A comparative study of data assimilation methods for oceanic models
type: Dataset
description: This thesis developed and implemented iterative data assimilation algorithms for a primitive equation ocean model, and compared them with other well established DA methods such as the 4Dvar and the Singular Evolutive Extended Kalman (SEEK) Filter/Smoother. The new proposed iterative algorithms, similarly to the Back and Forth Nudging (BFN), are all based on a sequence of alternating forward and backward model integrations. Namely, they are the Backward Smoother (BS), which uses the backward model to freely propagate “future” observations backward in time, and the Back and Forth Kalman Filter, which also uses the backward model to propagate the observations backward in time but, at every time an observation batch is available, an update step similar to the SEEK filter step is carried out. The Bayesian formalism was used to derive these methods, which means that they may be used with any algorithm that estimates the “a posteriori” conditional probability of the model state by means of sequential methods. The results show that the main advantage of the methods based on the BFN is the use of the backward model to propagate the observation informations backward in time. By this way, it avoids the use of the adjoint model, needed by the 4Dvar, and of unknown temporal correlations, needed by the Kalman Smoother, to produce initial states or past model trajectories. The advantages of using the Back and Forth (BF) idea rely on the implicit use of the unstable forward subspace, which became stable when stepping backwards, that allows the errors components projecting onto this subspace to be naturally damped during the backward integration.
landing page: https://theses.hal.science/tel-00976619
identifier: NNT: 2014NICE4011
keyword: assimilation-de-donnees
keyword: back-and-forth-nudging
keyword: bayesian-estimation
keyword: data-assimilation
keyword: estimation-bayesienne
keyword: infoeu-reposemanticsdoctoralthesis
keyword: nudging
keyword: sdustusciences-of-the-universe-physicsearth-sciences
keyword: theses
issued: 2026-05-05T15:32:35.740994
modified: 2026-05-05T15:32:35.740998
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

A comparative study of data assimilation methods for oceanic models
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00976619
issued: 2026-05-05T15:32:35.742180
modified: 2026-05-05T15:32:35.730838