Skip to main content
Log in

Methods for Observation Data Assimilation in Problems of Physics of Atmosphere and Ocean

  • Published:
Izvestiya, Atmospheric and Oceanic Physics Aims and scope Submit manuscript

Abstract

This paper presents a review and analysis of approaches to data assimilation in problems of geophysical hydrodynamics, from the simplest sequential assimilation schemes to modern variational methods. Special attention is paid to the study of the problem of variational assimilation in a weak formulation, in particular, to the construction of an optimality system and the estimation of the covariance matrices of the optimal solution errors. This is a new direction of research in which the author has obtained some results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. L. Richardson, Weather Prediction by Numerical Process (Cambridge University Press, Cambridge, 1922).

    Google Scholar 

  2. J. G. Charney, “The use of the primitive equations of motion in numerical prediction,” Tellus 7, 22–26 (1955).

    Article  Google Scholar 

  3. N. A. Phillips, “On the problem of initial data for the primitive equations,” Tellus 12, 121–126 (1960).

    Article  Google Scholar 

  4. A. H. Jazwinski, Stochastic Processes and Filtering Theory (Academic, London, 1970).

    Google Scholar 

  5. R. S. Bucy and P. D. Joseph, Filtering for Stochastic Processes with Applications to Guidance (Chelsea, New York, 1987).

    Google Scholar 

  6. J. L. Lions, Contrôle optimal des systèmes gouvernés par des équations aux dérivées partielles (Dunod, Paris, 1968).

    Google Scholar 

  7. G. I. Marchuk, Adjoint Equations and Analysis of Complex Systems (Kluwer, Dordrecht, 1995).

    Book  Google Scholar 

  8. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization (Academic, London, 1987).

    Google Scholar 

  9. G. I. Marchuk, V. I. Agoshkov, and V. P. Shutyaev, Adjoint Equations and Perturbation Algorithms in Nonlinear Problems (CRC, New York, 1996).

    Google Scholar 

  10. A. F. Bennett, Inverse Modeling of the Ocean and Atmosphere (Cambridge University Press, Cambridge, 2002).

    Book  Google Scholar 

  11. R. Daley, Atmospheric Data Analysis (Cambridge University Press, Cambridge, 1991).

    Google Scholar 

  12. M. Ghil and P. Malanotte-Rizzoli, “Data assimilation in meteorology and oceanography,” Adv. Geophys. 33, 141–266 (1991).

    Article  Google Scholar 

  13. E. Kalnay, Atmospheric Modeling. Data Assimilation and Predictability (Cambridge University Press, Cambridge, 2003).

    Google Scholar 

  14. H. Panofsky, “Objective weather-map analysis,” J. Appl. Meteorol. 6, 386–392 (1949).

    Article  Google Scholar 

  15. B. Gilchrist and G. Cressman, “An experiment in objective analysis,” Tellus 6 (4), 309–318 (1954).

    Article  Google Scholar 

  16. P. Bergthórsson and B. Döös, “Numerical weather map analysis,” Tellus 7 (3), 329–340 (1955).

    Google Scholar 

  17. G. Cressman, “An operational objective analysis system,” Mon. Weather Rev. 87, 367–374 (1959).

    Article  Google Scholar 

  18. J. Hoke and R. A. Anthes, “The initialization of numerical models by a dynamic initialization technique,” Mon. Weather. Rev. 104, 1551–1556 (1976).

    Article  Google Scholar 

  19. J. Verron, “Altimeter data assimilation into an ocean circulation model: Sensitivity to orbital parameters,” J. Geophys. Res. 95 (C7), 443–459 (1990).

    Article  Google Scholar 

  20. J. Verron and W. R. Holland, “Impact de données d’altimétrie satellitaire sur les simulations numériques des circulations générales océaniques aux latitudes moyennes,” Ann. Geophys. 7, 31–46 (1989).

    Google Scholar 

  21. E. Blayo, J. Verron, and J.-M. Molines, “Assimilation of TOPEX/POSEIDON altimeter data into a circulation model of the North Atlantic,” J. Geophys. Res. 99 (C12), 24691–24705 (1994).

    Article  Google Scholar 

  22. D. Auroux and J. Blum, “A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm,” Nonlinear Processes Geophys. 15, 305–319 (2008).

