Parameter Distributions for the Drag-Based Modeling of CME Propagation

TL;DR

This paper evaluates statistical distributions for input parameters in drag-based CME propagation models, using observational data to improve ensemble forecasting of CME arrival times and velocities in space weather prediction.

Contribution

It systematically assesses and refines the parameter distributions used in drag-based models based on observational data from past CME events.

Findings

Distributions used previously are generally appropriate.

Current methods can be refined by considering CME acceleration or deceleration.

The study supports the use of ensemble modeling for CME forecast uncertainties.

Abstract

In recent years, ensemble modeling has been widely employed in space weather to estimate uncertainties in forecasts. We here focus on the ensemble modeling of CME arrival times and arrival velocities using a drag-based model, which is well-suited for this purpose due to its simplicity and low computational cost. Although ensemble techniques have previously been applied to the drag-based model, it is still not clear how to best determine distributions for its input parameters, namely the drag parameter and the solar wind speed. The aim of this work is to evaluate statistical distributions for these model parameters starting from a list of past CME-ICME events. We employ LASCO coronagraph observations to measure initial CME position and speed, and in situ data to associate them with an arrival date and arrival speed. For each event we ran a statistical procedure to invert the model equations, producing parameters distributions as output. Our results indicate that the distributions employed in previous works were appropriately selected, even though they were based on restricted samples and heuristic considerations. On the other hand, possible refinements to the current method are also identified, such as the dependence of the drag parameter distribution on the CME being accelerated or decelerated by the solar wind, which deserve further investigation.