Reducing correlation sampling error in ensemble Kalman filter data assimilation

Good results with affordable ensemble sizes require enhancements to the basic algorithms to deal with insufficient ensemble variance and spurious ensemble correlations between observations and state variables. These challenges are often dealt with by using inflation and localization algorithms.

A new method for understanding and reducing some ensemble filter errors is introduced and tested. The method assumes that sampling error due to small ensemble size is the primary source of error. Sampling error in the ensemble correlations between observations and state variables is reduced by estimating the distribution of correlations as part of the ensemble filter algorithm.