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Open Access

Model Error Correction in Data Assimilation by Integrating Neural Networks

Jiangcheng Zhu, Shuang Hu, Rossella Arcucci, Chao Xu, Jihong Zhu, Yi-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.
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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

deep learningneural networksdata assimilationKalman filtervariational approach

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Big Data Mining and Analytics
Volume 2 Issue 2,
June 2019
Pages 83-91
DOI: 10.26599/BDMA.2018.9020033
Cite this article:
Zhu J, Hu S, Arcucci R, et al. Model Error Correction in Data Assimilation by Integrating Neural Networks. Big Data Mining and Analytics, 2019, 2(2): 83-91. https://doi.org/10.26599/BDMA.2018.9020033

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Received: 05 July 2018
Accepted: 19 September 2018
Published: 14 May 2019
© The author(s) 2019
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