Speaker
Description
Timely and accurate forecasting of interplanetary coronal mass ejections (ICMEs) is essential for mitigating their impact on space- and ground-based infrastructure. While significant advances have been made in predicting ICME arrival times and identifying their in situ signatures, integrating these steps into a continuous operational pipeline remains a challenge.
In this work, we present the next major development of the ARCANE framework by coupling it with arrival time forecasting and real-time magnetic flux rope reconstruction. The resulting automated pipeline combines:
(1) ELEvo - a drag-based model for predicting ICME arrival times
(2) ARCANE - a machine learning–based framework for automatic ICME detection in solar wind in situ data
(3) 3DCORE - a semi-empirical flux rope model that is now automatically triggered by ARCANE to perform real-time reconstruction of the ICME’s internal magnetic structure.
Here, we demonstrate the first fully automated pipeline capable of identifying the onset of an ICME’s magnetic obstacle in real-time and initiating immediate 3D modeling of its internal structure. By integrating detection and modeling into a unified system, we enable both improved nowcasting and short-term forecasting. We describe the technical implementation of this end-to-end framework, showcase initial results and discuss its potential for operational use in the future, combining physics-based models with AI for improved space weather forecasting.
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