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Ensemble Prediction of a Halo Coronal Mass Ejection Using Heliospheric Imagers
T Amerstorfer1, C Möstl1, P Hess2
1Space Research Institute Austrian Academy of Sciences Graz Austria.
The Solar Terrestrial Relations Observatory (STEREO) heliospheric imagers improve understanding of coronal mass ejection (CME) propagation. Ensemble modeling with ELEvoHI significantly reduced CME arrival time errors, suggesting operational feasibility.
Area of Science:
- Space Physics
- Heliophysics
- Solar Eruptions
Background:
- The Solar Terrestrial Relations Observatory (STEREO) and its heliospheric imagers (HIs) enable enhanced understanding of coronal mass ejection (CME) interplanetary propagation.
- HI-based methods can forecast CME arrival times and speeds, tracing their paths up to and beyond 1 AU.
Purpose of the Study:
- To utilize the ELEvoHI model with an ensemble approach for CME arrival prediction and uncertainty quantification.
- To analyze a specific CME event from November 3, 2010, using 339 model runs and comparing with in situ measurements.
Main Methods:
- Employed the ELEvoHI model for CME arrival prediction.
- Utilized an ensemble approach with 339 model runs to derive uncertainties in arrival time and impact speed.
- Analyzed frequency distributions of ELEvoHI output parameters (drag parameter, solar wind speed, initial distance, speed) to identify optimal predictions.
Main Results:
- A promising approach using frequency distributions of ELEvoHI outputs yielded predictions with smaller errors.
- Restricting the ensemble to runs with the most frequent parameter values reduced the mean absolute arrival time error at 1 AU from 3.5 ± 2.6 hr to 1.6 ± 1.1 hr.
- Remote HI observations from STEREO-B, showing a halo CME, were comparable to an L1 observer viewing an Earth-directed CME.
Conclusions:
- The L1 vantage point may be sufficient for observing Earth-directed CMEs with heliospheric imagers.
- Ensemble modeling with ELEvoHI shows potential for operational CME arrival time predictions.
- The study highlights the effectiveness of refining ensemble members based on parameter frequency distributions.
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