Improving CME Forecasting Capability: An Urgent Need

Abstract

Key Points CMEs are highly significant in terms of space weather Operational CME forecasting capability requires strengthening All stakeholders should be involved

EUHFORIA: European heliospheric forecasting information asset

“…Similar errors are reported by Mays et al (2015) who utilize ensemble modeling employing the WSA-ENLIL model. While predicting the time of arrival is certainly crucial, a major complication is that not all CMEs are equally geoeffective, and thus, it is paramount to improve our ability to predict their geo-effectiveness (see e.g., Siscoe 2007;Zheng 2013;Lavraud & Rouillard 2013). The main problem is that our ability to predict the magnetic field characteristics of CMEs, a major factor determining their geo-effectiveness, is minimal.…”

The application of heliospheric imaging to space weather operations: Lessons learned from published studies

“…The derived CME characteristics are used to initialize solar wind propagation models that yield predictions of CME arrival time, and speed, at Earth. At the current time, the combined WSA (Wang‐Sheeley‐Arge) [ Arge and Pizzo , ] + Enlil modeling framework provides the basis of the majority of operational CME arrival predictions [e.g., Zheng , ]. Uncertainties in characterizing CMEs—as well as the background solar wind through which they propagate (the latter is generally extrapolated from solar surface magnetic field measurements using the WSA component of the coupled model)—combined with potential breakdowns in the underlying assumptions inherent in all modeling endeavors, can lead, on occasion, to extremely poor predictions of CME arrival [e.g., Cash et al ., ].…”

“…The derived CME characteristics are used to initialize solar wind propagation models that yield predictions of CME arrival time, and speed, at Earth. At the current time, the combined WSA (Wang-Sheeley-Arge) [Arge and Pizzo, 2000] + Enlil modeling framework provides the basis of the majority of operational CME arrival predictions [e.g., Zheng, 2013].…”

“…Obviously, the appearance of CMEs in visible‐light heliospheric imagery lends itself to direct comparison with the results of such modeling; indeed, hopefully, in the future, the modeling will benefit from the direct ingestion of information on CMEs, and potentially the background solar wind, afforded by such imaging of the heliosphere. At the current time, the combined WSA‐Enlil modeling framework—used in conjunction with the CAT tool—provides the basis for the majority of operational CME arrival predictions [e.g., Zheng , ; Zhao and Dryer , ].…”

Space Weather Quarterly Volume 14, Issue 2, 2017

“…While CME and SEP forecasting is still in its infancy [Zheng, 2013;Luhmann et al, 2015], much research has already been undertaken in the field of solar flare forecasting. Early work focused on statistical methods based on historical flaring rates [McIntosh, 1990;Gallagher et al, 2002;Leka and Barnes, 2007;Wheatland, 2005]; however, more complex methods have been developed in recent years Georgoulis and Rust, 2007], and sophisticated computational techniques such as machine learning [Ahmed et al, 2013;Bobra and Couvidat, 2015] have become popular.…”