Numerical Research of the Pollution Surface and Deep-Sea Evolution in the Sea of Azov Using Satellite Observation Data

T. Ya. Shul’ga✉, V. V. Suslin, R. R. Stanichnaya

Marine Hydrophysical Institute, Russian Academy of Sciences, Sevastopol, Russian Federation

✉ e-mail: shulgaty@mail.ru

Abstract

Numerical hydrodynamic modeling of the Sea of Azov is done for 2013–2014 on basis of the Princeton Ocean Model at presetting of real atmospheric impact (the SKIRON model). The hydrodynamic model was applied in numerical studies to analyze the evolution of pollution on the basis of transport and diffusion equation solution. Level-2 data from MODIS at satellite Aqua with 1 km spatial resolution were used in the work. The following parameters were calculated according to satellite data: the ratio of normalized brightness of the light coming from under the water surface in two 531 and 488 nm spectral channels and light backscattering coefficient by the of the suspension particles at 555 nm wavelength. These data determine the presence of suspended matter (mineral suspended matter from river discharges or rising from the bottom as a result of a strong wind), and suspended matter of biological origin (coccolithophorides bloom). New model algorithms are applied to analyze the consistency of data obtained by remote sensing of the sea surface from space modeling solutions and their combinations. The paper discusses methods of sharing information, assessment of model forecast quality depending on the intervals between satellite data assimilation. It is shown that a serial scheme of data assimilation improves the pollution forecast by the model, even when the satellite images are not stable.

Keywords

Sea of Azov, evolution of passive admixture, remote observations, numerical modeling, comparative analysis of satellite and model data

For citation

Shul’ga, T.Ya., Suslin, V.V. and Stanichnaya, R.R., 2017. Numerical Research of the Pollution Surface and Deep-Sea Evolution in the Sea of Azov Using Satellite Observation Data. Physical Oceanography, (6), pp. 36-46. doi:10.22449/1573-160X-2017-6-36-46

