Abstract. Prediction of spatiotemporal chaotic systems is important in various
fields, such as numerical weather prediction (NWP). While data assimilation
methods have been applied in NWP, machine learning techniques, such as
reservoir computing (RC), have recently been recognized as promising tools to
predict spatiotemporal chaotic systems. However, the sensitivity of the
skill of the machine-learning-based prediction to the imperfectness of
observations is unclear. In this study, we evaluate the skill of RC with
noisy and sparsely distributed observations. We intensively compare the
performances of RC and local ensemble transform Kalman filter (LETKF) by
applying them to the prediction of the Lorenz 96 system. In order to
increase the scalability to larger systems, we applied a parallelized RC
framework. Although RC can successfully predict the Lorenz 96 system if the
system is perfectly observed, we find that RC is vulnerable to observation
sparsity compared with LETKF. To overcome this limitation of RC, we propose
to combine LETKF and RC. In our proposed method, the system is predicted by
RC that learned the analysis time series estimated by LETKF. Our proposed
method can successfully predict the Lorenz 96 system using noisy and
sparsely distributed observations. Most importantly, our method can predict
better than LETKF when the process-based model is imperfect.
Abstract. Prediction of spatio-temporal chaotic systems is important in various fields, such as Numerical Weather Prediction (NWP). While data assimilation methods have been applied in NWP, machine learning techniques, such as Reservoir Computing (RC), are recently recognized as promising tools to predict spatio-temporal chaotic systems. However, the sensitivity of the skill of the machine learning based prediction to the imperfectness of observations is unclear. In this study, we evaluate the skill of RC with noisy and sparsely distributed observations. We intensively compare the performances of RC and Local Ensemble Transform Kalman Filter (LETKF) by applying them to the prediction of the Lorenz 96 system. Although RC can successfully predict the Lorenz 96 system if the system is perfectly observed, we find that RC is vulnerable to observation sparsity compared with LETKF. To overcome this limitation of RC, we propose to combine LETKF and RC. In our proposed method, the system is predicted by RC that learned the analysis time series estimated by LETKF. Our proposed method can successfully predict the Lorenz 96 system using noisy and sparsely distributed observations. Most importantly, our method can predict better than LETKF when the process-based model is imperfect.
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