Accelerating the spin-up of Ensemble Kalman filtering

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 ofEnKFby 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 showthatwith 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.