Articles | Volume 22, issue 5
https://doi.org/10.5194/npg-22-601-2015
https://doi.org/10.5194/npg-22-601-2015
Research article
 | 
09 Oct 2015
Research article |  | 09 Oct 2015

A framework for variational data assimilation with superparameterization

I. Grooms and Y. Lee

Viewed

Total article views: 2,089 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,105 849 135 2,089 135 141
  • HTML: 1,105
  • PDF: 849
  • XML: 135
  • Total: 2,089
  • BibTeX: 135
  • EndNote: 141
Views and downloads (calculated since 20 Mar 2015)
Cumulative views and downloads (calculated since 20 Mar 2015)

Cited

Latest update: 27 Mar 2024
Download
Short summary
Superparameterization is a multiscale computational method that significantly improves the representation of cloud processes in global atmosphere and climate models. We present a framework for assimilating observational data into superparameterized models to initialize them for forecasts. The framework is demonstrated in the context of a new system of ordinary differential equations that constitutes perhaps the simplest model of superparameterization.