Observation-Based Noise Calibration: An Efficient Dynamics for the Ensemble Kalman Filter - INRIA 2 Access content directly
Book Sections Year : 2023

Observation-Based Noise Calibration: An Efficient Dynamics for the Ensemble Kalman Filter

Abstract

Abstract We investigate the calibration of the stochastic noise in order to guide the realizations towards the observational data used for the assimilation. This is done in the context of the stochastic parametrization under Location Uncertainty (LU) and data assimilation. The new methodology is rigorously justified by the use of the Girsanov theorem, and yields significant improvements in the experiments carried out on the Surface Quasi Geostrophic (SQG) model, when applied to Ensemble Kalman filters. The particular test case studied here shows improvements of the peak MSE from 85% to 93%.
Fichier principal
Vignette du fichier
978-3-031-18988-3_4.pdf (455.17 Ko) Télécharger le fichier
Origin : Publication funded by an institution

Dates and versions

hal-03910764 , version 1 (23-01-2023)

Identifiers

Cite

Benjamin Dufée, Etienne Mémin, Dan Crisan. Observation-Based Noise Calibration: An Efficient Dynamics for the Ensemble Kalman Filter. Stochastic Transport in Upper Ocean Dynamics, 10, Springer International Publishing, pp.43-56, 2023, Mathematics of Planet Earth, ⟨10.1007/978-3-031-18988-3_4⟩. ⟨hal-03910764⟩
350 View
51 Download

Altmetric

Share

Gmail Facebook X LinkedIn More