Ocean Science
Ocean Science
OS
Articles | Volume 17, issue 4
Articles | Volume 17, issue 4
https://doi.org/10.5194/os-17-891-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-17-891-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 4
Research article
 | 
06 Jul 2021
Research article |  | 06 Jul 2021

High-resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics

Georgy I. Shapiro, Jose M. Gonzalez-Ondina, and Vladimir N. Belokopytov

Viewed

Total article views: 2,577 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,611 882 84 2,577 88 86
  • HTML: 1,611
  • PDF: 882
  • XML: 84
  • Total: 2,577
  • BibTeX: 88
  • EndNote: 86
Views and downloads (calculated since 11 Jan 2021)
Cumulative views and downloads (calculated since 11 Jan 2021)

Viewed (geographical distribution)

Total article views: 2,577 (including HTML, PDF, and XML) Thereof 2,447 with geography defined and 130 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 12 Jun 2025
Download
Short summary
This paper presents an efficient method for high-resolution ocean modelling based on a combination of the deterministic and stochastic approaches. The method utilises mathematical tools similar to those developed for data assimilation in ocean modelling. The main difference is that instead of assimilating a relatively small number of observations, the SDD method assimilates all the data produced by a parent model. The method is applied to create an operational Stochastic Model of the Red Sea.
Read more
Share