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Annual Review of Marine Science

Volume 7, 2015

Regional Ocean Data Assimilation

Abstract

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

Keyword(s): data assimilation, ecosystem modeling, ocean modeling
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2015-01-03
2024-04-16
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Literature Cited

  1. Ades M, van Leeuwen PJ. 2013. An exploration of the equivalent weights particle filter. Q. J. R. Meteorol. Soc. 139:820–40 [Google Scholar]
  2. Anderson JL. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Weath. Rev. 129:2884–903 [Google Scholar]
  3. Anderson JL. 2003. A local least squares framework for ensemble filtering. Mon. Weath. Rev. 131:634–42 [Google Scholar]
  4. Anderson JL, Hoar T, Raeder K, Liu H, Collins N. et al. 2009. The Data Assimilation Research Testbed: a community facility. Bull. Am. Meteorol. Soc. 90:1283–96 [Google Scholar]
  5. Arango HG, Levin JC, Curchitser EN, Zhang B, Moore AM. et al. 2010. Development of a hindcast/forecast model for the Philippine Archipelago. Oceanography 24:158–69 [Google Scholar]
  6. Barth A, Alvera-Azcrate A, Beckers JM, Rixen M, Vandenbulcke L. 2007. Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea. J. Mar. Syst. 65:41–59 [Google Scholar]
  7. Béal D, Brasseur P, Brankart JM, Ourmières Y, Verron J. 2010. Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis. Ocean Sci. 6:247–62 [Google Scholar]
  8. Bell MJ, Forbes RM, Hines A. 2000. Assessment of the FOAM global data assimilation system for real-time operational ocean forecasting. J. Mar. Syst. 25:1–22 [Google Scholar]
  9. Bennett AF. 1992. Inverse Methods in Physical Oceanography Cambridge, UK: Cambridge Univ. Press
  10. Bennett AF. 2002. Inverse Modeling of the Ocean and Atmosphere Cambridge, UK: Cambridge Univ. Press
  11. Berline L, Brankart JM, Brasseur P, Ourmières Y, Verron J. 2007. Improving the physics of a coupled physical–biogeochemical model of the North Atlantic through data assimilation: impact on the ecosystem. J. Mar. Syst. 64:153–72 [Google Scholar]
  12. Bertino L, Lister K. 2008. The TOPAZ monitoring and prediction system for the Atlantic and Arctic Oceans. J. Oper. Oceanogr. 1:15–18 [Google Scholar]
  13. Bishop CH, Etherton BJ, Majumdar SJ. 2001. Adaptive sampling with ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Weath. Rev. 129:420–36 [Google Scholar]
  14. Brasseur P, Bahurel P, Bertino L, Birol F, Brankart JM. et al. 2005. Data assimilation for marine monitoring and prediction: the MERCATOR operational assimilation systems and the MERSEA developments. Q. J. R. Meteorol. Soc. 131:3561–82 [Google Scholar]
  15. Broquet G, Edwards CA, Moore AM, Powell BS, Veneziani M, Doyle JD. 2009. Application of 4D-variational data assimilation to the California Current System. Dyn. Atmos. Oceans 48:69–92 [Google Scholar]
  16. Broquet G, Moore AM, Arango HG, Edwards CA. 2011. Corrections to ocean surface forcing in the California Current System using 4D-variational data assimilation. Ocean Model. 36:116–32 [Google Scholar]
  17. Brusdal K, Brankart JM, Halberstadt G, Evensen G, Brasseur P. et al. 2003. A demonstration of ensemble-based assimilation methods with a layered OGCM from the perspective of operational ocean forecasting systems. J. Mar. Syst. 40–41:253–89 [Google Scholar]
  18. Burgers G, van Leeuwen PJ, Evensen G. 1998. On the analysis scheme in the ensemble Kalman filter. Mon. Weath. Rev. 126:1719–24 [Google Scholar]
