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: 2,100 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,109 852 139 2,100 136 143
  • HTML: 1,109
  • PDF: 852
  • XML: 139
  • Total: 2,100
  • BibTeX: 136
  • EndNote: 143
Views and downloads (calculated since 20 Mar 2015)
Cumulative views and downloads (calculated since 20 Mar 2015)

Cited

Latest update: 24 Apr 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.