Preprints
https://doi.org/10.5194/os-2020-119
https://doi.org/10.5194/os-2020-119

  11 Jan 2021

11 Jan 2021

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

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

Georgy I. Shapiro1, Jose M. Gonzalez-Ondina2, and Vladimir N. Belokopytov3 Georgy I. Shapiro et al.
  • 1School of Biological and Marine Sciences, University of Plymouth, Plymouth, PL4 8AA, UK
  • 2University of Plymouth Enterprise LTD, Plymouth, PL4 8AA, UK
  • 3Marine Hydrophysical Institute, Russian Academy of Sciences, Sevastopol, 299011, Russia

Abstract. High-resolution modelling of a large ocean domain requires significant computational resources. The main purpose of this study is to develop an efficient tool for downscaling the lower resolution data such as available from Copernicus Marine Environment Monitoring Service (CMEMS). Common methods of downscaling CMEMS ocean models utilize their lower resolution output as boundary conditions for local, higher resolution hydrodynamic ocean models. Such methods reveal greater details of spatial distribution of ocean variables; however, they increase the cost of computations, and often reduce the model skill due to the so called double penalty effect. This effect is a common problem for many high-resolution models where predicted features are displaced in space or time. This paper presents a Stochastic Deterministic Downscaling (SDD) method, which is an efficient tool for downscaling of ocean models based on the combination of deterministic and stochastic approaches. The ability of the SDD method is first demonstrated in an idealised case when the true solution is known a priori. Then the method is applied to create an operational eddy-resolving Stochastic Model of the Red Sea (SMORS) with the parent model being the eddy-permitting Mercator Global Ocean Analysis and Forecast System. The stochastic component is data-driven rather than equation-driven and applied to the areas smaller than the Rossby radius, where distributions of ocean variables are more coherent. The method, based on objective analysis, is similar to what is used for data assimilation in ocean models, and stems from the philosophy of 2D turbulence. The SMORS model produces higher resolution (1/24th degree latitude mesh) oceanographic data using the output from a coarser resolution (1/12th degree mesh) parent model available from CMEMS. The values on the high-resolution mesh are computed under condition of minimisation of the cost function which represents the error between the model and true solution. The SMORS model has been validated against Sea Surface Temperature and ARGO floats observations. Comparisons show that the model and observations are in good agreement and SMORS is not subject to the ‘double penalty’ effect. SMORS is very fast to run on a typical desktop PC and can be relocated to another area of the ocean.

Georgy I. Shapiro et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on os-2020-119', George Zodiatis, 27 Jan 2021
    • AC1: 'Reply on CC1', Georgy Shapiro, 12 Apr 2021
  • RC1: 'Comment on os-2020-119', Mike Bell, 16 Feb 2021
    • AC2: 'Reply on RC1', Georgy Shapiro, 12 Apr 2021
  • EC1: 'Comment on os-2020-119', John M. Huthnance, 26 Mar 2021
    • AC3: 'Reply on EC1', Georgy Shapiro, 12 Apr 2021

Georgy I. Shapiro et al.

Georgy I. Shapiro et al.

Viewed

Total article views: 420 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
341 66 13 420 1 1
  • HTML: 341
  • PDF: 66
  • XML: 13
  • Total: 420
  • BibTeX: 1
  • EndNote: 1
Views and downloads (calculated since 11 Jan 2021)
Cumulative views and downloads (calculated since 11 Jan 2021)

Viewed (geographical distribution)

Total article views: 363 (including HTML, PDF, and XML) Thereof 361 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Apr 2021
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.