Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5623–5635, 2021
Geosci. Model Dev., 14, 5623–5635, 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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Cited articles

Asanjan, A., Yang, T., Hsu, K., Sorooshian, S., Lin, J., and Peng, Q.: Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks, J. Geophys. Res.-Atmos., 123, 12543–12563,, 2018. 
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633,, 2017. 
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818,, 2013. 
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models, Nonlin. Processes Geophys., 26, 143–162,, 2019. 
Bocquet, M., Farchi, A., and Malartic, Q.: Online learning of both state and dynamics using ensemble Kalman filters, Found. Data Sci.,, 2020. 
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.