    Article  Google Scholar 

  23. L. C. Gandin, Objective Analysis of Hydrometeorological Fields (Gidrometizdat, Leningrad, 1963) [in Russian].

    Google Scholar 

  24. A. C. Lorenc, “A global three-dimensional multivariate statistical analysis scheme,” Mon. Weather Rev. 109 (4), 701–721 (1981).

    Article  Google Scholar 

  25. R. D. McPherson, K. H. Bergman, R. E. Kistler, G. E. Rasch, and D. S. Gordon, “The NMC operational global data assimilation system,” Mon. Weather Rev. 107 (11), 1445–1461 (1979).

    Article  Google Scholar 

  26. W. H. Lyne, R. Swinbank, and N. T. Birch, “A data assimilation experiment, with results showing the atmospheric circulation during the FGGE special observing periods,” Q. J. R. Meteorol. Soc. 108, 575–594 (1982).

    Article  Google Scholar 

  27. A. C. Lorenc, “Analysis methods for numerical weather prediction, " Q. J. R. Meteorol. Soc. 112 (474), 1177–1194 (1986).

    Article  Google Scholar 

  28. A. C. Lorenc, R. S. Bell, and B. Macpherson, “The Meteorological Office analysis correction data assimilation scheme,” Q. J. R. Meteorol. Soc. 117, 59–89 (1991).

    Article  Google Scholar 

  29. H. J. Thiebaux and M. A. Pedder, Spatial Objective Analysis (Academic, London, 1987).

    Google Scholar 

  30. H. Douville, P. Viterbo, J.-F. Mahfouf, and A. C. M. Beljaars, “Evaluation of the optimum interpolation and nudging techniques for soil moisture analysis using FIFE data,” Mon. Weather Rev. 128, 1733–1756 (2000).

    Article  Google Scholar 

  31. A. N. Bagrov and M. D. Tsirul’nikov, “Operational scheme of objective analysis of the Russian Hydrometeorological Center,” in 70 Years of the Russian Hydrometeorological Center (Gidrometeoizdat, St. Petersburg, 1999), pp. 59–69 [in Russian].

    Google Scholar 

  32. A. V. Frolov, A. I. Vazhnik, P. I. Svirenko, and V. I. Tsvetkov, Global System of Data Assimilation for Atmospheric Observation Data (Gidrometeoizdat, St. Petersburg, 2000) [in Russian].

    Google Scholar 

  33. J. A. Carton and E. C. Hackert, “Applications of multi-variate statistical objective analysis to the circulation in the tropical Atlantic,” Ocean. Dyn. Atmos. Oceans 13, 491–515 (1989).

    Article  Google Scholar 

  34. J. C. Derber and A. Rosati, “A global ocean data assimilation system,” J. Phys. Oceanogr., 19, 1333–1347 (1989).

    Article  Google Scholar 

  35. S. Smith, J. A. Cummings, and C. Rowley, Validation Test Report for the Navy Coupled Ocean Data Assimilation 3D Variational Analysis (NCODA-VAR) System, Version 3.43 (2012).

  36. G. Evensen, “The ensemble Kalman filter: Theoretical formulation and practical implementation,” Ocean Dyn. 53 (4), 343–367 (2003).

    Article  Google Scholar 

  37. P. Sakov and P. A. Sandery, “Comparison of EnOI and EnKF regional ocean reanalysis systems,” Ocean Modell. 89, 45–60 (2015).

    Article  Google Scholar 

  38. M. N. Kaurkin, R. A. Ibrayev, and K. P. Belyaev, “ARGO data assimilation into the ocean dynamics model with high spatial resolution using Ensemble Optimal Interpolation (EnOI),” Oceanology (Engl. Transl.) 56 (6), 774–781 (2016).

  39. R. E. Kalman, “A new approach to linear filter and prediction theory,” J. Basic Eng. 82, 35–45 (1960).

    Article  Google Scholar 

  40. R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,” J. Basic Eng. 83D, 95–108 (1961).

    Article  Google Scholar 

  41. M. Ghil, S. E. Cohn, and A. Dalcher, “Sequential estimation, data assimilation, and initialization,” in The Interaction between Objective Analysis and Initialization (Proceedings of the Fourteenth Stanstead Seminar), Ed. by D. Williamson (McGill University, Montreal, 1982), pp. 83–97.

