Estimation of Wind Resource Assessment at High-Resolution Using SAR Observations, Validated with Lidar Measurements

Abstract

Abstract The 18-year database of Envisat & Sentinel 1A-1B Synthetic Aperture Radar (SAR) provides worldwide surface wind measurements at a 1-km resolution. Through an innovative vertical extrapolation methodology published in 2022, these long-term, wide, and high-resolution observations can complement in-situ observations and mesoscale modelling for offshore wind resource assessment. The methodology is based on three steps: (i) derivation of the 10-min SAR surface winds from SAR sea surface roughness, and a site-independent machine learning algorithm based on a large buoy network to correct SAR winds due to biases inherent to the characteristics of the satellite sensors and wind retrieval methodology, (ii) extrapolation up to a few hundred meters based on a second machine learning algorithm trained with in-situ observations and physical parameters from a high-resolution mesoscale model related to e.g. atmospheric stability, and (iii) a final post-processing step to correct for low temporal sampling of the SAR database (one passage every two days for one satellite) and retrieve wind statistics. The resulting output is a 1-km resolution wind atlas in a large (> 3000 km2) offshore and/or coastal area where strong coastal gradients will be accounted for, or it can integrate directly the estimation of the extractible wind power. Here, an improvement of the extrapolation method is presented and applied to a French offshore area in South Brittany. The wind atlases obtained with SAR are found to display a much finer level of details and estimate more precisely the coastal gradient.