  19. Campbell JW. 1995. The lognormal distribution as a model for bio-optical variability in the sea. J. Geophys. Res. 100:13237–54 [Google Scholar]
  20. Cane MA, Kaplan A, Miller RN, Tang B, Hackert EC, Busalacchi AJ. 1996. Mapping tropical Pacific sea level: data assimilation via a reduced state Kalman filter. J. Geophys. Res. 101:22599–617 [Google Scholar]
  21. Carton JA, Giese BS. 2008. A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon. Weather Rev. 136:2999–3017 [Google Scholar]
  22. Chapman DC. 1985. Numerical treatment of cross-shelf open boundaries in a barotropic coastal ocean model. J. Phys. Oceanogr. 15:1060–75 [Google Scholar]
  23. Chassignet EP, Hurlburt HE, Metzger EJ, Smedstad OM, Cummings JA. et al. 2009. US GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM). Oceanography 22:264–75 [Google Scholar]
  24. Chen K, He R, Powell BS, Gawarkiewicz G, Moore AM, Arango HG. 2014. Data assimilative modeling investigation of Gulf Stream Warm Core Ring interaction with continental shelf and slope circulation. J. Geophys. Res. 1195968–91
  25. Chin TM, Haza AC, Mariano AJ. 2001. A reduced-order information filter for multi-layer shallow water models: profiling and assimilation of sea surface height. J. Atmos. Ocean. Technol. 19:517–33 [Google Scholar]
  26. Chin TM, Mariano AJ, Chassignet EP. 1999. Spatial regression and multi-scale approximations for sequential data assimilation in ocean models. J. Geophys. Res. 104:7991–8014 [Google Scholar]
  27. Ciavatta S, Torres R, Saux-Picart S, Allen JI. 2011. Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?. J. Geophys. Res. 116:C12043 [Google Scholar]
  28. Cohn SE, da Silva A, Guo J, Sienkiewicz M, Lamich D. 1998. Assessing the effects of data selection with the DAO physical-space statistical analysis system. Mon. Weath. Rev. 126:2913–26 [Google Scholar]
  29. Cohn SE, Todling R. 1996. Approximate data assimilation schemes for stable and unstable dynamics. J. Meteorol. Soc. Jpn. 74:63–75 [Google Scholar]
  30. Counillon F, Bertino L. 2009. Ensemble Optimal Interpolation: multivariate properties in the Gulf of Mexico. Tellus A 61:296–308 [Google Scholar]
  31. Counillon F, Sakov P, Bertino L. 2009. Application of a hybrid EnKF-OI to ocean forecasting. Ocean Sci. 5:389–401 [Google Scholar]
  32. Courtier P, Thépaut JN, Hollingsworth A. 1994. A strategy for operational implemenation of 4D-Var using an incremental approach. Q. J. R. Meteorol. Soc. 120:1367–88 [Google Scholar]
  33. Cummings JA, Bertino L, Brasseur P, Fukumori I, Kamachi M. et al. 2009. Ocean data assimilation systems for GODAE. Oceanography 22:396–109 [Google Scholar]
  34. Daley R. 1991. Atmospheric Data Analysis Cambridge, UK: Cambridge Univ. Press
  35. De Mey P, Robinson AR. 1987. Assimilation of altimeter eddy fields in a limited-area quasi-geostrophic model. J. Phys. Oceanogr. 17:2280–93 [Google Scholar]
  36. Debreu L, Marchesiello P, Penven P, Cambon G. 2012. Two-way nesting in split-explicit ocean models: algorithms, implementation and validation. Ocean Model. 49–50:1–21 [Google Scholar]
  37. Dobricic S, Pinardi N. 2008. An oceanographic three-dimensional variational data assimilation scheme. Ocean Model. 22:89–105 [Google Scholar]
  38. Dobricic S, Wikle CK, Milliff RF, Pinardi N, Berliner LB. 2014. Assimilation of oceanographic observations with estimates of vertical background-error covariances by a Bayesian hierarchical model. Q. J. R. Meteorol. Soc. In press. doi: 10.1002/qj.2348
