Probabilistic Drag-Based Ensemble Model (DBEM) Evaluation for Heliospheric Propagation of CMEs

TL;DR

This paper introduces an improved probabilistic ensemble model (DBEMv3) for predicting CME arrival times and speeds in the heliosphere, enhancing accuracy and uncertainty quantification over previous versions.

Contribution

The paper develops and evaluates the DBEMv3, a new ensemble model that incorporates variable input parameters to improve CME propagation predictions and quantify uncertainties.

Findings

DBEMv3 achieves a mean error of -11.3 hours in CME arrival time predictions.

The model's absolute error averages 17.3 hours, indicating improved accuracy.

Optimal input parameters are identified as higher drag coefficient and solar wind speed.

Abstract

The Drag-based Model (DBM) is a 2D analytical model for heliospheric propagation of Coronal Mass Ejections (CMEs) in ecliptic plane predicting the CME arrival time and speed at Earth or any other given target in the solar system. It is based on the equation of motion and depends on initial CME parameters, background solar wind speed, $w$ and the drag parameter $\gamma$. A very short computational time of DBM ($<$ 0.01s) allowed us to develop the Drag-Based Ensemble Model (DBEM) that takes into account the variability of model input parameters by making an ensemble of n different input parameters to calculate the distribution and significance of the DBM results. Thus the DBEM is able to calculate the most likely CME arrival times and speeds, quantify the prediction uncertainties and determine the confidence intervals. A new DBEMv3 version is described in detail and evaluated for the first time determing the DBEMv3 performance and errors by using various CME-ICME lists as well as it is compared with previous DBEM versions. The analysis to find the optimal drag parameter $\gamma$ and ambient solar wind speed $w$ showed that somewhat higher values ($\gamma \approx 0.3 \times 10^{-7}$ km$^{-1}$, $w \approx$ 425 km\,s$^{-1}$) for both of these DBEM input parameters should be used for the evaluation compared to the previously employed ones. Based on the evaluation performed for 146 CME-ICME pairs, the DBEMv3 performance with mean error (ME) of -11.3 h, mean absolute error (MAE) of 17.3 h was obtained. There is a clear bias towards the negative prediction errors where the fast CMEs are predicted to arrive too early, probably due to the model physical limitations and input errors (e.g. CME launch speed).