Preprints
https://doi.org/10.5194/wes-2024-77
https://doi.org/10.5194/wes-2024-77
16 Jul 2024
 | 16 Jul 2024
Status: this preprint is currently under review for the journal WES.

A New Gridded Offshore Wind Profile Product for US Coasts Using Machine Learning and Satellite Observations

James Frech, Korak Saha, Paige D. Lavin, Huai-Min Zhang, James Reagan, and Brandon Fung

Abstract. Offshore wind farms are a low-cost, efficient technology for green energy. They deliver significant economic benefits through their manufacturing and operation, and can be readily deployed at scale. Offshore wind also offers a route to opening up access to renewable energy for a global population, 40 % of whom live within 100 km of the coast. Presently, offshore wind speed data around wind turbine hub heights are fairly limited, available either through in situ observations from wind masts and floating Light Detection and Ranging (lidar) buoys at selected locations or as forecasting-model based output such as from the 2023 National Renewable Energy Laboratory (NREL) National Offshore Wind (NOW-23) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). In situ wind profiles are very sparse and costly to obtain en masse, whereas satellite-derived 10 m wind speeds have vast coverage at high resolution. In this study, we show the improvement of deploying machine learning techniques, in particular random forest regression (RFR), over conventional methods for accurately estimating offshore wind speed profiles on a high-resolution (0.25°) grid at 6-hourly resolution from 1987 to 2022 using satellite-derived surface wind speeds from the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information’s (NCEI) Blended Seawinds version 2.0 (NBSv2.0) product. We use 276,577 wind profiles from five publicly available lidar datasets over the Northeast US and California coasts to train and validate a RFR model to extrapolate wind speed profiles up to 200 m. A single extrapolation model applicable to the coastal regions of the contiguous US and Hawai’i is developed, instead of site-specific ones attempted in previous studies.

Our RFR outperforms conventional extrapolation methods at the five training stations under cross validation (where each station is held out from the training once and used for validation), especially under conditions of high vertical wind shear and at wind turbine hub heights (~100 m). This model is then tested on two lidar stations that were not used in the training data and profiles from six NOW-23 station locations to evaluate its performance on unseen data. The final model is applied to the NBSv2.0 data from 1987–2022 to create publicly available wind speed profiles over the coastal regions of the contiguous US and Hawai’i on a 0.25° grid, which are shown to outperform NOW-23 and ERA-5 reanalysis at 100 m using a correlated triple collocation method over five years of matchup data (2015–2019). Gridded maps of wind profiles in the marine boundary layer over US coastal waters will enable the development of a suite of wind energy resources and will help stakeholders in their decision making related to wind-based renewable energy development.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
James Frech, Korak Saha, Paige D. Lavin, Huai-Min Zhang, James Reagan, and Brandon Fung

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2024-77', Anonymous Referee #1, 31 Aug 2024
  • RC2: 'Comment on wes-2024-77', Anonymous Referee #2, 15 Oct 2024
  • RC3: 'Comment on wes-2024-77', Anonymous Referee #3, 04 Nov 2024
James Frech, Korak Saha, Paige D. Lavin, Huai-Min Zhang, James Reagan, and Brandon Fung
James Frech, Korak Saha, Paige D. Lavin, Huai-Min Zhang, James Reagan, and Brandon Fung

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Short summary
A machine learning model is developed using lidar stations around the US coasts to extrapolate wind speed profiles up to the hub heights of wind turbines from surface wind speeds. Independent validation shows that our model vastly outperforms traditional methods for vertical wind extrapolation. We produce a new long-term gridded dataset of wind speed profiles from 20 to 200 m at 0.25°, 6-hourly resolution from 1987 to 2022 by applying this model to the NOAA/NCEI Blended Seawinds product.
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