  39. Doron M, Brasseur P, Brankart JM, Losa SN, Melet A. 2013. Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical-biogeochemical model. J. Mar. Syst. 117–118:81–95 [Google Scholar]
  40. Dowd M. 2006. A sequential Monte Carlo approach for marine ecological prediction. Environmetrics 17:435–55 [Google Scholar]
  41. Dowd M. 2011. Estimating parameters for a stochastic dynamic marine ecological system. Environmetrics 22:501–15 [Google Scholar]
  42. Egbert GD, Bennett AF, Foreman MGG. 1994. TOPEX/POSEIDON tides estimated using a global inverse method. J. Geophys. Res. 99:24,821–52 [Google Scholar]
  43. Enomoto T, Miyoshi T, Moteki Q, Inoue J, Hattori M. et al. 2013. Observing-system research and ensemble data assimilation at JAMSTEC. See Park & Xu 2013 509–26
  44. Evensen G. 1992. Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model. J. Geophys. Res. 97:17905–24 [Google Scholar]
  45. Evensen G. 2006. Data Assimilation: The Ensemble Kalman Filter Berlin: Springer-Verlag
  46. Farrara JD, Chao Y, Li Z, Wang X, Jin X. et al. 2013. A data-assimilative ocean forecasting system for the Prince William sound and an evaluation of its performance during sound Predictions 2009. Cont. Shelf Res. 63:Suppl.S193–208 [Google Scholar]
  47. Farrell BF, Ioannou PJ. 2001. State estimation using a reduced-order Kalman filter. J. Geophys. Res. 58:3666–80 [Google Scholar]
  48. Fiechter J, Broquet G, Moore AM, Arango HG. 2011. A data assimilative, coupled physical-biological model for the Coastal Gulf of Alaska. Dyn. Atmos. Oceans 52:95–118 [Google Scholar]
  49. Fiechter J, Moore AM. 2012. Iron limitation impact on eddy-induced ecosystem variability in the coastal Gulf of Alaska. J. Mar. Syst. 92:1–15 [Google Scholar]
  50. Flather RA. 1976. A tidal model of the northwest European continental shelf. Mem. Soc. R. Sci. Liege 10:141–64 [Google Scholar]
  51. Fletcher SJ, Zupanski M. 2006. A data assimilation method for log-normally distributed observational errors. Q. J. R. Meteorol. Soc. 132:2505–19 [Google Scholar]
  52. Fontana C, Grenz C, Pinazo C. 2010. Sequential assimilation of a year-long time-series of SeaWiFS chlorophyll data into a 3D biogeochemical model on the French Mediterranean coast. Cont. Shelf Res. 30:1761–71 [Google Scholar]
  53. Fontana C, Grenz C, Pinazo C, Marsaleix P, Diaz F. 2009. Assimilation of SeaWiFS chlorophyll data into a 3D-coupled physical-biogeochemical model applied to a freshwater-influenced coastal zone. Cont. Shelf Res. 29:1397–409 [Google Scholar]
  54. Friedrichs MA, Hood RR, Wiggert JD. 2006. Ecosystem model complexity versus physical forcing: quantification of their relative impact with assimilated Arabian sea data. Deep-Sea Res. II 53:576–600 [Google Scholar]
  55. Fukumori I, Malanotte-Rizzoli P. 1995. An approximate Kalman filter for ocean data assimilation: an example with an idealized Gulf Stream model. J. Geophys. Res. 100:6777–93 [Google Scholar]
  56. Gandin LS. 1965. Objective Analysis of Meteorological Fields Jerusalem: Israel Program Sci. Transl.
  57. Gauthier P, Courtier P, Moll P. 1993. Assimilation of simulated wind lidar data with a Kalman filter. Mon. Weath. Rev. 121:1803–20 [Google Scholar]
  58. Gebbie G, Heimbach P, Wunsch C. 2006. Strategies for nested and eddy-permitting state estimation. J. Geophys. Res. 111:C10073 [Google Scholar]
  59. Ghil M, Malanotte-Rizzoli P. 1991. Data assimilation in meteorology and oceanography. Advances in Geophysics 33 R Dmowska, B Saltzman 141–266 San Diego, CA: Academic [Google Scholar]
