Preprints
https://doi.org/10.5194/wes-2024-76
https://doi.org/10.5194/wes-2024-76
04 Jul 2024
 | 04 Jul 2024
Status: this preprint is currently under review for the journal WES.

Linking weather patterns to observed and modelled turbine hub-height winds offshore U.S. West Coast

Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee

Abstract. The U.S. West Coast holds great potential for wind power generation, although its potential varies due to complex coastal processes. Characterizing and modelling turbine hub-height winds under different weather conditions are vital for wind resources 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. Distinct wind speed, wind direction, diurnal variation, and jet feature responses are observed for each LSMP and at both lidar locations. The wind speed at Humboldt is higher during the post-trough, pre-ridge, and California-high LSMPs and lower 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.

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Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee

Status: open (until 06 Aug 2024)

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Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee
Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee

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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. 
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