When the Sun Breaks Your GPS: Building an Explainable Early Warning System

Space Weather doesn’t just produce beautiful auroras: it can silently disrupt navigation systems, radio links, and satellite-based technologies we rely on every day.

Travelling Ionospheric Disturbances (TIDs) are wave-like structures in the ionosphere that affect GNSS accuracy and HF communications. From an ML perspective, forecasting TIDs is a challenging rare-event prediction problem involving imbalanced data and heterogeneous physical inputs.

In this talk, I will present an operational machine learning approach developed within the T-FORS project to forecast TID occurrence over Europe. The model is built using CatBoost and integrates data from space- and ground-based observations.

The talk focuses on model design and evaluation choices. In particular, I will show how SHAP can be used to debug model behaviour, validate feature relevance, and build trust in predictions in a high-risk operational context.

Along the way, I’ll share practical engineering lessons on:

• handling class imbalance,
• incorporating domain knowledge into ML pipelines,
• producing uncertainty-aware outputs via Conformal Prediction, and
• running interpretable models in real-time forecasting systems.

The talk is aimed at data scientists and ML practitioners interested in applied forecasting, interpretable models, uncertainty quantification and ML at the boundary between data and physics.

Talk outline

• 0-4: What is Space Weather and why should we care
• 4-7: Framing TID forecasting as an ML problem
• 7-10: Model design with CatBoost
• 10-13: Explainability with SHAP
• 13-18: Uncertainty quantification with Conformal Prediction
• 18-22: Cost-sensitive learning and real-time operations
• 22-25: Lessons learned
• 25-30: Q&A