Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models

A. V. Penenko, V. S. Konopleva, P. M. Golenko, V. V. Penenko

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

The development of efficient data assimilation algorithms for atmospheric chemistry models is an important part of modern air quality studies. In the data assimilation framework considered, the identification of the chosen model parameters is used to continue the model state function to the unobservable part of the domain. This continuation problem is solved sequentially on the set of time intervals called the data assimilation windows. The framework is illustrated on a low-dimensional atmospheric chemistry model.

Original languageEnglish
Title of host publication27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics
EditorsGennadii G. Matvienko, Oleg A. Romanovskii
PublisherSPIE
ISBN (Electronic)9781510646971
DOIs
Publication statusPublished - 2021
Event27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics 2021 - Moscow, Russian Federation
Duration: 5 Jul 20219 Jul 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11916
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics 2021
CountryRussian Federation
CityMoscow
Period05.07.202109.07.2021

Keywords

  • atmospheric chemistry
  • continuation problem
  • data assimilation
  • di erential evolution
  • reaction rates
  • uncertainty function
  • variational approach

OECD FOS+WOS

  • 1.01.PN MATHEMATICS, APPLIED
  • 2.02.IQ ENGINEERING, ELECTRICAL & ELECTRONIC
  • 1.03.UK PHYSICS, CONDENSED MATTER

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