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Variational methods for predicting climate-environmental processes with assimilation of observational data. / Penenko, Vladimir V.

23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. ed. / GG Matvienko; OA Romanovskii. Vol. 10466 SPIE, 2017. 104665Q (Proceedings of SPIE; Vol. 10466).

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

Harvard

Penenko, VV 2017, Variational methods for predicting climate-environmental processes with assimilation of observational data. in GG Matvienko & OA Romanovskii (eds), 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. vol. 10466, 104665Q, Proceedings of SPIE, vol. 10466, SPIE, 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, Irkutsk, Russian Federation, 03.07.2017. https://doi.org/10.1117/12.2287115

APA

Penenko, V. V. (2017). Variational methods for predicting climate-environmental processes with assimilation of observational data. In GG. Matvienko, & OA. Romanovskii (Eds.), 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics (Vol. 10466). [104665Q] (Proceedings of SPIE; Vol. 10466). SPIE. https://doi.org/10.1117/12.2287115

Vancouver

Penenko VV. Variational methods for predicting climate-environmental processes with assimilation of observational data. In Matvienko GG, Romanovskii OA, editors, 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. Vol. 10466. SPIE. 2017. 104665Q. (Proceedings of SPIE). doi: 10.1117/12.2287115

Author

Penenko, Vladimir V. / Variational methods for predicting climate-environmental processes with assimilation of observational data. 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. editor / GG Matvienko ; OA Romanovskii. Vol. 10466 SPIE, 2017. (Proceedings of SPIE).

BibTeX

@inproceedings{6c4f76825e864cf6b7e51f13d0ef053f,
title = "Variational methods for predicting climate-environmental processes with assimilation of observational data",
abstract = "Prospective issues of the organization of modeling technology for studying climatic and environmental processes and solving practical problems are discussed. We study the problems of predictability analysis and uncertainty estimates. To this goal, the combination of process models and observational data is carried out within the framework of the variational principle with weak constraints. This makes it possible to obtain the direct non-iterative algorithms for estimating the state and uncertainty functions.",
keywords = "data assimilation, forecasting, mathematical modeling, predictability, uncertainty assessment, variational approach, MODELS, CHEMISTRY",
author = "Penenko, {Vladimir V.}",
year = "2017",
month = jan,
day = "1",
doi = "10.1117/12.2287115",
language = "English",
isbn = "978-1-5106-1413-0",
volume = "10466",
series = "Proceedings of SPIE",
publisher = "SPIE",
editor = "GG Matvienko and OA Romanovskii",
booktitle = "23rd International Symposium on Atmospheric and Ocean Optics",
address = "United States",
note = "23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics ; Conference date: 03-07-2017 Through 07-07-2017",

}

RIS

TY - GEN

T1 - Variational methods for predicting climate-environmental processes with assimilation of observational data

AU - Penenko, Vladimir V.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Prospective issues of the organization of modeling technology for studying climatic and environmental processes and solving practical problems are discussed. We study the problems of predictability analysis and uncertainty estimates. To this goal, the combination of process models and observational data is carried out within the framework of the variational principle with weak constraints. This makes it possible to obtain the direct non-iterative algorithms for estimating the state and uncertainty functions.

AB - Prospective issues of the organization of modeling technology for studying climatic and environmental processes and solving practical problems are discussed. We study the problems of predictability analysis and uncertainty estimates. To this goal, the combination of process models and observational data is carried out within the framework of the variational principle with weak constraints. This makes it possible to obtain the direct non-iterative algorithms for estimating the state and uncertainty functions.

KW - data assimilation

KW - forecasting

KW - mathematical modeling

KW - predictability

KW - uncertainty assessment

KW - variational approach

KW - MODELS

KW - CHEMISTRY

UR - http://www.scopus.com/inward/record.url?scp=85043349026&partnerID=8YFLogxK

U2 - 10.1117/12.2287115

DO - 10.1117/12.2287115

M3 - Conference contribution

AN - SCOPUS:85043349026

SN - 978-1-5106-1413-0

VL - 10466

T3 - Proceedings of SPIE

BT - 23rd International Symposium on Atmospheric and Ocean Optics

A2 - Matvienko, GG

A2 - Romanovskii, OA

PB - SPIE

T2 - 23rd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

Y2 - 3 July 2017 through 7 July 2017

ER -

ID: 10455570