Variational data assimilation of airborne sensing profiles to the transport and transformation model of atmospheric chemistry

Alexey Penenko; Pavel Antokhin; Anastasia Grishina

doi:10.1117/12.2288830

Variational data assimilation of airborne sensing profiles to the transport and transformation model of atmospheric chemistry

Alexey Penenko, Pavel Antokhin, Anastasia Grishina

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › рецензирование

Аннотация

The variational data assimilation algorithm for the atmospheric chemistry transport and transformation model is applied to the airborne sensing profiles of chemical substances concentration. The data assimilation is performed quasiindependently on the separate stages of the splitting scheme. For the linear transport stage the direct algorithm is used. In the nonlinear transformation stage an iterative gradient-type one is applied. In the numerical experiment the realistic scenario of vertical ozone concentration profiles assimilation has been considered.

Язык оригинала	английский
Название основной публикации	23rd International Symposium on Atmospheric and Ocean Optics
Подзаголовок основной публикации	Atmospheric Physics
Редакторы	GG Matvienko, OA Romanovskii
Издатель	SPIE
Число страниц	5
Том	10466
ISBN (электронное издание)	9781510614130
ISBN (печатное издание)	978-1-5106-1413-0
DOI	https://doi.org/10.1117/12.2288830
Состояние	Опубликовано - 1 янв 2017
Событие	23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics - Irkutsk, Российская Федерация Продолжительность: 3 июл 2017 → 7 июл 2017

Серия публикаций

Название	Proceedings of SPIE
Издатель	SPIE-INT SOC OPTICAL ENGINEERING
Том	10466
ISSN (печатное издание)	0277-786X

Конференция

Конференция	23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics
Страна	Российская Федерация
Город	Irkutsk
Период	03.07.2017 → 07.07.2017

Доступ к документу

10.1117/12.2288830

Link to publication in Scopus

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Цитировать

Penenko, A., Antokhin, P., & Grishina, A. (2017). Variational data assimilation of airborne sensing profiles to the transport and transformation model of atmospheric chemistry. В GG. Matvienko, & OA. Romanovskii (Ред.), 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics (Том 10466). [1046674] (Proceedings of SPIE; Том 10466). SPIE. https://doi.org/10.1117/12.2288830

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title = "Variational data assimilation of airborne sensing profiles to the transport and transformation model of atmospheric chemistry",

abstract = "The variational data assimilation algorithm for the atmospheric chemistry transport and transformation model is applied to the airborne sensing profiles of chemical substances concentration. The data assimilation is performed quasiindependently on the separate stages of the splitting scheme. For the linear transport stage the direct algorithm is used. In the nonlinear transformation stage an iterative gradient-type one is applied. In the numerical experiment the realistic scenario of vertical ozone concentration profiles assimilation has been considered.",

keywords = "advectiondiffusion-reaction model, airborne sensing profiles, atmospheric chemistry, Data assimilation, splitting scheme, variational approach, advection-diffusion-reaction model, ALGORITHMS",

author = "Alexey Penenko and Pavel Antokhin and Anastasia Grishina",

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Penenko, A, Antokhin, P & Grishina, A 2017, Variational data assimilation of airborne sensing profiles to the transport and transformation model of atmospheric chemistry. в GG Matvienko & OA Romanovskii (ред.), 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. том. 10466, 1046674, Proceedings of SPIE, том. 10466, SPIE, 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, Irkutsk, Российская Федерация, 03.07.2017. https://doi.org/10.1117/12.2288830

Variational data assimilation of airborne sensing profiles to the transport and transformation model of atmospheric chemistry. / Penenko, Alexey; Antokhin, Pavel; Grishina, Anastasia.

23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. ред. / GG Matvienko; OA Romanovskii. Том 10466 SPIE, 2017. 1046674 (Proceedings of SPIE; Том 10466).

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › рецензирование

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N2 - The variational data assimilation algorithm for the atmospheric chemistry transport and transformation model is applied to the airborne sensing profiles of chemical substances concentration. The data assimilation is performed quasiindependently on the separate stages of the splitting scheme. For the linear transport stage the direct algorithm is used. In the nonlinear transformation stage an iterative gradient-type one is applied. In the numerical experiment the realistic scenario of vertical ozone concentration profiles assimilation has been considered.

AB - The variational data assimilation algorithm for the atmospheric chemistry transport and transformation model is applied to the airborne sensing profiles of chemical substances concentration. The data assimilation is performed quasiindependently on the separate stages of the splitting scheme. For the linear transport stage the direct algorithm is used. In the nonlinear transformation stage an iterative gradient-type one is applied. In the numerical experiment the realistic scenario of vertical ozone concentration profiles assimilation has been considered.

KW - advectiondiffusion-reaction model

KW - airborne sensing profiles

KW - atmospheric chemistry

KW - Data assimilation

KW - splitting scheme

KW - variational approach

KW - advection-diffusion-reaction model

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Penenko A, Antokhin P, Grishina A. Variational data assimilation of airborne sensing profiles to the transport and transformation model of atmospheric chemistry. В Matvienko GG, Romanovskii OA, редакторы, 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. Том 10466. SPIE. 2017. 1046674. (Proceedings of SPIE). https://doi.org/10.1117/12.2288830