Preprints
https://doi.org/10.5194/wes-2021-150
https://doi.org/10.5194/wes-2021-150
 
21 Dec 2021
21 Dec 2021
Status: a revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl Stephanie Redfern et al.
  • National Renewable Energy Laboratory, Golden, Colorado, USA

Abstract. 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.

Stephanie Redfern et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-150', Anonymous Referee #1, 02 Mar 2022
  • RC2: 'Comment on wes-2021-150', Anonymous Referee #2, 21 Apr 2022
  • AC1: 'Author Comment on wes-2021-150', Stephanie Redfern, 29 Aug 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-150', Anonymous Referee #1, 02 Mar 2022
  • RC2: 'Comment on wes-2021-150', Anonymous Referee #2, 21 Apr 2022
  • AC1: 'Author Comment on wes-2021-150', Stephanie Redfern, 29 Aug 2022

Stephanie Redfern et al.

Stephanie Redfern et al.

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Short summary
As wind farm developments expand offshore, it is becoming more important for us to accurately forecast winds above coastal waters so we can capitalize on the offshore wind energy resource as best as possible. Weather models rely on various inputs to generate their forecasts, and one of them is sea surface temperature (SST). In this study, we evaluate how the SST product used in a model (there are numerous options) can influence the forecast and find that it can make a significant difference.