Ensemble Kalman filtering with colored observation noise

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The Kalman filter (KF) is derived under the assumption of time-independent (white) observation noise. Although this assumption can be reasonable in many ocean and atmospheric applications, the recent increase in sensors coverage such as the launching of new constellations of satellites with global spatio-temporal coverage will provide high density of oceanic and atmospheric observations that are expected to have time-dependent (colored) error statistics. In this situation, the KF update has been shown to generally provide overconfident probability estimates, which may degrade the filter performance. Different KF-based schemes accounting for time-correlated observation noise were proposed for small systems by modeling the colored noise as a first-order autoregressive model driven by white Gaussian noise. This work introduces new ensemble Kalman filters (EnKFs) that account for colored observational noises for efficient data assimilation into large-scale oceanic and atmospheric applications. More specifically, we follow the standard and the one-step-ahead smoothing formulations of the Bayesian filtering problem with colored observational noise, modeled as an autoregressive model, to derive two (deterministic) EnKFs. We demonstrate the relevance of the colored observational noise-aware EnKFs and analyze their performances through extensive numerical experiments conducted with the Lorenz-96 model.
Original languageEnglish (US)
JournalQuarterly Journal of the Royal Meteorological Society
StatePublished - Oct 15 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-10-20
Acknowledged KAUST grant number(s): OSR, REP/1/3268-01-01
Acknowledgements: This work was supported by the Office of SponsoredResearch (OSR) at King Abdullah University of Science andTechnology (KAUST) under the Virtual Red Sea Initiative (Grant#REP/1/3268-01-01). The research made use of the KAUST super computing facility SHAHEEN

ASJC Scopus subject areas

  • Atmospheric Science


Dive into the research topics of 'Ensemble Kalman filtering with colored observation noise'. Together they form a unique fingerprint.

Cite this