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@ARTICLE{Chang:858700,
      author       = {Chang, Kai-Lan and Cooper, Owen R. and West, J. Jason and
                      Serre, Marc L. and Schultz, Martin G. and Lin, Meiyun and
                      Marécal, Virginie and Josse, Béatrice and Deushi, Makoto
                      and Sudo, Kengo and Liu, Junhua and Keller, Christoph A.},
      title        = {{A} new method ({M}$^3${F}usion-v1) for combining
                      observations and multiple model output for an improved
                      estimate of the global surface ozone distribution},
      journal      = {Geoscientific model development discussions},
      volume       = {183},
      issn         = {1991-962X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2018-07545},
      pages        = {1 - 34},
      year         = {2018},
      abstract     = {We have developed a new statistical approach (M3Fusion) for
                      combining surface ozone observations from thousands of
                      monitoring sites around the world with the output from
                      multiple atmospheric chemistry models to produce a global
                      surface ozone distribution with greater accuracy than can be
                      provided by any individual model. The ozone observations
                      from 4766 monitoring sites were provided by the Tropospheric
                      Ozone Assessment Report (TOAR) surface ozone database which
                      contains the world's largest collection of surface ozone
                      metrics. Output from six models was provided by the
                      participants of the Chemistry-Climate Model Initiative
                      (CCMI) and NASA's Global Modeling and Assimilation Office
                      (GMAO). We analyze the 6-month maximum of the maximum daily
                      8-hour average ozone value (DMA8) for relevance to ozone
                      health impacts. We interpolate the irregularly-spaced
                      observations onto a fine resolution grid by using integrated
                      nested Laplace approximations, and compare the ozone field
                      to each model in each world region. This method allows us to
                      produce a global surface ozone field based on TOAR
                      observations, which we then use to select the combination of
                      global models with the greatest skill in each of 8 world
                      regions; models with greater skill in a particular region
                      are given higher weight. This blended model product is
                      bias-corrected within two degrees of observation locations
                      to produce the final fused surface ozone product. We show
                      that our fused product has an improved mean squared error
                      compared to the simple multi-model ensemble mean.},
      cin          = {JSC},
      ddc          = {910},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF3-512 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.5194/gmd-2018-183},
      url          = {https://juser.fz-juelich.de/record/858700},
}