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@INPROCEEDINGS{Kaffashzadeh:864041,
      author       = {Kaffashzadeh, Najmeh and Schröder, Sabine and Schultz,
                      Martin},
      title        = {{A} {N}ovel {C}oncept for {A}utomated {Q}uality {C}ontrol
                      of {A}tmospheric {T}ime {S}eries},
      reportid     = {FZJ-2019-03957},
      year         = {2019},
      abstract     = {Measurements of atmospheric physical and chemical
                      parameters are essential for atmospheric model
                      evaluation,trend analysis, climate prediction, and other
                      applications. Particularly when the time series from various
                      measure-ment instruments or data providers are merged
                      together, assessing the quality of the data presents a major
                      challengeand often relies on subjective screening. The
                      quality of the time series can be affected by several error
                      types, suchas random error, systematic error due to
                      calibration errors, and gross error from malfunctioning
                      instruments, ordata processing errors, such as mistyped
                      values and improper date-time formats. Some of these errors
                      may havea considerable impact on the statistical analysis of
                      the time series. Thus, identifying the quality of the data,
                      i.e.quality control (QC), is an essential step for any data
                      analysis.Here, we present a software package for the
                      automated QC of the atmospheric time series based on the use
                      ofseveral algorithms that are in use at various
                      environmental agencies and research initiatives. The tool
                      can either beembedded in automated workflows to process
                      real-time data or be applied to a second-level analysis of
                      archivedmulti-year data. Several statistical tests are
                      grouped in categories with increasing complexity. Any number
                      of testscan be defined and run sequentially. The set of
                      statistical tests and any user arguments can easily be
                      configuredwith variable-specific control files in the JSON
                      format. This allows for easy integration into an automated
                      work-flow software and distributed data processing
                      services.For expressing the quality of a measured data
                      series, we introduced a probability concept which assigns
                      each valuea likelihood of being "good" data. Here, "good" is
                      interpreted in a statistical sense as belonging to an
                      expectedprobability distribution. Some of the tests
                      influence not only the probability of a single point but may
                      also impacton the probability of its neighboring points.We
                      tested the software with multi-annual hourly ozone and
                      temperature data from the database of the TroposphericOzone
                      Assessment Report (TOAR). Preliminary results indicate that
                      the concept works well and is able to dealwith a large and
                      heterogeneous dataset such as the global collection of ozone
                      data in the TOAR database.},
      month         = {Apr},
      date          = {2019-04-07},
      organization  = {European Geoscience Union (EGU),
                       Vienna (Austria), 7 Apr 2019 - 12 Apr
                       2019},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / IntelliAQ - Artificial Intelligence for Air
                      Quality (787576) / Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)787576 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/864041},
}