Theoretical and Applied Climatology

, Volume 102, Issue 1–2, pp 1–12

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

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

DOI: 10.1007/s00704-009-0233-7

Cite this article as:
Kassomenos, P., Vardoulakis, S., Borge, R. et al. Theor Appl Climatol (2010) 102: 1. doi:10.1007/s00704-009-0233-7

Abstract

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.

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • P. Kassomenos
    • 1
  • 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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