Magnetospheric impulse response for many levels of geomagnetic activity

TLDR

Linear prediction filtering is used to model the magnetospheric response to solar wind input, yielding time‑lagged coefficients that capture both direct solar‑wind coupling and delayed tail‑energy release. The study applied linear prediction filtering to 34 high‑resolution IMP 8 solar‑wind intervals ranked by median AL index to model the AL response to the VB s input. The filtering reveals two response pulses—20 min and 60 min—whose amplitudes vary with activity level, indicating that both direct solar‑wind driving and tail‑energy unloading contribute to magnetospheric response.

Abstract

The temporal relationship between the solar wind and magnetospheric activity has been studied using 34 intervals of high time resolution IMP 8 solar wind data and the corresponding AL auroral activity index. The median values of the AL index for each interval were utilized to rank the intervals according to geomagnetic activity level. The linear prediction filtering technique was then applied to model magnetospheric response as measured by the AL index to the solar wind input function VB s . The linear prediction filtering routine produces a filter of time‐lagged response coefficients which estimates the most general linear relationship between the chosen input and output parameters of the magnetospheric system. It is found that the filters are composed of two response pulses speaking at time lags of 20 and 60 min. The amplitude of the 60‐min pulse is the larger for moderate activity levels, while the 20‐min pulse is the larger for strong activity levels. A possible interpretation is that the 20‐min pulse represents magnetospheric activity driven directly by solar wind coupling and that the 60‐min pulse represents magnetospheric activity driven by the release of energy previously stored in the magnetotail. If this interpretation is correct, the linear filtering results suggest that both the driven and the unloading models of magnetospheric response are inportant facets of a more comprehensive response model.