Abstract: Empirical magnetic field models have long been important tools in the magnetospheric physics community as knowledge of the global 3D magnetic field and its time-evolution are crucial ingredients for understanding the physics of the magnetosphere. For instance, empirical magnetic field models are used to map spacecraft locations to the ionosphere, so that in-situ measurements can be compared to auroral signatures, or for determining magnetic field line conjunctions of spacecraft observations. These models are often employed to compute the adiabatic invariants for particle observations, and are also useful for tracing particle trajectories within the magnetosphere. The latest generation of these models utilize powerful data-mining algorithms to dynamically bin spaceborne magnetometer data, resulting in magnetic field models with an unprecedented ability to reconstruct the global dynamics of geomagnetic storms and substorms. This has elevated the status of empirical magnetic field models to being a scientific analysis resource in their own right. Here, we overview this approach and highlight our recent results.

Reference:

https://doi.org/10.1029/2018JA025843