New Methods to Improve the Vertical Extrapolation of Near-Surface Offshore Wind Speeds

Mike Optis, Nicola Bodini, Mithu Debnath, Paula Doubrawa

Research output: Contribution to journalArticle


Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in U.S. Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air-sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83?km from its training location, suggesting promising future applications in extrapolating 10-m wind speeds from spatially resolved satellite-based wind atlases.
Original languageAmerican English
Number of pages26
JournalWind Energy Science Discussions
StatePublished - 2021

Bibliographical note

See NREL/JA-5000-80459 for final paper as published in Wind Energy Science

NREL Publication Number

  • NREL/JA-5000-78614


  • machine learning
  • offshore wind
  • validation
  • vertical extrapolation


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