  60. Giering R, Kaminski T. 1998. Recipes for adjoint code construction. ACM Trans. Math. Softw. 24:437–74 [Google Scholar]
  61. Gopalakrishnan G, Cornuelle BD, Hoteit I, Rudnick DL, Owens WB. 2013. State estimates and forecasts of the loop current in the Gulf of Mexico using the MITgcm and its adjoint. J. Geophys. Res. 118:3292–314 [Google Scholar]
  62. Gregg WW. 2008. Assimilation of SeaWiFS ocean chlorophyll data into a three-dimensional global ocean model. J. Mar. Syst. 69:205–25 [Google Scholar]
  63. Greybush SJ, Kalnay E, Miyoshi T, Ide K, Hunt BR. 2011. Balance and ensemble Kalman filter localization techniques. Mon. Weath. Rev. 139:511–22 [Google Scholar]
  64. Haley PJ Jr, Lermusiaux PFJ, Robinson AR, Leslie WG, Logutov O. et al. 2009. Forecasting and reanalysis in the Monterey Bay/California Current region for the Autonomous Ocean Sampling Network-II Experiment. Deep-Sea Res. II 56:127–48 [Google Scholar]
  65. Halliwell GR, Srinivasan A, Kourafalou V, Yang H, Willey D. et al. 2014. Rigorous evaluation of a fraternal twin ocean OSSE system for the open Gulf of Mexico. J. Atmos. Ocean. Technol. 31:105–30 [Google Scholar]
  66. Hamill TM, Whitaker JS. 2011. What constrains spread growth in forecasts initialized from ensemble Kalman filters?. Mon. Weath. Rev. 139:117–31 [Google Scholar]
  67. Hamill TM, Whitaker JS, Anderson JL, Snyder C. 2009. Comments on “Sigma-point Kalman filter data assimilation methods for strongly nonlinear systems.”. J. Atmos. Sci. 66:3498–500 [Google Scholar]
  68. Hamill TM, Whitaker JS, Snyder C. 2001. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Weath. Rev. 129:2776–90 [Google Scholar]
  69. Hooten MB, Leeds WB, Fiechter J, Wikle CK. 2011. Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models. J. Agric. Biol. Environ. Stat. 16:475–94 [Google Scholar]
  70. Hoteit I, Cornuelle B, Heimbach P. 2010. An eddy-permitting, dynamically consistent adjoint-based assimilation system for the tropical Pacific: hindcast experiments in 2000. J. Geophys. Res. 115:C03001 [Google Scholar]
  71. Hoteit I, Cornuelle B, Kim SY, Forget G, Kohl A, Terrill E. 2009. Assessing 4D-VAR for dynamical mapping of coastal high-frequency radar in San Diego. Dyn. Atmos. Oceans 48:175–97 [Google Scholar]
  72. Hoteit I, Hoar T, Gopalakrishnan G, Anderson J, Collins N. et al. 2012. A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico. Dyn. Atmos. Oceans 63:1–23 [Google Scholar]
  73. Hoteit I, Pham DT, Blum J. 2002. A simplified reduced order Kalman filtering and application to altimetric data assimilation in tropical Pacific. J. Mar. Syst. 36:101–27 [Google Scholar]
  74. Hoteit I, Triantafyllou G, Petihakis G. 2005. Efficient data assimilation into a complex, 3-D physical-biogeochemical model using partially-local Kalman filters. Ann. Geophys. 23:3171–85 [Google Scholar]
  75. Houtekamer PL, Mitchell HL. 2005. Ensemble Kalman filtering. Q. J. R. Meteorol. Soc. 131:3269–89 [Google Scholar]
  76. Houtekamer PL, Mitchell HL, Deng X. 2009. Model error representation in an operational ensemble Kalman filter. Mon. Weath. Rev. 137:2126–43 [Google Scholar]
  77. Hu J, Fennel K, Mattern JP, Wilkin JL. 2012. Data assimilation with a local ensemble Kalman filter applied to a three-dimensional biological model of the Middle Atlantic Bight. J. Mar. Syst. 94:145–56 [Google Scholar]
