Drag-based model (DBM) tools for forecast of coronal mass ejection arrival time and speed

TL;DR

This paper reviews five versions of the drag-based model (DBM) used for forecasting coronal mass ejection (CME) arrival times and speeds, highlighting their features, differences, and applications in space weather prediction.

Contribution

It provides a comprehensive overview of five DBM tools, including their development, capabilities, and how they are used in CME forecasting.

Findings

Five DBM versions are available and tested.

The models differ in geometry and ensemble capabilities.

They are widely used by researchers and forecasters.

Abstract

Forecasting the arrival time of CMEs and their associated shocks is one of the key aspects of space weather research. One of the commonly used models is, due to its simplicity and calculation speed, the analytical drag-based model (DBM) for heliospheric propagation of CMEs. DBM relies on the observational fact that slow CMEs accelerate whereas fast CMEs decelerate, and is based on the concept of MHD drag, which acts to adjust the CME speed to the ambient solar wind. Although physically DBM is applicable only to the CME magnetic structure, it is often used as a proxy for the shock arrival. In recent years, the DBM equation has been used in many studies to describe the propagation of CMEs and shocks with different geometries and assumptions. Here we give an overview of the five DBM versions currently available and their respective tools, developed at Hvar Observatory and frequently used by researchers and forecasters. These include: 1) basic 1D DBM, a 1D model describing the propagation of a single point (i.e. the apex of the CME) or concentric arc (where all points propagate identically); 2) advanced 2D self-similar cone DBM, a 2D model which combines basic DBM and cone geometry describing the propagation of the CME leading edge which evolves self-similarly; 3) 2D flattening cone DBM, a 2D model which combines basic DBM and cone geometry describing the propagation of the CME leading edge which does not evolve self-similarly; 4) DBEM, an ensemble version of the 2D flattening cone DBM which uses CME ensembles as an input and 5) DBEMv3, an ensemble version of the 2D flattening cone DBM which creates CME ensembles based on the input uncertainties. All five versions have been tested and published in recent years and are available online or upon request. We provide an overview of these five tools, of their similarities and differences, as well as discuss and demonstrate their application.