Extreme space-weather events known as storms/sub-storms can have severe impacts on technological systems, on the ground and in space, including damage to satellites and power blackouts in severe cases. Quantitative understanding of the magnetospheric system during storms/sub-storms is important as our reliance on space based systems increases.

The data we will use for our network analysis is obtained from the SuperMAG collaboration which collates data from >100 ground-based magnetometers. Using recently released 1s high time resolution data it is possible to capture more dynamical detail of the magnetosphere during storms and substorms.

To understand these extreme space weather events, we capture information of oscillations along whole magnetic field lines known as Pc waves. We will use this Pc data in multiple frequency bands in our network analysis. Networks are a broadly used analysis tool and applied in areas such as social data, where people are linked based on various social relationships. In this project we link magnetometer stations based on similarity of their time series to build our dynamical Pc networks.

Eventual using our dynamical networks we aim to characterise Pc wave onset, resonance, propagation to translate the full spatio-temporal pattern into a few network parameters. We then aim to find how these network parameters correlate with wider behaviour of the system.