Hybrid Physical–Machine Learning Framework for Polar Vortex and Sudden Stratospheric Warming Detection
Abstract
Hybrid Δ±1 physical index combined with XGBoost captures non-linear interactions of space weather and atmospheric physics, offering a reproducible pipeline to detect rare stratospheric events. Synthetic data demonstrate methodological feasibility; observational validation is next.
Fields of Study & Subjects
Ionosphere and magnetosphere dynamics
Atmospheric Ozone and Climate
Meteorological Phenomena and Simulations
Publication details
- Publication date
- 1 January 2026
- Document type
- Preprint