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

Lyapina, Olga; Schultz, Martin G.; Hense, Andreas

doi:https://doi.org/10.5194/acp-16-6863-2016

Articles | Volume 16, issue 11

https://doi.org/10.5194/acp-16-6863-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/acp-16-6863-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 11

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, Martin G. Schultz, and Andreas Hense

Supplement

https://doi.org/10.5194/acp-16-6863-2016-supplement

Data sets

Airbase database EEA data service (European Environment Agency, http://www.eea.europa.eu) http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8

The MACC reanalysis: an 8 yr data set of atmospheric composition (data available at: http://apps.ecmwf.int/datasets/data/macc-reanalysis/) A. Inness, F. Baier, A. Benedetti, I. Bouarar, S. Chabrillat, H. Clark, C. Clerbaux, P. Coheur, R. J. Engelen, Q. Errera, J. Flemming, M. George, C. Granier, J. Hadji-Lazaro, V. Huijnen, D. Hurtmans, L. Jones, J. W. Kaiser, J. Kapsomenakis, K. Lefever, J. Leitão, M. Razinger, A. Richter, M. G. Schultz, A. J. Simmons, M. Suttie, O. Stein, J.-N. Thépaut, V. Thouret, M. Vrekoussis, C. Zerefos, and the MACC team https://doi.org/10.5194/acp-13-4073-2013

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Short summary

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).