  42. N. P. Budgell, “Stochastic filtering of linear shallow water wave processes,” SIAM J. Sci. Stat. Comput. 8 (2), 152–179 (1987).

    Article  Google Scholar 

  43. A. S. Sarkisyan, Modeling of Ocean Dynamics (Gidrometeoizdat, St. Petersburg, 1991) [in Russian].

    Google Scholar 

  44. B. A. Nelepo, V. V. Knysh, A. S. Sarkisyan, and I. E. Timchenko, “Study of synoptic variability of the ocean based on the dynamic–stochastic approach,” Dokl. Akad. Nauk SSSR 246 (4), 974–978 (1979).

    Google Scholar 

  45. G. K. Korotaev and V. N. Eremeev, Introduction to Operational Oceanography of the Black Sea (EKOSI-Gidrofizika, Sevastopol, 2006) [in Russian].

    Google Scholar 

  46. A. S. Sarkisyan, S. G. Demyshev, G. K. Korotaev, and V. A. Moiseenko, “An example of four-dimensional analysis of observational data from the Razrezy program for the Newfoundland energy active zone of the ocean,” in Scientific and Technological Results: Atmosphere, Ocean, Space—the Razrezy Program (VINITI, Moscow, 1986), vol. 6, pp. 88–89 [in Russian].

  47. V. V. Knysh, G. K. Korotaev, A. I. Mizyuk, and A. S. Sarkisyan, “Assimilation of hydrological observation data for calculating currents in seas and oceans,” Izv., Atmos. Ocean. Phys. 48 (1), 57–73 (2012).

    Article  Google Scholar 

  48. G. Evensen, Data Assimilation: The Ensemble Kalman Filter (Springer, Berlin, 2007).

    Google Scholar 

  49. E. Klimova, “A suboptimal data assimilation algorithm based on the ensemble Kalman filter,” Q. J. R. Meteorol. Soc. 138, 2079–2085 (2012).

    Article  Google Scholar 

  50. A. V. Shlyaeva, M. A. Tolstykh, V. G. Mizyak, and V. S. Rogutov, “Local ensemble transform Kalman filter data assimilation system for the global semi-Lagrangian atmospheric model,” Russ. J. Numer. Anal. Math. Modell. 28 (4), 419–441 (2013).

    Article  Google Scholar 

  51. Y. K. Sasaki, “An objective analysis based on the variational method,” J. Meteorol. Soc. Jpn. 36, 77–88 (1958).

    Article  Google Scholar 

  52. Y. Sasaki, “Some basic formalisms in numerical variational analysis,” Mon. Weather Rev. 98, 875–883 (1970).

    Article  Google Scholar 

  53. C. Provost and R. Salmon, “A variational method for inverting hydrographic data,” J. Mar. Res. 44, 1–34 (1986).

    Article  Google Scholar 

  54. V. V. Penenko and N. V. Obraztsov, “Variational method for the fields of meteorological variables,” Meteorol. Gidrol. 11, 1–11 (1976).

    Google Scholar 

  55. G. I. Marchuk and V. V. Penenko, “Application of optimization methods to the problem of mathematical simulation of atmospheric processes and environment,” in Modelling and Optimization of Complex Systems: Proc. of the IFIP-TC7 Working Conf., Ed. by G. I. Marchuk (Springer, New York, 1978), pp. 240–252.

  56. F.-X. Le Dimet and O. Talagrand, “Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects,” Tellus, Ser. A 38, 97–110 (1986).

    Article  Google Scholar 

  57. J. M. Lewis and J. C. Derber, “The use of adjoint equations to solve a variational adjustment problem with advective constraints,” Tellus, Ser. A 37, 309–322 (1985).

    Article  Google Scholar 

  58. P. Courtier and O. Talagrand, “Variational assimilation of meteorological observations with the adjoint vorticity equation. II. Numerical results,” Q. J. R. Meteorol. Soc. 111, 1329–1347 (1987).

    Article  Google Scholar 

  59. I. M. Navon, “A review of variational and optimization methods in meteorology,” in Variational Methods in Geosciences, Ed. by Y. K. Sasaki (Elsevier, New York, 1986), pp. 29–34.

