A preliminary investigation of dynamical complexity in Swarm Dst-like time series using information theory techniques

For 7 years now, the European Space Agency’s Swarm fleet of satellites surveys the Earth’s magnetic field, measuring magnetic and electric fields at low-Earth orbit (LEO) with unprecedented detail. We have recently demonstrated the feasibility of Swarm measurements to derive a Swarm Dst-like index for the intense magnetic storms of solar cycle 24. We have shown that the newly proposed Swarm Dst-like index monitors magnetic storm activity at least as good as the standard Dst index. The Swarm derived Dst index can be used to (1) supplement the standard Dst index in near-real-time geomagnetic applications and (2) replace the ‘prompt’ Dst index during periods of unavailability. Herein, we employ a series of information theory methods, namely Hurst exponent and various entropy measures, for analyzing Swarm Dst-like time series. The results show that information theory techniques can effectively detect the dissimilarity of complexity between the pre-storm activity and intense magnetic storms (Dst < 150 nT), which is convenient for space weather applications.