Heliophysics Science Division
Dimitris Vassiliadis - Abstract

Mapping Inner Magnetospheric Convection and Injections
from Ground and Geosynchronous Measurements

Dimitris Vassiliadis
USRA at NASA/GSFC, Greenbelt, Maryland

Ring current structure and activity is determined by several macroscopic processes: convection, particle injections, and compression. While ground magnetometer measurements and derived indices offer simple glimpses of these events, by combining data analysis methods one can reconstruct a significant amount of information often sufficient to quantify the determinant processes. The first part of the discussion concerns high-time-resolution index modeling (Dst, ASYM). The time scales of predictive models are functions of Dst activity, storm phase, and solar wind input. The impulse responses, measuring geoeffectiveness, also depend on storm phase. During the commencement and main phase the models show that the response is damped-oscillatory with periods of ~10 min and ~1 hour, respectively. The oscillations are found in spectrograms of the index and the original magnetograms. Comparisons with LANL geosynchronous data show that the 1-hour oscillations coincide with injection and drift echo signatures.

In the next part ground magnetograms and geosynchronous measurements are used to synthesize the longitudinal profile of the ring current activity. In this representation substorms and convection are seen to develop as two distinct response modes, both contributing to the ring current and having separable temporal and spatial features. With the substorm and solar wind compression effects removed, and via the DPS relation, the corrected magnetic disturbance gives an estimate of the total particle energy contributing to the ring current (and effectively a new, corrected stormtime index). By combining the ground magnetograms with the solar wind electric field input one can produce empirical predictive models of the geomagnetic disturbance. Nonlinear predictions based on these magnetometer models are significantly more accurate than earlier Dst-model predictions.