Evolution of the pollution in the Sea of Azov by satellite data and simulation results

T. Ya. Shul'ga

Vestnik Udmurtskogo universiteta. Matematika, mehanika, kompʹûternye nauki, Tome 27 (2017) no. 3, pp. 450-459

Résumé

Methods of sharing information obtained by methods of remote sensing of the sea surface from space and numerical solutions are discussed in this paper. The results of numerical modeling and data of satellite observations on the state of the waters of the Sea of Azov for the period 2013–2014 are summarized on the basis of developed algorithms. Three-dimensional hydrodynamic modeling of water dynamics and evolution of contaminants in the Sea of Azov is performed using the model of POM (Princeton Ocean Model) with a real atmospheric forcing SKIRON. The joint analysis of numerical solutions and data of space monitoring Aqua (MODIS) allows to study the features of the spatial and temporal distribution of pollution in the Sea of Azov. New model algorithms are used to analyze the consistency of the results of numerical solutions, satellite observation data and their combination. The dynamic-stochastic method of assimilating satellite information is used, which makes it possible to obtain an estimate of the quality of the model prediction depending on the intervals between the assimilation of satellite data.

Détail

Keywords: Sea of Azov, remote observations, numerical modeling, Kalman filter.
Mots-clés : evolution of passive admixture, assimilation of satellite data

Bibliographie

[1] Matishov G. G., Chikin A. L., Berdnikov S. V., Sheverdyaev I. V., “The extreme flood in the Don river delta, March 23–24, 2013, and determining factors”, Doklady Earth Sciences, 455:1 (2014), 360–363 | DOI | DOI

[2] Ide K., Courter Ph., Ghil M., Lorenc A., “Unified notation for data assimilation: operational, sequential and variation”, Journal of the Meteorological Society of Japan. Ser. II, 75:1B (1997), 181–189 | DOI

[3] Blumberg A. F., Mellor G. L., “A description of three dimensional coastal ocean circulation model”, Three-Dimensional Coastal Ocean Models, v. 4, ed. N. Heaps, American Geophysical Union, Washington, D.C., 1987, 1–16 | DOI

[4] Cherkesov L. V., Ivanov V. A., Khartiev S. M., Introduction into hydrodynamics and wave theory, Gidrometeoizdat, Saint Petersburg, 1992, 264 pp.

[5] Mellor G. L., Yamada T., “Development of a turbulence closure model for geophysical fluid problems”, Reviews of Geophysics, 20:4 (1982), 851–857 | DOI

[6] Smagorinsky J., “General circulation experiments with the primitive equations. I. The basic experiment”, Monthly Weather Review, 91:3 (1963), 99–164 | 2.3.CO;2 class='badge bg-secondary rounded-pill ref-badge extid-badge'>DOI

[7] Wannawong W., Humphries U. W., Wongwises P., Vongvisessomjai S., “Mathematical modeling of storm surge in three dimensional primitive equations”, International Journal of Computational and Mathematical Sciences, 5:1 (2011), 44–53 http://waset.org/publications/6330

[8] Grant W. D., Madsen O. S., “Combined wave and current interaction with a rough bottom”, Journal of Geophysical Research: Oceans, 84:C4 (1979), 1797–1808 | DOI

[9] Courant R., Friedrichs K. O., Lewy H., “On the partial difference equations of mathematical physics”, IBM Journal of Research and Development, 11:2 (1967), 215–234 | DOI | MR | Zbl

[10] Kallos G., Boukas L., Jovic D., Kakaliagou O., Misirlis N., Nickovic S., Papadopoulos A., Mimikou N., Sakellaridis G., Papageorgiou J., Anadranistakis E., Manousakis M., “The regional weather forecasting system SKIRON and its capability for forecasting dust uptake and transport”, Proceedings of the First LAS/WMO International Symposium on Sand and Dust Storms (Damascus, Syria, 1997), WMO, 1998, 9

[11] NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group, Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Ocean Color Data, NASA OB.DAAC, Greenbelt, MD, USA, 2014 | DOI

[12] NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group, Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Ocean Color Data, NASA OB.DAAC, Greenbelt, MD, USA, 2014 | DOI

[13] Suslin V., Churilova T., “A regional algorithm for separating light absorption by chlorophyll-$\alpha$ and coloured detrital matter in the Black Sea, using 480–560 nm bands from ocean colour scanners”, International Journal of Remote Sensing, 37:18 (2016), 4380–4400 | DOI

[14] Suetin V. S., Suslin V. V., Korolev S. N., Kucheryavyi A. A., “Estimation of the variability of optical properties of water in the Black Sea according to the satellite device SeaWiFS”, Morskoi Gidrofizicheskii Zhurnal, 2002, no. 6, 44–54 (in Russian)

[15] Suslin V. V., Suetin V. S., Korolev S. N., Kucheryavyi A. A., “Use of SeaWiFS data to estimate water optical properties of the Black Sea”, Current Research on Remote Sensing, Laser Probing, and Imagery in Natural Waters (April 13, 2007), Proc. SPIE, 6615, 661509 | DOI

[16] Suslin V., Churilova T., Ivanchik M., Pryahina S., Golovko N., “A simple approach for modeling of downwelling irradiance in the Black Sea based on satellite data”, Current Problems in Optics of Natural Waters, ONW'2011, Proceedings of VI International Conference, Nauka, Saint-Petersburg, 2011, 199–203

[17] Klimova E. G., “Numerical experiments on meteorological data assimilation using a suboptimal Kalman filter”, Russian Meteorology and Hydrology, 2003, no. 10, 40–50