Abstract
The problem of stability and sensitivity of functionals of the optimal solution of the variational data assimilation of sea surface temperature for the model of sea thermodynamics is considered. The variational data assimilation problem is formulated as an optimal control problem to find the initial state and the boundary heat flux. The sensitivity of the response functions as functionals of the optimal solution with respect to the observation data is studied. Computing the gradient of the response function reduces to the solution of a non-standard problem being a coupled system of direct and adjoint equations with mutually dependent initial and boundary values. The algorithm to compute the gradient of the response function is presented, based on the Hessian of the original cost functional. Stability analysis of the response function with respect to uncertainties of input data is given. Numerical examples are presented for the Black and Azov seas thermodynamics model.
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Funding: The work was supported by the Russian Science Foundation (project {20-11-20057}, in the part of research of Sections 2–5) and the Moscow Center for Fundamental and Applied Mathematics (agreement with the Ministry of Education and Science of the Russian Federation No. 075-15-2019-1624).
References
[1] V. I. Agoshkov, E. I. Parmuzin, and V. P. Shutyaev, Numerical algorithm of variational assimilation of the ocean surface temperature data. J. Comp. Math. Math. Phys. 48 (2008), 1371–1391.10.1134/S0965542508080046Search in Google Scholar
[2] V. V. Alekseev and V. B. Zalesny, Numerical model of the large-scale ocean dynamics. In: Computational Processes and Systems. Nauka, Moscow, 1993 (in Russian).Search in Google Scholar
[3] J. F. Bonnans and A. Shapiro, Perturbation Analysis of Optimization Problems. New York, Springer, 2000.10.1007/978-1-4612-1394-9Search in Google Scholar
[4] N. A. Diansky, A. V. Bagno, and V. B. Zalesny, Sigma model of global ocean circulation and its sensitivity to variations in wind stress. Izv. Atmos. Ocean. Phys. 38 (2002), No. 4, 477–494.Search in Google Scholar
[5] G. Chavent, About the stability of the optimal control solution of inverse problems. Mathematical and Numerical Methods of Inverse and Improperly Posed Problems (Ed. G. Anger). Akademie Verlag, Berlin, 1979.10.1515/9783112480281-005Search in Google Scholar
[6] I. Gejadze, F.-X. Le Dimet, and V. P. Shutyaev, On analysis error covariances in variational data assimilation. SIAM J. Sci. Comput. 30 (2008), No. 4, 1847–1874.10.1137/07068744XSearch in Google Scholar
[7] I. Gejadze, F.-X. Le Dimet, and V. P. Shutyaev, On optimal solution error covariances in variational data assimilation problems. J. Comp. Phys. 229 (2010), 2159–2178.10.1016/j.jcp.2009.11.028Search in Google Scholar
[8] I. Yu. Gejadze, G. J. M. Copeland, F.-X. Le Dimet, and V. Shutyaev, Computation of the analysis error covariance in variational data assimilation problems with nonlinear dynamics. J. Comp. Phys. 230 (2011), 7923–7943.10.1016/j.jcp.2011.03.039Search in Google Scholar
[9] I. Gejadze, V. P. Shutyaev, and F.-X. Le Dimet, Analysis error covariance versus posterior covariance in variational data assimilation. Quart. J. Royal Meteorol. Soc. 139 (2013), 1826–1841.10.1002/qj.2070Search in Google Scholar
[10] I. Yu. Gejadze, V. P. Shutyaev, and F.-X. Le Dimet, Hessian-based covariance approximations in variational data assimilation. Russ. J. Numer. Anal. Math. Modelling 33 (2018), No. 1, 25–39.10.1515/rnam-2018-0003Search in Google Scholar
[11] I. Karagali, J. Hoyer, and C. B. Hasager, SST diurnal variability in the North Sea and the Baltic Sea. Remote Sensing of Environment 121 (2012), 159–170.10.1016/j.rse.2012.01.016Search in Google Scholar
[12] F.-X. Le Dimet, I. M. Navon, and D. N. Daescu, Second-order information in data assimilation. Monthly Weather Review 130 (2002), No. 3, 629–648.10.1175/1520-0493(2002)130<0629:SOIIDA>2.0.CO;2Search in Google Scholar
