Abstract

Abstract. We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from −0.8 to 0.6 m s−1. Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m s−1, while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s−1. The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s−1 for SAR and less than 0.2 m s−1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

Highlights

  • Offshore wind energy has been established on the continental shelf of northern Europe since 2001 with a total installed capacity of 15 780 MW (Wind Europe, 2018)

  • We present the first wind atlas for the US East Coast based on intercalibrated synthetic aperture radar (SAR) wind fields from four different sensors

  • Wind Integration National Dataset Toolkit (WTK) wind speed contours are smoother than those from SAR for two reasons: (i) SAR wind speeds are based on high-resolution observations that can resolve sub-kilometrescale variation in the wind fields, and (ii) SAR-derived mean winds are derived from fewer samples while WTK winds are based on 7 full years of hourly modelled wind speeds

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Summary

Introduction

Offshore wind energy has been established on the continental shelf of northern Europe since 2001 with a total installed capacity of 15 780 MW (Wind Europe, 2018). The US East Coast is similar in water depths and population density and could be well-suited for offshore wind farms (Kempton et al, 2007). The Bureau of Offshore Energy Management (BOEM) has leased out areas designated for offshore wind farm development along the US East Coast (BOEM, 2018), and the first wind plant became operational in 2016 (Block Island Wind Farm, Rhode Island). Accurate and long-term wind statistics across broad geographic areas (i.e. wind atlases) are needed to support offshore wind energy deployment. Wind atlases can be developed from local in situ measurements, i.e. buoys or meteorological masts (Troen and Petersen, 1989); numerical weather prediction models; (Dvorak et al, 2013; Hahmann et al, 2015); or satellite-based remote sensing (Christiansen et al, 2006; Hasager et al, 2015).

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