Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model

Alexey Penenko; Vladimir Penenko; Roman Nuterman; Alexander Baklanov; Alexander Mahura

doi:10.1117/12.2206008

Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model

Alexey Penenko, Vladimir Penenko, Roman Nuterman, Alexander Baklanov, Alexander Mahura

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

3 Citations (Scopus)

Abstract

Atmospheric chemistry dynamics is studied with convection-diffusion-reaction model. The numerical Data Assimilation algorithm presented is based on the additive-Averaged splitting schemes. It carries out ''fine-grained'' variational data assimilation on the separate splitting stages with respect to spatial dimensions and processes i.e.The same measurement data is assimilated to different parts of the split model. This design has efficient implementation due to the direct data assimilation algorithms of the transport process along coordinate lines. Results of numerical experiments with chemical data assimilation algorithm of in situ concentration measurements on real data scenario have been presented. In order to construct the scenario, meteorological data has been taken from EnviroHIRLAM model output, initial conditions from MOZART model output and measurements from Airbase database.

Original language	English
Title of host publication	21st International Symposium on Atmospheric and Ocean Optics : Atmospheric Physics
Editors	Gennadii G. Matvienko, Oleg A. Romanovskii
Publisher	SPIE
Publication date	2015
Article number	968076
ISBN (Electronic)	9781628419085
DOIs	https://doi.org/10.1117/12.2206008
Publication status	Published - 2015
Event	21st International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics - Tomsk, Russian Federation Duration: 22 Jun 2015 → 26 Jun 2015

Conference

Conference	21st International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics
Country/Territory	Russian Federation
City	Tomsk
Period	22/06/2015 → 26/06/2015
Sponsor	Russian Foundation for Basic Research, Siberian Branch of Russian Academy of Sciences

Series	Proceedings of SPIE - The International Society for Optical Engineering
Volume	9680
ISSN	0277-786X

Bibliographical note

Publisher Copyright:
© 2015 SPIE.

Keywords

advection-diffusion-reaction model
Chemical data assimilation
discrete-Analytical schemes
fine-grained data assimilation
splitting method
variational approach

Access to Document

10.1117/12.2206008

Cite this

Penenko, A., Penenko, V., Nuterman, R., Baklanov, A., & Mahura, A. (2015). Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model. In G. G. Matvienko, & O. A. Romanovskii (Eds.), 21st International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics [968076] SPIE. Proceedings of SPIE - The International Society for Optical Engineering Vol. 9680 https://doi.org/10.1117/12.2206008

Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model. / Penenko, Alexey; Penenko, Vladimir; Nuterman, Roman ; Baklanov, Alexander; Mahura, Alexander.

21st International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. ed. / Gennadii G. Matvienko; Oleg A. Romanovskii. SPIE, 2015. 968076 (Proceedings of SPIE - The International Society for Optical Engineering, Vol. 9680).

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

Penenko, A, Penenko, V, Nuterman, R , Baklanov, A & Mahura, A 2015, Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model. in GG Matvienko & OA Romanovskii (eds), 21st International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics., 968076, SPIE, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9680, 21st International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, Tomsk, Russian Federation, 22/06/2015. https://doi.org/10.1117/12.2206008

Penenko A, Penenko V, Nuterman R , Baklanov A, Mahura A. Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model. In Matvienko GG, Romanovskii OA, editors, 21st International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. SPIE. 2015. 968076. (Proceedings of SPIE - The International Society for Optical Engineering, Vol. 9680). https://doi.org/10.1117/12.2206008

Penenko, Alexey ; Penenko, Vladimir ; Nuterman, Roman ; Baklanov, Alexander ; Mahura, Alexander. / Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model. 21st International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. editor / Gennadii G. Matvienko ; Oleg A. Romanovskii. SPIE, 2015. (Proceedings of SPIE - The International Society for Optical Engineering, Vol. 9680).

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abstract = "Atmospheric chemistry dynamics is studied with convection-diffusion-reaction model. The numerical Data Assimilation algorithm presented is based on the additive-Averaged splitting schemes. It carries out ''fine-grained'' variational data assimilation on the separate splitting stages with respect to spatial dimensions and processes i.e.The same measurement data is assimilated to different parts of the split model. This design has efficient implementation due to the direct data assimilation algorithms of the transport process along coordinate lines. Results of numerical experiments with chemical data assimilation algorithm of in situ concentration measurements on real data scenario have been presented. In order to construct the scenario, meteorological data has been taken from EnviroHIRLAM model output, initial conditions from MOZART model output and measurements from Airbase database.",

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AB - Atmospheric chemistry dynamics is studied with convection-diffusion-reaction model. The numerical Data Assimilation algorithm presented is based on the additive-Averaged splitting schemes. It carries out ''fine-grained'' variational data assimilation on the separate splitting stages with respect to spatial dimensions and processes i.e.The same measurement data is assimilated to different parts of the split model. This design has efficient implementation due to the direct data assimilation algorithms of the transport process along coordinate lines. Results of numerical experiments with chemical data assimilation algorithm of in situ concentration measurements on real data scenario have been presented. In order to construct the scenario, meteorological data has been taken from EnviroHIRLAM model output, initial conditions from MOZART model output and measurements from Airbase database.

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Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model

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Keywords

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