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@ARTICLE{Lyapina:810021,
      author       = {Lyapina, Olga and Schultz, Martin and Hense, Andreas},
      title        = {{C}luster analysis of {E}uropean surface ozone observations
                      for evaluation of {MACC} reanalysis data},
      journal      = {Atmospheric chemistry and physics},
      volume       = {16},
      number       = {11},
      issn         = {1680-7324},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2016-02904},
      pages        = {6863 - 6881},
      year         = {2016},
      abstract     = {The high density of European surface ozone monitoring sites
                      provides unique opportunities for the investigation of
                      regional ozone representativeness and for the evaluation of
                      chemistry climate models. The regional representativeness of
                      European ozone measurements is examined through a cluster
                      analysis (CA) of 4 years of 3-hourly ozone data from 1492
                      European surface monitoring stations in the Airbase
                      database; the time resolution corresponds to the output
                      frequency of the model that is compared to the data in this
                      study. K-means clustering is implemented for
                      seasonal–diurnal variations (i) in absolute mixing ratio
                      units and (ii) normalized by the overall mean ozone mixing
                      ratio at each site. Statistical tests suggest that each CA
                      can distinguish between four and five different ozone
                      pollution regimes. The individual clusters reveal
                      differences in seasonal–diurnal cycles, showing typical
                      patterns of the ozone behavior for more polluted stations or
                      more rural background. The robustness of the clustering was
                      tested with a series of k-means runs decreasing randomly the
                      size of the initial data set or lengths of the time series.
                      Except for the Po Valley, the clustering does not provide a
                      regional differentiation, as the member stations within each
                      cluster are generally distributed all over Europe. The
                      typical seasonal, diurnal, and weekly cycles of each cluster
                      are compared to the output of the multi-year global
                      reanalysis produced within the Monitoring of Atmospheric
                      Composition and Climate (MACC) project. While the MACC
                      reanalysis generally captures the shape of the diurnal
                      cycles and the diurnal amplitudes, it is not able to
                      reproduce the seasonal cycles very well and it exhibits a
                      high bias up to 12 nmol mol−1. The bias decreases from
                      more polluted clusters to cleaner ones. Also, the seasonal
                      and weekly cycles and frequency distributions of ozone
                      mixing ratios are better described for clusters with
                      relatively clean signatures. Due to relative sparsity of CO
                      and NOx measurements these were not included in the CA.
                      However, simulated CO and NOx mixing ratios are consistent
                      with the general classification into more polluted and more
                      background sites. Mean CO mixing ratios are within
                      140–145 nmol mol−1 (CL1–CL3) and
                      130–135 nmol mol−1 (CL4 and CL5), and NOx mixing
                      ratios are within 4–6 nmol mol−1 and
                      2–3 nmol mol−1, respectively. These results confirm
                      that relatively coarse-scale global models are more suitable
                      for simulation of regional background concentrations, which
                      are less variable in space and time. We conclude that CA of
                      surface ozone observations provides a powerful and robust
                      way to stratify sets of stations, being thus more suitable
                      for model evaluation.},
      cin          = {IEK-8},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IEK-8-20101013},
      pnm          = {243 - Tropospheric trace substances and their
                      transformation processes (POF3-243)},
      pid          = {G:(DE-HGF)POF3-243},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000378354600013},
      doi          = {10.5194/acp-16-6863-2016},
      url          = {https://juser.fz-juelich.de/record/810021},
}