Network analysis of geomagnetic substorms using the SuperMAG database of ground-based magnetometer stations

TL;DR

This study introduces a novel network-based method to quantitatively analyze and differentiate geomagnetic substorm dynamics using ground-based magnetometer data, providing new insights into substorm characterization.

Contribution

First application of dynamical network analysis to ground-based magnetometer data for substorm characterization, including optimization of network parameters for better differentiation.

Findings

Substorm onsets show consistent network responses in spatial correlation.

Network responses differ between substorms, steady convection, and no activity.

Method enables quantitative categorization of substorm dynamics.

Abstract

The overall morphology and dynamics of magnetospheric substorms is well established in terms of the observed qualitative auroral features seen in ground-based magnetometers. This paper focuses on the quantitative characterization of substorm dynamics captured by ground-based magnetometer stations. We present the first analysis of substorms using dynamical networks obtained from the full available set of ground-based magnetometer observations in the Northern Hemisphere. The stations are connected in the network when the correlation between the vector magnetometer time series from pairs of stations within a running time window exceeds a threshold. Dimensionless parameters can then be obtained that characterize the network and by extension, the spatiotemporal dynamics of the substorm under observation. We analyze four isolated substorm test cases as well as a steady magnetic convection (SMC) event and a day in which no substorms occur. These test case substorms are found to give a consistent characteristic network response at onset in terms of their spatial correlation. Such responses are differentiable from responses to the SMC event and nonsubstorm times. We present a method to optimize network parametrization with respect to the different individual station responses, the spatial inhomogeneity of stations in the Northern Hemisphere, and the choice of correlation window sizes. Our results suggest that dynamical network analysis has potential to quantitatively categorize substorms.