The ensemble Kalman filter (EnKF) has been widely tested as a possible candidate for the next generation of meteorological and oceanographic data assimilation algorithms. While a number of tests with models of varying realism have been successfully performed, the EnKF has been seldom evaluated in an operational regional NWP environment at realistic spatial resolution. In this work one particular EnKF implementation (Local Ensemble Transform Kalman Filter, LETKF) has been implemented and its performance evaluated in comparison with CNMCA operational 3D-Var.
One of the most important issues in EnKF implementations lies in the filter tendency to become underdispersive for practical ensemble sizes. While multiplicative (or additive) covariance inflation has been used to deal with this problem, tuning its values is an expensive and possibly never-ending task. Following ideas from linear estimation theory, we test an adaptive estimation procedure to evaluate forecast covariance inflation factors and observation errors. Our results show that, differently from previous experiences, the online estimation technique can be successfully employed in a realistic state-of-the-art NWP system. More generally the LETKF analysis is shown to be of superior quality with respect to the operational 3D-Var and a likely candidate for its replacement in the not-too-distant future. Copyright © 2008 Royal Meteorological Society