Identification of a Pollution Source Power in the Kazantip Bay Applying the Variation Algorithm
V.S. Kochergin✉, S.V. Kochergin
Marine Hydrophysical Institute, Russian Academy of Sciences, Sevastopol, Russian Federation
✉ e-mail: vskocher@gmail.com
Abstract
The transport model of passive admixture in the Azov Sea is considered. On its basis the variational algorithm of identification power source pollution, including a variable in space, is implemented. The algorithm operability of optimum space distribution search of power source with measurements data is shown on a test example. Test calculations for the Kazantip Bay under east wind stress were carried out. The measurement data assimilation algorithm in the passive admixture transfer model is implemented applying gradient methods for optimal estimate retrieval. The retrieval is carried out by means of minimizing a quadratic function of prediction quality. The linked problem solving is used in the gradient of quality functional construction. On the basis of the variational method of data assimilation, the optimal estimate retrieval algorithm for pollution source power identification is constructed. In application of the algorithm, the integration of the main, linked and variational problems is implemented. The latter is solved to determine an iteration parameter when performing gradient descent. Integration problems are solved using TVD approximations. For the application of the procedure, the Sea of Azov flow fields and turbulent diffusion coefficients are obtained using the sigma coordinate ocean model (POM) under the eastern wind stress conditions being dominant at the observed period of time. Furthermore, the results can be used to perform numerical data assimilation on loads of suspended matter.
Keywords
power source identification, variation algorithm, discrepancy functional, concentration field, measurement data assimilation, the Azov Sea
For citation
Kochergin, V.S. and Kochergin, S.V., 2015. Identification of a Pollution Source Power in the Kazantip Bay Applying the Variation Algorithm. Physical Oceanography, (2), pp. 69-76. doi:10.22449/1573-160X-2015-2-69-76
DOI
10.22449/1573-160X-2015-2-69-76
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