    Google Scholar 

  60. V. I. Agoshkov and G. I. Marchuk, “On solvability and numerical solution of data assimilation problems,” Russ. J. Numer. Anal. Math. Modell. 8 (1), 1–16 (1993).

    Article  Google Scholar 

  61. G. I. Marchuk and V. B. Zalesny, “A numerical technique for geophysical data assimilation problem using Pontryagin’s principle and splitting-up method,” Russ. J. Numer. Anal. Math. Modell. 8 (4), 311–326 (1993).

    Article  Google Scholar 

  62. G. I. Marchuk and V. P. Shutyaev, “Iteration methods for solving a data assimilation problem,” Russ. J. Numer. Anal. Math. Modell. 9 (3), 265–279 (1994).

    Article  Google Scholar 

  63. G. I. Marchuk and V. P. Shutyaev, “Adjoint equations and iterative algorithms in variational data assimilation problems,” Tr. Inst. Mat. Mekh. Ural. Otd. Ross. Akad. Nauk 17 (2), 136–150 (2011).

    Google Scholar 

  64. V. I. Agoshkov, E. I. Parmuzin, V. B. Zalesny, V. P. Shutyaev, N. B. Zakharova, and A. V. Gusev, “Variational assimilation of observation data in the mathematical model of the Baltic Sea dynamics,” Russ. J. Numer. Anal. Math. Modell. 30 (4), 203–212 (2015).

    Google Scholar 

  65. V. I. Agoshkov, M. Assovskii, V. B. Zalesny, N. B. Zakharova, E. I. Parmuzin, and V. P. Shutyaev, “Variational assimilation of observation data in the mathematical model of the Black Sea taking into account the tide-generating forces,” Russ. J. Numer. Anal. Math. Modell. 30 (3), 129–142 (2015).

    Google Scholar 

  66. M. Ventsel’ and V. B. Zalesny, “Data assimilation in the one-dimensional model of heat convection–diffusion in the ocean,” Izv. Akad. Nauk: Fiz. Atmos. Okeana 32 (5), 613–629 (1996).

  67. V. P. Shutyaev, Control Operators and Iterative Algorithms in Problems in Variational Data Assimilation Problems (Nauka, Moscow, 2001) [in Russian].

    Book  Google Scholar 

  68. V. I. Agoshkov, E. I. Parmuzin, and V. P. Shutyaev, “Numerical algorithm for variational assimilation of sea surface temperature data,” Comput. Math. Math. Phys. 48 (8), 1293–1312 (2008).

    Article  Google Scholar 

  69. D. F. Parrish and J. C. Derber, “The National Meteorological Center’s spectral statistical interpolation analysis scheme,” Mon. Weather Rev. 120, 1747–1763 (1992).

    Article  Google Scholar 

  70. P. Courtier, E. Andersson, W. Heckley, J. Pailleux, D. Vasiljevic, M. Hamrud, A. Hollingsworth, F. Rabier, and M. Fisher, “The ECMWF implementation of three-dimensional variational assimilation (3D-Var). I. Formulation,” Q. J. R. Meteorol. Soc. 124, 1783–1807 (1998).

    Google Scholar 

  71. P. Courtier, J. N. Thepaut, and A. Hollingsworth, “A strategy for operational implementation of 4D-Var, using an incremental approach,” Q. J. R. Meteorol. Soc. 120, 1389–1408 (1994).

    Article  Google Scholar 

  72. K. Ide, P. Courtier, M. Ghil, and A. C. Lorenc, “Unified notation for data assimilation: Operational, sequential and variational,” J. Meteorol. Soc. Jpn. 75, 181–189 (1997).

    Article  Google Scholar 

  73. K. Mogensen, M. A. Balmaseda, A. T. Weaver, M. Martin, and A. Vidard, “NEMOVAR: a variational data assimilation system for the NEMO ocean model,” ECMWF Tech. Mem., No. 120 (2009).

  74. Yu. G. Evtushenko, E. S. Zasukhina, and V. I. Zubov, “Numerical optimization of solutions to Burgers problem by means of boundary conditions,” Comput. Math. Math. Phys. 37 (12), 1406–1414 (1997).

    Google Scholar 

  75. V. M. Ipatova, Data Assimilation for an Ocean General Circulation Model in the quasi-geostrophic approximation (VINITI, Moscow, 1992), No. 2333-V92 [in Russian].

