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

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Status: closed
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ian Grooms on behalf of the Authors (28 May 2015)  Author's response   Manuscript 
ED: Reconsider after major revisions (further review by Editor and Referees) (10 Jun 2015) by Zoltan Toth
AR by Ian Grooms on behalf of the Authors (18 Jun 2015)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (06 Jul 2015) by Zoltan Toth
RR by Anonymous Referee #2 (07 Jul 2015)
RR by Anonymous Referee #1 (28 Jul 2015)
ED: Publish subject to minor revisions (further review by Editor) (24 Aug 2015) by Zoltan Toth
AR by Ian Grooms on behalf of the Authors (25 Aug 2015)  Author's response   Manuscript 
ED: Publish as is (28 Sep 2015) by Zoltan Toth
AR by Ian Grooms on behalf of the Authors (30 Sep 2015)
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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.