EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Quantifying space-weather events using dynamical network analysis of ground based magnetometers

Shahbaz Chaudhry, Sandra Chapman, and Jesper Gjerloev
Shahbaz Chaudhry et al.
  • University of Warwick, Physics, United Kingdom of Great Britain – England, Scotland, Wales (

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 highly non-linear magnetospheric system during storms/sub-storms is important as our reliance on space based systems increases. We perform network analysis on the 100+ ground-based magnetometer stations collated by SuperMAG. One of the key geomagnetic responses to space weather events are Pc waves which are oscillations along whole magnetospheric magnetic field lines. Recently SuperMAG has offered the full set of Pc measurements collating magnetometer data globally. High quality Pc wave data has only been available locally across magnetometer chains. However, now with SuperMAG these measurements these are available globally with uniform background calibration and time-base. To fully exploit this data requires a new application of analysis tools, for the first time we apply dynamical network analysis to this data set. Obtaining Pc waves over a range of frequencies allows us to probe multiple time and length scales, likely corresponding to different physical generation mechanisms. We will aim to obtain the global Pc wave dynamical networks over individual space weather events in order to quantify the full spatio-temporal response of the magnetosphere to storms/sub-storms with a few network parameters.

To create the network we first band-pass filter magnetometer time series data into four known frequency intervals. Next the data is time-lagged-cross-correlated (TLXC) for each band ensuring a window at least twice the Pc wave period of interest. We then we use noise surrogates to establish a threshold to filter out insignificant peak TLXC values. For each windowed TLXC we build a peak-classification routine (PCR) to determine whether a signal is wavelike or not to then determine the phase difference. The PCR determines whether a network connection is directed or undirected between two geospatially located magnetometer stations for each time window. If the signal/time-series phase difference is found as non-zero for the TLXC function there is a directed network connection pair, otherwise an undirected network connection pair is formed. We perform the TLXC and PCR for each frequency band and between all magnetometer time-series pairs to obtain four dynamical directed and four dynamical undirected networks. The undirected networks quantify the onset time, and spatial extent, of large-scale coherent Pc wave activity. While directed networks also quantify how Pc wave activity is propagating across the magnetosphere for non-coherent Pc wave activity.

Quantifying the full spatio-temporal response of the magnetosphere across 100s of ground based magnetometers with a few parameters also forms the basis of statistical studies across many events.

How to cite: Chaudhry, S., Chapman, S., and Gjerloev, J.: Quantifying space-weather events using dynamical network analysis of ground based magnetometers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5804,, 2021.


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