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 language||English (US)|
|Number of pages||17|
|Journal||Tellus, Series A: Dynamic Meteorology and Oceanography|
|State||Published - 2009|
ASJC Scopus subject areas
- Atmospheric Science