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

Model code and software

RC_Analysis_Loren96 Futo Tomizawa and Yohei Sawada https://doi.org/10.5281/zenodo.3907291

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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.