Articles | Volume 16, issue 11
Atmos. Chem. Phys., 16, 6863–6881, 2016
https://doi.org/10.5194/acp-16-6863-2016
Atmos. Chem. Phys., 16, 6863–6881, 2016
https://doi.org/10.5194/acp-16-6863-2016

Research article 06 Jun 2016

Research article | 06 Jun 2016

Cluster analysis of European surface ozone observations for evaluation of MACC reanalysis data

Olga Lyapina et al.

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Cited articles

Ashmore, M. R.: Assessing the future global impacts of ozone on vegetation, Plant Cell Environ., 28, 949–964, 2005.
Beaver, S. and Palazoglu, A.: Cluster Analysis of Hourly Wind Measurements to Reveal Synoptic Regimes Affecting Air Quality, J. Appl. Meteorol. Clim., 45, 1710–1726, https://doi.org/10.1175/JAM2437.1, 2006.
Beirle, S., Platt, U., Wenig, M., and Wagner, T.: Weekly cycle of NO2 by GOME measurements: a signature of anthropogenic sources, Atmos. Chem. Phys., 3, 2225–2232, https://doi.org/10.5194/acp-3-2225-2003, 2003.
Bell, M. L., Peng, R. D., and Dominici, F.: The exposure-response curve for ozone and risk of mortality and the adequacy of current ozone regulations, Environ. Health Persp., 114, 532–536, 2006.
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This study applies numerical clustering for the classification of about 1500 ozone data sets in Europe. We show the usefulness of cluster analysis (CA) for the quantitative evaluation of a global model: pre-selection of stations and validation of a global model in a phase-space produce clearer and more interpretable results. CA can be easily updated for new stations, different length of data, and other type of input properties, as well as other type of data (for example, meteorological).
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