  78. Janjić T, Nerger L, Albertella A, Schröter J, Skachko S. 2011. On domain localization in ensemble-based Kalman filter algorithms. Mon. Weath. Rev. 139:2046–60 [Google Scholar]
  79. Jazwinski AH. 1970. Stochastic Processes and Filtering Theory San Diego, CA: Academic
  80. Kalman R. 1960. A new approach to linear filtering and prediction problems. Trans. ASME D 82:35–45 [Google Scholar]
  81. Keppenne C, Rienecker M. 2002. Initial testing of a massively parallel ensemble Kalman filter with the Poseidon Isopycnal Ocean General Circulation Model. Mon. Weath. Rev. 130:2951–65 [Google Scholar]
  82. Kim SB, Fukumori I, Lee T. 2006. The closure of the ocean mixed layer temperature budget using level-coordinate model fields. J. Atmos. Ocean. Technol. 23:840–53 [Google Scholar]
  83. Korres G, Nittis K, Perivoliotis L, Tsiaras K, Papadopoulos A. et al. 2010. Forecasting the Aegean Sea hydrodynamics within the POSEIDON-II operational system. J. Oper. Oceanogr. 38:37–49 [Google Scholar]
  84. Korres G, Triantafyllou G, Petihakis G, Raitsos D, Hoteit I. et al. 2012. A data assimilation tool for the Pagasitikos Gulf ecosystem dynamics: methods and benefits. J. Mar. Syst. 94:Suppl.S102–17 [Google Scholar]
  85. Kurapov AL, Egbert GD, Allen JS, Miller RN. 2009. Representer-based analyses in the coastal upwelling system. Dyn. Atmos. Oceans 48:198–218 [Google Scholar]
  86. Kurapov AL, Foley D, Strub PT, Egbert GD, Allen JS. 2011. Variational assimilation of satellite observations in a coastal ocean model off Oregon. J. Geophys. Res. 116:C05006 [Google Scholar]
  87. Leeds WB, Wikle CK, Fiechter J, Brown J, Milliff RF. 2013. Modeling 3-D spatio–temporal biogeochemical processes with a forest of 1-D statistical emulators. Environmetrics 24:1–12 [Google Scholar]
  88. Lermusiaux P, Robinson A. 1999. Data assimilation via error subspace statistical estimation. Part I: theory and schemes. Mon. Weath. Rev. 127:1385–407 [Google Scholar]
  89. Li Z, Chao Y, McWilliams JC, Ide K. 2008. A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System. J. Atmos. Ocean. Technol. 25:2074–90 [Google Scholar]
  90. Li Z, Navon IM. 2001. Optimality of variational data assimilation and its relationship with the Kalman filter and smoother. Q. J. R. Meteorol. Soc. 127:661–83 [Google Scholar]
  91. Lima JAM, Martins RP, Tanajura CAS, de Moraes Paiva A, Cirano M. et al. 2013. Design and implementation of the Oceanographic Modeling and Observation Network (REMO) for operational oceanography and ocean forecasting. Rev. Bras. Geofìs. 31:209–28 [Google Scholar]
  92. Lorenc A. 1992. Iterative analysis using covariance functions and filters. Q. J. R. Meteorol. Soc. 118:569–91 [Google Scholar]
  93. Losa SN, Kivman GA, Schröter J, Wenzel M. 2003. Sequential weak constraint parameter estimation in an ecosystem model. J. Mar. Syst. 43:31–49 [Google Scholar]
  94. Luo X, Hoteit I. 2011. Robust ensemble filtering and its relation to covariance inflation in the ensemble Kalman filter. Mon. Weath. Rev. 139:3938–53 [Google Scholar]
  95. Madec G, Delecluse P, Imbard M, Lévy C. 1998. OPA 8.1 Ocean General Circulation Model Reference Manual Notes Pôle Modél. 11 Paris: Inst. Pierre Simon Laplace Sci. Environ. Glob.
  96. Malanotte-Rizzoli P. 1996. Modern Approaches to Data Assimilation in Ocean Modeling Elsevier Oceanogr. Ser. 61 Amsterdam: Elsevier
  97. Marchesiello P, McWilliams JC, Shchepetkin A. 2001. Open boundary conditions for long-term integration of regional oceanic models. Ocean Model. 3:1–20 [Google Scholar]
