Preprints
https://doi.org/10.5194/os-2021-77
https://doi.org/10.5194/os-2021-77

  18 Aug 2021

18 Aug 2021

Review status: this preprint is currently under review for the journal OS.

Model-to-model data assimilation method for fine resolution ocean modelling

Georgy I. Shapiro1 and Jose M. Gonzalez-Ondina2 Georgy I. Shapiro and Jose M. Gonzalez-Ondina
  • 1School of Biological and Marine Sciences, University of Plymouth, Plymouth, PL4 8AA, United Kingdom
  • 2University of Plymouth Enterprise Ltd, Plymouth, PL4 8AA, United Kingdom

Abstract. An effective and computationally efficient method is presented for data assimilation in a high-resolution (child) ocean model, which is nested into a coarse-resolution good quality data assimilating (parent) model. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) reduces bias and root mean square errors (RMSE) of the child model and does not allow the child model to drift away from reality. The basic idea is to assimilate data from the parent model instead of actual observations. In this way, the child model is physically aware of observations via the parent model. The method allows to avoid a complex process of assimilating the same observations which were already assimilated into the parent model. The method consists of two stages: (1) downscaling the parent model output onto the child model grid using Stochastic-Deterministic Downscaling, and (2) applying a simplified Kalman gain formula to each of the fine grid nodes. The method is illustrated in a synthetic case where the true solution is known, and the child model forecast (before data assimilation) is simulated by adding various types of errors. The SDDA method reduces the child model bias to the same level as in the parent model and reduces the RMSE typically by a factor of 2 to 5.

Georgy I. Shapiro and Jose M. Gonzalez-Ondina

Status: open (until 13 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Georgy I. Shapiro and Jose M. Gonzalez-Ondina

Georgy I. Shapiro and Jose M. Gonzalez-Ondina

Viewed

Total article views: 262 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
226 31 5 262 0 0
  • HTML: 226
  • PDF: 31
  • XML: 5
  • Total: 262
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 18 Aug 2021)
Cumulative views and downloads (calculated since 18 Aug 2021)

Viewed (geographical distribution)

Total article views: 232 (including HTML, PDF, and XML) Thereof 232 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Sep 2021
Download
Short summary
An effective method is developed for data assimilation in a high-resolution (child) ocean model in the case when the output from a coarse-resolution data-assimilating model (parent) is available. The basic idea is to assimilate data from the coarser model instead of actual observations. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) does not allow the child model to drift away from reality as it is indirectly controlled by observations via the parent model.