  76. V. I. Agoshkov and V. M. Ipatova, “Solvability of the observation data assimilation problem in the three-dimensional model of ocean dynamics,” Differ. Equations 43 (8), 1088–1100 (2007).

    Article  Google Scholar 

  77. V. I. Agoshkov and V. M. Ipatova, “Existence theorems for a three-dimensional ocean dynamics model and a data assimilation problem,” Dokl. Math. 75 (1), 28–30 (2007).

    Article  Google Scholar 

  78. Z. Sirkes and E. Tziperman, “Finite difference of adjoint or adjoint of finite difference?,” Mon. Weather Rev. 125, 3373–3378 (1997).

    Article  Google Scholar 

  79. E. I. Parmuzin, V. P. Shutyaev, and N. A. Diansky, “Numerical solution of a variational data assimilation problem for a 3D ocean thermohydrodynamics model with a nonlinear vertical heat exchange,” Russ. J. Numer. Anal. Math. Modell. 22 (2), 177–198 (2007).

    Article  Google Scholar 

  80. F.-X. Le Dimet and I. Charpentier, “Méthodes de second ordre en assimilation de données,” in Équations aux Dérivées Partielles et Applications (Articles dédiés à Jacques-Louis Lions) (Elsevier, Paris, 1998), pp. 623–639.

    Google Scholar 

  81. A. S. Lawless, N. K. Nichols, and S. P. Balloid, “A comparison of two methods for developing the linearization of a shallow-water model,” Q. J. R. Meteorol. Soc. 129, 1237–1254 (2003).

    Article  Google Scholar 

  82. W. C. Chao and L. -P. Chang, “Development of a four-dimensional variational analysis system using the adjoint method at GLA Part I. Dynamics,” Mon. Weather Rev. 120, 1661–1672 (1992).

    Article  Google Scholar 

  83. R. Giering and T. Kaminski, “Recipes for adjoint code constructions,” ACM Trans. Math. Software 24, 437–474 (1998).

    Article  Google Scholar 

  84. M. B. Giles and N. A. Pierce, “An introduction to the adjoint approach to design,” Flow, Turbul. Combust. 65, 393–415 (2000).

    Article  Google Scholar 

  85. F.-X. Le Dimet and V. P. Shutyaev, “On deterministic error analysis in variational data assimilation,” Nonlinear Processes Geophys. 12, 481–490 (2005).

    Article  Google Scholar 

  86. I. Gejadze, F.-X. Le Dimet, and V. Shutyaev, “On analysis error covariances in variational data assimilation,” SIAM J. Sci. Comput. 30 (4), 1847–1874 (2008).

    Article  Google Scholar 

  87. V. P. Shutyaev and E. I. Parmuzin, “Some algorithms for studying solution sensitivity in the problem of variational assimilation of observation data for a model of ocean thermodynamics,” Russ. J. Numer. Anal. Math. Modell. 24 (2), 145–160 (2009).

    Google Scholar 

  88. V. I. Agoshkov, E. I. Parmuzin, and V. P. Shutyaev, “Observational data assimilation in the problem of Black Sea circulation and sensitivity analysis of its solution,” Izv., Atmos. Ocean. Phys. 49 (6), 592–602 (2013).

    Article  Google Scholar 

  89. V. P. Shutyaev, F.-X. Le Dimet, V. I. Agoshkov, and E. I. Parmuzin, “Sensitivity of functionals in problems of variational assimilation of observational data,” Izv., Atmos. Ocean. Phys. 51 (3), 342–350 (2015).

    Article  Google Scholar 

  90. V. P. Shutyaev and E. I. Parmuzin, “Studying the sensitivity of the optimal solution of the variational data assimilation problem for the Baltic Sea thermodynamics model,” Russ. Meteorol. Hydrol. 40 (6), 411–419 (2015).

    Article  Google Scholar 

  91. F.-X. Le Dimet, V. Shutyaev, and E. I. Parmuzin, “Sensitivity of functionals with respect to observations in variational data assimilation,” Russ. J. Numer. Anal. Math. Modell. 31 (2), 81–91 (2016).

    Article  Google Scholar 

  92. V. B. Zalesny, V. I. Agoshkov, V. P. Shutyaev, F. Le Dimet, and V. O. Ivchenko, “Numerical modeling of ocean hydrodynamics with variational assimilation of observational data,” Izv., Atmos. Ocean. Phys. 52 (4), 431–442 (2016).

