A Physics-Constrained Super-Resolution Framework for Wind Resource Mapping: Application to Brazil

High-resolution wind resource maps are essential for wind farm siting and renewable-energy planning, but national-scale assessment is often limited by sparse meteorological stations and the coarse resolution of reanalysis products. This study introduces a self-supervised Physics-Constrained Super-Resolution CNN (PC-SRCNN) that enhances ERA5 wind fields from 0.25° to 0.025°, for settings lacking a high-resolution, hourly-resolved reference wind field, by embedding kinematic and vertical-profile consistency as soft regularization penalties, without solving the full Navier-Stokes momentum balance. The framework was evaluated against 190 independent INMET stations, alongside interpolation, data-driven, and climatological baselines, using a station-wise bootstrap and the Diebold-Mariano test. The full model reduced the bootstrap MAE from 1.22 to 1.04 m/s relative to native ERA5, a 14.85% improvement confirmed by both tests, and achieved a vertical-profile R 2 of 0.90 against baselines below 0.70. Ablation shows physical regularization is not automatically beneficial: a variant constrained only by horizontal kinematics performed significantly worse than ERA5, with gains obtained only when vertical logarithmic-profile consistency was included. All other baselines evaluated were statistically indistinguishable from native ERA5 against station observations. These results indicate that sharper spatial detail alone is insufficient to improve wind-resource estimates unless supported by physically meaningful, vertically consistent constraints.