Articles | Volume 15, issue 20
Development and technical paper
20 Oct 2022
Development and technical paper |  | 20 Oct 2022

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

Colin Grudzien and Marc Bocquet

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Cited articles

Ait-El-Fquih, B. and Hoteit, I.: Filtering with One-Step-Ahead Smoothing for Efficient Data Assimilation, in: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), edited by: Park, S. K. and Xu, L., Springer, Cham, 69–96,, 2022. a, b
Ait-El-Fquih, B., El Gharamti, M., and Hoteit, I.: A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology, Hydrol. Earth Syst. Sci., 20, 3289–3307,, 2016. a
Asch, M., Bocquet, M., and Nodet, M.: Data Assimilation: Methods, Algorithms, and Applications, SIAM, ISBN 978-1-61197-453-9,, 2016. a, b, c, d, e
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633,, 2017. a
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V.: Julia: A fresh approach to numerical computing, SIAM Rev., 59, 65–98,, 2017. a
Short summary
Iterative optimization techniques, the state of the art in data assimilation, have largely focused on extending forecast accuracy to moderate- to long-range forecast systems. However, current methodology may not be cost-effective in reducing forecast errors in online, short-range forecast systems. We propose a novel optimization of these techniques for online, short-range forecast cycles, simultaneously providing an improvement in forecast accuracy and a reduction in the computational cost.