Observation bias correction with an ensemble Kalman filter
Elana Judith Fertig, Seung-Jong Baek, Brian R. Hunt, Edward Ott, Istvan Szunyogh, José A. Aravéquia, Eugenia Kalnay, Hong Li, Junjie Liu · Tellus A Dynamic Meteorology and Oceanography · 2009
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.