Observation bias correction with an ensemble Kalman filter Academic Article uri icon


  • 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. 2009 The Authors Journal compilation 2009 Blackwell Munksgaard.

published proceedings


altmetric score

  • 0.5

author list (cited authors)

  • Fertig, E. J., Baek, S., Hunt, B. R., Ott, E., Szunyogh, I., Aravequia, J. A., ... Liu, J.

citation count

  • 48

complete list of authors

  • Fertig, Elana J||Baek, Seung-Jong||Hunt, Brian R||Ott, Edward||Szunyogh, Istvan||Aravequia, Jose A||Kalnay, Eugenia||Li, Hong||Liu, Junjie

publication date

  • March 2009