Input parameters variational identifying procedures of the passive impurity transfer model
UDC
51.37DOI:
https://doi.org/10.31429/vestnik-18-3-41-45Abstract
The paper presents a method for input parameters variational identification of passive impurity transfer model as a result of assimilation of measurement data. The assimilation is carried out by minimizing the quadratic functional of the forecast quality, and the solution of the adjoint problem is used to construct gradients of the functional in the parameter space. Formulas for calculating such gradients in the case of identification of the initial concentration fields, turbulent diffusion coefficients and the power of pollution sources are presented. When solving environmental problems based on the use of numerical dynamic models and models describing the processes of propagation of certain pollutants, the task naturally arises of improving the predicted model fields by identifying the input parameters of the models. One of the ways of such identification is the variational assimilation method based on minimizing the forecast residuals and solving conjugate problems of a special type. The development of such algorithms is an important and urgent task due to the increasing anthropogenic load and the need to create environmental monitoring systems.
Acknowledgement
References
- Ivanov V.A., Fomin V.V. Matematicheskoe modelirovanie dinamicheskih processov v zone more–susha [Mathematical modeling of dynamic processes in the sea–land zone]. EKOSI-gidrofizika, Sevastopol', 2008. (In Russian)
- Fomin V.V. CHislennaya model' cirkulyacii vod Azovkogo morya [Numerical model of the Azov Sea water circulation]. Nauchnye trudy UkrNIGMI [Scientific works of UkrNIGMI], 2002, vol. 249, pp. 246–255. (In Russian)
- Marchuk G.I., Penenko V.V. Application of optimization methods to the problem of mathematical simulation of atmospheric processes and environment. In Marchuk G.I. (ed.) Modelling and Optimization of Complex Systems. Proc. of the IFIP-TC7 Working conf. Springer, NewYork, 1978, pp. 240–252.
- Penenko V.V. Metody chislennogo modelirovaniya atmosfernyh processov [Methods for numerical modeling of atmospheric processes]. Gidrometeoizdat, Leningrad, 1981. (In Russian)
- Agoshkov V.I., Parmuzin E.I., Shutyaev V.P. Assimilyaciya dannyh nablyudenij v zadache cirkulyacii Chernogo morya i analiz chuvstvitel'nosti eyo resheniya [Assimilation of observational data in the problem of circulation of the Black Sea and analysis of the sensitivity of its solution]. Izvestiya RAN. Fizika atmosfery i okeana [Izvestiya RAN. Physics of the atmosphere and ocean], 2013, vol. 49, no. 6, pp. 643–654. (In Russian)
- Shutyaev V.P. Adjoint equations in variational data assimilation problem. Russian Journal of Numerical Analysis and Mathematical Modelling, 2018, vol. 33, no. 2, pp. 137–147. DOI: 10.1515/rnam-2018-0012
- Kochergin V.S., Kochergin S.V. Ispol'zovanie variacionnyh principov i resheniya sopryazhennoj zadachi pri identifikacii vhodnyh parametrov modeli perenosa passivnoj primesi [The use of variational principles and the solution of the conjugate problem in the identification of the input parameters of the model of the transfer of a passive admixture]. Ekologicheskaya bezopasnost' pribrezhnoj i shel'fovoj zon i kompleksnoe ispol'zovanie resursov shel'fa [Environmental safety of coastal and shelf zones and integrated use of shelf resources], 2010, vol. 22, pp. 240–244. (In Russian)
- Kochergin V.S., Kochergin S.V. Opredelenie peremennoj po vremeni moshchnosti tochechnogo istochnika zagryazneniya v Azovskom more na osnove variacionnogo algoritma assimilyacii [Determination of the time variable of the power of a point source of pollution in the Sea of Azov based on the variational assimilation algorithm]. Processy v geosredah [Processes in geoenvironments], 2020, no. 1, pp. 594–598. (In Russian)
- Kochergin V.S., Kochergin S.V., Stanichnyj S.V. Variacionnaya assimilyaciya sputnikovyh dannyh poverhnostnoj koncentracii vzveshennogo veshchestva v Azovskom more [Variational assimilation of satellite data on the surface concentration of suspended matter in the Sea of Azov]. Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa [Modern problems of remote sensing of the Earth from space]. 2020. vol. 17, no. 2, pp. 40–48. DOI: 10.21046/2070-7401-2020-17-2-40-48 (In Russian)
- Kochergin V.S., Kochergin S.V. Identification of a Pollution Source Power in the Kazantip Bay Applying the Variation Algorithm. Physical Oceanography, 2015, no. 2, pp. 69–76.
- Kochergin S.V., Fomin, V.V. Variational Identification of Input Parameters in the Model of Distribution of the Pollutants from the Underwater Source. Physical Oceanography, 2019, vol. 26, iss. 6, pp. 547–556. DOI: 10.22449/1573-160X-2019-6-547-556
Downloads
Submitted
Published
How to Cite
Copyright (c) 2021 Kochergin V.S., Kochergin S.V.
This work is licensed under a Creative Commons Attribution 4.0 International License.
