Quantifying errors in 3D CME parameters derived from synthetic data using white-light reconstruction techniques
Christine Verbeke, M. Leila Mays, Christina Kay, Pete Riley, Erika, Palmerio, Mateja Dumbovi\'c, Marilena Mierla, Camilla Scolini, Manuela, Temmer, Evangelos Paouris, Laura A. Balmaceda, Hebe Cremades, J\"urgen, Hinterreiter

TL;DR
This study quantifies the uncertainties in 3D CME parameters derived from white-light coronagraph images using synthetic data, highlighting the importance of multiple viewpoints for accurate space weather forecasting.
Contribution
It introduces a novel approach to quantify errors in CME parameter estimation using synthetic scenarios and evaluates the impact of viewing configurations on reconstruction accuracy.
Findings
Single viewpoint reconstructions have larger errors.
Adding a second viewpoint reduces errors significantly.
Estimated minimum error bars for CME parameters are provided.
Abstract
(Shortened version) Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make predictions, methods have been developed to derive the true CME speed, size and position, among others. Difficulties in determining input parameters for CME forecasting arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques. We explore how subjectivity affects the 3D CME parameters that are obtained from the GCS reconstruction technique. We have designed two different synthetic scenarios where the ``true'' geometric parameters are known in order to quantify such uncertainties for the first time. We explore this as follows: 1) Using the…
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