High-Precision Wind Resource Observation and Assessment Framework for Offshore Wind Farms

Abstract

Abstract To address the challenges of sparse in situ observations, limited spatial resolution of long-term datasets, and insufficient representation of regional wind-field details in offshore wind resource assessment, this study develops a high-accuracy wind resource observation and assessment framework based on multi-source satellite remote sensing, artificial intelligence-based downscaling, and meteorological mast validation. The framework establishes an integrated processing chain from long-term sea-surface wind fields to wind resource parameters at wind turbine development heights. A 12.5 km sea-surface wind field over China’s coastal waters, derived from the fusion of scatterometer and microwave radiometer observations, is used as the continuous large-scale background, while Sentinel-1 OCN products provide kilometer-scale spatial structure information. To address label mismatch caused by inconsistent satellite overpass times and cross-sensor biases, zero-mean spatial textures are extracted from Sentinel-1 OCN wind fields and superimposed onto the fused background to construct training targets with a unified wind-speed reference. A Residual U-Net is employed to reconstruct a constrained high-resolution baseline field, while a conditional residual denoising diffusion probabilistic model (Residual DDPM) further compensates for local gradients and fine-scale spatial heterogeneity. The downscaled sea-surface wind fields are subsequently subjected to vertical extrapolation and wind resource statistical analysis, followed by site-level validation using offshore meteorological mast observations. Approximately 7,000 matched scenes were divided into training, validation, and test sets at a ratio of 7:2:1, and the training and validation losses stabilized after approximately 30 epochs. Representative test samples show that the 1 km outputs preserve the large-scale wind-speed distribution while exhibiting richer local gradients and spatial textures. In the evaluation of mean wind speed at 37 meteorological mast sites, the downscaled satellite wind field product achieved an MAE of 0.321 m s−1, an RMSE of 0.464 m s−1, and a correlation coefficient of 0.912. Using standardized 12.5 km fused wind fields as inference inputs, the model can be applied to batch processing of the long-term 2000–2024 wind field record. The proposed framework decouples the numerical reference provided by long-term satellite wind fields from the spatial structures contained in high-resolution observations, and generates 1 km wind fields through the coordinated use of deterministic baseline reconstruction and probabilistic residual compensation. By separating the training and inference stages, sparse high-resolution satellite observations are transformed into reusable spatial structural priors. This framework therefore provides a practical technical pathway for offshore wind farm site screening, refined wind resource assessment, and the generation of high-resolution wind atlas products.