Articles | Volume 14, issue 9
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

Related authors

Ensemble Kalman filter in geoscience meets model predictive control
Yohei Sawada
EGUsphere,,, 2024
Short summary
A signal-processing-based interpretation of the Nash–Sutcliffe efficiency
Le Duc and Yohei Sawada
Hydrol. Earth Syst. Sci., 27, 1827–1839,,, 2023
Short summary
Global assessment of subnational drought impact based on the Geocoded Disasters dataset and land reanalysis
Yuya Kageyama and Yohei Sawada
Hydrol. Earth Syst. Sci., 26, 4707–4720,,, 2022
Short summary
Impact of cry wolf effects on social preparedness and the efficiency of flood early warning systems
Yohei Sawada, Rin Kanai, and Hitomu Kotani
Hydrol. Earth Syst. Sci., 26, 4265–4278,,, 2022
Short summary
Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration
Yohei Sawada and Risa Hanazaki
Hydrol. Earth Syst. Sci., 24, 4777–4791,,, 2020
Short summary

Related subject area

Numerical methods
VISIR-2: ship weather routing in Python
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
Geosci. Model Dev., 17, 4355–4382,,, 2024
Short summary
Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211,,, 2024
Short summary
Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0
Federica Castino, Feijia Yin, Volker Grewe, Hiroshi Yamashita, Sigrun Matthes, Simone Dietmüller, Sabine Baumann, Manuel Soler, Abolfazl Simorgh, Maximilian Mendiguchia Meuser, Florian Linke, and Benjamin Lührs
Geosci. Model Dev., 17, 4031–4052,,, 2024
Short summary
Developing meshing workflows in Gmsh v4.11 for the geologic uncertainty assessment of high-temperature aquifer thermal energy storage
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485,,, 2024
Short summary
Development and preliminary validation of a land surface image assimilation system based on the Common Land Model
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465,,, 2024
Short summary

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.