Morphological Fingerprints of Forbush Decreases and Their Relation to Geomagnetic Storm Severity

TL;DR

This paper introduces a graph-based method to analyze Forbush decrease events, extracting morphological fingerprints from station response networks to predict geomagnetic storm severity.

Contribution

It presents a novel graph-based event representation and fingerprinting approach that improves the quantitative comparison and severity prediction of Forbush decreases.

Findings

Fingerprints enable multi-class classification of storm intensity.

High sensitivity in binary severity screening for severe events.

Partial least squares regression shows positive explained variance.

Abstract

Forbush decreases (FDs) are transient depressions in the galactic cosmic-ray flux observed by global neutron-monitor networks and are commonly associated with interplanetary disturbances driven by coronal mass ejections and related shocks. Despite extensive observational work, quantitatively comparing FD morphology across events and linking it to storm severity remains challenging due to heterogeneous station responses, coverage gaps, and the multivariate nature of the network. This work introduces a graph-based event representation in which each FD is mapped to an event network constructed from pairwise dissimilarities between station response time series. A controlled sparse backbone is obtained via the minimum spanning tree, enabling comparable event graphs across cases. From each graph, a compact set of geometric/topological fingerprints is computed, including global integration measures, spectral summaries, mesoscopic structure, centrality aggregates, and complexity descriptors. Predictive skill is assessed using strict leave-one-event-out validation over a pre-defined grid of distance metrics and distance-domain transformations, with selection criteria fixed \emph{a priori}. The proposed fingerprints exhibit measurable signal for three tasks: (i) multi-class classification of geomagnetic storm intensity (G3/G4/G5) with moderate but consistent performance and errors dominated by adjacent categories; (ii) stronger binary severity screening ($\ge$G4 vs. G3) with high sensitivity to severe events; and (iii) drop regression with partial least squares achieving positive explained variance relative to a fold-wise mean baseline.