‘Ensemble Modeling’ of the September 2017 CME Event Observed at Earth, STEREO-A, and Mars (Invited Presentation)

The Wang-Sheeley-Arge(WSA)-Enlil-cone modeling system is routinely used for making arrival time forecasts of the Earth-impacting coronal mass ejections (CMEs). At the same time, this modeling system has been valuable for providing global context to observations of CME propagation and impact at other vantage points, such as STEREO-A along 1 AU (e.g., Luhmann et al.) and Mars at ~1.5 AU (e.g., Lee et al.). The current progression along the Cycle 24 solar minimum provides a unique opportunity for us to revisit a recent modeling effort of the September 2017 CME structure that impacted all three observer locations. When the fast and wide CME erupted from active region 12673 (AR2673) on 10 September 2017, the space weather impact was widespread: the CME structure impacted Earth and Mars, while the SEPs accelerated by the moving CME shock front impacted Earth, Mars, and STEREO-A. During this time, Mars and STEREO-A were more than 90 degrees away from the Sun-Earth line. The modeling of this CME event using WSA-Enlil-cone and modeling of the SEP transport with the SPEMOD code was challenging: the initial cone fits yielded results that poorly matched the in situ CME and SEP measurements at all three observer locations. Only by varying the cone parameters, i.e., the angular width and propagation direction, over several iterations were we able to obtain modeling results that were consistent with those that were observed. In particular, the modeled arrival time at Mars was within 3 hours of the observed arrival time.

We will discuss our challenges in our ‘ensemble modeling’ of the September 2017 event period and the lessons learned in using WSA-Enlil-cone to model this widely-observed CME structure. In general, the modeling of real events like this one with any approach illustrates the challenges faced by those seeking to forecast them. Finding optimum strategies for ensemble modeling needs to be a key part of space weather prediction research.