Predictability-Guided Multiscale Probabilistic Forecasting of Wind Direction under Extreme Shear

Accurate multi-horizon wind direction forecasting is critical for turbine yaw control and grid security. Rapid directional shear (turning $\ge {90}^{\circ }$) challenges models via non-Euclidean geometry on $S^1$, multiscale dynamics, and regime-dependent uncertainty. Conventional discrete models and foundation models suffer from mid-frequency phase lag and turning misalignments. We show that directional predictability decays at disparate rates across frequency subbands, rendering monolithic mechanisms suboptimal. We propose a predictability-guided paradigm: slow synoptic drift $\to$ deterministic regression; intermediate turning $\to$ continuous latent differential flows; unresolved turbulence $\to$ conditional residual diffusion; followed by causal recalibration. On a 10,000-sequence multi-year benchmark, our framework maintains calm-weather accuracy (Test MCE ${38.48}^{\circ }$) while reducing extreme-turning error (Case 1 MCE ${60.69}^{\circ }$ vs ${70.42}^{\circ }$ for zero-shot foundation models). The circular CRPS reaches ${22.36}^{\circ }$, with 93.88% coverage at nominal 95% (91.01% out-of-distribution). Density estimation further reveals near-antipodal bimodal structure under severe shear (13.39%–15.43% tail mass $\ge {135}^{\circ }$), exposing a geometric bound where single-center calibration under-covers (81.56%), motivating multimodal circular manifold learning.