    Article  Google Scholar 

  93. A. Lorenc, “The potential of the ensemble Kalman filter for NWP—a comparison with 4D-Var,” Q. J. R. Meteorol. Soc. 129, 3183–3203 (2003).

    Article  Google Scholar 

  94. E. Kalnay, H. Li, T. Miyoshi, S.-C. Yang, J. Ballabrera-Poy, “4D-Var or ensemble Kalman filter?,” Tellus, Ser. A 59, 758–773 (2007).

    Article  Google Scholar 

  95. A. Caya, J. Sun, and C. Snyder, “A Comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation,” Mon. Weather Rev. 133 (11), 3081–3094 (2005).

    Article  Google Scholar 

  96. N. Gustafsson, “Discussion on “4D-Var Or EnKF”,” Tellus, Ser. A 59, 774–777 (2007).

    Google Scholar 

  97. E. J. Fertig, J. Harlim, and B. R. Hunt, “A comparative study of 4D-VAR and a 4D ensemble Kalman filter: Perfect model simulations with Lorenz-96,” Tellus, Ser. A 59, 96–100 (2007).

    Article  Google Scholar 

  98. M. Buehner, P. Houtekamer, C. Charette, H. Mitchell, and B. He, “Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I. Description and single-observation experiments,” Mon. Weather Rev. 138, 1550–1566 (2010).

    Article  Google Scholar 

  99. D. Fairbairn, S. R. Pring, A. C. Lorenc, and I. Roulstone, “A comparison of 4DVar with ensemble data assimilation methods,” Q. J. R. Meteorol. Soc. 140, 281–294 (2014).

    Article  Google Scholar 

  100. X. Tian, J. Xie, and A. Dai, “An ensemble-based explicit 4D-Var assimilation method,” J. Geophys. Res. 113, D21124 (2008).

    Article  Google Scholar 

  101. F. Q. Zhang, M. Zhang, and J. A. Hansen, “Coupling ensemble Kalman filter with four dimensional variational data assimilation,” Adv. Atmos. Sci. 26 (1), 1–8 (2009).

    Article  Google Scholar 

  102. A. M. Clayton, A. C. Lorenc, and D. M. Barker, “Operational implementation of a hybrid ensemble/4D-Var global data assimilation at the Met Office,” Q. J. R. Meteorol. Soc. 139, 1445–1461 (2013).

    Article  Google Scholar 

  103. N. Gustafsson, J. Bojarova, and O. Vignes, “A hybrid variational ensemble data assimilation for the HIgh Resolution Limited Area Model (HIRLAM),” Nonlinear Processes Geophys. 21, 303–323 (2014).

    Article  Google Scholar 

  104. M. Bonavita, E. Hólm, L. Isaksen, and M. Fisher, “The evolution of the ECMWF hybrid data assimilation system,” Q. J. R. Meteorol. Soc. 142, 287–303 (2016).

    Article  Google Scholar 

  105. C. Liu, Q. Xiao, and B. Wang, “An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test,” Mon. Weather Rev. 136, 3363–3373 (2008).

    Article  Google Scholar 

  106. C. Liu, Q. Xiao, and B. Wang, “An ensemble-based four-dimensional variational data assimilation scheme. Part II: Observing system simulation experiments with Advanced Research WRF (ARW),” Mon. Weather Rev. 137, 1687–1704 (2009).

    Article  Google Scholar 

  107. C. Liu and Q. Xiao, “An ensemble-based four-dimensional variational data assimilation scheme. Part III: Antarctic applications with advanced WRF using real data,” Mon. Weather Rev. 141, 2721–2739 (2013).

    Article  Google Scholar 

  108. G. Desroziers, J.-T. Camino, and L. Berre, “4DEnVar: Link with 4D state formulation of variational assimilation and different possible implementations,” Q. J. R. Meteorol. Soc. 140, 2097–2110 (2014).

    Article  Google Scholar 

  109. N. Gustafsson and J. Bojarova, “Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM),” Nonlinear Processes Geophys. 21, 745–762 (2014).

    Article  Google Scholar 

  110. M. Asch, M. Bocquet, and M. Nodet, Data Assimilation: Methods, Algorithms, and Applications (SIAM, Philadelphia, 2016).

