A catalogue of observed geo-effective CME/ICME characteristics

TL;DR

This paper compiles a comprehensive database of geo-effective CMEs, evaluates the drag-based model's effectiveness in predicting their propagation, and provides insights into CME characteristics and forecasting limitations.

Contribution

It introduces a validated, homogeneous CME database and assesses the drag-based model's accuracy using a Monte Carlo approach, highlighting its strengths and limitations.

Findings

The database reliably links in-situ and remote CME observations.

The drag-based model effectively predicts CME arrival times and speeds.

Certain CME events require more complex modeling due to model limitations.

Abstract

One of the goals of Space Weather studies is to achieve a better understanding of impulsive phenomena, such as Coronal Mass Ejections (CMEs), in order to improve our ability to forecast them and mitigate the risk to our technologically driven society. The essential part of achieving this goal is to assess the performance of forecasting models. To this end, the quality and availability of suitable data are of paramount importance. In this work, we have merged already publicly available data of CMEs from both in-situ and remote instrumentation in order to build a database of CME properties. To evaluate the accuracy of such a database and confirm the relationship between in-situ and remote observations, we have employed the drag-based model (DBM) due to its simplicity and inexpensive cost of computational resources. In this study, we have also explored the parameter space for the drag parameter and solar wind speed using a Monte Carlo approach to evaluate how well the DBM determines the propagation of CMEs for the events in the dataset. The dataset of geoeffective CMEs constructed as a result of this work provides validation of the initial hypothesis about DBM, and solar wind speed and also yields further insight into CME features like arrival time, arrival speed, lift-off time, etc. Using a data-driven approach, this procedure allows us to present a homogeneous, reliable, and robust dataset for the investigation of CME propagation. On the other hand, possible CME events are identified where DBM approximation is not valid due to model limitations and higher uncertainties in the input parameters, those events require more thorough investigation.