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 journalArticle

Abstract

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
Volume61
Issue number2
DOIs
StatePublished - Mar 5 2009

ASJC Scopus subject areas

  • Oceanography
  • Atmospheric Science

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  • Cite this

    Fertig, E. J., Baek, S. J., Hunt, B. R., Ott, E., Szunyogh, I., Aravéquia, J. A., Kalnay, E., Li, H., & Liu, J. (2009). Observation bias correction with an ensemble Kalman filter. Tellus, Series A: Dynamic Meteorology and Oceanography, 61(2), 210-226. https://doi.org/10.1111/j.1600-0870.2008.00378.x