A Probabilistic Approach to the Drag-Based Model

TL;DR

This paper introduces a probabilistic method for CME arrival time forecasting using the drag-based model, improving uncertainty estimation and showing promising accuracy with potential for real-time application.

Contribution

It presents a novel statistical approach that incorporates probability distributions into the drag-based model for better uncertainty quantification in CME arrival forecasts.

Findings

Average forecast error of 9.1 hours

Half of the residuals within estimated errors

Promising results for future real-time implementation

Abstract

The forecast of the time of arrival of a coronal mass ejection (CME) to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i.e. the error associated to the estimate. We propose a statistical approach for the computation of the time of arrival using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.