Editorial Type:
Article
Article Type:
Research Article

A 4DVAR System for the Navy Coastal Ocean Model. Part I: System Description and Assimilation of Synthetic Observations in Monterey Bay

Hans Ngodock Naval Research Laboratory, Stennis Space Center, Mississippi

Search for other papers by Hans Ngodock in
Current site
Google Scholar
PubMed Close
and
Matthew Carrier Naval Research Laboratory, Stennis Space Center, Mississippi

Search for other papers by Matthew Carrier in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jun 2014
DOI:
https://doi.org/10.1175/MWR-D-13-00221.1
Page(s):
2085–2107
Restricted access

Abstract

A 4D variational data assimilation system was developed for assimilating ocean observations with the Navy Coastal Ocean Model. It is described in this paper, along with initial assimilation experiments in Monterey Bay using synthetic observations. The assimilation system is tested in a series of twin data experiments to assess its ability to fit assimilated and independent observations by controlling the initial conditions and/or the external forcing while assimilating surface and/or subsurface observations. In all strong and weak constraint experiments, the minimization of the cost function is done with both the gradient descent method (in the control space) and the representer method (observation space). The accuracy of the forecasts following the analysis and the relevance of the retrieved forcing correction in the case of weak constraints are evaluated. It is shown that the assimilation system generally fits the assimilated and nonassimilated observations well in all experiments, yielding lower forecast errors.

Naval Research Laboratory Contribution Number JA/7320-13-1821.

Corresponding author address: Hans Ngodock, Naval Research Laboratory, Code 7321, Stennis Space Center, MS 39529. E-mail: hans.ngodock@nrlssc.navy.mil

Abstract

A 4D variational data assimilation system was developed for assimilating ocean observations with the Navy Coastal Ocean Model. It is described in this paper, along with initial assimilation experiments in Monterey Bay using synthetic observations. The assimilation system is tested in a series of twin data experiments to assess its ability to fit assimilated and independent observations by controlling the initial conditions and/or the external forcing while assimilating surface and/or subsurface observations. In all strong and weak constraint experiments, the minimization of the cost function is done with both the gradient descent method (in the control space) and the representer method (observation space). The accuracy of the forecasts following the analysis and the relevance of the retrieved forcing correction in the case of weak constraints are evaluated. It is shown that the assimilation system generally fits the assimilated and nonassimilated observations well in all experiments, yielding lower forecast errors.

Naval Research Laboratory Contribution Number JA/7320-13-1821.

Corresponding author address: Hans Ngodock, Naval Research Laboratory, Code 7321, Stennis Space Center, MS 39529. E-mail: hans.ngodock@nrlssc.navy.mil
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Amodei, L., 1995: Solution approchée pour un problème d’assimilation de données avec prise en compte de l’erreur du modèle. C. R. Acad. Sci.,321, 1087–1094.

  • Barron, C. N., R. C. Rhodes, L. F. Smedstad, C. D. Rowley, P. J. Martin, and A. B. Kara, 2003: Global ocean nowcasts and forecasts with the Navy Coastal Ocean Model (NCOM). 2003 NRL Review, Naval Research Laboratory, 175–178.

  • Barron, C. N., A. B. Kara, H. E. Hurlburt, C. Rowley, and L. F. Smedstad, 2004: Sea surface height predictions from the global Navy Coastal Ocean Model (NCOM) during 1998–2001. J. Atmos. Oceanic Technol., 21, 1876–1894, doi:10.1175/JTECH-1680.1.

    • Search Google Scholar
    • Export Citation

  • Barron, C. N., A. B. Kara, P. J. Martin, R. C. Rhodes, and L. F. Smedstad, 2006: Formulation, implementation and examination of vertical coordinate choices in the Global Navy Coastal Ocean Model (NCOM). Ocean Modell., 11, 347–375, doi:10.1016/j.ocemod.2005.01.004.

    • Search Google Scholar
    • Export Citation

  • Bennett, A. F., 1992: Inverse Methods in Physical Oceanography. Cambridge University Press, 347 pp.

  • Bennett, A. F., 2002: Inverse Modeling of the Ocean and Atmosphere. Cambridge University Press, 260 pp.

  • Bennett, A. F., B. S. Chua, and L. M. Leslie, 1996: Generalized inversion of a global numerical weather prediction model. Meteor. Atmos. Phys., 60, 165–178, doi:10.1007/BF01029793.

