Observation bias correction with an ensemble Kalman filter

Elana J. Fertig, Seung Jong Baek, Brian R. Hunt, Edward Ott, Istvan Szunyogh, José A. Aravéquia, Eugenia Kalnay, Hong Li, Junjie Liu

Research output: Contribution to journalArticlepeer-review


This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases. Our approach is to use state-space augmentation to estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate our approach by applying it to a particular ensemble scheme - the local ensemble transform Kalman filter (LETKF) - to assimilate simulated biased atmospheric infrared sounder brightness temperature observations from 15 channels on the simplified parameterizations, primitive-equation dynamics (SPEEDY) model. The scheme we present successfully reduces both the observation bias and analysis error in perfect-model simulations.

Original languageEnglish (US)
Pages (from-to)210-226
Number of pages17
JournalTellus, Series A: Dynamic Meteorology and Oceanography
Issue number2
StatePublished - 2009

ASJC Scopus subject areas

  • Oceanography
  • Atmospheric Science

Fingerprint Dive into the research topics of 'Observation bias correction with an ensemble Kalman filter'. Together they form a unique fingerprint.

Cite this