Entering the era of automated CME recognition: A review of existing tools

We consider the problem of the observational identification of CMEs. The ever growing importance of space weather has led to new requirements on the timeliness and objectiveness of CME detection. It is not sufficient any more to simply detect CMEs, a complete set of characteristics (speed, direction, mass, chirality) must be reported as soon as possible to estimate its geoeffectiveness. Recent developments in (solar) feature recognition greatly improved the ability to address these new needs. Progress was achieved in automating the detection of CMEs in coronagraphic data. This has led to near-real-time messages alerting the space weather community day and night. In attempting to generate ever-prompter alerts, we can employ a far broader set of solar observations than coronagraphic data alone. At present an extensive set of automatic recognition tools exists for a number of CME-related phenomena occurring in the lower corona. This paper deals with detection techniques for disappearing filaments in H alpha images, dimmings, EIT waves and erupting prominences in radio data. We believe that incorporating all automatically generated alerts into one report per CME can provide valuable CME information, especially when no coronagraphic images are available. This paper is thus a quest to reach a maximal success rate with the help of an integrated system of tools acting on a variety of data. Future grid-technology systems will greatly facilitate this.