  98. Mason E, Molemaker J, Shchepetkin AF, Colas F, McWilliams JC, Sangrà P. 2010. Procedures for offline grid nesting in regional ocean models. Ocean Model. 35:1–15 [Google Scholar]
  99. Matear RJ. 1995. Parameter optimization and analysis of ecosystem models using simulated annealing: a case study at Station P. J. Mar. Res. 53:571–607 [Google Scholar]
  100. Mattern JP, Dowd M, Fennel K. 2010. Sequential data assimilation applied to a physical-biological model for the Bermuda Atlantic time series station. J. Mar. Syst. 79:144–56 [Google Scholar]
  101. Mattern JP, Dowd M, Fennel K. 2013. Particle filter-based data assimilation for a three-dimensional biological ocean model and satellite observations. J. Geophys. Res. 118:2746–60 [Google Scholar]
  102. Mattern JP, Fennel K, Dowd M. 2012. Estimating time-dependent parameters for a biological ocean model using an emulator approach. J. Mar. Syst. 96–97:32–47 [Google Scholar]
  103. Matthews D, Powell BS, Janeković I. 2012. Analysis of four-dimensional variational state estimation of the Hawaiian waters. J. Geophys. Res. 117:C03013 [Google Scholar]
  104. Miyoshi T. 2011. The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Mon. Weath. Rev. 139:1519–35 [Google Scholar]
  105. Moore AM, Arango HG, Broquet G, Powell BS, Weaver AT, Zavala-Garay J. 2011a. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems. Part I: system overview and formulation. Prog. Oceanogr. 91:34–49 [Google Scholar]
  106. Moore AM, Arango HG, Broquet G, Edwards CA, Veneziani M. et al. 2011b. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems. Part II: performance and application to the California Current System. Prog. Oceanogr. 91:50–73 [Google Scholar]
  107. Moore AM, Edwards CA, Fiechter J, Drake P, Arango HG. et al. 2013. A prototype for an operational regional ocean data assimilation system. See Park & Xu 2013 346–66
  108. Nerger L, Hiller A, Schröter J. 2005. A comparison of error subspace Kalman filters. Tellus A 57:715–35 [Google Scholar]
  109. Ngodock H, Carrier M. 2013. A weak constraint 4D-Var assimilation system for the Navy Coastal Ocean Model using the representer method. See Park & Xu 2013 367–90
  110. Oke PR, Allen JS, Miller RN, Egbert GD, Kosro PM. 2008a. Assimilation of surface velocity data into a primitive equation coastal ocean model. J. Geophys. Res. 107:3122 [Google Scholar]
  111. Oke PR, Brassington GB, Griffin DA, Schiller A. 2008b. The Bluelink Ocean Data Assimilation System (BODAS). Ocean Model. 21:46–70 [Google Scholar]
  112. Oliger J, Sundström A. 1978. Theoretical and practical aspects of some initial boundary value problems in fluid dynamics. SIAM J. Appl. Math. 35:419–46 [Google Scholar]
  113. Orlanski I. 1976. A simple boundary condition for unbounded hyperbolic flows. J. Comput. Phys. 21:251–69 [Google Scholar]
  114. Ourmières Y, Brasseur P, Lévy M, Brankart JM, Verron J. 2009. On the key role of nutrient data to constrain a coupled physical-biogeochemical assimilative model of the North Atlantic Ocean. J. Mar. Syst. 75:100–15 [Google Scholar]
  115. Paduan JD, Washburn L. 2013. High-frequency radar observations of ocean surface currents. Annu. Rev. Mar. Sci. 5:115–36 [Google Scholar]
  116. Pan C, Yaremchuk M, Nechaev D, Ngodock H. 2011. Variational assimilation of glider data in Monterey Bay. J. Mar. Res. 69:331–46 [Google Scholar]
  117. Park SK, Xu L. 2013. Data Assimilation for Atmospheric, Oceanic and Hydrological Applications 2 Berlin: Springer-Verlag
