Speaker
Description
Coronal Mass Ejections (CMEs) are among the most impactful solar transients, capable of driving intense space weather phenomena, including geomagnetic storms and solar energetic particle (SEP) events. The near-Sun CME velocity plays a vital role in space weather forecasting, serving as a key parameter for estimating ICME arrival time, SEP intensity, and overall geoeffectiveness. While considerable progress has been made in predicting CME occurrence, arrival, and impact using machine learning and various solar proxies, the direct prediction of CME speed remains relatively underexplored. One of the primary challenges is the absence of consistent magnetic proxies that correlate reliably with CME velocity.
Previous statistical studies (e.g., Tiwari et al., 2015; Vijayalakshmi et al., 2023) have shown that CME velocity exhibits positive correlations with the magnetic properties of their source active regions (ARs), particularly for fast or radio-loud CMEs. In contrast, slow or radio-quiet CMEs display weak or inconsistent relationships, especially when only instantaneous AR properties at the time of eruption are considered.
In this study, we examine whether time-averaged magnetic diagnostics can improve predictability. We analyze sunspot-associated CMEs from the raising phase of Solar Cycle 24 and compare their deprojected velocities, derived using the Graduated Cylindrical Shell (GCS) method, with SHARP parameters averaged over a range of lead times (3 to 24 hours, in 3-hour intervals) prior to eruption. We classify the events into fast (≥ 900 km/s) and slow (< 900 km/s) CMEs and observe that this separation enhances the correlation strength for fast CMEs. Interestingly, we identify a subset of slow CMEs that exhibit inverse relationships with certain SHARP parameters, thereby suppressing overall correlation in the slow group. We further investigate the velocity threshold at which this inversion becomes statistically significant.
Our results offer new insight into the temporal and magnetic conditions that influence CME dynamics and highlight the feasibility of forecasting CME velocity using pre-eruption AR properties—laying the groundwork for future predictive modeling efforts.
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