Which Upstream Solar Wind Conditions Matter Most in Predicting Bz Within Coronal Mass Ejections

Accurately predicting the z-component of the interplanetary magnetic field, particularly during the passage of an interplanetary coronal mass ejection (ICME), is a crucial objective for space weather predictions. Currently, only a handful of techniques have been proposed and they remain limited in scope and accuracy. Recently, a robust machine learning technique was developed for predicting the minimum value of B_z within ICMEs based on a set of 42 "features," that is, variables calculated from measured quantities upstream of the ICME and within its sheath region. In this study, we investigate these so-called explanatory variables in more detail, focusing on those that were (a) statistically significant and (b) most important. We find that number density and magnetic field strength accounted for a large proportion of the variability. These features capture the degree to which the ICME compresses the ambient solar wind ahead. Intuitively, this makes sense: Energy made available to coronal mass ejections (CMEs) as they erupt is partitioned into magnetic and kinetic energy. Thus, more powerful CMEs are launched with larger flux-rope fields (larger B_z), at greater speeds, resulting in more sheath compression (increased number density and total field strength).