Journal cover Journal topic
Ocean Science An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 2.539 IF 2.539
  • IF 5-year value: 3.129 IF 5-year
    3.129
  • CiteScore value: 2.78 CiteScore
    2.78
  • SNIP value: 1.217 SNIP 1.217
  • IPP value: 2.62 IPP 2.62
  • SJR value: 1.370 SJR 1.370
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 48 Scimago H
    index 48
  • h5-index value: 32 h5-index 32
OS | Articles | Volume 15, issue 2
Ocean Sci., 15, 443–457, 2019
https://doi.org/10.5194/os-15-443-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: The Copernicus Marine Environment Monitoring Service (CMEMS):...

Ocean Sci., 15, 443–457, 2019
https://doi.org/10.5194/os-15-443-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Apr 2019

Research article | 26 Apr 2019

A multiscale ocean data assimilation approach combining spatial and spectral localisation

Ann-Sophie Tissier et al.

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 Ann-Sophie Tissier on behalf of the Authors (04 Mar 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (18 Mar 2019) by Marina Tonani
AR by Ann-Sophie Tissier on behalf of the Authors (20 Mar 2019)  Author's response    Manuscript
ED: Publish as is (21 Mar 2019) by Marina Tonani
AR by Ann-Sophie Tissier on behalf of the Authors (28 Mar 2019)  Author's response    Manuscript
Publications Copernicus
Download
Short summary
To better exploit the observational information available for all scales in data assimilation systems, we investigate a new method to introduce scale separation in the algorithm. It consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The performance is then checked explicitly and separately for all scales. Results show that accuracy can be improved for the large scales while preserving reliability at all scales.
To better exploit the observational information available for all scales in data assimilation...
Citation