Mixed-Resolution Ensemble Data Assimilation

Abstract

nsemble Kalman filters perform data assimilation by forming a background covariance matrix from an ensemble forecast. Most of the literature on ensemble Kalman filters assumes that all ensemble members come from the same model. This article presents and tests a modified local ensemble transform Kalman filter (LETKF) that takes its background covariance from a combination of a high-resolution ensemble and a low-resolution ensemble. The computational time and the accuracy of this mixed-resolution LETKF are explored and compared to the standard LETKF on a high-resolution ensemble, using simulated observation experiments with the Lorenz models II and III (more complex versions of the Lorenz-96 model). In a variety of scenarios, mixed-resolution analysis can obtain higher accuracy with similar computation time (or similar accuracy with a reduced computation time) compared to single-resolution analysis.