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In this paper, we suggest a new methodology which combines Neural Networks (NN) into Data Assimilation (DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. The NNs are iteratively trained as observational data is updated. The main DA models used here are the Kalman filter and the variational approaches. The effectiveness of the proposed algorithm is validated by examples and by a sensitivity study.


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Model Error Correction in Data Assimilation by Integrating Neural Networks

Show Author's information Jiangcheng ZhuShuang HuRossella ArcucciChao XuJihong ZhuYi-ke Guo( )
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Data Science Institute, Imperial College London, London SW7 2AZ, UK.

Abstract

In this paper, we suggest a new methodology which combines Neural Networks (NN) into Data Assimilation (DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. The NNs are iteratively trained as observational data is updated. The main DA models used here are the Kalman filter and the variational approaches. The effectiveness of the proposed algorithm is validated by examples and by a sensitivity study.

Keywords:

data assimilation, deep learning, neural networks, Kalman filter, variational approach
Received: 05 July 2018 Accepted: 19 September 2018 Published: 14 May 2019 Issue date: June 2019
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Publication history

Received: 05 July 2018
Accepted: 19 September 2018
Published: 14 May 2019
Issue date: June 2019

Copyright

© The author(s) 2019

Acknowledgements

This work was supported by the EPSRC Grand Challenge grant "Managing Air for Green Inner Cities" (MAGIC) EP/N010221/1.

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