A Time-Efficient, Data Driven Modelling Approach For Predicting The Geomagnetic Impact of Coronal Mass Ejections

TL;DR

This paper presents a fast, data-driven modeling approach using 3D magnetohydrodynamics to predict geomagnetic impacts of coronal mass ejections, improving space weather forecasting with high correlation to observed indices.

Contribution

It introduces a simplified, time-efficient flux rope-magnetosphere interaction model that accurately estimates geomagnetic storm intensity using fewer parameters.

Findings

Simulated impacts correlate well with Dst/SYM-H indices.

The method is faster and requires fewer parameters than complex models.

Flux rope erosion has minimal effect on predictions.

Abstract

To understand the global-scale physical processes behind coronal mass ejection (CME)-driven geomagnetic storms and predict their intensity as a space weather forecasting measure, we develop an interplanetary CME flux rope-magnetosphere interaction module using 3D magnetohydrodynamics. The simulations adequately describe ICME-forced dynamics of the magnetosphere including the imposed magnetotail torsion. These interactions also result in induced currents which is used to calculate the geomagnetic perturbation. Through a suitable calibration, we estimate a proxy of geoeffectiveness -- the Storm Intensity index (STORMI) -- that compares well with the Dst/SYM-H Index. Simulated impacts of two contrasting coronal mass ejections quantified by the STORMI index exhibit a high linear correlation with the corresponding Dst and SYM-H indices. Our approach is relatively simple, has fewer parameters to be fine-tuned, is time-efficient compared to complex fluid-kinetic methods. Furthermore, we demonstrate that flux rope erosion does not significantly affect our results. Thus our method has the potential to significantly extend the time window for predictability -- an outstanding challenge in geospace environment forecasting -- if early predictions of near-Earth CME flux rope structures based on near-Sun observations are available as inputs. This study paves the way for early warnings based on operational predictions of CME-driven geomagnetic storms.