An automated pipeline forecasts CME magnetic fields at L1 using initial magnetic obstacle data, achieving errors of roughly 5 hours in timing and 10 nT in strength comparable to full-event reconstructions.
J., Nieves-Chinchilla, T., & M¨ ostl, C
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CMEs during active solar phases are faster with stronger magnetic fields than in quiet phases (even after speed matching), while toroidal and poloidal field components decay similarly with distance and front-to-rear asymmetry increases.
MHD modeling of the 2024 October 26 CME demonstrates that specific pre-eruptive magnetic flux rope footpoint locations and near-real-time background fields are required to reproduce observed complex morphology from multiple viewpoints without fine-tuning.
citing papers explorer
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Towards a Fully Automated Pipeline for Short-Term Forecasting of In Situ Coronal Mass Ejection Magnetic Field Structure
An automated pipeline forecasts CME magnetic fields at L1 using initial magnetic obstacle data, achieving errors of roughly 5 hours in timing and 10 nT in strength comparable to full-event reconstructions.
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Evolution of Coronal Mass Ejection Properties through Superposed Epoch Analysis from 0.2 to 2.2 au
CMEs during active solar phases are faster with stronger magnetic fields than in quiet phases (even after speed matching), while toroidal and poloidal field components decay similarly with distance and front-to-rear asymmetry increases.
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Understanding the complex morphology of a CME II: how pre-eruptive conditions shape CME evolution
MHD modeling of the 2024 October 26 CME demonstrates that specific pre-eruptive magnetic flux rope footpoint locations and near-real-time background fields are required to reproduce observed complex morphology from multiple viewpoints without fine-tuning.