Model Error Correction in Data Assimilation by Integrating Neural Networks

• 作者: Chao Xu Jiangcheng Zhu Jihong Zhu Rossella Arcucci Shuang Hu Yi-ke Guo
• 来源期刊: 大数据挖掘与分析(英文)
• 学科: 工学
• 关键词: data ASSIMILATION deep learning neural networks KALMAN filter VARIATIONAL approach
• 年，卷（期）: 2019,（2）
• 页码范围: 83-91
• 页数: 9页
• 分类号: TP183

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.