Predictability-Guided Multiscale Probabilistic Forecasting of Wind Direction under Extreme Shear
Abstract
Accurate multi-horizon wind direction forecasting is critical for turbine yaw control and grid security. Rapid directional shear (turning ≥ 90∘) challenges models via non-Euclidean geometry on S1, 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 → deterministic regression; intermediate turning → continuous latent differential flows; unresolved turbulence → 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∘) while reducing extreme-turning error (Case 1 MCE 60.69∘ vs 70.42∘ for zero-shot foundation models). The circular CRPS reaches 22.36∘, 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 ≥ 135∘), exposing a geometric bound where single-center calibration under-covers (81.56\%), motivating multimodal circular manifold learning.
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BibTeX
@misc{shu2026predictabilityguided,
title = {{Predictability-Guided Multiscale Probabilistic Forecasting of Wind Direction under Extreme Shear}},
author = {Hailong Shu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.16707},
eprint = {2609.16707},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16707},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Predictability-Guided Multiscale Probabilistic Forecasting of Wind Direction under Extreme Shear
AU - Hailong Shu
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2609.16707
UR - https://arxiv.org/abs/2609.16707
AB - Accurate multi-horizon wind direction forecasting is critical for turbine yaw control and grid security. Rapid directional shear (turning ≥ 90^∘) 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 → deterministic regression; intermediate turning → continuous latent differential flows; unresolved turbulence → 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^∘) while reducing extreme-turning error (Case 1 MCE 60.69^∘ vs 70.42^∘ for zero-shot foundation models). The circular CRPS reaches 22.36^∘, 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 ≥ 135^∘), exposing a geometric bound where single-center calibration under-covers (81.56\%), motivating multimodal circular manifold learning.
ER -