Predicting the magnetic vectors within coronal mass ejections arriving at Earth: 1. Initial Architecture

TL;DR

This paper introduces an initial architecture combining a helicity rule and flux rope topology to predict magnetic vectors in Earth-directed CMEs, aiming to improve space weather forecasting accuracy.

Contribution

It presents a novel proof-of-concept model that predicts CME magnetic vectors by integrating solar origin helicity with flux rope topology, tested on multiple events.

Findings

Angular rotation in predictions matches in situ data

Early CME evolution impacts forecasting accuracy

Time-varying magnetic field estimates enable Kp index prediction

Abstract

The process by which the Sun affects the terrestrial environment on short timescales is predominately driven by the amount of magnetic reconnection between the solar wind and Earth's magnetosphere. Reconnection occurs most efficiently when the solar wind magnetic field has a southward component. The most severe impacts are during the arrival of a coronal mass ejection (CME) when the magnetosphere is both compressed and magnetically connected to the heliospheric environment. Unfortunately, forecasting magnetic vectors within coronal mass ejections remains elusive. Here we report how, by combining a statistically robust helicity rule for a CME's solar origin with a simplified flux rope topology the magnetic vectors within the Earth-directed segment of a CME can be predicted. In order to test the validity of this proof-of-concept architecture for estimating the magnetic vectors within CMEs, a total of eight CME events (between 2010 and 2014) have been investigated. With a focus on the large false alarm of January 2014, this work highlights the importance of including the early evolutionary effects of a CME for forecasting purposes. The angular rotation in the predicted magnetic field closely follows the broad rotational structure seen within the in situ data. This time-varying field estimate is implemented into a process to quantitatively predict a time-varying Kp index that is described in detail in paper II. Future statistical work, quantifying the uncertainties in this process, may improve the more heuristic approach used by early forecasting systems.