Advances in Science and Research www.adv-sci-res.net 1992-0628 1992-0636 2 7th EMS Annual Meeting and 8th European Conference on Applications of Meteorology 2007 2008 10.5194/asr-2-65-2008 http://www.adv-sci-res.net/2/65/2008/ http://www.adv-sci-res.net/2/65/2008/asr-2-65-2008.html http://www.adv-sci-res.net/2/65/2008/asr-2-65-2008.pdf 65 70 2008-05-22 Influence of meteorological input data on backtrajectory cluster analysis – a seven-year study for southeastern Spain M. Cabello J. A. G. Orza V. Galiano G. Ruiz SCOLAb, Física Aplicada, Universidad Miguel Hernández, Elche, Spain Backtrajectory differences and clustering sensitivity to the meteorological input data are studied. Trajectories arriving in Southeast Spain (Elche), at 3000, 1500 and 500 m for the 7-year period 2000–2006 have been computed employing two widely used meteorological data sets: the NCEP/NCAR Reanalysis and the FNL data sets. Differences between trajectories grow linearly at least up to 48 h, showing faster growing after 72 h. A k-means cluster analysis performed on each set of trajectories shows differences in the identified clusters (main flows), partially because the number of clusters of each clustering solution differs for the trajectories arriving at 3000 and 1500 m. Trajectory membership to the identified flows is in general more sensitive to the input meteorological data than to the initial selection of cluster centroids. Brankov, E., Rao, S. T., and Porter, P. S.: A trajectory-clustering-correlation methodology for examining the long-range transport of air pollutants, Atmos Environ., 32, 1525–1534, 1998. Cabello, M., Orza, J. A. G., and Galiano, V.: Air mass origin and its influence over the aerosol size distribution: a study in SE Spain, Adv. Sci. Res., 2, 47–52, 2008. Dorling, S. R., Davies, T. D., and Pierce, C. E.: Cluster analysis: A technique for estimating the synoptic meteorological controls on air and precipitation chemistry-Method and applications, Atmos Environ., 26, 2575–2581, 1992. Draxler, R. R. and Rolph, G. D.: HYSPLIT Model access via NOAA ARL READY Website (http://www.arl.noaa.gov/ready/hysplit4.html), NOAA Air Resources Laboratory, 2003. Harris, J. M., Draxler, R. R., and Oltmans, S. J.: Trajectory model sensitivity to differences in input data and vertical transport method, J Geophys Res., 110, D14109, doi:10.1029/2004JD005750, 2005. Jorba, O., Pérez, C., Rocadenbosch, F., and Baldasano, J. M.: Cluster analysis of 4-day back trajectories arriving in the Barcelona Area, Spain, from 1997 to 2002, J Appl Meteorol. 43, 887–901, 2004. Mattis, I.: Compilation of trajectory data. EARLINET: A European Aerosol Research Lidar Network to Establish an Aerosol Climatology, Scientific Report for the period Febr. 2000 to Jan. 2001, J Bösenberg, Max Planck Inst. für Meteorologie, 26–29, 2001, available at: http://lidarb.dkrz.de/earlinet/scirep1.pdf, 2005. Millan, M. M., Salvador, R., Mantilla, E., and Kallos, G.: Photo-oxidant dynamics in the Mediterranean basin in summer: results from European research projects, J Geophys Res., 102, 8811–8823, 1997. Rolph, G. D. and Draxler, R. R.: Sensitivity of three-dimensional trajectories to the spatial and temporal densities of the wind field, J Appl Meteorol., 29, 1043–1054, 1990. Salvador, P., Art\'\iñano, B., Alonso, D. G., Querol, X., and Alastuey, A.: Identification and characterisation of sources of PM10 in Madrid (Spain) by statistical methods, Atmos Environ., 38, 435–447, 2004. Stohl, A.: Computation, accuracy and applications of trajectories – a review and bibliography, Atmos Environ., 32, 947–966, 1998. Stohl, A., Wotawa, G., Seibert, P., Kromp-Kolb, H.: Interpolation errors in wind fields as a function of spatial and temporal resolution and their impact on different types of kinematic trajectories, J Appl Meteorol., 34, 2149–2165, 1995. Stohl, A., Eckhardt, S., Forster, C., James, P., Spichtinger, N., and Seibert, P.: A replacement for simple back trajectory calculations in the interpretation of atmospheric trace substance measurements, Atmos Environ., 36, 4635–4648, 2002.