StormSeeker: A Machine-Learning-Based Mediterranean Storm Tracer

  • Raffaele MontellaEmail author
  • Diana Di Luccio
  • Angelo Ciaramella
  • Ian Foster
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11874)


The Mediterranean area is subject to a range of destructive weather events, including middle-latitudes storms, Mediterranean sub-tropical hurricane-like storms (“medicanes”), and small-scale but violent local storms. Although predicting large-scale atmosphere disturbances is a common activity in numerical weather prediction, the tasks of recognizing, identifying, and tracing trajectories of such extreme weather events within weather model outputs remains challenging. We present here a new approach to this problem, called StormSeeker, that uses machine learning techniques to recognize, classify, and trace the trajectories of severe storms in atmospheric model data. We report encouraging results detecting weather hazards in a heavy middle-latitude storm that struck the Ligurian coast in October 2018, causing disastrous damages to public infrastructure and private property.


Machine learning Distributed computing Computational environmental data science Extreme weather forecast 



This research was supported by project PAUN (ex RIPA PON03PE_00164) and DOE Contract DE-AC02-06CH11357. We are grateful to the University of Napoli “Parthenope” forecast service ( for know-how and HPC facilities.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Science and Technologies DepartmentUniversity of Naples “Parthenope”NaplesItaly
  2. 2.Computer Science DepartmentUniversity of ChicagoChicagoUSA
  3. 3.Data Science and Learning DivisionArgonne National LaboratoryArgonneUSA

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