Abstract/Summary

Ensemble Kalman Filters are used extensively in all geoscience areas. Often a stochastic variant is used, in which each ensemble member is updated via the Kalman Filter equation with an extra perturbation in the innovation. These perturbations are essential for the correct ensemble spread in a stochastic Ensemble Kalman Filter, and are applied either to the observations or to the modeled observations. This paper investigates if there is a preference for any of these two perturbation methods. Both versions lead to the same posterior mean and covariance when the prior and the likelihood are Gaussian in the state. However, ensemble verification methods, Bayes Theorem and the Best Linear Unbiased Estimate (BLUE) suggest that one should perturb the modeled observations. Furthermore, it is known that in non-Gaussian settings the perturbed modeled observation scheme is preferred, illustrated here for a skewed likelihood. Existing reasons for the perturbed observation scheme are shown to be incorrect, and no new arguments in favor of that scheme have been found. Finally, a new and consistent derivation and interpretation of the stochastic version of the EnKF equations is derived based on perturbing modeled observations. It is argued that these results have direct consequences for (iterative) Ensemble Kalman Filters and Smoothers, including 'perturbed observation' 3D- and 4DVars, both in terms of internal consistency and implementation.