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

Journal metrics

IF value: 2.864
IF2.864
IF 5-year value: 3.337
IF 5-year
3.337
CiteScore value: 4.5
CiteScore
4.5
SNIP value: 1.259
SNIP1.259
IPP value: 3.07
IPP3.07
SJR value: 1.326
SJR1.326
Scimago H <br class='widget-line-break'>index value: 52
Scimago H
index
52
h5-index value: 30
h5-index30
Preprints
https://doi.org/10.5194/os-2020-43
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2020-43
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Jun 2020

03 Jun 2020

Review status
A revised version of this preprint is currently under review for the journal OS.

Data assimilation of sea surface temperature and salinity using basin-scale EOF reconstruction: a feasibility study in the NE Baltic Sea

Mihhail Zujev, Jüri Elken, and Priidik Lagemaa Mihhail Zujev et al.
  • Department of Marine Systems, Tallinn University of Technology, Tallinn, EE12618, Estonia

Abstract. The tested data assimilation (DA) method based on EOF (Empirical Orthogonal Functions) reconstruction of observations decreased RMSD of surface temperature (SST) and salinity (SSS) in reference to observations in the NE Baltic Sea by 22 % and 34 %, respectively, compared to the control run without DA. The method is based on the covariance estimates from the long period model data. The amplitudes of the pre-calculated gravest EOF modes are estimated from point observations using least-squares optimization; the method builds the variables on the regular grid. The study used FerryBox observations along four ship tracks from 1 May to 31 December 2015, and observations from research vessels. In the reconstruction, this data amount was compressed into daily averages over 5’ N X 10’ E coarse grid. Skill was tested based on daily averages on the 0.5’ N X 1’ E original fine grid of the model. DA with EOF reconstruction technique was found feasible for further implementation studies, since: 1) the method that works on the large-scale patterns (mesoscale features are neglected by taking only the gravest EOF modes) improves the high-resolution model performance by comparable or even better degree than in the other published studies, 2) the method is computationally effective.

Mihhail Zujev et al.

Interactive discussion

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Topic Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Mihhail Zujev et al.

Mihhail Zujev et al.

Viewed

Total article views: 354 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
219 93 42 354 43 39
  • HTML: 219
  • PDF: 93
  • XML: 42
  • Total: 354
  • BibTeX: 43
  • EndNote: 39
Views and downloads (calculated since 03 Jun 2020)
Cumulative views and downloads (calculated since 03 Jun 2020)

Viewed (geographical distribution)

Total article views: 267 (including HTML, PDF, and XML) Thereof 267 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 28 Sep 2020
Publications Copernicus
Download
Short summary
The proposed method of data assimilation is capable of effectively correcting basin-scale mismatch of oceanographic model when the domain is under nearly coherent external forcing. The method uses basin scale EOF modes, calculated from the long-term model statistics. These modes are used to reconstruct gridded fields from point observations, which are further fed to the model using relaxation. Tests with sea surface temperature and salinity in the NE Baltic Sea were successful.
The proposed method of data assimilation is capable of effectively correcting basin-scale...
Citation