Mother’s Day 2024 Extreme Solar Event: Modelling and Learning How to Improve Space Weather Forecasting

Between 10 and 14 May 2024, the Sun produced an extraordinary sequence of eruptions, the Mother’s Day event, culminating in the most intense geomagnetic storm in decades. This event was driven by at least ten interacting coronal mass ejections (CMEs), accompanied by solar flares and filament eruptions, forming highly complex heliospheric structures with exceptional geoeffectiveness. Such CME–CME interactions present significant challenges for operational space-weather forecasting. 

This study addresses the complexity of modelling extreme events within operational space weather frameworks. A key difficulty lies in constraining CME and solar wind input parameters, especially for halo and partial-halo CMEs, where parameter sensitivity is heightened. Accurate representation of CME–CME interactions necessitates physics-based magnetohydrodynamic (MHD) modelling rather than empirical approaches. 

We employ multi-point remote observations in white light and extreme ultraviolet to identify CME sources and derive their kinematic and geometric properties. These parameters drive three-dimensional MHD simulations of CME evolution and heliospheric propagation using the EUHFORIA model. Our best-performing simulation reproduced the storm’s arrival time within approximately two hours and estimated its peak intensity with ~70% accuracy. Crucially, the inclusion of all contributing CMEs was essential for achieving this level of predictive reliability. 

Our findings underscore the need for improved observational infrastructure and enhanced modelling capabilities to address the inherent complexity of extreme space weather events. Advancing the speed and accuracy of MHD-based forecasting tools is critical for mitigating the impacts of future solar superstorms. We highlight how the Mother’s Day 2024 event serves as a benchmark for understanding the limitations of current models and a call for the urgent requirement for community-wide investment in both observational and computational resources.