@article{Zhu2019, 
author = {Jiangcheng Zhu and Shuang Hu and Rossella Arcucci and Chao Xu and Jihong Zhu and Yi-ke Guo},
title = {Model Error Correction in Data Assimilation by Integrating Neural Networks},
year = {2019},
journal = {Big Data Mining and Analytics},
volume = {2},
number = {2},
pages = {83-91},
keywords = {data assimilation, deep learning, neural networks, Kalman filter, variational approach},
url = {https://www.sciopen.com/article/10.26599/BDMA.2018.9020033},
doi = {10.26599/BDMA.2018.9020033},
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.}
}