Examining the response of Earth's magnetosphere to extreme southward turnings of the Interplanetary
Research focussing on studying how the magnetosphere responds to extreme changes in solar wind driving, specifically to large and rapid north-south turnings of the IMF.
Chiara Lazzeri
Supervised by: Colin Forsyth, Andrey Samsonov, Andrew Fazakerley
Changes in the orientation of the interplanetary magnetic field (IMF) can strongly affect our planet’s magnetosphere. While under northward IMF geomagnetic activity is reduced, under southward IMF the transfer of energy from the solar wind into the magnetosphere, and consequently the level of geomagnetic activity, is enhanced. My research focuses on studying how the magnetosphere responds to extreme changes in solar wind driving, specifically to large and rapid north-south turnings of the IMF.
In the first project of my PhD, I conducted a statistical analysis of the magnetospheric response to these southward turnings as quantified by geomagnetic indices measured by ground magnetometers. These indices are indicators of different types of geomagnetic activity and can inform us of the strength and timescales of their response. The results of this work were published in the Journal of Geophysical Research (see ). https://doi.org/10.1029/2023JA032160
In the second project of my PhD, I investigated the excitation of magnetospheric Ultra Low Frequency (ULF) waves following the arrival of an IMF southward turning; this is an aspect of the magnetospheric response that cannot be captured by geomagnetic indices. The project involved the analysis of in-situ data provided by the THEMIS and GOES missions and of ground magnetometer data, as well as the use of global magnetohydrodynamics (MHD) modelling of the magnetosphere.
I am currently working on evaluating and comparing the performance of empirical magnetosheath models to determine whether they are suitable for obtaining simulations of the SMILE mission Soft X-ray Imager (SXI) output. While global MHD models of the magnetosphere are usually employed for this purpose, using empirical models could reduce computational costs and simplify forward modelling.