@phdthesis{79620,
  keywords = {workshop, model uncertainty},
  author = {Neill Bowler},
  title = {On the diagnosis of model error statistics using weak-constraint data assimilation},
  abstract = {<p>
  Outputs from a data assimilation system may be used to diagnose observation and background error statistics, as has been demonstrated by previous researchers. In this study, that technique is extended to diagnose model-error statistics using a weak-constraint data assimilation. It deals with a set of observations over a time window and uses the temporal distribution to separate model errors from errors in the background forecast. In idealised tests this method is shown to be able to successfully distinguish between model, background and observation errors. The success of this method depends on the prior assumptions included in the weak-constraint data assimilation and how well these describe the true nature of the system being modelled.</p>
},
  year = {2016},
  journal = {ECMWF/WWRP Workshop: Model Uncertainty},
  month = {07/2016},
  address = {ECMWF, Reading},
  language = {eng},
}