Tracking the split: a non-linear iterative approach to the monitoring of recent SAA evolution

Still today vaguely defined, the South Atlantic Anomaly (SAA) is the vast
geographic region where the Earth’s magnetic field is weakest relative to an
ideal Earth-centered dipole field, and the inner radiation belt comes closest
to the planet. Nonetheless it represents a major concern to the space science
community, since the local reduced magnetic intensity often results in satellite
outages and radiation hazard to humans, especially in geomagnetically disturbed
periods.
Since 1958, relentless investigation of the various morphological and dynamic
features of the SAA has been taking place, robustly relying on field, plasma and
particle measurements from Low-Earth-Orbit (LEO) satellites since the late
1970s.
New readings provided by magnetometers operating at LEO altitudes show that,
within the past decade, an apparent second center of minimum field intensity
has begun to be clearly resolved southwest of Africa, suggesting a possible rapid
splitting of the SAA into two cells. In addition to magnetic determinations, the
tracking of fluxes of sub-MeV electrons that are lost to the atmosphere when
drifting into the SAA due to its increased bounce loss cone, offers a specular
view of the same phenomenon. This multi-messenger approach from different
platforms is best suited to catch fine details of the splitting.
Directly stemming from the data-adaptive Empirical Mode Decomposition (EMD)
developed at NASA in the 1990s for the analysis of non-stationary signals, the
Fast Iterative Filtering (FIF) class of signal mode decompositions is recently
taking center stage due to enhanced rigorous formalization in terms of con-
vergence and stability. Multidimensional and Multivariate FIF (MMFIF) is a
brand-new extension that handles multidimensional and multichannel datasets.
The application of MMFIF techniques to magnetic-field and particle data from
an ensemble of LEO satellites has allowed us to best characterize the dynamic
evolution of the SAA lobes in the 2010s, and compare it to analogous data in
the literature from the previous decades.