  The so-called drag-based model (DBM) simulates analytically the propagation
of coronal mass ejections (CMEs) in interplanetary space and allows the
prediction of their arrival times and impact speeds at any point in the
heliosphere ("target"). The DBM is based on the assumption that beyond a
distance of about 20 solar radii from the Sun, the dominant force acting on
CMEs is the "aerodynamic" drag force. In the standard form of DBM, the user
provisionally chooses values for the model input parameters, by which the
kinematics of the CME over the entire Sun--"target" distance range is defined.
The choice of model input parameters is usually based on several previously
undertaken statistical studies. In other words, the model is used by ad hoc
implementation of statistics-based values of the input parameters, which are
not necessarily appropriate for the CME under study. Furthermore, such a
procedure lacks quantitative information on how well the simulation reproduces
the coronagraphically observed kinematics of the CME, and thus does not provide
an estimate of the reliability of the arrival prediction. In this paper we
advance the DBM by adopting it in a form that employs the CME observations over
a given distance range to evaluate the most suitable model input parameters for
a given CME by means of the least-squares fitting. Furthermore, the new version
of the model automatically responds to any significant change of the conditions
in the ambient medium (solar wind speed, density, CME--CME interactions, etc.)
by changing the model input parameters according to changes in the CME
kinematics. The advanced DBM is shaped in a form that can be readily employed
in an operational system for real-time space-weather forecasting by promptly
adjusting to a successively expanding observational dataset, thus providing a
successively improving prediction of the CME arrival.
