J. B., Benjamin, S. G., Carley, J. R., Bianco, L., Djalalova, I. V., Wilczak, J. M., Hardesty, R. M., Cline, J., and Marquis, M. C.: Evaluating and Improving NWP Forecast Models for the Future: How the Needs of Offshore Wind Energy Can Point the Way, B. Am. Meteorol. Soc., 99, 1155–1176, 2017.
Berg, L. K., Liu, Y., Yang, B., Qian, Y., Krishnamurthy, R., Sheridan, L., and Olson, J.: Time Evolution and Diurnal Variability of the Parametric Sensitivity of Turbine-Height Winds in the MYNN-EDMF Parameterization, J. Geophys. Res.-Atmos., 126, e2020JD034000, https://doi.org/10.1029/2020jd034000, 2021.
Bianco, L., Djalalova, I. V., Wilczak, J. M., Olson, J. B., Kenyon, J. S., Choukulkar, A., Berg, L. K., Fernando, H. J. S., Grimit, E. P., Krishnamurthy, R., Lundquist, J. K., Muradyan, P., Pekour, M., Pichugina, Y., Stoelinga, M. T., and Turner, D. D.: Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2), Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, 2019.
Bodini, N., Optis, M., Redfern, S., Rosencrans, D., Rybchuk, A., Lundquist, J. K., Pronk, V., Castagneri, S., Purkayastha, A., Draxl, C., Krishnamurthy, R., Young, E., Roberts, B., Rosenlieb, E., and Musial, W.: The 2023 National Offshore Wind data set (NOW-23), Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, 2024.
BOEM – Bureau of Ocean Energy Management: California Activities, https://www.boem.gov/renewable-energy/state-activities/california (last access: 20 June 2024), 2024.
Brewer, M. C., Mass, C. F., and Potter, B. E.: The West Coast Thermal Trough: Climatology and Synoptic Evolution, Mon. Weather Rev., 140, 3820–3843, 2012.
Burk, S. D. and Thompson, W. T.: The Summertime Low-Level Jet and Marine Boundary Layer Structure along the California Coast, Mon. Weather Rev., 124, 668–686, 1996.
Carroll, B. J., Demoz, B. B., and Delgado, R.: An overview of low-level jet winds and corresponding mixed layer depths during PECAN, J. Geophys. Res., 124, 9141–9160, 2019.
Dadashazar, H., Crosbie, E., Majdi, M. S., Panahi, M., Moghaddam, M. A., Behrangi, A., Brunke, M., Zeng, X., Jonsson, H. H., and Sorooshian, A.: Stratocumulus cloud clearings: statistics from satellites, reanalysis models, and airborne measurements, Atmos. Chem. Phys., 20, 4637–4665, https://doi.org/10.5194/acp-20-4637-2020, 2020.
Dowell, D. C., Alexander, C. R., James, E. P., Weygandt, S. S., Benjamin, S. G., Manikin, G. S., Blake, B. T., Brown, J. M., Olson, J. B., Hu, M., Smirnova, T. G., Ladwig, T., Kenyon, J. S., Ahmadov, R., Turner, D. D., Duda, J. D., and Alcott, T. I.: The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description, Weather Forecast., 37, 1371–1395, 2022.
Fovell, R. G. and Gallagher, A.: An Evaluation of Surface Wind and Gust Forecasts from the High-Resolution Rapid Refresh Model, Weather Forecast., 37, 1049–1068, 2022.
Frías-Paredes, L., Mallor, F., Gastón-Romeo, M., and León, T.: Assessing energy forecasting inaccuracy by simultaneously considering temporal and absolute errors, Energy Convers. Manage., 142, 533–546, 2017.
Gaudet, B. J., García-Medina, G., Krishnamurthy, R., Shaw, W. J., Sheridan, L. M., Yang, Z., Newsom, R. K., and Pekour, M.: Evaluation of coupled wind / wave model simulations of offshore winds in the Mid-Atlantic Bight using lidar-equipped buoys, Mon. Weather Rev., 150, 1377–1395, https://doi.org/10.1175/mwr-d-21-0166.1, 2022.
Gilliam, R. C., Raman, S., and Niyogi, D. D. S.: Observational and Numerical Study on the Influence of Large-Scale Flow Direction and Coastline Shape on Sea-Breeze Evolution, Bound.-Lay. Meteorol., 111, 275–300, 2004.
Hallgren, C., Arnqvist, J., Ivanell, S., Körnich, H., Vakkari, V., and Sahlée, E.: Looking for an Offshore Low-Level Jet Champion among Recent Reanalyses: A Tight Race over the Baltic Sea, Energies, 13, 3670, https://doi.org/10.3390/en13143670, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6 (last access: 20 June 2024), 2023.
