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https://doi.org/10.5194/egusphere-2023-1765
https://doi.org/10.5194/egusphere-2023-1765
21 Sep 2023
 | 21 Sep 2023
Status: this preprint has been withdrawn by the authors.

On the transition from strong to weak constraint 4DVar using asimple one-dimensional advection equation for a passive tracer

Noureddine Semane

Abstract. In contrast to strong constraint 4DVar, the weak constraint takes into account the model imperfection in the minimisation process. Relaying on a simple one-dimensional advection equation for a passive tracer, this study shows that the transition from strong to weak constraint, accounting for both observations and model biases, reduces the analysis bias.

This preprint has been withdrawn.

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Noureddine Semane

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Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1765', zezhong zhang, 27 Oct 2023
  • RC2: 'Comment on egusphere-2023-1765', Anonymous Referee #2, 08 Dec 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1765', zezhong zhang, 27 Oct 2023
  • RC2: 'Comment on egusphere-2023-1765', Anonymous Referee #2, 08 Dec 2023
Noureddine Semane
Noureddine Semane

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This preprint has been withdrawn.

Short summary
4DVar assumes random zero-mean random errors. Therefore, to build an unbiased analysis, variational bias correction and weak constraint are designed to conjointly remove observations and model biases as shown in Fig. 1. Throughout an idealised experiment, this study demonstrates the added-value of weak constraint in the reduction of the analysis bias when compared to the strong constraint.