    • Search Google Scholar
    • Export Citation

  • Book, J. W., P. J. Martin, I. Janekovic, M. Kuzmic, and M. Wimbush, 2009: Vertical structure of bottom Ekman tidal flows: Observations, theory, and modeling from the northern Adriatic. J. Geophys. Res., 114, C01S06, doi:10.1029/2008JC004736.

    • Search Google Scholar
    • Export Citation

  • Broquet, G., C. A. Edwards, A. M. Moore, B. S. Powell, M. Veneziani, and J. D. Doyle, 2009: Application of 4D-variational data assimilation to the California Current System. Dyn. Atmos. Oceans, 48, 69–92, doi:10.1016/j.dynatmoce.2009.03.001.

    • Search Google Scholar
    • Export Citation

  • Broquet, G., A. M. Moore, H. G. Arango, and C. A. Edwards, 2011: Corrections to ocean surface forcing in the California Current System using 4D variational data assimilation. Ocean Modell., 36, 116–132, doi:10.1016/j.ocemod.2010.10.005.

    • Search Google Scholar
    • Export Citation

  • Brushett, B. A., B. A. King, and C. J. Lemckert, 2011: Evaluation of met-ocean forecast data effectiveness for tracking drifters deployed during operational oil spill response in Australian waters. J. Coastal Res.,64, 991–994.

  • Burrage, D. M., J. W. Book, and P. J. Martin, 2009: Eddies and filaments of the western Adriatic Current near Cape Gargano: Analysis and prediction. J. Mar. Syst., 78, S205–S226, doi:10.1016/j.jmarsys.2009.01.024.

    • Search Google Scholar
    • Export Citation

  • Chao, Y., and Coauthors, 2009: Development, implementation and evaluation of a data-assimilative ocean forecasting system off the central California coast. Deep-Sea Res. II,56,100–126, doi:10.1016/j.dsr2.2008.08.011.

    • Search Google Scholar
    • Export Citation

  • Cheng, Y. C., X. F. Li, Q. Xu, O. Garcia-Pineda, O. B. Andersen, and W. G. Pichel, 2011: SAR observation and model tracking of an oil spill event in coastal waters. Mar. Pollut. Bull., 62, 350–363, doi:10.1016/j.marpolbul.2010.10.005.

    • Search Google Scholar
    • Export Citation

  • Chua, B. S., and A. F. Bennett, 2001: An inverse ocean modeling system. Ocean Modell., 3, 137–165, doi:10.1016/S1463-5003(01)00006-3.

  • Daley, R., 1992: Atmospheric Data Analysis. Cambridge University Press, 472 pp.

  • Derber, J., and A. Rosati, 1989: A global oceanic data assimilation system. J. Phys. Oceanogr., 19, 1333–1347, doi:10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Di Lorenzo, E., A. Moore, H. Arango, Chua, B. D. Cornuelle, A. J. Miller, B. Powell, and A. Bennett, 2007: Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System (ROMS): Development and application for a baroclinic coastal upwelling system. Ocean Modell., 16, 160–187, doi:10.1016/j.ocemod.2006.08.002.

    • Search Google Scholar
    • Export Citation

  • Egbert, G. D., A. F. Bennett, and M. G. G. Foreman, 1994: TOPEX/POSEIDON tides estimated using a global inverse method. J. Geophys. Res., 99, 24 821–24 852, doi:10.1029/94JC01894.

    • Search Google Scholar
    • Export Citation

  • Erwig, M., Z. Fu, and B. Pflaum, 2007: Parametric Fortran: Program generation in scientific computing. J. Software Maint. Evol., 19, 155–182, doi:10.1002/smr.346.

    • Search Google Scholar
    • Export Citation

  • Goerss, J. S., and P. A. Phoebus, 1992: The Navy’s operational atmospheric analysis. Wea. Forecasting, 7, 232–249, doi:10.1175/1520-0434(1992)007<0232:TNOAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Haley, P. J., and Coauthors, 2009: Forecasting and reanalysis in the Monterey Bay/California Current region for the Autonomous Ocean Sampling Network-II experiment. Deep-Sea Res. II,56,127–148, doi:10.1016/j.dsr2.2008.08.010.

    • Search Google Scholar
    • Export Citation

  • Haza, A. C., L. I. Piterbarg, P. Martin, T. M. Ozgokmen, and A. Griffa, 2007: A Lagrangian subgridscale model for particle transport improvement and application in the Adriatic Sea using the Navy Coastal Ocean Model. Ocean Modell., 17, 68–91, doi:10.1016/j.ocemod.2006.10.004.

    • Search Google Scholar
    • Export Citation

  • Hodur, R. M., 1997: The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev., 125, 1414–1430, doi:10.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Jacobs, G. A., and H. E. Ngodock, 2003: The maintenance of conservative physical laws within data assimilation systems. Mon. Wea. Rev., 131, 2595–2607, doi:10.1175/1520-0493(2003)131<2595:TMOCPL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Jolliff, J. K., J. C. Kindle, I. Shulman, B. Penta, M. A. M. Friedrichs, R. Helber, and R. A. Arnone, 2009: Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment. J. Mar. Syst., 76, 64–82, doi:10.1016/j.jmarsys.2008.05.014.

    • Search Google Scholar
    • Export Citation

  • Kara, A. B., C. N. Barron, P. J. Martin, L. F. Smedstad, and R. C. Rhodes, 2006: Validation of interannual simulations from the 1/8 degree global Navy Coastal Ocean Model (NCOM). Ocean Modell., 11, 376–398, doi:10.1016/j.ocemod.2005.01.003.

    • Search Google Scholar
    • Export Citation

  • Liu, Y. G., P . MacCready, B. M . Hickey, E. P. Dever, P. M . Kosro, and N. S. Banas, 2009: Evaluation of a coastal ocean circulation model for the Columbia River plume in summer 2004. J. Geophys. Res., 114, C00B04, doi:10.1029/2008JC004929.

    • Search Google Scholar
    • Export Citation

  • Marotzke, J., R. Giering, K. Q. Zhang, D. Stammer, C. Hill, and T. Lee, 1999: Construction of the adjoint MIT ocean general circulation model and application to Atlantic heat transport sensitivity. J. Geophys. Res., 104, 29 529–29 547, doi:10.1029/1999JC900236.

    • Search Google Scholar
    • Export Citation

  • Martin P., 2000: Description of the Navy Coastal Ocean Model Version 1.0. NRL Rep. NRL/FR/7322—00-9962, 45 pp. [Available online at http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA387444.]

  • Moore, A. M., H. G. Arango, E. Di Lorenzo, B. D. Cornuelle, A. J. Miller, and D. J. Neilson, 2004: A comprehensive ocean prediction and analysis system based on the tangent linear and adjoint of a regional ocean model. Ocean Modell., 7, 227–258, doi:10.1016/j.ocemod.2003.11.001.

    • Search Google Scholar
    • Export Citation

  • Morey, S. L., P. J. Martin, J. J. O'Brien, A. A. Wallcraft, and J. Zavala-Hidalgo, 2003: Export pathways for river discharged fresh water in the northern Gulf of Mexico. J. Geophys. Res., 108, 3303, doi:10.1029/2002JC001674.

    • Search Google Scholar
    • Export Citation

  • Ngodock, H. E., 2005: Efficient implementation of covariance multiplication for data assimilation with the representer method. Ocean Modell., 8, 237–251, doi:10.1016/j.ocemod.2003.12.005.

    • Search Google Scholar
    • Export Citation

  • Ngodock, H. E., and M. J. Carrier, 2013: A weak constraint 4D-Var assimilation system for the Navy Coastal Ocean Model using the representer method. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. II, S. K. Park and L. Xu, Eds., Springer-Verlag, doi:10.1007/978-3-642-35088-7_15.

  • Ngodock, H. E., and M. J. Carrier, 2014: A 4DVAR system for the Navy Coastal Ocean Model. Part II: Strong and weak constraint assimilation experiments with real observations in Monterey Bay. Mon. Wea. Rev., 142, 2108–2117, doi:10.1175/MWR-D-13-00220.1.

    • Search Google Scholar
    • Export Citation

  • Ngodock, H. E., B. S. Chua, and A. F. Bennett, 2000: Generalized inversion of a reduced gravity primitive equation ocean model and tropical atmosphere ocean data. Mon. Wea. Rev., 128, 1757–1777, doi:10.1175/1520-0493(2000)128<1757:GIOARG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ngodock, H. E., S. R. Smith, and G. A. Jacobs, 2007: Cycling the representer algorithm for variational data assimilation with the Lorenz attractor. Mon. Wea. Rev., 135, 373–386, doi:10.1175/MWR3281.1.

    • Search Google Scholar
    • Export Citation

  • Ngodock, H. E., S. R. Smith, and G. A. Jacobs, 2009: Cycling the representer method with nonlinear models. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, S. K. Park and L. Xu, Eds., Springer, 321–340, doi:10.1007/978-3-540-71056-1_17.

  • Pullen, J., J. D. Doyle, and R. P. Signell, 2006: Two-way air–sea coupling: A study of the Adriatic. Mon. Wea. Rev., 134, 1465–1483, doi:10.1175/MWR3137.1.

    • Search Google Scholar
    • Export Citation

  • Pullen, J., J. D. Doyle, T. Haack, C. Dorman, R. P. Signell, and C. M. Lee, 2007: Bora event variability and the role of air-sea feedback. J. Geophys. Res., 112, C03S18, doi:10.1029/2006JC003726.

    • Search Google Scholar
    • Export Citation

  • Rosmond, T. E., 1992: The design and testing of the Navy Operational Global Atmospheric Prediction System. Wea. Forecasting, 7, 262–262, doi:10.1175/1520-0434(1992)007<0262:TDATOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Schroeder, K., A. C. Haza, A. Griffa, T. M. Ozgokmen, P. M. Poulain, R. Gerin, G. Peggion, and M. Rixen, 2011: Relative dispersion in the Liguro-Provencal basin: From sub-mesoscale to mesoscale. Deep-Sea Res. I, 58, 209–228, doi:10.1016/j.dsr.2010.11.004.

    • Search Google Scholar
    • Export Citation

  • Shulman, I., C. R. Wu, J. K. Lewis, J. D. Paduan, L. K. Rosenfeld, J. C. Kindle, S. R. Ramp, and C. A. Collins, 2002: High resolution modeling and data assimilation in The Monterey Bay. Cont. Shelf Res., 22, 1129–1151, doi:10.1016/S0278-4343(01)00100-5.

    • Search Google Scholar
    • Export Citation

  • Shulman, I., and Coauthors, 2007: Modeling of upwelling/relaxation events with the Navy Coastal Ocean Model. J. Geophys. Res., 112, C06023, doi:10.1029/2006JC003946.

    • Search Google Scholar
    • Export Citation

  • Shulman, I., and Coauthors, 2009: Impact of glider data assimilation on the Monterey Bay Model. Deep-Sea Res., 56, 188–198, doi:10.1016/j.dsr2.2008.08.003.

    • Search Google Scholar
    • Export Citation

  • Shulman, I., M. A. Moline, B. Penta, S. Anderson, M. Oliver, and S. H. D. Haddock, 2011: Observed and modeled bio-optical, bioluminescent, and physical properties during a coastal upwelling event in Monterey Bay, California. J. Geophys. Res., 116, C01018, doi:10.1029/2010JC006525.

    • Search Google Scholar
    • Export Citation

  • Stammer, D., and Coauthors, 2002: The global ocean circulation during 1992–1997, estimated from ocean observations and a general circulation model. J. Geophys. Res.,107, 3118, doi:10.1029/2001JC000888.

  • Talagrand, O., 1999: A posteriori evaluation and verification of analysis and assimilation algorithms. Proc. Workshop on Diagnosis of Data Assimilation Systems, Reading, United Kingdom, ECMWF, 17–28.

  • Weaver, A., and P. Courtier, 2001: Correlation modeling on the sphere using a generalized diffusion equation. Quart. J. Roy. Meteor. Soc., 127, 1815–1846, doi:10.1002/qj.49712757518.

    • Search Google Scholar
    • Export Citation

  • Weaver, A., J. Vialard, and D. L. T. Anderson, 2003: Three- and four-dimensional variational assimilation with a general circulation model of the tropical Pacific Ocean. Part I: Formulation, internal diagnostics, and consistency checks. Mon. Wea. Rev., 131, 1360–1378, doi:10.1175/1520-0493(2003)131<1360:TAFVAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wunsch, C., 2006: Discrete Inverse and State Estimation Problems: With Geophysical Fluid Applications. Cambridge University Press, 371 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 275 82 7
PDF Downloads 157 48 5