Huang, H., Qian, Y., Liu, Y., He, C., Zheng, J., Zhang, Z., and Gkikas, A.: Where does the dust deposited over the Sierra Nevada snow come from?, Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, 2022.
James, E. P., Benjamin, S. G., and Marquis, M.: A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model, Renew. Energy, 102, 390–405, 2017.
James, E. P., Benjamin, S. G., and Marquis, M.: Offshore wind speed estimates from a high-resolution rapidly updating numerical weather prediction model forecast dataset, Wind Energy, 21, 264–284, 2018.
Jeon, H., Hartman, B., Cutler, H., Hill, R., Hu, Y. C., Lu, T., Shields, M., and Turner, D. D.: Estimating the economic impacts of improved wind speed forecasts in the United States electricity sector, J. Renewable Sustainable Energy, 14, 036101, https://doi.org/10.1063/5.0081905, 2022.
Juliano, T. W., McGinnis, S., Haupt, S. E., Brummet, T., Bukovsky, M. S., Liu, Y., and Kristnamurthy, R.: Examining future changes in coastal low-level jet properties offshore California through dynamical downscaling, Environ. Res. Lett., 20, 034023, https://doi.org/10.1088/1748-9326/adb16b, 2025.
Kalverla, P. C., Duncan Jr., J. B., Steeneveld, G.-J., and Holtslag, A. A. M.: Low-level jets over the North Sea based on ERA5 and observations: together they do better, Wind Energ. Sci., 4, 193–209, https://doi.org/10.5194/wes-4-193-2019, 2019.
Kohonen, T.: Self-organized formation of topologically correct feature maps, Biol. Cybern., 43, 59–69, 1982.
Krishnamurthy, R., García Medina, G., Gaudet, B., Gustafson Jr., W. I., Kassianov, E. I., Liu, J., Newsom, R. K., Sheridan, L. M., and Mahon, A. M.: Year-long buoy-based observations of the air–sea transition zone off the US west coast, Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, 2023.
Lima, D. C. A., Soares, P. M. M., Nogueira, M., and Semedo, A.: Global coastal low-level wind jets revisited through the new ERA5 reanalysis, Int. J. Climatol., 42, 4491–4507, 2022.
Liu, Y., Qian, Y., and Berg, L. K.: Local-thermal-gradient and large-scale-circulation impacts on turbine-height wind speed forecasting over the Columbia River Basin, Wind Energ. Sci., 7, 37–51, https://doi.org/10.5194/wes-7-37-2022, 2022.
Liu, Y., Feng, S., Qian, Y., Huang, H., and Berg, L. K.: How do North American weather regimes drive wind energy at the sub-seasonal to seasonal timescales?, npj Climate Atmos. Sci., 6, 100, https://doi.org/10.1038/s41612-023-00403-5, 2023.
Liu, Y., Gaudet, B., Krishnamurthy, R., Tai, S.-L., Berg, L. K., Bodini, N., Rybchuk, A., and Kumler, A.: Identifying Meteorological Drivers for Errors in Modeled Winds along the Northern California Coast, Mon. Weather Rev., 152, 455–469, 2024.
Meenal, R., Binu, D., Ramya, K. C., Michael, P. A., Vinoth Kumar, K., Rajasekaran, E., and Sangeetha, B.: Weather Forecasting for Renewable Energy System: A Review, Arch. Comput. Methods Eng., 29, 2875–2891, 2022.
Mingoti, S. A. and Lima, J. O.: Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms, Eur. J. Oper. Res., 174, 1742–1759, 2006.
Misra, S., Li, H., and He, J.: Robust geomechanical characterization by analyzing the performance of shallow-learning regression methods using unsupervised clustering methods, in: Machine Learning for Subsurface Characterization, Gulf Professional Publishing, 1st edn., 129–155, 2020.
Musial, W., Spitsen, P., Duffy, P., Beiter, P., Shields, M., Mulas Hernando, D., Hammond, R., Marquis, M., King, J., and Sathish, S.: Offshore Wind Market Report: 2023 Edition, Office of Scientific and Technical Information (OSTI), https://doi.org/10.2172/2001112, 2023.
Myers, T. A., Van Ormer, A., Turner, D. D., Wilczak, J. M., Bianco, L., and Adler, B.: Evaluation of hub-height wind forecasts over the New York Bight, Wind Energy, 27, 1063–1073, 2024.
NOAA: NOAA High-Resolution Rapid Refresh (HRRR) Model, NOAA [data set], https://registry.opendata.aws/noaa-hrrr-pds (last access: 20 June 2024), 2024.
