Numerical Investigation of the Direct Variational Algorithm of Data Assimilation in the Urban Scenario


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Abstract

The performance of a direct variational data assimilation algorithm with quasi-independent data assimilation at individual steps of the splitting scheme has been studied in a realistic scenario of air pollution assessment in the city of Novosibirsk by monitoring system data. For operation under conditions of a sparse monitoring network, an algorithm with minimization of the spatial derivative of the uncertainty (control) function adjusted to data assimilation is proposed. The use of the spatial derivative minimization increases the smoothness of the uncertainty (control functions) reconstructed, which has a positive effect on the reconstruction quality in the scenario considered.

About the authors

A. V. Penenko

Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch

Author for correspondence.
Email: a.penenko@yandex.ru
Russian Federation, Novosibirsk, 630090

Zh. S. Mukatova

Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch

Email: a.penenko@yandex.ru
Russian Federation, Novosibirsk, 630090

V. V. Penenko

Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch

Email: a.penenko@yandex.ru
Russian Federation, Novosibirsk, 630090

A. V. Gochakov

Siberian Regional Hydrometeorological Research Institute

Email: a.penenko@yandex.ru
Russian Federation, Novosibirsk, 630099

P. N. Antokhin

V.E. Zuev Institute of Atmospheric Optics, Siberian Branch

Email: a.penenko@yandex.ru
Russian Federation, Tomsk, 634055

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