Standard

Sequential variational data assimilation algorithms at the splitting stages of a numerical atmospheric chemistry model. / Penenko, Alexey; Penenko, Vladimir; Tsvetova, Elena и др.

Large-Scale Scientific Computing - 11th International Conference, LSSC 2017, Revised Selected Papers. ред. / Lirkov; S Margenov. Springer-Verlag GmbH and Co. KG, 2018. стр. 536-543 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 10665 LNCS).

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

Harvard

Penenko, A, Penenko, V, Tsvetova, E, Grishina, A & Antokhin, P 2018, Sequential variational data assimilation algorithms at the splitting stages of a numerical atmospheric chemistry model. в Lirkov & S Margenov (ред.), Large-Scale Scientific Computing - 11th International Conference, LSSC 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 10665 LNCS, Springer-Verlag GmbH and Co. KG, стр. 536-543, 11th International Conference on Large-Scale Scientific Computations, LSSC 2017, Sozopol, Болгария, 11.09.2017. https://doi.org/10.1007/978-3-319-73441-5_59

APA

Penenko, A., Penenko, V., Tsvetova, E., Grishina, A., & Antokhin, P. (2018). Sequential variational data assimilation algorithms at the splitting stages of a numerical atmospheric chemistry model. в Lirkov, & S. Margenov (Ред.), Large-Scale Scientific Computing - 11th International Conference, LSSC 2017, Revised Selected Papers (стр. 536-543). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 10665 LNCS). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-319-73441-5_59

Vancouver

Penenko A, Penenko V, Tsvetova E, Grishina A, Antokhin P. Sequential variational data assimilation algorithms at the splitting stages of a numerical atmospheric chemistry model. в Lirkov, Margenov S, Редакторы, Large-Scale Scientific Computing - 11th International Conference, LSSC 2017, Revised Selected Papers. Springer-Verlag GmbH and Co. KG. 2018. стр. 536-543. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-73441-5_59

Author

Penenko, Alexey ; Penenko, Vladimir ; Tsvetova, Elena и др. / Sequential variational data assimilation algorithms at the splitting stages of a numerical atmospheric chemistry model. Large-Scale Scientific Computing - 11th International Conference, LSSC 2017, Revised Selected Papers. Редактор / Lirkov ; S Margenov. Springer-Verlag GmbH and Co. KG, 2018. стр. 536-543 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{ab2d8a1e971a4c81b3e5a400d7142a72,
title = "Sequential variational data assimilation algorithms at the splitting stages of a numerical atmospheric chemistry model",
abstract = "A variational data assimilation algorithm is studied numerically. In situ concentration measurement data are assimilated into transport and transformation model of atmospheric chemistry. The algorithm is based on decomposition and splitting methods with solution of variational data assimilation problems for separate splitting stages. A direct algorithm without iterations is used for the linear transport stage. An iterative gradient algorithm is applied for data assimilation at the non-linear chemical transformation stage. In a realistic numerical experiment, the contributions of data assimilation algorithms for the different splitting stages are compared.",
keywords = "Advection-diffusion-reaction models, Splitting, Variational data assimilation",
author = "Alexey Penenko and Vladimir Penenko and Elena Tsvetova and Anastasia Grishina and Pavel Antokhin",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; 11th International Conference on Large-Scale Scientific Computations, LSSC 2017 ; Conference date: 11-09-2017 Through 15-09-2017",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-73441-5_59",
language = "English",
isbn = "9783319734408",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag GmbH and Co. KG",
pages = "536--543",
editor = "Lirkov and S Margenov",
booktitle = "Large-Scale Scientific Computing - 11th International Conference, LSSC 2017, Revised Selected Papers",
address = "Germany",

}

RIS

TY - GEN

T1 - Sequential variational data assimilation algorithms at the splitting stages of a numerical atmospheric chemistry model

AU - Penenko, Alexey

AU - Penenko, Vladimir

AU - Tsvetova, Elena

AU - Grishina, Anastasia

AU - Antokhin, Pavel

N1 - Publisher Copyright: © Springer International Publishing AG 2018.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - A variational data assimilation algorithm is studied numerically. In situ concentration measurement data are assimilated into transport and transformation model of atmospheric chemistry. The algorithm is based on decomposition and splitting methods with solution of variational data assimilation problems for separate splitting stages. A direct algorithm without iterations is used for the linear transport stage. An iterative gradient algorithm is applied for data assimilation at the non-linear chemical transformation stage. In a realistic numerical experiment, the contributions of data assimilation algorithms for the different splitting stages are compared.

AB - A variational data assimilation algorithm is studied numerically. In situ concentration measurement data are assimilated into transport and transformation model of atmospheric chemistry. The algorithm is based on decomposition and splitting methods with solution of variational data assimilation problems for separate splitting stages. A direct algorithm without iterations is used for the linear transport stage. An iterative gradient algorithm is applied for data assimilation at the non-linear chemical transformation stage. In a realistic numerical experiment, the contributions of data assimilation algorithms for the different splitting stages are compared.

KW - Advection-diffusion-reaction models

KW - Splitting

KW - Variational data assimilation

UR - http://www.scopus.com/inward/record.url?scp=85041715534&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-73441-5_59

DO - 10.1007/978-3-319-73441-5_59

M3 - Conference contribution

AN - SCOPUS:85041715534

SN - 9783319734408

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 536

EP - 543

BT - Large-Scale Scientific Computing - 11th International Conference, LSSC 2017, Revised Selected Papers

A2 - Lirkov, null

A2 - Margenov, S

PB - Springer-Verlag GmbH and Co. KG

T2 - 11th International Conference on Large-Scale Scientific Computations, LSSC 2017

Y2 - 11 September 2017 through 15 September 2017

ER -

ID: 10452401