[13] F.-X. Le Dimet, L. B. Ngodock, and J. Verron, Sensitivity analysis in variational data assimilation. J. Meteorol. Soc. Japan 75 (1997), No. 1B, 245–255.10.2151/jmsj1965.75.1B_245Search in Google Scholar
[14] F.-X. Le Dimet, V. Shutyaev, and T. H. Tran, General sensitivity analysis in data assimilation. Russ. J. Numer. Anal. Math. Modelling 29 (2014), No. 2, 107–127.10.1515/rnam-2014-0009Search in Google Scholar
[15] F.-X. Le Dimet and O. Talagrand, Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus 38A (1986), 97–110.10.1111/j.1600-0870.1986.tb00459.xSearch in Google Scholar
[16] J. L. Lions, Contrôle optimal des systèmes gouvernés par des équations aux dérivées partielles. Paris, Dunod, 1968.Search in Google Scholar
[17] E. A. Lupyan, A. A. Matveev, I. A. Uvarov, T. Yu. Bocharova, O. Yu. Lavrova, and M. I. Mityagina, ‘See the Sea’ satellite service, instrument for studying processes and phenomena on the ocean surface. In: Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa (Problems in Remote Sensing of the Earth from Space) 9 (2012), No. 2, 251–261.Search in Google Scholar
[18] G. I. Marchuk, Adjoint Equations and Analysis of Complex Systems. Dordrecht, Kluwer, 1995.10.1007/978-94-017-0621-6Search in Google Scholar
[19] G. I. Marchuk, V. I. Agoshkov, and V. P. Shutyaev, Adjoint Equations and Perturbation Algorithms in Nonlinear Problems. New York, CRC Press Inc., 1996.Search in Google Scholar
[20] V. P. Shutyaev, Control Operators and Iterative Algorithms for Variational Data Assimilation Problems. Nauka, Moscow, 2001.10.1515/jiip.2001.9.2.177Search in Google Scholar
[21] V. P. Shutyaev, F.-X. Le Dimet, and E. I. Parmuzin, Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation. Nonlin. Processes Geophys. 25 (2018), 429–439.10.5194/npg-25-429-2018Search in Google Scholar
[22] V. P. Shutyaev and E. I. Parmuzin, Numerical solution of the problem of variational data assimilation to restore heat fluxes and initial state for the ocean thermodynamics model. Russ. J. Numer. Anal. Math. Modelling 36 (2021), No. 1, 43–53.10.1515/rnam-2021-0004Search in Google Scholar
[23] N. B. Zakharova, V. I. Agoshkov, and E. I. Parmuzin, The new method of ARGO buoys system observation data interpolation. Russ. J. Numer. Anal. Math. Modelling 28 (2013), No. 1, 67–84.10.1515/rnam-2013-0005Search in Google Scholar
[24] N. B. Zakharova and S. A. Lebedev, Interpolation of operative data of ARGO buoys for data assimilation in World ocean circulation model. In: Contemporary Problems of Earth Remote Sensing from Space: Physical Grounds, Methods, Environment Monitoring Technology, Potential Dangerous Phenomena and Objects. Collected papers. Domira, Moscow, 2010, Vol. 7, No. 4. pp. 104–111.Search in Google Scholar
[25] V. B. Zalesny, N. F. Diansky, V. V. Fomin, S. N. Moshonkin, and S. G. Demyshev, Numerical model of the circulation of the Black Sea and the Sea of Azov. Russ. J. Numer. Anal. Math. Modelling 27 (2012), No. 1. 95–112.10.1515/rnam-2012-0006Search in Google Scholar
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Articles in the same Issue
- Frontmatter
- An equilibrated a posteriori error estimator for an Interior Penalty Discontinuous Galerkin approximation of the p-Laplace problem
- Numerical-statistical study of the prognostic efficiency of the SEIR model
- Stability analysis of functionals in variational data assimilation with respect to uncertainties of input data for a sea thermodynamics model
- General finite-volume framework for saddle-point problems of various physics
Articles in the same Issue
- Frontmatter
- An equilibrated a posteriori error estimator for an Interior Penalty Discontinuous Galerkin approximation of the p-Laplace problem
- Numerical-statistical study of the prognostic efficiency of the SEIR model
- Stability analysis of functionals in variational data assimilation with respect to uncertainties of input data for a sea thermodynamics model
- General finite-volume framework for saddle-point problems of various physics