Predicting CME Arrivals with Heliospheric Imagers from L5: A Data Assimilation Approach

The Solar TErrestrial RElations Observatory (STEREO) mission has laid a foundation for advancing real-time space weather forecasting by enabling the evaluation of heliospheric imager (HI) data for predicting coronal mass ejection (CME) arrivals at Earth. This study employs the ELEvoHI model to assess how incorporating STEREO/HI data from the Lagrange 5 (L5) perspective can enhance prediction accuracy for CME arrival times and speeds. Our investigation, preparing for the upcoming ESA Vigil mission, explores whether the progressive incorporation of HI data in real-time enhances forecasting accuracy. The role of human tracking variability is evaluated by comparing predictions based on observations by three different scientists, highlighting the influence of manual biases on forecasting outcomes. Furthermore, the study examines the efficacy of deriving CME propagation directions using HI-specific methods versus coronagraph-based techniques, emphasising the trade-offs in prediction accuracy. Our results demonstrate the potential of HI data to significantly improve operational space weather forecasting when integrated with other observational platforms, especially when HI data from beyond 35° elongation are used. These findings pave the way for optimising real-time prediction methodologies, providing valuable groundwork for the forthcoming Vigil mission and enhancing preparedness for CME-driven space weather events.

💡 Research Summary

The prediction of Coronal Mass Ejection (CME) arrival times and velocities is a critical component of space weather forecasting, essential for protecting Earth’s technological infrastructure. This paper presents a sophisticated study on enhancing these predictions by utilizing Heliospheric Imager (HI) data from the L5 Lagrange point, specifically through a data assimilation approach using the ELEvoHI model. As the scientific community prepares for the upcoming ESA Vigil mission, this research provides a vital evaluation of how integrating L5-based observations can transform real-time forecasting capabilities.

The core of the investigation lies in the systematic application of data assimilation. The researchers explored how the progressive incorporation of STEREO/HI data into the ELEvoHI model affects the accuracy of predicting CME arrival at Earth. A significant finding of the study is the identification of a critical threshold: the integration of HI data becomes particularly effective at improving prediction accuracy when the data covers an elongation of more than 35° from the Sun. This suggests that the side-view perspective provided by the L5 vantage point is most potent during the mid-to-late stages of CME propagation.

Beyond the data integration itself, the study addresses the “human element” in space weather forecasting. By comparing predictions derived from the manual tracking efforts of three different scientists, the authors quantified the impact of human-induced variability and manual bias on forecasting outcomes. This analysis highlights a significant source of uncertainty in current methodologies and underscores the urgent need for standardized, automated tracking algorithms to ensure consistent and reliable predictions.

Furthermore, the paper evaluates the technical trade-offs between different methods of determining CME propagation directions. It compares HI-specific derivation techniques with traditional coronagraph-based methods, analyzing how each approach influences the overall error margin in arrival time and speed predictions. This comparative analysis is crucial for optimizing the multi-platform observational strategies required for the next generation of space weather monitoring.

In conclusion, the study demonstrates that the synergy between L5-based heliospheric imaging and advanced numerical models like ELEvoHI holds immense potential for the future of space weather operational forecasting. By providing a technical roadmap for data assimilation and highlighting the importance of overcoming human-centric biases, this research serves as a foundational pillar for the success of the ESA Vigil mission and the global effort to mitigate the impacts of CME-driven geomagnetic storms.