Accounting for Model Errors in Ensemble Data Assimilation

Abstract

Abstract This study addresses the issue of model errors with the ensemble Kalman filter. Observations generated from the NCEP–NCAR reanalysis fields are assimilated into a low-resolution AGCM. Without an effort to account for model errors, the performance of the local ensemble transform Kalman filter (LETKF) is seriously degraded when compared with the perfect-model scenario. Several methods to account for model errors, including model bias and system noise, are investigated. The results suggest that the two pure bias removal methods considered [Dee and Da Silva (DdSM) and low dimensional (LDM)] are not able to beat the multiplicative or additive inflation schemes used to account for the effects of total model errors. In contrast, when the bias removal methods are augmented by additive noise representing random errors (DdSM+ and LDM+), they outperform the pure inflation schemes. Of these augmented methods, the LDM+, where the constant bias, diurnal bias, and state-dependent errors are estimated from a large sample of 6-h forecast errors, gives the best results. The advantage of the LDM+ over other methods is larger in data-sparse regions than in data-dense regions.