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

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.

References

  1. Artíñano B, Querol X, Salvador P, Rodríguez S, Alonso DG, Alastuey A (2001) Assessment of airborne particulate levels in Spain in relation to the new EU-Directive. Atmos Environ 35:S43–S53CrossRefGoogle Scholar
  2. Borge R, Lumbreras J, Vardoulakis S, Kassomenos P, Rodriguez E (2007) Analysis of long-range transport influences on urban PM10 using two-stage atmospheric trajectory clusters. Atmos Environ 41:4434–4450CrossRefGoogle Scholar
  3. Cape JN, Methven J, Hudson LE (2000) The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland. Atmos Environ 34:3651–3663CrossRefGoogle Scholar
  4. Dorling SR, Davis TD (1995) Extending cluster analysis-synoptic meteorology links to characterize chemical climates at six northwest European monitoring stations. Atmos Environ 29:145–167CrossRefGoogle Scholar
  5. Dorling SR, Davies TD, Pierce CE (1992) Cluster analysis: a technique for estimating the synoptic meteorological controls on air and precipitation chemistry. Atmos Environ 26:2575–2581CrossRefGoogle Scholar
  6. Draxler RR, Hess GD (1998) An overview of the HYSPLIT 4 modelling system for trajectories, dispersion and deposition. Aust Meteorol Mag 47:295–308Google Scholar
  7. Everitt B (1980) Cluster analysis. Halstead, New York, p 136Google Scholar
  8. Grivas G, Chaloulakou A, Kassomenos P (2008) An overview of the PM10 pollution problem, in the Metropolitan Area of Athens, Greece. Assessment of controlling factors and potential impact of long range transport. Sci Total Environ 389:165–177CrossRefGoogle Scholar
  9. Johnson SC (1967) Hierarchical Clustering Schemes. Psychometrika 2:241–254CrossRefGoogle Scholar
  10. Jorba O, Perez C, Rocadenbosch F, Baldasano J (2004) Cluster Analysis of 4-Day back Trajectories arriving in the Barcelona area, Spain, from 1997 to 2002. J Appl Meteorol 43:887–900CrossRefGoogle Scholar
  11. Kanamitsu M (1989) Description of the NMC Global Data Assimilation and Forecast System. Weather Forecasting 4:335–342CrossRefGoogle Scholar
  12. Kassomenos P (2003a) Anatomy of the synoptic conditions occurring over southern Greece during the second half of 20th century. Part I. Summer and Winter. Theor Appl Climatol 75(1–2):65–77Google Scholar
  13. Kassomenos P (2003b) Anatomy of the synoptic conditions occurring over southern Greece during the second half of 20th century. Part II. Spring and Autumn. Theor Appl Climatol 75(1–2):79–92Google Scholar
  14. Kocak M, Mihalopoulos N, Kubilay N (2007) Contributions of natural sources to high PM10 and PM2.5 events in the eastern Mediterranean. Atmos Environ 41:3806–3818CrossRefGoogle Scholar
  15. Kohonen T (2001) Self organizing maps. SpringerGoogle Scholar
  16. Kolehmainen M, Martikainen H, Hiltunen T, Ruuskanen J (2000) Forecasting air quality parameters using hybrid neural network modelling. Environ Monit Assess 65:277–286CrossRefGoogle Scholar
  17. Kolehmainen M, Martikainen H, Ruuskanen J (2001) Neural networks and periodic components used in air quality forecasting. Atmos Environ 35:815–825CrossRefGoogle Scholar
  18. Leavey M, Sweeny J (1990) The influence of long range transport of air pollutants on summer visibility at Dublin. Int J Climatol 10:191–201CrossRefGoogle Scholar
  19. McQueen JB (1967) Some methods for classification and Analysis of Multivariate Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press, Los Angeles, pp 281–297Google Scholar
  20. Melas D, Ziomas I, Klemm O, Zerefos CS (1998) Anatomy of the Sea breeze circulation in Athens area under weak large-scale ambient winds. Atmos Environ 32:2223–2237CrossRefGoogle Scholar
  21. Mihalopoulos N, Stephanou E, Kanakidou M, Pilitsidis S, Bousquet P (1997) Troposheric aerosol ionic composition in the Eastern Mediterranean region. Tellus B Chem Phys Meteorol 49:314–326CrossRefGoogle Scholar
  22. Moody JL, Oltmans SJ, Levy H II, Merrill T (1995) Transport climatology of tropospheric ozone. Bermuda, 1988–1991. J Geophys Res 100:7179–7194CrossRefGoogle Scholar
  23. Newell R, Thuret V, Cho J, Stoller P, Marenco A, Smit H (1999) Ubiquity of quasi-horizontal layers in the troposphere. Nature 398:316–319CrossRefGoogle Scholar
  24. Rolph G.D., 2003. READY: Real time Environmental Applications and Display system. NOAA Air resources Laboratory (http://www.arl.noaa.gov/ready.html).
  25. Salvador S, Chan P (2005) Learning States and rules for detecting anomalies in Time Series. Appl Intell 23:241–255CrossRefGoogle Scholar
  26. Schädler G, Sasse R (2006) Analysis of the connection between precipitation and synoptic scale processes in the Eastern Mediterranean using self-organizing maps. Meteorol Z 15(3):273–278CrossRefGoogle Scholar
  27. Schlink U, Herbarth O, Richter M, Dorling S, Nunnari G, Cawley G, Pelikan E (2006) Statistical models to assess the health effects and to forecast ground-level ozone. Environ Model Softw 21:547–558CrossRefGoogle Scholar
  28. Stohl A, Eckhartdt S, Forster C, James P, Spichtinger N, Seibert P (2002) A replacement for simple back trajectory calculations in the interpretation of atmospheric trace substance measurement. Atmos Environ 36:4635–4648CrossRefGoogle Scholar
  29. Sinnott RW (1984) Virtues of the Haversine. Sky Telescope 68:159Google Scholar
  30. Vardoulakis S, Kassomenos P (2008) Comparison of factors influencing PM10 levels in Athens (Greece) and Birmingham (UK). Atmos Environ 42:3949–3963CrossRefGoogle Scholar
  31. Wernli H, Davies H (1997) A Langrangian-based analysis of extratropical cyclones. I: the method and some applications. Q J R Meteorol Soc 123:467–489CrossRefGoogle Scholar
  32. Yao CS (1998) A loading correlation model for climatic classification in terms of synoptic climatology. Theor Appl Climatol 61:113–120CrossRefGoogle Scholar

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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