On 15 October 2016, Dr. Gianluca Napoletano received his Master Degree in Physics (top marks cum laude) from the University of L’Aquila discussing his thesis “A probabilistic approach to the Drag-Based Model for the forecasting of Interplanetary CMEs arrivals“.
Here we report an excerpt from his introduction:
Coronal Mass Ejections are violent phenomena of solar activity with repercussions throughout the entire heliosphere. It is now well established that Interplanetary Coronal Mass Ejections, i.e. their manifestation into interplanetary space and solar wind, are the major cause of geomagnetic disturbances, with enormous economic consequences on our hi-tech society, in addition to severe risks for space missions, telecommunication disturbances and the potential hazard for high-flying aircraft and astronauts during missions of space exploration. All these effects contribute in making the techniques for monitoring, tracking and predicting the arrival of such events at Earth one of the primary tasks in space-weather forecasting.
A detailed description of the motion of an ICME by means of magneto-hydrodynamics equations is in general extremely expensive, since numerical simulations involve a large amount of data, time and computational power.
For this reason, it is interesting to develop real-time forecasting techniques which rely on a less direct approach, involving statistical or kinematic methods based on various relationship established between measured parameters and observed characteristics of the ICME propagation such as arrival time, speed and state of the solar activity.
The purpose of this work is to investigate an analytic, hydro-dynamical based model to describe ICME propagation, analysing its potentials for Space Weather forecasting and possibilities of improvement.
The results here obtained point out the importance of taking into account the CME geometry and the limited accuracy of the input parameters, and lead to the formulation and validation of a forecasting method based on the statistical analysis of the distributions of the time of arrival.
This approach is an effective tool for the purpose of forecasting, deserving further attention and studies.