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

doi:10.1117/12.2603422

15 December 2021 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

Author Affiliations +

Proceedings Volume 11916, 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics; 119168O (2021) https://doi.org/10.1117/12.2603422
Event: 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics, 2021, Moscow, Russian Federation

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.

Citation Download Citation

A. V. Penenko, V. S. Konopleva, P. M. Golenko, and V. V. Penenko "Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models", Proc. SPIE 11916, 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics, 119168O (15 December 2021); https://doi.org/10.1117/12.2603422

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