Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5623-2021
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

Related authors

A signal-processing-based interpretation of the Nash–Sutcliffe efficiency
Le Duc and Yohei Sawada
Hydrol. Earth Syst. Sci., 27, 1827–1839, https://doi.org/10.5194/hess-27-1827-2023,https://doi.org/10.5194/hess-27-1827-2023, 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, https://doi.org/10.5194/hess-26-4707-2022,https://doi.org/10.5194/hess-26-4707-2022, 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, https://doi.org/10.5194/hess-26-4265-2022,https://doi.org/10.5194/hess-26-4265-2022, 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, https://doi.org/10.5194/hess-24-4777-2020,https://doi.org/10.5194/hess-24-4777-2020, 2020
Short summary
Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?
Yohei Sawada
Hydrol. Earth Syst. Sci., 24, 3881–3898, https://doi.org/10.5194/hess-24-3881-2020,https://doi.org/10.5194/hess-24-3881-2020, 2020
Short summary

Related subject area

Numerical methods
LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023,https://doi.org/10.5194/gmd-16-2391-2023, 2023
Short summary
Fast approximate Barnes interpolation: illustrated by Python-Numba implementation fast-barnes-py v1.0
Bruno K. Zürcher
Geosci. Model Dev., 16, 1697–1711, https://doi.org/10.5194/gmd-16-1697-2023,https://doi.org/10.5194/gmd-16-1697-2023, 2023
Short summary
Strategies for conservative and non-conservative monotone remapping on the sphere
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023,https://doi.org/10.5194/gmd-16-1537-2023, 2023
Short summary
Modeling large‐scale landform evolution with a stream power law for glacial erosion (OpenLEM v37): benchmarking experiments against a more process-based description of ice flow (iSOSIA v3.4.3)
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343, https://doi.org/10.5194/gmd-16-1315-2023,https://doi.org/10.5194/gmd-16-1315-2023, 2023
Short summary
A mixed finite-element discretisation of the shallow-water equations
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276, https://doi.org/10.5194/gmd-16-1265-2023,https://doi.org/10.5194/gmd-16-1265-2023, 2023
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, https://doi.org/10.1029/2018JD028375, 2018. 
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. 
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, 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, https://doi.org/10.5194/npg-26-143-2019, 2019. 
Bocquet, M., Farchi, A., and Malartic, Q.: Online learning of both state and dynamics using ensemble Kalman filters, Found. Data Sci., https://doi.org/10.3934/fods.2020015, 2020. 
Download
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.