Articles | Volume 11, issue 6
https://doi.org/10.5194/wes-11-2257-2026
https://doi.org/10.5194/wes-11-2257-2026
Research article
 | 
29 Jun 2026
Research article |  | 29 Jun 2026

Performance of reanalysis and mesoscale models off the coast of Hawai'i

Lindsay M. Sheridan, Raghavendra Krishnamurthy, Tien Manh Nguyen, Yi-Leng Chen, William I. Gustafson Jr., Ye Liu, Feng Hsiao, Rob K. Newsom, Preston Spicer, Evgueni Kassianov, Mikhail Pekour, Nicola Bodini, and Mark Severy

Data sets

10 min Lidar Winds/Derived Data DOE (U.S. Department of Energy) https://wdh.energy.gov/ds/buoy/lidar.z07.c0

1Hz Lidar Winds/Reviewed Data DOE (U.S. Department of Energy) https://wdh.energy.gov/ds/buoy/lidar.z07.b0

Hawai'i - Wind Sentinel (120), Oahu, Hawai'i/Reviewed Data DOE (U.S. Department of Energy) https://wdh.energy.gov/ds/buoy/buoy.z07.b0

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Short summary
Wind simulations can contain significant errors, which can lead to inaccurate estimates of wind energy generation. Using observations from a floating lidar off Hawai'i, we hypothesize and establish that distinct simulation datasets will exhibit diverse ranges of errors in this offshore environment. The most commonly used simulation dataset produces the largest wind speed biases due to underestimation of fast wind speeds and misrepresentation of how wind speed varies throughout the day and night.
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