Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Comparison of statistical clustering techniques for the classification of modelled atmospheric trajectories
Año:2009
Áreas de investigación
  • Política del medio ambiente
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
THEORETICAL AND APPLIED CLIMATOLOGY
ISSN
0177-798X
Factor de impacto JCR
1,621
Información de impacto
Volumen
DOI
10.1007/s00704-009-0233-7
Número de revista
0
Desde la página
1735
Hasta la página
1747
Mes
DICIEMBRE
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: C. Papaloukas (University of Ioannina)
  • Autor: S. Karakitsios (University of Ioannina)
  • Autor: Sotiris Vardoulakis (London School of Hygiene and Tropical Medicine)
  • Autor: Pavlos Kassomenos (University of Ioannina)
  • Autor: Rafael Borge Garcia (UPM)
  • Autor: Julio Lumbreras Martin (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Tecnologías Ambientales y Recursos Industriales
  • Departamento: Ingeniería Química Industrial y del Medio Ambiente
S2i 2021 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
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