Speaker
Description
Meteor observation networks are generating increasingly large,
precise, and geographically distributed datasets of meteoroid
atmospheric entries. These observations offer an opportunity to move
beyond detection and trajectory reconstruction towards automated
physical characterisation and classification of incoming bodies. The
α–β method (Gritsevich 2009) provides a physically grounded and
computationally efficient way for interpreting atmospheric-entry
dynamics and assessing their relevance to planetary defence (e.g.
Gritsevich et al. 2012; Sansom et al. 2019). Its extension to
realistic atmospheric conditions (Lyytinen and Gritsevich 2016) makes
it well suited to the automated analysis of large fireball datasets.
This contribution explores how integrating the method with automated
detection and trajectory analysis can enable rapid, consistent
assessment of atmospheric deceleration, ablation, penetration, and
potential meteorite survival.
The α–β method characterises meteoroid atmospheric entry using two
dimensionless parameters, α and β, which describe the coupled effects
of atmospheric deceleration and mass loss. The parameter α primarily
reflects the ballistic behaviour and degree of atmospheric
deceleration, while β characterises the relationship between
aerodynamic energy loss and ablation. The parametrisation is further
extended through the shape-change coefficient μ, which accounts for
changes in the effective cross-sectional area and evolution in the
shape of the body as mass is lost during atmospheric flight. Together,
α, β and μ allow fireballs with different sizes, velocities, entry
angles and physical characteristics to be compared within a common
parameter space. The method is successfully used by the observing
networks wordwide, including the Finnish Fireball Network, the Desert
Fireball Network, the Spanish Meteor Network, the BOOTES network in
cooperation with the University of Málaga, the Polish Fireball Network
and FRIPON. The applications include fireball classification,
estimation of pre-atmospheric mass and bulk density, rapid
identification of meteorite falls and prioritisation of events for
detailed follow up analysis.
We highlight accessible opportunities to integrate the method into
reproducible, open-access analysis pipelines, harmonise
classifications across observing networks, and develop collaborative
datasets and joint publications. These efforts would help establish
standardised procedures for translating routine meteor observations
into physically interpretable parameters and prioritised event
classifications. This provides a scalable pathway towards more
efficient fireball analysis and improved characterisation of small
near-Earth objects entering Earth's atmosphere, strengthening the
connection between automated meteor monitoring and planetary-defence
objectives.