    Book  Google Scholar 

  111. I. Gejadze, F.-X. Le Dimet, and V. P. Shutyaev, “On optimal solution error covariances in variational data assimilation problems,” J. Comput. Phys. 229, 2159–2178 (2010).

    Article  Google Scholar 

  112. I. Gejadze, V. P. Shutyaev, and F.-X. Le Dimet, “Analysis error covariance versus posterior covariance in variational data assimilation,” Q. J. R. Meteorol. Soc. 139, 1826–1841 (2013).

    Article  Google Scholar 

  113. I. Yu. Gejadze and V. P. Shutyaev, “On Gauss-verifiability of optimal solutions in variational data assimilation problems with nonlinear dynamics,” J. Comput. Phys. 280, 439–456 (2015).

    Article  Google Scholar 

  114. D. C. Liu and J. Nocedal, “On the limited memory BFGS Method for large scale minimization,” Math. Programming 45 (1–3), 503–528 (1989).

    Article  Google Scholar 

  115. F. Veerse, D. Auroux, and M. Fisher, “Limited-memory BFGS diagonal pre-conditioners for a data assimilation problem in meteorology,” Optim. Eng. 1, 323–339 (2000).

    Article  Google Scholar 

  116. Y. Trémolet, “Model-error estimation in 4D-Var,” Q. J. R. Meteorol. Soc. 133 (626), 1267–1280 (2007).

    Article  Google Scholar 

  117. A. Carrassi and S. Vannitsem, “Accounting for model error in variational data assimilation: A deterministic approach,” Mon. Weather Rev. 138, 875–883 (2010).

    Article  Google Scholar 

  118. D. Furbish, M. Y. Hussaini, F.-X. Le Dimet, et al., “On discretization error and its control in variational data assimilation,” Tellus, Ser. A 60, 979–991 (2008).

    Article  Google Scholar 

  119. A. K. Griffith and N. K. Nichols, “Adjoint methods in data assimilation for estimating model error,” Flow, Turbul. Combust. 65 (3–4), 469–488 (2000).

    Article  Google Scholar 

  120. A. Vidard, A. Piacentini, and F.-X. Le Dimet, “Variational data analysis with control of the forecast bias,” Tellus, Ser. A 56, 1–12 (2004).

    Article  Google Scholar 

  121. S. Akella and I. Navon, “Different approaches to model error formulation in 4D-Var: A study with high resolution advection schemes,” Tellus, Ser. A 61, 112–128 (2009).

    Article  Google Scholar 

  122. V. V. Penenko, “Variational methods of data assimilation and inverse problems for studying the atmosphere, ocean, and environment,” Numer. Anal. Appl. 2 (4), 341–351 (2009).

    Article  Google Scholar 

  123. M. D. Tsyrulnikov, “Stochastic modelling of model errors: A simulation study,” Q. J. R. Meteorol. Soc. 131, 3345–3371 (2005).

    Article  Google Scholar 

  124. I. Gejadze, H. Oubanas, and V. Shutyaev, “Implicit treatment of model error using inflated observation-error covariance,” Q. J. R. Meteorol. Soc. 143, 2496–2508 (2017).

    Article  Google Scholar 

  125. V. Shutyaev, I. Gejadze, A. Vidard, and F.-X. Le Dimet, “Optimal solution error quantification in variational data assimilation involving imperfect models,” Int. J. Numer. Methods Fluids 83 (3), 276–290 (2017).

    Article  Google Scholar 

  126. A. M. Stuart, “Inverse problems: A Bayesian perspective,” Acta Numerica 9, 451–559 (2010).

    Article  Google Scholar 

Download references

ACKNOWLEDGMENTS

This study was supported in part by the Russian Science Foundation (Sections 1, 2, and 4), project no. 17-77-30001, and by the Russian Foundation for Basic Research (Section 3), project no. 18-01-00267.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. P. Shutyaev.

Additional information

Translated by O. Pismenov

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shutyaev, V.P. Methods for Observation Data Assimilation in Problems of Physics of Atmosphere and Ocean. Izv. Atmos. Ocean. Phys. 55, 17–31 (2019). https://doi.org/10.1134/S0001433819010080

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0001433819010080

Keywords:

Navigation