A fast, single-iteration ensemble Kalman smoother for sequential data assimilation

This paper, led by my colleague Colin Grudzien, formerly at the Reno University of Nevada, and now at SCRIPPS, is both a rather extensive review on the fundamental of the ensemble iterative smoothers for sequential data assimilation, and the introduction to a new, cheaper iterative ensemble Kalman smoother. This single IEnKS, then SIEnKS, combines the advantages of the standard ensemble Kalman smoother (EnKS) and of the iterative ensemble Kalman filter (IEnKS). Some numerical illustration and validation experiments are provided.

The paper is published in Geoscientific Model Development and can be found here.