Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7641-2022
https://doi.org/10.5194/gmd-15-7641-2022
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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Latest update: 15 Apr 2024
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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.