Theoretical and Applied Climatology

, Volume 102, Issue 1–2, pp 1–12 | Cite as

Comparison of statistical clustering techniques for the classification of modelled atmospheric trajectories

  • P. KassomenosEmail author
  • S. Vardoulakis
  • R. Borge
  • J. Lumbreras
  • C. Papaloukas
  • S. Karakitsios
Original Paper


In this study, we used and compared three different statistical clustering methods: an hierarchical, a non-hierarchical (K-means) and an artificial neural network technique (self-organizing maps (SOM)). These classification methods were applied to a 4-year dataset of 5 days kinematic back trajectories of air masses arriving in Athens, Greece at 12.00 UTC, in three different heights, above the ground. The atmospheric back trajectories were simulated with the HYSPLIT Vesion 4.7 model of National Oceanic and Atmospheric Administration (NOAA). The meteorological data used for the computation of trajectories were obtained from NOAA reanalysis database. A comparison of the three statistical clustering methods through statistical indices was attempted. It was found that all three statistical methods seem to depend to the arrival height of the trajectories, but the degree of dependence differs substantially. Hierarchical clustering showed the highest level of dependence for fast-moving trajectories to the arrival height, followed by SOM. K-means was found to be the least depended clustering technique on the arrival height. The air quality management applications of these results in relation to PM10 concentrations recorded in Athens, Greece, were also discussed. Differences of PM10 concentrations, during certain clusters, were found statistically different (at 95% confidence level) indicating that these clusters appear to be associated with long-range transportation of particulates. This study can improve the interpretation of modelled atmospheric trajectories, leading to a more reliable analysis of synoptic weather circulation patterns and their impacts on urban air quality.


Cluster Technique PM10 Concentration Sahara Desert Back Trajectory Circulation Regime 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge the NOAA Air resources (ARL) for the provision of the FNL-HYSPLIT data, the HYSPLIT transport and dispersion model and the READY web site ( used in this work. The authors would also like to thank the two anonymous reviewers for their valuable and constructive suggestions that improve this work substantially.


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • P. Kassomenos
    • 1
    Email author
  • S. Vardoulakis
    • 2
  • R. Borge
    • 3
  • J. Lumbreras
    • 3
  • C. Papaloukas
    • 4
  • S. Karakitsios
    • 4
  1. 1.Department of Physics, Laboratory of MeteorologyUniversity of IoanninaIoanninaGreece
  2. 2.Public and Environmental Health Research UnitLondon School of Hygiene and Tropical MedicineLondonUK
  3. 3.Department of Chemical and Environmental EngineeringTechnical University of Madrid, (UPM)MadridSpain
  4. 4.Department of Biological Applications and TechnologyUniversity of IoanninaIoanninaGreece

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