most citedMachine learning for predicting the Bz magnetic field component from upstream in situ observations of solar coronal mass ejections

25 citations · 46 across the 4 of their papers we have counts for

collaborators

4 papers

physics.space-ph2024★ 20 cited

First observations of a geomagnetic superstorm with a sub-L1 monitor

Eva Weiler, Christian Möstl, Emma E. Davies +10

Forecasting the geomagnetic effects of solar coronal mass ejections (CMEs) is currently an unsolved problem. CMEs, responsible for the largest values of the north-south component o…

physics.space-ph2024

Flux rope modeling of the 2022 Sep 5 CME observed by Parker Solar Probe and Solar Orbiter from 0.07 to 0.69 au

Emma E. Davies, Hannah T. Rüdisser, Ute V. Amerstorfer +19

As both Parker Solar Probe (PSP) and Solar Orbiter (SolO) reach heliocentric distances closer to the Sun, they present an exciting opportunity to study the structure of CMEs in the…

astro-ph.SR2024★ 1 cited

Understanding the effects of spacecraft trajectories through solar coronal mass ejection flux ropes using 3DCOREweb

Hannah Theresa Rüdisser, Andreas Jeffrey Weiss, Justin Le Louëdec +4

This study investigates the impact of spacecraft positioning and trajectory on in situ signatures of coronal mass ejections (CMEs). Employing the 3DCORE model, a 3D flux rope model…

physics.space-ph2021★ 25 cited

Machine learning for predicting the Bz magnetic field component from upstream in situ observations of solar coronal mass ejections

Martin A. Reiss, Christian Möstl, Rachel L. Bailey +6

Predicting the Bz magnetic field embedded within ICMEs, also known as the Bz problem, is a key challenge in space weather forecasting. We study the hypothesis that upstream in situ…