Accelerating the spin‐up of Ensemble Kalman Filtering

Abstract

Abstract Ensemble Kalman Filter (EnKF) may have a longer spin‐up time to reach its asymptotic level of accuracy than the corresponding spin‐up time in variational methods (3D‐Var or 4D‐Var). During the spin‐up EnKF has to fulfill two independent requirements, namely that the ensemble mean be close to the true state, and that the ensemble perturbations represent the ‘errors of the day’. As a result, there are cases, such as radar observations of a severe storm, or regional forecast of a hurricane, where EnKF may spin‐up too slowly to be useful. A heuristic scheme is proposed to accelerate the spin‐up of EnKF by applying a no‐cost Ensemble Kalman Smoother, and using the observations more than once in each assimilation window during spin‐up in order to maximize the initial extraction of information. The performance of this scheme is tested with the Local Ensemble Transform Kalman Filter (LETKF) implemented in a quasi‐geostrophic model, which requires a very long spin‐up time when initialized from random initial perturbations from a uniform distribution. Results show that with the new ‘running in place’ (RIP) scheme the LETKF spins up and converges to the optimal level of error faster than 3D‐Var or 4D‐Var, even in the absence of any prior information. Additional computations (2 to 12 iterations for each assimilation window) are only required during the initial spin‐up, since the scheme naturally returns to the original LETKF after spin‐up is achieved. RIP also accelerates spin‐up when the initial perturbations are drawn from a well‐tuned 3D‐Var background‐error covariance, rather than being uniform noise, and fewer iterations and RIP cycles are required than in the case without such prior information. Copyright © 2010 Royal Meteorological Society