Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
GMD | Articles | Volume 12, issue 2
Geosci. Model Dev., 12, 629–649, 2019
https://doi.org/10.5194/gmd-12-629-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 12, 629–649, 2019
https://doi.org/10.5194/gmd-12-629-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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
Download
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
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
Publications Copernicus
Download
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.
This work describes DATeS, a highly extensible data assimilation package. DATeS seeks to provide...
Citation