Local ensemble transform Kalman filter data assimilation system for the global semi-Lagrangian atmospheric model

Abstract

Recently, ensemble Kalman filters have come into practical data assimilation for numerical weather prediction models. We give an overview of ensemble Kalman filters and problems that arise with practical implementation of ensemble methods. We present our implementation of the local ensemble transform Kalman filter, one of ensemble square root filters using observation localization. Multiplicative and additive inflations are used to prevent filter divergence and to account for the model error. The implemented assimilation system is tested with the global semi-Lagrangian atmospheric model SL-AV using real observations for 2 months of cyclic assimilation (August and September 2012). The system works stably. Application of the ensemble filter significantly reduces first guess (background) errors and corrects the forecast biases.