arXiv:2605.24038v1 Announce Type: cross Abstract: Forecasting aurora borealis visibility matters for space weather research and aurora tourism. Visibility at a site and night depends on two distinct factors: (1) whether aurora is physically occurring, driven by solar wind-magnetosphere coupling, and (2) whether observing conditions allow naked-eye detection, mainly cloud cover and lunar illumination. We present Aurora Hunter, a two-stage cascade that decouples these factors. Stage 1 predicts P(occurring) with XGBoost using 51 physics-driven features trained on joint Tromso+Kiruna data (about 1
The development of 'Aurora Hunter' reflects ongoing advancements in AI applications for increasingly complex, real-world forecasting challenges, leveraging sophisticated machine learning models.
This AI model demonstrates the expanding capabilities of machine learning to predict natural phenomena with higher accuracy, which has implications for various fields beyond space weather, including climate science and disaster prediction.
The ability to accurately forecast aurora visibility through a two-stage AI framework improves scientific understanding and offers new tools for industries dependent on environmental conditions, such as tourism and space operations.
- · AI/ML researchers
- · Space weather researchers
- · Tourism industry (aurora)
- · Earth observation sectors
Improved probabilistic forecasting of aurora borealis visibility becomes available to the public and scientific community.
Enhanced predictability could lead to new commercial opportunities around aurora tourism and better protection of space assets vulnerable to space weather.
The success of this two-stage model could inspire similar decoupled AI frameworks for other complex, multi-factor forecasting problems across different scientific domains.
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Read at arXiv cs.LG