Aurora visibility forecasts turn an emotionally charged natural spectacle into a practical daily decision for travelers and photographers by answering when the northern lights will actually appear. The Aurora Hunter preprint (arXiv:2605.24038, May 2026) introduces a two-stage cascade that first predicts auroral occurrence via XGBoost on 51 physics features from 16,600 hourly Tromsø-Kiruna samples (2015-2023), labeled by an all-sky image classifier, then estimates clear-sky probability with logistic regression on 21 cloud and lunar features. This yields ROC-AUC scores of 0.937 on Tromsø hold-out and 0.905 on independent Kiruna 2024 data, outperforming single-stage baselines by 0.087 while generalizing to Skibotn 2022-2025. SHAP analysis highlights Kp-nightside interactions and oval distance as dominant drivers. Unlike NOAA's Kp-based alerts, which conflate physical activity with local observability, this framework explicitly separates magnetospheric drivers from terrestrial blockers, addressing a gap in operational space-weather products. Limitations include site-specific training that may underperform at lower latitudes and reliance on hourly resolution that misses substorm dynamics. Peer-reviewed validation remains pending for this preprint. Related work from the 2023 Nature Communications paper on solar-wind coupling and a 2024 Space Weather journal study on cloud-corrected forecasts reinforces the value of cascaded probabilistic models for tourism applications.