An iterative approach for handling satellite radiance bias correction and non-gaussian observation errors in the Ensemble Kalman Filter
Here we present an iterative method mimicing VarBC and VarQC in the EnKF, which we refer to as EnKF-QC and EnKF-BC. Both methods require updates of ensemble mean observation priors (O - B) values during the iteration. In the case of EnKF-QC, these updated values are used to iteratively estimate the probablity of a "gross" observation error. In the case of EnKF-BC, the updated values are used to update the radiance bias correction coefficients. If the process is iterated until convergence, it is algorithmically identical to VarBC and VarQC.
We demonstrate the use of EnKF-BC and EnKF-QC in an EnKF assimilation system based upon the NCEP Global Forecast System (GFS). The EnKF-BC is shown to perform just as well for satellite radiances as the VarBC component of the NCEP Global Statistical Interpolation (GSI) variational system. The EnKF-QC system is demonstrated by assimilating only surface pressure observations for the month of December, 1999. For the two severe storms that battered Europe on Dec 26 and 27th, the EnKF-rQC was able to "redeem" several important observations that were rejected by a simple ensemble-based background check that uses assumes Gaussian observation errors.