Research Article

A catalogue of observed geo-effective CME/ICME characteristics

¹ Department of Physics, University of Rome “Tor Vergata”, Via della Ricerca Scientifica 1, I-00133 Rome, Italy
² SP2RC, School of Mathematics and Statistics, University of Sheffield, Hicks Building, 10 Broomhall, Sheffield S3 7RH, England
³ Institute of Astrophysics and Space Science, University of Coimbra, 3004-531 Coimbra, Portugal
⁴ Dipartimento di Scienze Fisiche e Chimiche, Università dell’Aquila, Via Vetoio, 67100 L’Aquila, Italy
⁵ Ecole Centrale de Lyon, CNRS, Universite Claude Bernard Lyon 1, INSA Lyon, LMFA, UMR5509, 69130 Ecully, France
⁶ Dipartimento di Fisica, Sapienza Università di Roma, P. le A. Moro 2, Roma, Italy
⁷ Space Weather Research Technology Education Center (SWx-TREC), University of Colorado, Boulder, CO 80309, USA
⁸ Queen Mary University of London, London E1 4NS, UK
⁹ Department of Astronomy, Eötvös Loránd University, Pázmány Péter sétány 1/A, H-1112 Budapest, Hungary, Budapest, Hungary
¹⁰ Gyula Bay Zoltán Solar Observatory (GSO), Hungarian Solar Physics Foundation, Petőfi tér 3, 24, H-5700 Gyula, Hungary

^* Corresponding author: ronish@roma2.infn.it

Received: 19 May 2023
Accepted: 19 February 2024

Abstract

One of the goals of Space Weather studies is to achieve a better understanding of impulsive phenomena, such as Coronal Mass Ejections (CMEs), to improve our ability to forecast their propagation characteristics and mitigate the risks 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 merged publicly available data of CMEs from both in-situ and remote observations in order to build a dataset of CME properties. To evaluate the accuracy of the dataset and confirm the relationship between in-situ and remote observations, we have employed the Drag-Based Model (DBM) due to its simplicity and modest consumption 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 efficiently the DBM determines the propagation of CMEs for the events in the dataset. The geoeffective CMEs selected as a result of this work are compliant with the hypothesis of DBM (isolated CME, constant solar wind speed beyond 20 R_⊙) and also yield further insight into CME features such as arrival time and arrival speed at L1 point, lift-off time, speed at 20 R_⊙ and other similar quantities. Our analysis based on the acceptance rate in the DBM inversion procedure shows that almost 50% of the CME events in the dataset are well described by DBM as they propagate in the heliosphere. The dataset includes statistical metrics for the DBM model parameters. The probability distribution functions (PDFs) for the free parameters of DBM have been derived through a Monte Carlo-like inversion procedure. Probability distribution functions obtained from this work are comparable to PDFs employed in previous works. The analysis showed that there exist two different most probable values (median values) of solar wind speed for DBM input based on slow (w_slow ≈ 386 km/s) and fast (w_fast ≈ 547 km/s) solar wind type. The most probable value for the drag parameter (γ ≈ 0.687 × 10⁻⁷ km⁻¹) in our study is somewhat higher than the values reported in previous studies. Using a data-driven approach, this procedure allows us to present a homogeneous, reliable, and robust dataset for the investigation of CME propagation. Additionally, possible CME events are identified where the DBM prediction is not valid due to model limitations and higher uncertainties in the input parameters. These events require further thorough investigation in the future.