Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models

被引：0

作者：

Penenko, A., V ^{[1
,2
]}

Konopleva, V. S. ^{[1
,2
]}

Golenko, P. M. ^{[1
,2
]}

Penenko, V. V. ^{[1
,2
]}

机构：

[1] ICM&MG SB RAS, Inst Computat Math & Math Geophys, Prospekt Akad Lavrentjeva 6, Novosibirsk 630090, Russia

[2] Novosibirsk State Univ, Pirogova Str 2, Novosibirsk 630090, Russia

来源：

27TH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS, ATMOSPHERIC PHYSICS | 2021年 / 11916卷

基金：

俄罗斯基础研究基金会;

关键词：

data assimilation; atmospheric chemistry; uncertainty function; continuation problem; reaction rates; variational approach; differential evolution;

D O I：

10.1117/12.2603422

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

The development of efficient data assimilation algorithms for atmospheric chemistry models is an important part of modern air quality studies. In the data assimilation framework considered, the identification of the chosen model parameters is used to continue the model state function to the unobservable part of the domain. This continuation problem is solved sequentially on the set of time intervals called the data assimilation windows. The framework is illustrated on a low-dimensional atmospheric chemistry model.

引用

页数：7

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