Bias Correction in an Ensemble Kalman Filter Data Assimilation System

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Wednesday, 7 January 2015
Eric J. Kostelich, Arizona State University, Tempe, AZ; and I. Szunyogh

The classical Kalman filter assumes that the data and process noise is unbiased and gaussian. Bias correction schemes relax this hypothesis. I will describe one such scheme in the context of the Local Ensemble Transform Kalman Filter, where we apply bias correction to the processing of surface pressure observations in the Global Forecast System numerical model and assess its effect on analysis and forecast errors.