Model Error Correction in Data Assimilation by Integrating Neural Networks

被引：13

作者：

Zhu, Jiangcheng ^{[1
]}

Hu, Shuang ^{[2
,3
]}

Arcucci, Rossella ^{[3
]}

Xu, Chao ^{[1
]}

Zhu, Jihong ^{[2
,3
]}

Guo, Yi-ke ^{[3
]}

机构：

[1] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China

[2] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China

[3] Imperial Coll London, Data Sci Inst, London SW7 2AZ, England

来源：

BIG DATA MINING AND ANALYTICS | 2019年 / 2卷 / 02期

基金：

英国工程与自然科学研究理事会;

关键词：

data assimilation; deep learning; neural networks; Kalman filter; variational approach; ROUTINE;

D O I：

10.26599/BDMA.2018.9020033

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：83 / 91

页数：9

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