• Arhan, M., and Colin de Verdiére A. , 1985: Dynamics of eddy motions in the eastern North Atlantic. J. Phys. Oceanogr., 15, 153170, doi:10.1175/1520-0485(1985)015<0153:DOEMIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • AVISO, 2015: SSALTO/DUACS user handbook: (M)SLA and (M)ADT near-real time and delayed time products. Issue 4.4, CNES CLS-DOS-NT-06-034, SALP-MU-P-EA-21065-CLS, 66 pp. [Available online at http://www.aviso.oceanobs.com/fileadmin/documents/data/tools/hdbk_duacs.pdf.]

  • Bretherton, F. P., Davis R. E. , and Fandry C. B. , 1976: A technique for objective analysis and design of oceanographic experiment applied to MODE-73. Deep-Sea Res. Oceanogr. Abstr., 23, 559582, doi:10.1016/0011-7471(76)90001-2.

    • Search Google Scholar
    • Export Citation
  • Ducet, N., Le Traon P.-Y. , and Reverdin G. , 2000: Global high-resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and -2. J. Geophys. Res., 105, 19 47719 498, doi:10.1029/2000JC900063.

    • Search Google Scholar
    • Export Citation
  • Hill, C., Menemenlis D. , Ciotti B. , and Henze C. , 2007: Investigating solution convergence in a global ocean model using a 2048-processor cluster of distributed shared memory machines. Sci. Program., 15, 107115, doi:10.1155/2007/458463.

    • Search Google Scholar
    • Export Citation
  • Le Traon, P.-Y., Nadal F. , and Ducet N. , 1998: An improved mapping method of multisatellite altimeter data. J. Atmos. Oceanic Technol., 15, 522534, doi:10.1175/1520-0426(1998)015<0522:AIMMOM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Menemenlis, D., Fukumori I. , and Lee T. , 2005: Using Green’s functions to calibrate an ocean general circulation model. Mon. Wea. Rev., 133, 12241240, doi:10.1175/MWR2912.1.

    • Search Google Scholar
    • Export Citation
  • Menemenlis, D., Campin J. , Heimbach P. , Hill C. , Lee T. , Nguyen A. , Schodlok M. , and Zhang H. , 2008: ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, No. 31, Mercator Ocean, Ramonville Saint-Agne, France, 13–21.

    • Search Google Scholar
    • Export Citation
  • Ubelmann, C., Klein P. , and Fu L.-L. , 2015: Dynamic interpolation of sea surface height and potential applications for future high-resolution altimetry mapping. J. Atmos. Oceanic Technol., 32, 177184, doi:10.1175/JTECH-D-14-00152.1.

    • Search Google Scholar
    • Export Citation
  • Wunsch, C., 1996: The Ocean Circulation Inverse Problem. Cambridge University Press, 442 pp.

  • Wunsch, C., 1997: The vertical partition of oceanic horizontal kinetic energy. J. Phys. Oceanogr., 27, 17701794, doi:10.1175/1520-0485(1997)027<1770:TVPOOH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xu, Y., and Fu L.-L. , 2011: Global variability of the wavenumber spectrum of oceanic mesoscale turbulence. J. Phys. Oceanogr., 41, 802809, doi:10.1175/2010JPO4558.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 12 12 12
PDF Downloads 7 7 7

Dynamic Mapping of Along-Track Ocean Altimetry: Method and Performance from Observing System Simulation Experiments

View More View Less
  • 1 Jet Propulsion Laboratory, Pasadena, California
  • | 2 Scripps Institution of Oceanography, La Jolla, California
  • | 3 Jet Propulsion Laboratory, Pasadena, California
Restricted access

Abstract

Simulated along-track ocean altimetry data were used to implement the use of a nonlinear dynamic propagator to perform three-dimensional (time and 2D space) interpolation of mesoscale sea surface height (SSH). The method is an inverse approach to processing altimetry data unevenly sampled in time and space into high-level gridded altimetry maps. The inverse approach, similar to the standard objective mapping, contains some correction terms to the innovation vectors to account for nonlinear dynamics. Another key improvement is to solve for the covariance functions through a Green’s function approach. From the Observing System Simulation Experiments carried out to simulate a three-satellite constellation over the Gulf Stream region, the new method can significantly reduce mapping errors and improve the resolving capabilities compared to the standard linear objective analysis such as that used by the AVISO gridding.

Corresponding author address: Clement Ubelmann, Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91109. E-mail: clement.ubelmann@jpl.nasa.gov

Abstract

Simulated along-track ocean altimetry data were used to implement the use of a nonlinear dynamic propagator to perform three-dimensional (time and 2D space) interpolation of mesoscale sea surface height (SSH). The method is an inverse approach to processing altimetry data unevenly sampled in time and space into high-level gridded altimetry maps. The inverse approach, similar to the standard objective mapping, contains some correction terms to the innovation vectors to account for nonlinear dynamics. Another key improvement is to solve for the covariance functions through a Green’s function approach. From the Observing System Simulation Experiments carried out to simulate a three-satellite constellation over the Gulf Stream region, the new method can significantly reduce mapping errors and improve the resolving capabilities compared to the standard linear objective analysis such as that used by the AVISO gridding.

Corresponding author address: Clement Ubelmann, Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91109. E-mail: clement.ubelmann@jpl.nasa.gov
Save