Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds
This work addresses the need for high-quality, high-resolution wind data for wind-farm siting and extreme-weather risk assessment, representing an incremental improvement with a novel method for a known bottleneck.
This study tackled the problem of generating high-resolution hub-height wind speeds by introducing WindSR, a diffusion model with data assimilation for super-resolution downscaling, which outperformed baseline methods in efficiency and accuracy and reduced model bias by approximately 20% relative to independent observations.
High-quality observations of hub-height winds are valuable but sparse in space and time. Simulations are widely available on regular grids but are generally biased and too coarse to inform wind-farm siting or to assess extreme-weather-related risks (e.g., gusts) at infrastructure scales. To fully utilize both data types for generating high-quality, high-resolution hub-height wind speeds (tens to ~100m above ground), this study introduces WindSR, a diffusion model with data assimilation for super-resolution downscaling of hub-height winds. WindSR integrates sparse observational data with simulation fields during downscaling using state-of-the-art diffusion models. A dynamic-radius blending method is introduced to merge observations with simulations, providing conditioning for the diffusion process. Terrain information is incorporated during both training and inference to account for its role as a key driver of winds. Evaluated against convolutional-neural-network and generative-adversarial-network baselines, WindSR outperforms them in both downscaling efficiency and accuracy. Our data assimilation reduces WindSR's model bias by approximately 20% relative to independent observations.