Composing a surrogate observation operator for sequential data assimilation

被引：0

作者：

Akita, Kosuke ^{[1
]}

Miyatake, Yuto ^{[2
]}

Furihata, Daisuke ^{[2
]}

机构：

[1] Osaka Univ, Grad Sch Informat Sci & Technol, 1-5 Yamadaoka, Suita, Osaka 5650871, Japan

[2] Osaka Univ, Cybermedia Ctr, 1-32 Machikaneyama, Toyonaka, Osaka 5600043, Japan

来源：

JSIAM LETTERS | 2022年 / 14卷

关键词：

data assimilation; machine learning; neural network; observation operator; state-space model;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In data assimilation, state estimation is not straightforward when the observation operator is unknown. This study proposes a method for composing a surrogate operator when the true operator is unknown. A neural network is used to improve the surrogate model iteratively to decrease the difference between the observations and the results of the surrogate model. A twin experiment suggests that the proposed method outperforms approaches that tentatively use a specific operator throughout the data assimilation process.

引用

页码：123 / 126

页数：4

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