Recurrence based quantification of dynamical complexity in the Earth's magnetosphere at geospace storm timescales

TL;DR

This study applies advanced nonlinear recurrence analysis techniques to geomagnetic data, distinguishing storm-time magnetospheric activity driven by external solar wind influences from internal variability, aiding in space weather prediction.

Contribution

It introduces a novel application of recurrence quantification and network analysis to classify magnetic storm periods based on surface observations.

Findings

Successfully discriminates external vs. internal magnetospheric variability.

Provides a physically meaningful classification of storm periods.

Offers potential improvements for space weather forecasting.

Abstract

Magnetic storms are the most prominent global manifestations of out-of-equilibrium magnetospheric dynamics. Investigating the dynamical complexity exhibited by geomagnetic observables can provide valuable insights into relevant physical processes as well as temporal scales associated with this phenomenon. In this work, we utilize several innovative data analysis techniques enabling a quantitative nonlinear analysis of the nonstationary behavior of the disturbance storm time (Dst) index together with some of the main drivers of its temporal variability, the $VB_{South}$ electric field component, the vertical component of the interplanetary magnetic field, $B_z$, and the dynamic pressure of the solar wind, $P_{dyn}$. Using recurrence quantification analysis (RQA) and recurrence network analysis (RNA), we obtain several complementary complexity measures that serve as markers of different physical processes underlying quiet and storm time magnetospheric dynamics. Specifically, our approach discriminates the magnetospheric activity in response to external (solar wind) forcing from primarily internal variability and provides a physically meaningful classification of magnetic storm periods based on observations made at the Earth's surface. In this regard, the proposed methodology could provide a relevant step towards future improved space weather and magnetic storm forecasts.