@phdthesis{79560,
  keywords = {workshop, model uncertainty},
  author = {Darryl Holm},
  title = {Stochastic parametrisation models for GFD},
  abstract = {<p>
  In next-generation weather and climate models, stochastic parameterisation should be an important element in providing reliable estimates of model uncertainty. A fundamental conclusion of Berner, Jung &amp; Palmer [2012] is that</p>

<p>
  “a posteriori addition of stochasticity to an already tuned model is simply not viable [satisfactory]. This in turn suggests that stochasticity must be incorporated at a very basic level within the design of physical process parameterisations and improvements to the dynamical core."</p>

<p>
  This talk responded to the workshop’s challenge of “How do we improve the physical basis for model uncertainty schemes?” It proposed an a priori introduction of stochasticity for GFD models at various levels of approximation, by introducing the methodology of Holm [2015] as a potential framework for quantifying model transport error. In turn, the stochastic representation of model transport error would introduce stochasticity into the parameterisations of subgrid scale processes.</p>

<p>
  &nbsp;</p>
},
  year = {2016},
  journal = {ECMWF/WWRP Workshop: Model Uncertainty},
  month = {05/2016},
  address = {ECMWF, Reading},
  language = {eng},
}