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arXiv · 2602.04427

Cosmic Ray Inter-Station Correlation Variations as Precursors of Geomagnetic Storms: A Statistical Study and Multi-Parameter Early Warning Framework

Haoyang Li·Zongyuan Ge·Zhaoming Wang
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Abstract

The modulation of galactic cosmic rays (GCRs) by interplanetary disturbances, manifested as Forbush decreases (FDs), has long been recognized as a signature of coronal mass ejection (CME) passages through the heliosphere. While individual FD events have been extensively studied, systematic investigations of how GCR inter-station correlation variations relate to geomagnetic storm (GS) intensity have not been established. Here we analyze the relationship between GCR characteristics (from a global NM network) and GSs, aiming to understand the physical mechanisms of heliospheric disturbances and to develop complementary predictive capabilities beyond existing L1 solar wind monitoring. By applying a newly introduced anisotropy characteristic method alongside correlation analysis to 25 years of hourly NM data (1995-2020, seven stations), we demonstrate significant correlations between GCR parameters and geomagnetic activity. Inter-station relative differences and anisotropy enhancements show distinct precursor signatures depending on storm intensity, with extreme events displaying detectable signals 48-96 hours in advance. Based on these intensity-dependent response patterns, we propose a "two-stage multi-level" early warning framework: mid-term identification (48-96 hr) triggered by sustained anisotropy increases, followed by short-term grading (0-48 hr) based on inter-station relative difference variations and high-latitude flux changes. Validated on the extreme November 2003 and severe August 2018 geomagnetic storms, our approach successfully identifies precursor signals, providing a potential means to extend GS prediction windows.

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Haoyang Li, Zongyuan Ge, Zhaoming Wang. 2026-02-04. Cosmic Ray Inter-Station Correlation Variations as Precursors of Geomagnetic Storms: A Statistical Study and Multi-Parameter Early Warning Framework. https://arxiv.org/abs/2602.04427

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