  118. Pelc JS. 2013. Data assimilation for marine ecosystem models PhD Thesis, Delft Univ. Technol., Delft, Neth.
  119. Pelc JS, Ehouarn S, Bertino L, El Serafy G, Heemink AW. 2012. Application of model reduced 4D-Var to a 1D ecosystem model. Ocean Model. 57–58:16 [Google Scholar]
  120. Pham DT. 2001. Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon. Weath. Rev. 129:1194–207 [Google Scholar]
  121. Pham DT, Verron J, Roubaud MC. 1998. Singular evolutive Kalman filter with EOF initialization for data assimilation in oceanography. J. Mar. Syst. 16:323–40 [Google Scholar]
  122. Pinardi N, Allen I, Demirov E, De Mey P, Korres G. et al. 2003. The Mediterranean ocean forecasting system: first phase of implementation (1998–2001). Ann. Geophys. 21:3–20 [Google Scholar]
  123. Powell BS, Arango HG, Moore AM, Di Lorenzo E, Milliff RF, Foley D. 2008. 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS). Ocean Model. 23:130–45 [Google Scholar]
  124. Powell BS, Moore AM, Arango HG, Lorenzo ED, Milliff RF, Leben R. 2009. Near real-time assimilation and prediction in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS). Dyn. Atmos. Oceans 48:46–68 [Google Scholar]
  125. Robinson AR, Carton JA, Mooers CNK, Walstad LJ, Carter EF. et al. 1984. A real-time dynamical forecast of ocean synoptic/mesoscale eddies. Nature 309:781–83 [Google Scholar]
  126. Robinson AR, Carton JA, Pinardi N, Mooers CNK. 1986. Dynamical forecasting and dynamical interpolation: an experiment in the California Current. J. Phys. Oceanogr. 16:1561–79 [Google Scholar]
  127. Robinson AR, Lermusiaux PFJ, Sloan NQ III. 1998. Data assimilation. The Sea 10 The Global Coastal Ocean: Processes and Method KH Brink, AR Robinson 541–94 New York: Wiley & Sons [Google Scholar]
  128. Robinson AR, Leslie WG. 1985. Estimation and prediction of ocean eddy fields. Prog. Oceanogr. 14:485–510 [Google Scholar]
  129. Rudnick DL, Davis RE, Eriksen CC, Fratantoni DM, Perry MJ. 2004. Underwater gliders for ocean research. Mar. Technol. Soc. J. 38:73–84 [Google Scholar]
  130. Sakov P, Bertino L. 2011. Relation between two common localisation methods for the EnKF. Comput. Geosci. 15:225–37 [Google Scholar]
  131. Sasaki Y. 1970. Some basic formulations in numerical variational analysis. Mon. Weather Rev. 98:875–83 [Google Scholar]
  132. Shulman I, Frolov S, Anderson S, Penta B, Gould R. et al. 2013. Impact of bio-optical data assimilation on short-term coupled physical, bio-optical model predictions. J. Geophys. Res. 118:2215–30 [Google Scholar]
  133. Shuman FG. 1989. History of numerical weather prediction at the National Meteorological Center. Weath. Forecast. 4:286–96 [Google Scholar]
  134. Simon E, Bertino L. 2009. Application of the Gaussian anamorphosis to assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic with the EnKF: a twin experiment. Ocean Sci. 5:495–510 [Google Scholar]
  135. Smedstad OM, Hurlburt HE, Metzger EJ, Rhodes RC, Shriver JF. et al. 2003. An operational eddy-resolving 1/16° global ocean nowcast/forecast system. J. Mar. Syst. 40–41:341–61 [Google Scholar]
  136. Song H, Edwards CA, Moore AM, Fiechter J. 2012. Incremental four-dimensional variational data assimilation of positive-definite oceanic variables using a logarithm transformation. Ocean Model. 54–55:1–17 [Google Scholar]
  137. Srinivasan A, Chassignet EP, Bertino L, Brankart JM, Brasseur P. et al. 2001. A comparison of sequential assimilation schemes for ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM): twin experiments with static forecast error covariances. Ocean Model. 37:85–111 [Google Scholar]
  138. Stammer D, Wunsch C, Giering R, Eckert C, Heimbach P. et al. 2003. Volume, heat, and freshwater transports of the global ocean circulation 1993–2000, estimated from a general circulation model constrained by World Ocean Circulation Experiment (WOCE) data. J. Geophys. Res. 108:3007–29 [Google Scholar]
  139. Tanajura CAS, Costa FB, da Silva RR, Ruggiero GA, Daher VB. 2013. Assimilation of sea surface height anomalies into HYCOM with an optimal interpolation scheme over the Atlantic Ocean METAREA V. Rev. Bras. Geofìs. 31:257–70 [Google Scholar]
  140. Tippett M, Anderson J, Bishop C, Hamill T, Whitacker J. 2003. Ensemble square root filters. Mon. Weath. Rev. 131:1490–85 [Google Scholar]
