Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5623–5635, 2021
https://doi.org/10.5194/gmd-14-5623-2021
Geosci. Model Dev., 14, 5623–5635, 2021
https://doi.org/10.5194/gmd-14-5623-2021

Development and technical paper 13 Sep 2021

Development and technical paper | 13 Sep 2021

Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models

Futo Tomizawa and Yohei Sawada

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Futo Tomizawa on behalf of the Authors (13 Nov 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (26 Nov 2020) by Adrian Sandu
RR by Anonymous Referee #1 (26 Nov 2020)
RR by Anonymous Referee #4 (27 May 2021)
ED: Publish subject to minor revisions (review by editor) (27 May 2021) by Adrian Sandu
AR by Futo Tomizawa on behalf of the Authors (02 Jun 2021)  Author's response    Manuscript
ED: Publish as is (25 Jul 2021) by Adrian Sandu
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Short summary
A new method to predict chaotic systems from observation and process-based models is proposed by combining machine learning with data assimilation. Our method is robust to the sparsity of observation networks and can predict more accurately than a process-based model when it is biased. Our method effectively works when both observations and models are imperfect, which is often the case in geoscience. Therefore, our method is useful to solve a wide variety of prediction problems in this field.