A Monte Carlo Markov Chain Approach to the Probabilistic Drag Based Model for Coronal Mass Ejection Propagation
Abstract
Awareness that space weather phenomena pose a real threat to Earth's technologicalinfrastructure has grown considerably in recent decades. Coronal Mass Ejections (CMEs) are hugeclouds of plasma and magnetic field expelled from the Corona that can travel towards the Earth andcause significant space weather effects. The Drag-Based Model (DBM) describes the heliosphericpropagation of CMEs in the ambient solar wind as analogous to an aerodynamic drag. The Drag-based approximation is popular because it is a simple analytical model which mainly depends on twoparameters, the drag parameter γ and the solar wind speed w. DBM thus allows to obtain estimates ofCME transit time at low computational cost. Previous work proposed a probabilistic version of DBM, theProbabilistic Drag Based Model (P-DBM), which allows the evaluation of the uncertainties associatedwith the predictions. In this work, we infer the a-posteriori Probability Distribution Functions (PDFs) ofthe γ and w parameters of the DBM by exploiting a popular Bayesian inference technique: the MonteCarlo Markov Chains (MCMC) method. Among the results of this approach, we found that the posteriorγ PDFs for CMEs travelling either in Fast or Slow Solar wind are compatible with each other. Last, weobtained robust PDFs to be employed in the operational forward application of the P-DBM to obtainforecasts of CME arrival times and velocities in quasi-real-time.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMNG52A0165C