From optimal supply decisions to anticipatory control systems, wind-based energy applications rely heavily upon accurate, local, short-term forecasts of future wind speed. Recent studies have shown continuous ranked probability score (CRPS) minimizing models with Gaussian assumptions to be effective for well-researched sites where those assumptions are appropriate. We consider the more general case where Gaussianity is not assumed and access to historical data may be constrained. Deriving a CRPS expression for a minimum Extreme Value distribution, we use it to propose a site-adaptive Weibull-based CRPS-minimizing model, which is tested and shown to perform better than both deterministic and probabilistic reference models on a ground-based array of weather observation sites in northern Japan.