AUTHOR=Dumbović Mateja , Čalogović Jaša , Martinić Karmen , Vršnak Bojan , Sudar Davor , Temmer Manuela , Veronig Astrid TITLE=Drag-Based Model (DBM) Tools for Forecast of Coronal Mass Ejection Arrival Time and Speed JOURNAL=Frontiers in Astronomy and Space Sciences VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2021.639986 DOI=10.3389/fspas.2021.639986 ISSN=2296-987X ABSTRACT=

Forecasting the arrival time of coronal mass ejections (CMEs) and their associated shocks is one of the key aspects of space weather research. One of the commonly used models is the analytical drag-based model (DBM) for heliospheric propagation of CMEs due to its simplicity and calculation speed. The DBM relies on the observational fact that slow CMEs accelerate whereas fast CMEs decelerate and is based on the concept of magnetohydrodynamic (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 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. In this study, we provide an overview of the five DBM versions currently available and their respective tools, developed at Hvar Observatory and frequently used by researchers and forecasters (1) basic 1D DBM, a 1D model describing the propagation of a single point (i.e., the apex of the CME) or a 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 in a self-similar manner; (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 in a self-similar manner; (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, as well as of their similarities and differences, and discuss and demonstrate their application.