  141. Todd RE, Rudnick DL, Mazloff MR, Davis RE, Cornuelle BD. 2011. Poleward flows in the southern California Current System: glider observations and numerical simulation. J. Geophys. Res. 116:C02026 [Google Scholar]
  142. Triantafyllou G, Korres G, Hoteit I, Petihakis G, Banks AC. 2007. Assimilation of ocean colour data into a biogeochemical flux model of the eastern Mediterranean Sea. Ocean Sci. 3:397–410 [Google Scholar]
  143. Verlaan M, Heemink AW. 1997. Tidal flow forecasting using reduced rank square root filters. Stoch. Hydrol. Hydraul. 11:349–68 [Google Scholar]
  144. Vermeulen PTM, Heemink AW. 2006. Model-reduced variational data assimilation. Mon. Weath. Rev. 134:2888–99 [Google Scholar]
  145. Verron J, Gourdeau L, Pham DT, Murtugudde R, Busalacchi AJ. 1999. An extended Kalman filter to assimilate satellite altimeter data into a nonlinear numerical model of the tropical Pacific: method and validation. J. Geophys. Res. 104:5441–58 [Google Scholar]
  146. Wang X, Bishop CH, Julier SJ. 2004. Which is better, an ensemble of positive–negative pairs or a centered spherical simplex ensemble?. Mon. Weath. Rev. 132:1590–605 [Google Scholar]
  147. Ward BA, Friedrichs MA, Anderson TR, Oschlies A. 2010. Parameter optimisation techniques and the problem of underdetermination in marine biogeochemical models. J. Mar. Syst. 81:34–43 [Google Scholar]
  148. Weaver A. 2013. Aspects of covariance modelling in variational ocean data assimilation HDR Thesis, Inst. Natl. Polytech. Toulouse, Toulouse, France
  149. Weaver A, Courtier P. 2001. Correlation modelling on the sphere using a generalized diffusion equation. Q. J. R. Meteorol. Soc. 127:1815–46 [Google Scholar]
  150. Whitaker JS, Hamill TM. 2002. Ensemble data assimilation without perturbed observations. Mon. Weath. Rev. 130:1913–24 [Google Scholar]
  151. Whitaker JS, Hamill TM. 2012. Evaluating methods to account for system errors in ensemble data assimilation. Mon. Weath. Rev. 140:3078–89 [Google Scholar]
  152. Wikle CK, Berliner LB. 2007. A Bayesian tutorial for data assimilation. Physica D 230:1–16 [Google Scholar]
  153. Wilkin JL, Hunter EJ. 2013. An assessment of the skill of real-time models of Mid-Atlantic Bight continental shelf circulation. J. Geophys. Res. 118:2919–33 [Google Scholar]
  154. Wunsch C. 1996. The Ocean Circulation Inverse Problem Cambridge, UK: Cambridge Univ. Press
  155. Wunsch C, Heimbach P, Ponte R, Fukumori I. ECCO-GODAE Consort. Memb 2009. The global general circulation of the ocean estimated by the ECCO-Consortium. Oceanography 22:288–103 [Google Scholar]
  156. Xu FH, Oey LY, Miyazawa Y, Hamilton P. 2013. Hindcasts and forecasts of Loop Current and eddies in the Gulf of Mexico using local ensemble transform Kalman filter and optimum-interpolation assimilation schemes. Ocean Model. 69:22–38 [Google Scholar]
  157. Yu P, Kurapov AL, Egbert GD, Allen JS, Kosro PM. 2012. Variational assimilation of HnF radar surface currents in a coastal ocean model off Oregon. Ocean Model. 49–50:86–104 [Google Scholar]
  158. Zavala-Garay J, Wilkin JL, Arango HG. 2012. Predictability of mesoscale variability in the East Australia Current given strong-constraint data assimilation. J. Phys. Oceanogr. 42:1402–20 [Google Scholar]
  159. Zavala-Garay J, Wilkin JL, Levin J. 2014. Data assimilation on coastal oceanography: IS4DVAR in the Regional Ocean Modelling System (ROMS). Advanced Data Assimilation for Geosciences É Blayo, M Bocquet, E Cosme, LF Cugliandolo Oxford, UK: Oxford Univ. Press In press [Google Scholar]
  160. Zhang WG, Wilkin JL, Arango HG. 2010a. Towards building an integrated observation and modeling system in the New York Bight using variational methods. Part I: 4DVAR data assimilation. Ocean Model. 35:119–33 [Google Scholar]
  161. Zhang WG, Wilkin JL, Levin JC. 2010b. Towards building an integrated observation and modeling system in the New York Bight using variational methods. Part II: representer-based observing system evaluation. Ocean Model. 35:134–45 [Google Scholar]
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Regional Ocean Data Assimilation
Annual Review of Marine Science 7, 21 (2015); https://doi.org/10.1146/annurev-marine-010814-015821
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