Standard

Comparison of inverse and data assimilation problems in the inverse modeling of atmospheric chemistry. / Penenko, Alexey V.; Konopleva, Viktoria; Penenko, Vladimir В.

Proceedings Volume 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. Vol. 12341 SPIE-Intl Soc Optical Eng, 2022. p. 210 123416O.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

Harvard

Penenko, AV, Konopleva, V & Penenko, VВ 2022, Comparison of inverse and data assimilation problems in the inverse modeling of atmospheric chemistry. in Proceedings Volume 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. vol. 12341, 123416O, SPIE-Intl Soc Optical Eng, pp. 210. https://doi.org/10.1117/12.2644951

APA

Penenko, A. V., Konopleva, V., & Penenko, V. В. (2022). Comparison of inverse and data assimilation problems in the inverse modeling of atmospheric chemistry. In Proceedings Volume 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics (Vol. 12341, pp. 210). [123416O] SPIE-Intl Soc Optical Eng. https://doi.org/10.1117/12.2644951

Vancouver

Penenko AV, Konopleva V, Penenko VВ. Comparison of inverse and data assimilation problems in the inverse modeling of atmospheric chemistry. In Proceedings Volume 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. Vol. 12341. SPIE-Intl Soc Optical Eng. 2022. p. 210. 123416O doi: 10.1117/12.2644951

Author

Penenko, Alexey V. ; Konopleva, Viktoria ; Penenko, Vladimir В. / Comparison of inverse and data assimilation problems in the inverse modeling of atmospheric chemistry. Proceedings Volume 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. Vol. 12341 SPIE-Intl Soc Optical Eng, 2022. pp. 210

BibTeX

@inbook{3b28715ee9754e579483d9f06890f3fb,
title = "Comparison of inverse and data assimilation problems in the inverse modeling of atmospheric chemistry",
abstract = "Data assimilation algorithms are an important part of modern air quality modeling techniques. To study the real-time operation mode features of the data assimilation algorithms we numerically compare its performance to the solution in the “inverse problem mode”, when the same set of data is available “at once”. The objective of the paper is to compare the gradient-based (variational) and derivative-free solvers in the data assimilation mode to the solution of the reference inverse problem of reconstructing unobservable chemical species concentrations for the atmospheric chemistry model with a derivative-free solver.",
author = "Penenko, {Alexey V.} and Viktoria Konopleva and Penenko, {Vladimir В.}",
note = "Публикация для корректировки.",
year = "2022",
doi = "10.1117/12.2644951",
language = "English",
isbn = "9781510657540",
volume = "12341",
pages = "210",
booktitle = "Proceedings Volume 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics",
publisher = "SPIE-Intl Soc Optical Eng",

}

RIS

TY - CHAP

T1 - Comparison of inverse and data assimilation problems in the inverse modeling of atmospheric chemistry

AU - Penenko, Alexey V.

AU - Konopleva, Viktoria

AU - Penenko, Vladimir В.

N1 - Публикация для корректировки.

PY - 2022

Y1 - 2022

N2 - Data assimilation algorithms are an important part of modern air quality modeling techniques. To study the real-time operation mode features of the data assimilation algorithms we numerically compare its performance to the solution in the “inverse problem mode”, when the same set of data is available “at once”. The objective of the paper is to compare the gradient-based (variational) and derivative-free solvers in the data assimilation mode to the solution of the reference inverse problem of reconstructing unobservable chemical species concentrations for the atmospheric chemistry model with a derivative-free solver.

AB - Data assimilation algorithms are an important part of modern air quality modeling techniques. To study the real-time operation mode features of the data assimilation algorithms we numerically compare its performance to the solution in the “inverse problem mode”, when the same set of data is available “at once”. The objective of the paper is to compare the gradient-based (variational) and derivative-free solvers in the data assimilation mode to the solution of the reference inverse problem of reconstructing unobservable chemical species concentrations for the atmospheric chemistry model with a derivative-free solver.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85159690177&origin=inward&txGid=1106e6569a0f3abd88b8fcfec58d9316

UR - https://www.mendeley.com/catalogue/b8103f5b-8b47-3711-b8b3-b2a66f8eda57/

U2 - 10.1117/12.2644951

DO - 10.1117/12.2644951

M3 - Chapter

SN - 9781510657540

VL - 12341

SP - 210

BT - Proceedings Volume 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

PB - SPIE-Intl Soc Optical Eng

ER -

ID: 55717288