Data-based Modeling of the Geomagnetosphere: Lessons, Challenges, Promises

Nikolai Tsyganenko
Raytheon ITSS at NASA/GSFC

Modeling of the global geomagnetic field has an important place in the Sun-Earth connection studies, since that field underlies all processes in the geospace environment: it links the interplanetary medium with the ionosphere, traps energetic particles into the radiation belts, directs electric currents, controls the auroral plasma, and stores the energy, dissipated during magnetospheric disturbances.

A huge amount of magnetospheric and concurrent solar wind data exists from several decades of spaceflight, and an abundant flow of new measurements continues to add to it. The goal of the data-based modeling is to bridge the gap between these data and theory, extract full information from observations, and answer the fundamental question: "What do the data tell us about the magnetic structure of the geospace and its response to changing interplanetary conditions ?"

Creating an empirical model involves three essentially different tasks. First, one has to develop flexible and physically sensible mathematical representation of the B-field on a global scale. Second, one needs to compile large sets of space magnetometer data and tag them by the concurrent information on the solar wind state. Third, a meaningful choice should be made of the input parameters.

This talk will overview all the above aspects and discuss recent progress in the area of the empirical field modeling, based on new advanced methods and new data.