Offshore Wind Energy Forecasting Sensitivity to Sea Surface Temperature Input in the Mid-Atlantic

Stephanie Redfern, Mike Optis, Geng Xia, Caroline Draxl

Research output: Contribution to journalArticle


As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer time scales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting Model (WRF) 4.2.1 to two different SST products - a daily, spatially coarser resolution data set (the Operational Sea Surface Temperature and Ice Analysis, or OSTIA), and an hourly, spatially finer resolution product (SSTs from the Geostationary Operational Environmental Satellite 16, or GOES-16). We find that in the Mid-Atlantic, although OSTIA SSTs validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height wind characterization performance on monthly time scales. Additionally, during flagged events that show statistically significant wind speed deviations between the two simulations, the GOES-16-forced simulation outperforms that forced by OSTIA.
Original languageAmerican English
Number of pages31
JournalWind Energy Science Discussions
StatePublished - 2021

Bibliographical note

See NREL/JA-5000-85375 for final paper as published in Wind Energy Science

NREL Publication Number

  • NREL/JA-5000-81474


  • offshore wind
  • Weather Research and Forecasting model
  • wind forecasting sensitivity
  • wind resource characterization


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