Issue |
J. Space Weather Space Clim.
Volume 14, 2024
Topical Issue - CMEs, ICMEs, SEPs: Observational, Modelling, and Forecasting Advances
|
|
---|---|---|
Article Number | 1 | |
Number of page(s) | 15 | |
Section | Research Article | |
DOI | https://doi.org/10.1051/swsc/2023032 | |
Published online | 02 February 2024 |
A Bayesian approach to the drag-based modelling of ICMEs
1
SP2RC, School of Mathematics and Statistics, University of Sheffield, Hicks Building, Broomhall, Sheffield S3 7RH, UK
2
Department of Physics, University of Rome “Tor Vergata”, Via della Ricerca Scientifica 1, Rome I-00133, Italy
3
IA, Instituto De Astrofisica E Ciências Do Espaço, University of Coimbra, Coimbra 3004-531, Portugal
4
Univ Lyon, CNRS, École Centrale de Lyon, INSA Lyon, Univ Claude Bernard Lyon I, LMFA UMR 5509, Ecully cedex F-69134, France
5
Dipartimento di Scienze Fisiche e Chimiche, Università dell’Aquila, Via Vetoio, L’Aquila 67100, Italy
6
Space Weather Research Technology Education Center (SWx-TREC), University of Colorado, Boulder, CO 80309, USA
7
Queen Mary University of London, London E1 4NS, UK
8
Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome 00185, Italy
9
Department of Astronomy, Eötvös Loránd University, Pázmány Péter sétány 1/A, Budapest H-1112, Hungary
10
Hungarian Solar Physics Foundation, Petőfi tér 3, Gyula H-5700, Hungary
* Corresponding author: s.chierichini@sheffield.ac.uk
Received:
23
August
2023
Accepted:
29
November
2023
Coronal Mass Ejections (CMEs) are huge clouds of magnetised plasma expelled from the solar corona that can travel towards the Earth and cause significant space weather effects. The Drag-Based Model (DBM) describes the propagation of CMEs in an ambient solar wind as analogous to an aerodynamic drag. The drag-based approximation is popular because it is a simple analytical model that depends only on two parameters, the drag parameter and the solar wind speed . DBM thus allows us to obtain reliable estimates of CME transit time at low computational cost. Previous works proposed a probabilistic version of DBM, the Probabilistic Drag Based Model (P-DBM), which enables the evaluation of the uncertainties associated with the predictions. In this work, we infer the “a-posteriori” probability distribution functions (PDFs) of the and parameters of the DBM by exploiting a well-established Bayesian inference technique: the Monte Carlo Markov Chains (MCMC) method. By utilizing this Bayesian method through two different approaches, an ensemble and an individual approach, we obtain specific DBM parameter PDFs for two ensembles of CMEs: those travelling with fast and slow solar wind, respectively. Subsequently, we assess the operational applicability of the model by forecasting the arrival time of CMEs. While the ensemble approach displays notable limitations, the individual approach yields promising results, demonstrating competitive performances compared to the current state-of-the-art, with a Mean Absolute Error (MAE) of 9.86 ± 4.07 h achieved in the best-case scenario.
Key words: Coronal Mass Ejections / Drag Based Model / Space weather
© S. Chierichini et al., Published by EDP Sciences 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.