Identifying the origins of extreme rainfall using storm track classification

Andy Barnes, Marcus Suassuna Santos, Carlos Garijo, Luis Mediero, Ilaria Prosdocimi, Nick McCullen, Thomas Kjeldsen

Research output: Contribution to journalArticlepeer-review

5 Citations (SciVal)
40 Downloads (Pure)


Identifying patterns in data relating to extreme rainfall is important for classifying and estimating rainfall and flood frequency distributions routinely used in civil engineering design and flood management. This study demonstrates the novel use of several self-organising map (SOM) models to extract the key moisture pathways for extreme rainfall events applied to example data in northern Spain. These models are trained using various subsets of a backwards trajectory data set generated for extreme rainfall events between 1967 and 2016. The results of our analysis show 69.2% of summer rainfall extremes rely on recirculatory moisture pathways concentrated on the Iberian Peninsula, whereas 57% of winter extremes rely on deep-Atlantic pathways to bring moisture from the ocean. These moisture pathways have also shown differences in rainfall magnitude, such as in the summer where peninsular pathways are 8% more likely to deliver the higher magnitude extremes than their Atlantic counterparts.
Original languageEnglish
Pages (from-to)296-309
Number of pages14
Issue number2
Early online date23 Oct 2019
Publication statusPublished - 31 Mar 2020


  • Storms
  • Trajectories
  • self organising maps
  • extreme event
  • rainfall
  • spain
  • atmospheric trajectories
  • clustering
  • analysis

ASJC Scopus subject areas

  • Water Science and Technology
  • Computer Science Applications


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