Extreme Solar Storm Reveals Causal Interactions in Space Weather

TL;DR

This paper introduces an information-theoretic framework to analyze and predict extreme solar geomagnetic storms, revealing new causality patterns and improving forecasting accuracy for space weather events.

Contribution

It presents a novel causal analysis method for space weather dynamics and demonstrates its effectiveness in predicting extreme solar storms using extensive datasets.

Findings

Uncovered auroral spatial causality patterns during May 2024 storms.

Integrated causal constraints improve forecasting accuracy.

Framework outperforms existing models in predicting extreme events.

Abstract

Solar storms perturb Earth's magnetosphere, triggering geomagnetic storms that threaten space-based systems and infrastructure. Despite advances in spaceborne and ground-based observations, the causal chain driving solar-magnetosphere-ionosphere dynamics remains elusive due to multiphysics coupling, nonlinearity, and cross-scale complexity. This study presents an information-theoretic framework to decipher interaction mechanisms in extreme solar geomagnetic storms across intensity levels within space weather causal chains, using 1980-2024 datasets. Unexpectedly, we uncover auroral spatial causality patterns associated with space weather threats in the Arctic during May 2024 extreme storms. By integrating causal consistency constraints into spatiotemporal modeling, SolarAurora outperforms existing frameworks, achieving superior accuracy in forecasting May/October 2024 events. These results advance understanding of space weather dynamics and establish a promising framework for scientific discovery and forecasting extreme space weather events.