Towards a Fully Automated Pipeline for Short-Term Forecasting of In Situ Coronal Mass Ejection Magnetic Field Structure

Abstract

We present an automated pipeline for operational short-term forecasting of coronal mass ejection (CME) magnetic field structure at L1, coupling arrival time prediction, in situ detection, and iterative flux rope reconstruction, following near-real-time remote-sensing CME identification. The system is triggered by new entries in the CCMC DONKI database and first applies the drag-based ELEvo model to determine whether an Earth impact is expected and estimate arrival time. This estimate defines a temporal window constraining the search for CME signatures in real-time L1 in situ solar wind data, where the magnetic obstacle (MO) is automatically detected using the deep learning model ARCANE. Upon MO onset, iterative reconstructions with the semi-empirical flux rope model 3DCORE are performed, using a Monte Carlo fitting scheme, producing continuously updated forecasts of the remaining magnetic field profile. We evaluate the pipeline using 3870 archived DONKI entries and archived NOAA real-time in situ data from 2013-2025, assessing forecast performance at different stages of MO observation. For 61 events with an associated ground-truth counterpart in the ICMECAT catalog, forecasts based on initial MO data already achieve performance comparable to full-event reconstructions. Typical errors are ~5 hours in timing of magnetic field extrema and ~10 nT in field strength metrics, with limited systematic improvement as more of the event is observed. Results show substantial event variability and systematic underestimation of extrema, indicating deviations from ideal flux rope assumptions. These findings demonstrate the potential of fully autonomous real-time forecasting while highlighting limitations imposed by event complexity and model representational capacity.