Model Error Correction in Data Assimilation by Integrating Neural Networks

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英文篇名：Model Error Correction in Data Assimilation by Integrating Neural Networks 作者：Jiangcheng ; Zhu ; Shuang ; Hu ; Rossella ; Arcucci ; Chao ; Xu ; Jihong ; Zhu ; Yi-ke ; Guo 英文作者：Jiangcheng Zhu;Shuang Hu;Rossella Arcucci;Chao Xu;Jihong Zhu;Yi-ke Guo;State Key Laboratory of Industrial Control Technology, Zhejiang University;Data Science Institute, Imperial College London;Department of Computer Science and Technology, Tsinghua University; 英文关键词：data assimilation;;deep learning;;neural networks;;Kalman filter;;variational approach 中文刊名：BDMA 英文刊名：大数据挖掘与分析(英文) 机构：State Key Laboratory of Industrial Control Technology, Zhejiang University;Data Science Institute, Imperial College London;Department of Computer Science and Technology, Tsinghua University; 出版日期：2019-04-10 出版单位：Big Data Mining and Analytics 年：2019 期：v.2 基金：supported by the EPSRC Grand Challenge grant “Managing Air for Green Inner Cities” (MAGIC) EP/N010221/1 语种：英文; 页：BDMA201902002 页数：9 CN：02 ISSN：10-1514/G2 分类号：13-21

摘要

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.
        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.

引文

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