Ensemble modeling of CMEs using the WSA-ENLIL+Cone model

TL;DR

This paper presents real-time ensemble modeling of CME propagation using the WSA-ENLIL+Cone model, providing probabilistic forecasts of CME arrival times and assessing their accuracy and reliability.

Contribution

It introduces a real-time ensemble modeling approach for CMEs with sensitivity analysis and evaluates forecast accuracy using multiple metrics over 35 events.

Findings

Mean absolute arrival time error of 12.3 hours

Forecast reliability depends on initial CME parameter distribution

Kp index predictions have a mean absolute error of 1.3

Abstract

Ensemble modeling of CMEs provides a probabilistic forecast of CME arrival time which includes an estimation of arrival time uncertainty from the spread and distribution of predictions and forecast confidence in the likelihood of CME arrival. The real-time ensemble modeling of CME propagation uses the WSA-ENLIL+Cone model installed at the CCMC and executed in real-time. The current implementation evaluates the sensitivity of WSA-ENLIL+Cone model simulations of CME propagation to initial CME parameters. We discuss the results of real-time ensemble simulations for a total of 35 CME events between January 2013 - July 2014. For the 17 events where the CME was predicted to arrive at Earth, the mean absolute arrival time prediction error was 12.3 hours, which is comparable to the errors reported in other studies. For predictions of CME arrival at Earth the correct rejection rate is 62% and the false-alarm rate is 38%. The arrival time was within the range of the ensemble arrival predictions for 8 out of 17 events. The Brier Score for CME arrival predictions is 0.15 (where 1 is a perfect forecast), indicating that on average, the predicted likelihood of CME arrival is fairly accurate. The reliability of ensemble CME arrival predictions is heavily dependent on the initial distribution of CME input parameters, particularly the median and spread. Preliminary analysis of the probabilistic forecasts suggests undervariability, indicating that these ensembles do not sample a wide enough spread in CME input parameters. Prediction errors can also arise from ambient model parameters, the accuracy of the solar wind background derived from coronal maps, or other model limitations. Finally, predictions of the Kp geomagnetic index differ from observed values by less than one for 11 out of 17 of the ensembles and Kp prediction errors computed from the mean predicted Kp show a mean absolute error of 1.3.