Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 2
Articles | Volume 12, issue 2
https://doi.org/10.5194/gmd-12-629-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-629-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 2
Development and technical paper
 | 
12 Feb 2019
Development and technical paper |  | 12 Feb 2019

DATeS: a highly extensible data assimilation testing suite v1.0

Ahmed Attia and Adrian Sandu

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Interactive discussion

Status: closed
Status: closed
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 Ahmed Attia on behalf of the Authors (29 Jul 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (16 Aug 2018) by Ignacio Pisso
RR by Anonymous Referee #3 (29 Aug 2018)
RR by Kody Law (26 Sep 2018)
ED: Publish subject to minor revisions (review by editor) (22 Oct 2018) by Ignacio Pisso
AR by Ahmed Attia on behalf of the Authors (29 Oct 2018)  Author's response   Manuscript 
ED: Publish subject to technical corrections (07 Dec 2018) by Ignacio Pisso
AR by Ahmed Attia on behalf of the Authors (13 Dec 2018)  Manuscript 
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Short summary
This work describes DATeS, a highly extensible data assimilation package. DATeS seeks to provide a unified testing suite for data assimilation applications that allows researchers to easily compare different methodologies in different settings with minimal coding effort. The core of DATeS is written in Python. The main functionalities, such as model propagation and assimilation, can however be written in low-level languages such as C or Fortran to attain high levels of computational efficiency.
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