Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast

Journal Article · Mon Feb 24 00:00:00 EST 2025 · Wind Energy Science (Online)

DOI:https://doi.org/10.5194/wes-10-483-2025· OSTI ID:2523618

Liu, Ye ^[1]; Juliano, Timothy W. ^[2]; Krishnamurthy, Raghavendra ^[1]; Gaudet, Brian J. ^[1]; Lee, Jungmin ^[3]

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1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
2. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
3. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

The US West Coast holds great potential for wind power generation, although its potential varies due to the complex coastal climate. Characterizing and modeling turbine hub-height winds under different weather conditions are vital for wind resource assessment and management. This study uses a two-stage machine learning algorithm to identify five large-scale meteorological patterns (LSMPs): post-trough, post-ridge, pre-ridge, pre-trough, and California high. The LSMPs are linked to offshore wind patterns, specifically at lidar buoy locations within lease areas for future wind farm development off Humboldt and Morro Bay. While each LSMP is associated with characteristic large-scale atmospheric conditions and corresponding differences in wind direction, diurnal variation, and jet features at the two lidar sites, substantial variability in wind speeds can still occur within each LSMP. Wind speeds at Humboldt increase during the post-trough, pre-ridge, and California-high LSMPs and decrease during the remaining LSMPs. Morro Bay has smaller responses in mean speeds, showing increased wind speed during the post-trough and California-high LSMPs. Besides the LSMPs, local factors, including the land–sea thermal contrast and topography, also modify mean winds and diurnal variation. The High-Resolution Rapid Refresh model analysis does a good job of capturing the mean and variation at Humboldt but produces large biases at Morro Bay, particularly during the pre-ridge and California-high LSMPs. The findings are anticipated to guide the selection of cases for studying the influence of specific large-scale and local factors on California offshore winds and to contribute to refining numerical weather prediction models, thereby enhancing the efficiency and reliability of offshore wind energy production.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
• Grant/Contract Number: AC05-76RL01830; AC52-07NA27344
• OSTI ID: 2523618
• Alternate ID(s): OSTI ID: 2537986
• Report Number(s): LLNL--JRNL-863111; PNNL-SA--200213
• Journal Information: Wind Energy Science (Online), Journal Name: Wind Energy Science (Online) Journal Issue: 2 Vol. 10; ISSN 2366-7451
• Publisher: Copernicus Publications
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY