Journal of the korean society of oceanography

The Korean Society of Oceanography (한국해양학회)
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Data Assimilation for Oceanographic Application: A Brief Overview

  • Park, Seon-K. (Department of Environmental Science and Engineering, Ewha Womans University)
  • Published : 2003.06.01
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Abstract

In this paper, a brief overview on data assimilation is provided in the context of oceanographic application. The ocean data assimilation needs to ingest various types of data such as satellites and floats, thus essentially requires dynamically-consistent assimilation methods. For such purpose, sequential and variational approaches are discussed and compared. The major advantage of the Kalman filter (KF) is that it can forecast error covariances at each time step. However, for models with very large dimension of state vector, the KF Is exceedingly expensive and computationally less efficient than four-dimensional variational assimilation (4D-Var). For operational application, simplified 4D-Var schemes as well as ensemble KF may be considered.

Keywords

References

  1. J. Phys. Oceanogr. v.19 Initialization of equatorial waves in ocean models. Qnderson, D.L.T.;A.M. Moore https://doi.org/10.1175/1520-0485(1989)019<0116:IOEWIO>2.0.CO;2
  2. Ocean Circulation Models: Combining Data and Dynamics. Kluwer Anderxon, D.L.T.;J. Willebrand
  3. Bull. Amer. Meteor, Soc. v.67 Observing-systems simulation ewperiments: Past, Presint, and future. Atnold, C.P.;Jr;C.H. Dey. https://doi.org/10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2
  4. Mon. Wea. Rev. v.125 Am adjoint examination of anudging method for data assimilation. Bao, J.W.;R.M. Errico https://doi.org/10.1175/1520-0493(1997)125<1355:AAEOAN>2.0.CO;2
  5. J. Qppl. Meteor. v.3 A technique for maximizing details in numerical map anaoysis. Barnes, S.
  6. Inverse Methods in physical Oceanography. Bennett, A.F.
  7. J. Meteor. Soc. Japan v.75 no.1B Oceanic observations. Busalacdhi, A.J. https://doi.org/10.2151/jmsj1965.75.1B_131
  8. J. Phys. Oceanogr. v.14 A numerical model for low-frequency equatorial dynamics. Cane, M.A.;r.J. Patton https://doi.org/10.1175/1520-0485(1984)014<1853:ANMFLF>2.0.CO;2
  9. J. Geophys. Res. v.101 no.C10 Mapping tropical pacific sea level:data assimilation via a reduced state kplman filter. Cane, M.A.;A. Kaplan, R.N. Miller;B. Tang, E.C. Hackert;A.J. Busalacchi https://doi.org/10.1029/96JC01684
  10. An inverse ocean modeling system. Ocean Modeling v.3 Chua, B.S.;A.F. Bennett
  11. J. Geophys. Res. v.103 no.C4 Variational assimilation of Geosat altimeter data into a two-layer quasi-geo-strophic model over the Newfoundland ridge and basin. Cong, L.Z.;M. Ikeda;R.M. Hendry https://doi.org/10.1029/97JC00098
  12. J. Meteor. Soc. Japan v.75 no.1B Variational methods. Courtier, P. https://doi.org/10.2151/jmsj1965.75.1B_211
  13. Quaut. J. Roy. Meteor. Soc. v.120 A strategy for operational implementation of 4-DVAR using an incremental approach. Courtier, P.;J.-N. Uhepaut;A. Hollingsworth https://doi.org/10.1002/qj.49712051912
  14. Quart. J. Roy. Meteor. Soc v.124 the ECMWF implementation of three-dimensional variational assimilation (3D-Var). I: Formulation. courtier, P.;E. Andrtsson;W. Heckley, J. Pailleux; D. Vadiljevic;M. Hamrud;A. Hollingsworth;F. Ravier;M. Fisher
  15. Mon. Wea. Rev. v.87 An operational objective analysis system. Cressman, G.P. https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
  16. Atmospheric Data Analysis. Daley, R.
  17. J. Meteor. Soc. Japan v.75 no.1B Atmospheric data assimilation. Daley, R. https://doi.org/10.2151/jmsj1965.75.1B_319
  18. Quart. J. Roy. Maeteor. Soc. v.117 Simplification of the Kalama filter for meteorological data assimilation. Dee, D.P. https://doi.org/10.1002/qj.49711749806
  19. J.. Meteor. Soc. Japan v.75 no.1B Data assimilation at the oceanic mesoscale: A riview. De Mey, P. https://doi.org/10.2151/jmsj1965.75.1B_415
  20. J. Phys. Oceanogr v.19 A global data assimilation system. Cerber, J.C;A. Roshti https://doi.org/10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2
  21. Rev. geophys v.29 The emergence of concurrent high resolution physical and bio-optical measurements in the upper ocean and their applications. Dickey, T.D. https://doi.org/10.1029/91RG00578
  22. J. Mar. Systems v.41-41 Emerging ocean observations for interdisciplinary data assimilation systems. Dickey, T.D.
  23. J. Geopys. Res. v.99 no.C12 TOPEX/POSEIDON tides estimated using a global inverse model. Egbert, G.D.;A.F. Bennett;M.G.G. Foreman https://doi.org/10.1029/94JC01894
  24. J. Roy. Meteor. Soc. v.118 Four-dimensional data assimilation: comparison of variational and sequential algorithms. Ehrendorfer, M. https://doi.org/10.1002/qj.49711850605
  25. J. Geophys. res. v.97 no.C11 Using the extended Kalaman filter with a multilayer quasigeostrophic ocean model. Evensen, G.
  26. J. Geophys. Res. v.99 no.C5 Sequential data assimilation with a monlinearquasi-geostrophic model using Monte Carlo methods to forecast error statistics. Evensen, G. https://doi.org/10.1029/94JC00572
  27. J. Phys. Oceanogr. v.23 Fitting dynamic models to Geosat sea-level observations in the tropical Pacific ocean: Ⅱ. A linear, wind-driven model. Fu, L.L.;I. Fukumori; R.N. Miller https://doi.org/10.1175/1520-0485(1993)023<2162:FDMTTG>2.0.CO;2
  28. J. Geophys. Res. v.100 no.C12 Assimilation of Topex sea level measurements with a reducedgravity, shallow water model of the tropical Pacific ocean. Fukumori, I. https://doi.org/10.1029/95JC02083
  29. L.-L. and A. Cazenave, Data assimilation by models. In: satellite Altimetry and Earth Sciences, edited by Fu Fukrmori, I.
  30. J. Geophys. Res. v.100 no.C4 An approximate Kalman filter for ocean data assimilation: an example with anidealized Gulr stream model. Fukumori, I.;P. Malannotte-Rizzoil https://doi.org/10.1029/94JC03084
  31. J. Phys. Oceanogr. v.23 Assimilation of sea surface topography into an ocean circulation model using a steady state smoother. Fukumori, I.;J. Benveniste;C. Wunsch;D.B. Haidvogel https://doi.org/10.1175/1520-0485(1993)023<1831:AOSSTI>2.0.CO;2
  32. Leningrad. English translation by Israeli Program for Scientific Translation Jerusalem Objective Analysis of Meteorological Fields.GIMIZ Gandin, L.S.
  33. Applied Optimal Estimation Gelb, A.(Ed.)
  34. J. Meteor. Soc. Japan v.75 no.1B Advances in sequential estimation for atmosphere and oceanic flow. Ghil, M. https://doi.org/10.2151/jmsj1965.75.1B_289
  35. Qdv. Geophys. v.33 Data assomolation in meteorology and oceanography. Ghil, M.;P. Malanotte-Rizzoli https://doi.org/10.1016/S0065-2687(08)60442-2
  36. Applications of etimation theory to numerical weather predicto\ion. In: Dynamic Neteorology: Data Assimilation Methods Ghil, M.;S. Cohn;J. Tavantzis;K. Bube;E. Isaacson;Bengtsson, L.(ed.);M. Ghil(ed.);E. Kallen
  37. Accounting for model error in data assimilation using adjoint models. In: computational differentiation: Techniques Guiffith, A.K.;N.K. Nichols
  38. Ocean general circulation modeling. In: Climate Sustem Modeling Haidvogel, D.B.;F.O. Bryan;Trenberth, K.E.(ed.)
  39. Bull. Amer. Meteor, Soc. v.67 Observing-systems simulation ewperiments: Past, Presint, and future. Atnold, C.P.;Jr;C.H. Dey. https://doi.org/10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2
  40. Mon. Wea. Rev. v.126 Data assimilation usong an ensembok kalman filter technique. Houtekamer, P.L.;H.L. Mitchell https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2
  41. Using an adjoint model to improve an optimum iaterpolation-based data assimilation system. tellus v.49A Huant, X.-Y.;A. Cederskov;Kallen, E.
  42. J. Mar. Systems v.6 Comparison of sequential updating, Kalman filter and varational methods for assimilating Rossby waves in the simulated Geosat altimeter data into a quasi-geostrophic model. Ikeda, M.;G. Evensen;L. Cong https://doi.org/10.1016/0924-7963(94)00015-4
  43. Stochastic Processes and filtering Theory. Jazwinski, A.H.
  44. cited2003. The Joint Global Ocean Flux Study. Available online from "http//www.uib.no/jgofs/jgofs.html" JGOFS
  45. Trans. ASME. Series D, J.Basic Eng. v.52 A new approach to linear filtering and predicton problems. Kalman, R.E.
  46. Atmsopheric Modeling, Data Assimilation and Predictabillity. Kalnay, E.
  47. Mon. Wea. Rev. v.128 Application of the quasi-inverse method to data assimilation. Kalnay, E.;S.K. Park;Z.-X. Pu;J. Gao https://doi.org/10.1175/1520-0493(2000)128<0864:AOTQIM>2.0.CO;2
  48. Prog. Oceanogr. v.40 Wsak constraint data assimilation for tides in the Arctic Ocean. Kivman, G.A. https://doi.org/10.1016/S0079-6611(98)00005-6
  49. Observing the Oceans in the 21st Century. GODAE Koblinsky, C.J.;N.R. Smith(Eds.)
  50. J. Geophys. Res. v.102 no.C5 Imferring meridional mass and heat transports of the Indian-Ocean by fitting a general-circulation model to climatological data. Lee, T.;J. Marotzke https://doi.org/10.1029/97JC00464
  51. Quart. J. Roy. Meteor. Soc. v.127 Optimality of variational data assimilation and its rdlationship with the Kalman filter and smoother. Li, A.;I.M. Navon https://doi.org/10.1002/qj.49712757220
  52. Optimal Control of Systems Governed by Partial Differential Equations. Lions, J.L.
  53. J. Phys. Oceanogr. v.16 Data constraints applied to models of the ocean general circulation. I. The steady case. Malanotte-Rizzli, P;W.R. Holland https://doi.org/10.1175/1520-0485(1986)016<1665:DCATMO>2.0.CO;2
  54. Appl. Nath. Optimization v.2 Formulation of theory of perturbations for complicated models. Marchuk, G.I. https://doi.org/10.1007/BF01458193
  55. J. Phys. Oceanogr. v.19 A Kalman filter analysis of sea level hekght in the tropical Pacific. Miller, R.N.;M. Cane https://doi.org/10.1175/1520-0485(1989)019<0773:AKFAOS>2.0.CO;2
  56. J. Moteor. Soc. Japan v.75 no.1B Applecation of data assimilation to analysis of the ocean on large scales. Miller, R.N.;A.J. Busalacchi;E.C. Hackert https://doi.org/10.2151/jmsj1965.75.1B_445
  57. J. Phys. Oceanogr. v.17 Data assimilation in models of the Indian Icean. Moore, A.M.;N.S. Cooper;D.l.T. Andetson https://doi.org/10.1175/1520-0485(1987)017<1965:IADAIM>2.0.CO;2
  58. J. Geophys. Res. v.100 no.C12 Adjoint assimilation of altimetric, surface drtfter and hydrographic data in a QG model of the Azores Current. Morrow, R.;P.De Mey https://doi.org/10.1029/95JC02315
  59. cited 2003. Ocean Surface Topography from Space. Available online from " ＼http://topex-www.jpl.nasa.gov/mission/topex.html" NASA
  60. The ocean circulation. In:Climate System Modeling Niiler, P.P.;Trenberth, K.E(ed.)
  61. The Coaprehensive Ocean-Atmo-sphere Data Set Project. Available online from "＼http:www.ncdc.noaa.gov/oa/climate/coads/" NOAA
  62. cited 2003b. The Expendable Bathythermograph(XBT). Qvailable online from "＼hwwp://www.aoml.noaa.gov/phod/uot/uot―xbrt.html" NOAA
  63. Meteor. Atmos. Phys. v.82 Four-dimensional variational data Assimilation for mesoscale and storm-scale applications. Park, S.K.;D. Zupanski https://doi.org/10.1007/s00703-001-0586-7
  64. J. Mar. Systems v.16 A singular evolutive extended Kalman filter for data assimilatios in oceanography. Pham, D.T.;J. Verron;M.C.Roubaud https://doi.org/10.1016/S0924-7963(97)00109-7
  65. J. Coimate v.7 Improved global sea surface temperature analysis using optimum interpolation. Reynolds, R.W.;T.M. Smith
  66. Dyn. Atmos. Oceans v.13 Data assimilation and dynamical interpolation in Gulfcast experiments. Robinson, A.R.;N.A. Spall;L.J. walstad;W.G. Leslie https://doi.org/10.1016/0377-0265(89)90043-2
  67. Data assimilation. In: the Sea Robinson, A.R.;P.F.J. Lernusiaux;N.Q. Sloan Ⅲ;Brink, K.H.(ed.);A.R. Robinson(ed.)
  68. Mon. Wea. Rev. v.98 Some basic formalisms in nrmerical variational analysis. Sasaki, Y.K. https://doi.org/10.1175/1520-0493(1970)098<0875:SBFINV>2.3.CO;2
  69. Mon. Wea. Rev. v.98 Numerical variational analysis with weak constraint and application to surface analysis of severe storn gust. Sasaki, Y.K. https://doi.org/10.1175/1520-0493(1970)098<0899:NVAWWC>2.3.CO;2
  70. Variational Methods in Geosciences. Elsevier Sasaki, Y.K.
  71. Optimal determination of nudging coefficient using the adjoint equations. Tullus v.45A Stauffer, D.R.;J.-W. Bao
  72. J. Meteor. Soc. Japan v.75 no.1B Assimilation of observations, an introduction. Talagrand, O. https://doi.org/10.2151/jmsj1965.75.1B_191
  73. Adv. Appl. Mech. v.13 Large scale ocean circulation. Veronis, G. https://doi.org/10.1016/S0065-2156(08)70143-1
  74. Oceanus v.42 Launching the Argo Armada: Taking the dcean's pulse with 3,000 free-ranging floats. Wilson, S.
  75. WCRP Publication Series, No. 3. Scientific Plan for the Tropical Ocean and Global Atmosphere Programme. WCRP
  76. The World Ocean Circulation Experiment (WOCE) 1990-2002. Available online from "＼http://www. xic.soton.ac.uk/OTHERS/woceipo/ipo.html" WCRP
  77. Technical remarks on snoother algorithms. DAO Office Note 99-02, MASA/GSFC Zhu, Y.;R. Todling;S. E. Cohn
  78. Wea. Torecasting v.17 Fineresolution 4DVAR data assimilation for the Great Plains tornado outbreak of 3 May 1999. Zuapnski, D;M. Zupanski, E.Togers;D.F. Parrish;G.J. DkMego https://doi.org/10.1175/1520-0434(2002)017<0506:FRDAFT>2.0.CO;2