  Detection of thunderstorms is important to the wind hazard community to
better understand extreme winds field characteristics and associated wind
induced load effects on structures. This paper contributes to this effort by
proposing a new course of research that uses machine learning techniques,
independent of wind statistics based parameters, to autonomously identify and
separate thunderstorms from large databases containing high frequency sampled
continuous wind speed measurements. In this context, the use of Shapelet
transform is proposed to identify key individual attributes distinctive to
extreme wind events based on similarity of shape of their time series. This
novel shape based representation when combined with machine learning algorithms
yields a practical event detection procedure with minimal domain expertise. In
this paper, the shapelet transform along with Random Forest classifier is
employed for the identification of thunderstorms from 1 year of data from 14
ultrasonic anemometers that are a part of an extensive in situ wind monitoring
network in the Northern Mediterranean ports. A collective total of 235
non-stationary records associated with thunderstorms were identified using this
method. The results lead to enhancing the pool of thunderstorm data for more
comprehensive understanding of a wide variety of thunderstorms that have not
been previously detected using conventional gust factor-based methods.
