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

We present the automated pipeline NEXUS (Near-real-time Event detection and eXtrapolation using a Unified Space weather 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. Triggered by entries in the CCMC DONKI database, NEXUS first applies the drag-based ELEvo model to determine whether an Earth impact is expected and estimates arrival time. This defines a temporal window for detecting the magnetic obstacle (MO) in real-time L1 in situ solar wind data, 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 NEXUS using 3870 archived DONKI entries and NOAA real-time in situ data from 2013–2025, assessing forecast performance at different stages of MO observation. For 61 events with an associated counterpart in the ICMECAT catalog, forecasts based on initial MO data already achieve performance comparable to full-event reconstructions. Typical errors are ∼5 hr 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-to-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 constraints.