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Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models. / Penenko, A. V.; Konopleva, V. S.; Golenko, P. M. и др.

27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics. ред. / Gennadii G. Matvienko; Oleg A. Romanovskii. SPIE, 2021. 119168O (Proceedings of SPIE - The International Society for Optical Engineering; Том 11916).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Penenko, AV, Konopleva, VS, Golenko, PM & Penenko, VV 2021, Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models. в GG Matvienko & OA Romanovskii (ред.), 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics., 119168O, Proceedings of SPIE - The International Society for Optical Engineering, Том. 11916, SPIE, 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics 2021, Moscow, Российская Федерация, 05.07.2021. https://doi.org/10.1117/12.2603422

APA

Penenko, A. V., Konopleva, V. S., Golenko, P. M., & Penenko, V. V. (2021). Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models. в G. G. Matvienko, & O. A. Romanovskii (Ред.), 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics [119168O] (Proceedings of SPIE - The International Society for Optical Engineering; Том 11916). SPIE. https://doi.org/10.1117/12.2603422

Vancouver

Penenko AV, Konopleva VS, Golenko PM, Penenko VV. Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models. в Matvienko GG, Romanovskii OA, Редакторы, 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics. SPIE. 2021. 119168O. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2603422

Author

Penenko, A. V. ; Konopleva, V. S. ; Golenko, P. M. и др. / Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models. 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics. Редактор / Gennadii G. Matvienko ; Oleg A. Romanovskii. SPIE, 2021. (Proceedings of SPIE - The International Society for Optical Engineering).

BibTeX

@inproceedings{e58c20f15e4f4517b9e6a87839982e20,
title = "Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models",
abstract = "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. ",
keywords = "atmospheric chemistry, continuation problem, data assimilation, di erential evolution, reaction rates, uncertainty function, variational approach",
author = "Penenko, {A. V.} and Konopleva, {V. S.} and Golenko, {P. M.} and Penenko, {V. V.}",
note = "Funding Information: The work was supported by Russian Foundation for Basic Research project No. 20-01-00560 (in the part of continuation data assimilation framework implementation and analysis) and by Russian Foundation for Basic Research project No. 19-07-01135 (in the part of coefficient identification algorithm development). Publisher Copyright: {\textcopyright} 2021 SPIE.; 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics 2021 ; Conference date: 05-07-2021 Through 09-07-2021",
year = "2021",
doi = "10.1117/12.2603422",
language = "English",
isbn = "9781510646971",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Matvienko, {Gennadii G.} and Romanovskii, {Oleg A.}",
booktitle = "27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics",
address = "United States",

}

RIS

TY - GEN

T1 - Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models

AU - Penenko, A. V.

AU - Konopleva, V. S.

AU - Golenko, P. M.

AU - Penenko, V. V.

N1 - Funding Information: The work was supported by Russian Foundation for Basic Research project No. 20-01-00560 (in the part of continuation data assimilation framework implementation and analysis) and by Russian Foundation for Basic Research project No. 19-07-01135 (in the part of coefficient identification algorithm development). Publisher Copyright: © 2021 SPIE.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - atmospheric chemistry

KW - continuation problem

KW - data assimilation

KW - di erential evolution

KW - reaction rates

KW - uncertainty function

KW - variational approach

UR - http://www.scopus.com/inward/record.url?scp=85124693890&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/a3c2df06-0d07-3ff3-a0eb-6d7330828c8a/

U2 - 10.1117/12.2603422

DO - 10.1117/12.2603422

M3 - Conference contribution

AN - SCOPUS:85124693890

SN - 9781510646971

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics

A2 - Matvienko, Gennadii G.

A2 - Romanovskii, Oleg A.

PB - SPIE

T2 - 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics 2021

Y2 - 5 July 2021 through 9 July 2021

ER -

ID: 35538975