A Fixed-Lag Kalman Smoother for Retrospective Data Assimilation

Abstract

Analyses incorporating future data, as well as current and past data, are termed retrospective analyses. In this paper, the fixed-lag Kalman smoother (FLKS) is proposed as a means of providing retrospective analysis capability in data assimilation. The FLKS is a direct generalization of the Kalman filter. It incorporates all data observed up to and including some fixed amount of time past each analysis time. A computationally efficient form of the FLKS is derived. A simple scalar examination of the FLKS demonstrates that incorporating future data improves analyses the most in the presence of dynamical instabilities, for accurate models and for accurate observations. An implementation of the FLKS for two-dimensional linear shallow-water model corroborates the scalar analysis. The numerical experiments also demonstrate the ability of the FLKS to propagate information upstream as well as downstream, thus improving analysis quality substantially in data voids.