Ohba, M., Kadokura, S., and Nohara, D.: Impacts of synoptic circulation patterns on wind power ramp events in East Japan, Renewable Energy, 96, 591–602, 2016.
Pichugina, Y. L., Banta, R. M., Bonin, T., Brewer, W. A., Choukulkar, A., Mccarty, B. J., Baidar, S., Draxl, C., Fernando, H. J. S., Kenyon, J., Krishnamurthy, R., Marquis, M., Olson, J., Sharp, J., and Stoelinga, M.: Spatial Variability of Winds and HRRR–NCEP Model Error Statistics at Three Doppler-Lidar Sites in the Wind-Energy Generation Region of the Columbia River Basin, J. Appl. Meteorol. Climatol., 58, 1633–1656, 2019.
Pichugina, Y. L., Banta, R. M., Brewer, W. A., Bianco, L., Draxl, C., Kenyon, J., Lundquist, J. K., Olson, J. B., Turner, D. D., Wharton, S., Wilczak, J., Baidar, S., Berg, L. K., Fernando, H. J. S., McCarty, B. J., Rai, R., Roberts, B., Sharp, J., Shaw, W. J., Stoelinga, M. T., and Worsnop, R.: Evaluating the WFIP2 updates to the HRRR model using scanning Doppler lidar measurements in the complex terrain of the Columbia River Basin, J. Renewable Sustainable Energy, 12, 043301, https://doi.org/10.1063/5.0009138, 2020.
Shaw, W. J., Berg, L. K., Cline, J., Draxl, C., Djalalova, I., Grimit, E. P., Lundquist, J. K., Marquis, M., McCaa, J., Olson, J. B., Sivaraman, C., Sharp, J., and Wilczak, J. M.: The Second Wind Forecast Improvement Project (WFIP2): General Overview, B. Am. Meteorol. Soc., 100, 1687–1699, 2019.
Sheridan, L. M., Krishnamurthy, R., Gustafson Jr., W. I., Liu, Y., Gaudet, B. J., Bodini, N., Newsom, R. K., and Pekour, M.: Offshore low-level jet observations and model representation using lidar buoy data off the California coast, Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, 2024.
Shutaywi, M. and Kachouie, N. N.: Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering, Entropy, 23, 759, https://doi.org/10.3390/e23060759, 2021.
Spassiani, A. C. and Mason, M. S.: Application of Self-organizing Maps to classify the meteorological origin of wind gusts in Australia, J. Wind Eng. Ind. Aerodyn., 210, 104529, https://doi.org/10.1016/j.jweia.2021.104529, 2021.
Turner, D. D., Cutler, H., Shields, M., Hill, R., Hartman, B., Hu, Y., Lu, T., and Jeon, H.: Evaluating the Economic Impacts of Improvements to the High-Resolution Rapid Refresh (HRRR) Numerical Weather Prediction Model, B. Am. Meteorol. Soc., 103, E198–E211, 2022.
Vesanto, J. and Alhoniemi, E.: Clustering of the self-organizing map, IEEE Trans. Neural Netw., 11, 586–600, 2000.
Wilczak, J. M., Banta, R. M., Marquis, M., White, A. B., Stoelinga, M., McCaa, J., Bickford, J., Berg, L. K., Chand, D., Shaw, W. J., Sharp, J., McCaffrey, K., Bianco, L., Djalalova, I., Choukulkar, A., Long, C. N., Bonin, T., Pichugina, Y., Lantz, K., Olson, J. B., Draxl, C., Scott, G., Lundquist, J. K., Muradyan, P., Cook, D. R., Eckman, R., Leo, L., Fernando, H. J. S., Otarola-Bustos, S., Worsnop, R. P., Bodini, N., Clifton, A., Cline, J., Friedrich, K., Krishnamurthy, R., and Wharton, S.: The Second Wind Forecast Improvement Project (WFIP2): Observational Field Campaign The Second Wind Forecast Improvement Project (WFIP2): Observational Field Campaign, B. Am. Meteorol. Soc., 100, 1701–1723, 2019.
Zhou, J., Xiang, J., and Huang, S.: Marine Boundary Layer Height Obtained by New Numerical Regularization Method Based on GPS Radio Occultation Data, Sensors, 20, 4762, https://doi.org/10.3390/s20174762, 2020.
Short summary
Our study reveals how different weather patterns influence wind conditions off the US West Coast. We identified key weather patterns affecting wind speeds at potential wind farm sites using advanced machine learning. This research helps improve weather prediction models, making wind energy production more reliable and efficient.
Our study reveals how different weather patterns influence wind conditions off the US West...
Altmetrics
Final-revised paper
Preprint