Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 22
Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8395-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8395-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 22
Development and technical paper
 | 
18 Nov 2022
Development and technical paper |  | 18 Nov 2022

An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0

Shun Ohishi, Tsutomu Hihara, Hidenori Aiki, Joji Ishizaka, Yasumasa Miyazawa, Misako Kachi, and Takemasa Miyoshi

Viewed

Total article views: 2,586 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,968 547 71 2,586 57 51
  • HTML: 1,968
  • PDF: 547
  • XML: 71
  • Total: 2,586
  • BibTeX: 57
  • EndNote: 51
Views and downloads (calculated since 04 Mar 2022)
Cumulative views and downloads (calculated since 04 Mar 2022)

Viewed (geographical distribution)

Total article views: 2,586 (including HTML, PDF, and XML) Thereof 2,420 with geography defined and 166 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 May 2025
Download
Short summary
We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which satellite and in situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and accuracy based on sensitivity experiments focused on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.
Read more
Share