Preprints
https://doi.org/10.5194/os-2021-77
https://doi.org/10.5194/os-2021-77
 
18 Aug 2021
18 Aug 2021
Status: this preprint has been withdrawn by the authors.

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.

This preprint has been withdrawn.

Georgy I. Shapiro and Jose M. Gonzalez-Ondina

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on os-2021-77', Anonymous Referee #1, 17 Sep 2021
    • AC1: 'Reply on RC1', Georgy Shapiro, 12 Oct 2021
  • RC2: 'Comment on os-2021-77', Anonymous Referee #2, 22 Sep 2021
    • AC2: 'Reply on RC2', Georgy Shapiro, 12 Oct 2021
  • EC1: 'Comment on os-2021-77', Andrew Moore, 23 Sep 2021
    • AC3: 'Reply on EC1', Georgy Shapiro, 12 Oct 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on os-2021-77', Anonymous Referee #1, 17 Sep 2021
    • AC1: 'Reply on RC1', Georgy Shapiro, 12 Oct 2021
  • RC2: 'Comment on os-2021-77', Anonymous Referee #2, 22 Sep 2021
    • AC2: 'Reply on RC2', Georgy Shapiro, 12 Oct 2021
  • EC1: 'Comment on os-2021-77', Andrew Moore, 23 Sep 2021
    • AC3: 'Reply on EC1', Georgy Shapiro, 12 Oct 2021

Georgy I. Shapiro and Jose M. Gonzalez-Ondina

Georgy I. Shapiro and Jose M. Gonzalez-Ondina

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This preprint has been withdrawn.

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.