DOI

10.22449/1573-160X-2017-6-36-46

References

  1. Matishov, G.G. and Matishov, D.G., 2013. Current Natural and Social Risks in the Azov-Black Sea Region. Herald of the Russian Academy of Sciences, [e-journal] 83(6), pp. 490-498. doi:10.1134/S1019331613090062
  2. Klenkin, А.А., Korpakova, I.G., Pavlenko, L.F. and Temerdashev, Z.A., 2007. Ekosistema Azovskogo Morya: Antropogennoe Zagryaznenie [Ecosystem of the Sea of Azov: Anthropogenic Pollution]. Krasnodar: Education South Publ., 324 p. (in Russian).
  3. Bufetova, M.V., 2015. Zagryaznenie Vod Azovskogo Morya Tyazhelymi Metallami [Pollution of Sea of Azov with Heavy Metals]. South of Russia: Ecology, Development, [e-journal] 10(3), pp. 112-120 (in Russian). doi:10.18470/1992-1098-2015-3-112-120
  4. Matishov, G.G., Berdnikov, S.V., Bespalova, L.A., Ivlieva, O.V., Tsygankova, A.E., Khartiev, S.M., Ioshpa, A.R., Arkhipova, O.E., Kropyanko, L.V. [et al.], 2015. Sovremennye Opasnye Ekzogennye Protsessy v Beregovoy Zone Azovskogo Morya [Modern Dangerous Exogenous Processes in the Coastal Zone of the Azov Sea]. Rostov-on-Don: Southern Federal University, 321 p. (in Russian).
  5. Drozdov, V.V., 2010. Osobennosti Mnogoletney Dinamiki Ekosistemy Azovskogo Morya pod Vliyaniem Klimaticheskikh i Antropogennykh Faktorov [Features of Long-Term Dynamics of an Ecosystem of Sea of Azov under the Influence of Climatic and Anthropogenous Factors]. Uchenye zapiski RSHMU = Proceedings of the Russian State Hydrometeorological University. A theoretical research journal. St. Petersburg: RSHU Publishers. Iss.15, pp. 155-176 (in Russian).
  6. Lavrova, O.Yu., Kostianoy, A.G., Lebedev, S.A., Mityagina, M.I. Ginzburg, A.I. and Sheremet, N.A., 2011. Kompleksniy Sputnikoviy Monitoring Morey Rossii [Complex Satellite Monitoring of the Russian Seas]. Мoscow: SRI RAS, 480 p. (in Russian).
  7. Ivanov, V.A., Cherkesov, L.V. and Shulga, T.Ya., 2014. Dynamic Processes and their Influence on the Transformation of the Passive Admixture in the Sea of Azov. Oceanology, [e-journal] 54(4), pp. 426-434. doi:10.1134/S0001437014030023
  8. Blumberg, A.F. and Mellor, G.L., 1987. A Description of a Three-Dimensional Coastal Ocean Circulation Model. In: N. Heaps, ed., 1987. Three-Dimensional Coastal Ocean Models. Washington, D. C.: American Geophysical Union, pp. 1-16. doi:10.1029/CO004
  9. Fomin, V.V., 2002. Chislennaya Model' Tsirkulyatsii Vod Azovskogo Morya [Numerical Model of Water Circulation in the Azov Sea]. In: UHMI, 2002. Scientific Works of UkrNDGMI. Kiev: Nika-center. Iss. 249, pp. 246-255 (in Russian).
  10. Cherkesov, L.V., Ivanov, V.A. and Khartiev, S.M., 1992. Vvedenie v Gidrodinamiku i Teoriyu Voln [Introduction into Hydrodynamics and Wave Theory]. St. Petersburg: Gidrometeoizdat, 264 p. (in Russian).
  11. Mellor, G.L. and Yamada, T., 1982. Development of a Turbulence Closure Model for Geophysical Fluid Problems. Rev. Geophys., [e-journal] 20(4), pp. 851-875. doi:10.1029/RG020i004p00851
  12. Smagorinsky, J, 1963. General Circulation Experiments with Primitive Equations: I. The Basic Experiment. Mon. Wea. Rev., [e-journal] 91(3), pp. 99-164. doi:10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2
  13. Wannawong, W., Wongwises, U. and Vongvisessomjai, S., 2011. Mathematical Modeling of Storm Surge in Three Dimensional Primitive Equations. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, [e-journal] 5(6), pp. 797-806. Available at: http://waset.org/publications/6330/mathematical-modeling-of-storm-surge-in-three-dimensional-primitive-equations [Accessed: 04 July 2017].
  14. Grant, W.D. and Madsen, O.S., 1979. Combined Wave and Current Interaction with a Rough Bottom, J. Geophys. Res., [e-journal] 84(C4), pp. 1797-1808. doi:10.1029/JC084iC04p01797
  15. Courant, R., Friedrichs, K.O. and Lewy, H., 1967. On the Partial Difference Equations of Mathematical Physics. IBM J. Res. Dev., [e-journal] 11(2), pp. 215-234. doi:10.1147/rd.112.0215
  16. Kallos, G., Nickovic, S., Jovic, D., Kakaliagou, O., Papadopoulos, A., Misirlis, N., Boukas, L., Mimikou, N., Sakellaridis, G., Papageorgiou, J., Anadranistakis, E. and Manousakis, M., 1997. The Regional Weather Forecasting System SKIRON and its Capability for Forecasting Dust Uptake and Transport. In: WMO, 1997. Proceedings of the WMO Conference on Dust Storms, Damascus, 1-6 Nov. Damascus, Syria: WMO, p. 9.
  17. NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group, 2017. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Ocean Color Data; 2014 Reprocessing. Greenbelt, MD, USA: NASA OB.DAAC. doi:10.5067/AQUA/MODIS_OC.2014.0
  18. Suslin, V. and Churilova, T., 2016. A Regional Algorithm for Separating Light Absorption by Chlorophyll-a and Colored Detrital Matter in the Black Sea, Using 480-560 nm Bands from Ocean Color Scanners. Int. J. Remote Sens., [e-journal] 37(18), pp. 4380-4400. doi:10.1080/01431161.2016.1211350
  19. Suslin, V., Churilova, T., Ivanchik, M., Pryahina, S. and Golovko, N, 2011. A Simple Approach for Modeling of Downwelling Irradiance in the Black Sea Based on Satellite Data. Proc. of VI International Conference “Current Problems in Optics of Natural Waters” (ONW'2011). Saint-Petersburg: Nauka, pp. 199-203.
  20. Kalman, R., 1960. A New Approach to Linear Filtering and Prediction Problems. J. Basic Eng., [e-journal] 82(1), pp. 35-45. doi:10.1115/1.3662552
  21. Klimova, E.G., 2003. Numerical Experiments on Meteorological Data Assimilation Using a Suboptimal Kalman Filter. Russian Meteorology and Hydrology, (10), pp. 40-50.
  22. Ghil, M. and Malanotte-Rizzolli, P., 1991. Data Assimilation in Meteorology and Oceanography. In: R. Dmowska and B. Saltzman, eds., 1991. Advances in Geophysics. Academic Press. Vol. 33, pp. 141-266. doi:10.1016/s0065-2687(08)60442-2

Download the article (PDF)