• Behringer, D. W., M. Ji, and A. Leetmaa, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part II: The coupled model. Mon. Wea. Rev., 126, 10221034, https://doi.org/10.1175/1520-0493(1998)126<1022:AICMFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Birrien, F., B. Castelle, V. Marieu, and B. Dubarbier, 2013: On a data-model assimilation method to inverse wave-dominated beach bathymetry using heterogeneous video-derived observations. Ocean Eng., 73, 126138, https://doi.org/10.1016/j.oceaneng.2013.08.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Böhme, L., and U. Send, 2005: Objective analyses of hydrographic data for referencing profiling float salinities in highly variable environments. Deep. Sea Res. II, 52, 651664, https://doi.org/10.1016/j.dsr2.2004.12.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caires, S., J. Kim, and J. Groeneweg, 2018: Korean east coast wave predictions by means of ensemble Kalman filter data assimilation. Ocean Dyn., 68, 15711592, https://doi.org/10.1007/s10236-018-1214-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chassignet, E. P., and et al. , 2009: Global ocean prediction with The Hybrid Coordinate Ocean Model (HYCOM). Oceanography, 22 (2), 6475, https://doi.org/10.5670/oceanog.2009.39.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, C., and F. J. Millero, 1977: Speed of sound in seawater at high pressures. J. Acoust. Soc. Amer., 62, 11291135, https://doi.org/10.1121/1.381646.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, C., B. Lei, Y. L. Ma, and R. Duan, 2016: Investigating sound speed profile assimilation: An experiment in the Philippine Sea. Ocean Eng., 124, 135140, https://doi.org/10.1016/j.oceaneng.2016.07.062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, C., Y. Ma, and Y. Liu, 2018: Reconstructing sound speed profiles worldwide with sea surface data. Appl. Ocean Res., 77, 2633, https://doi.org/10.1016/j.apor.2018.05.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 13671387, https://doi.org/10.1002/qj.49712051912.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ezer, T., and G. L. Mellor, 2004: A generalized coordinate ocean model and a comparison of the bottom boundary layer dynamics in terrain-following and in z-level grids. Ocean Modell., 6, 379403, https://doi.org/10.1016/S1463-5003(03)00026-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fox, D. N., C. N. Barron, M. R. Carnes, M. Booda, G. Peggion, and J. van Gurley, 2002: The Modular Ocean Data Assimilation System. Oceanography, 15 (1), 2228, https://doi.org/10.5670/oceanog.2002.33.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, J., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457469, https://doi.org/10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ha, J. H., and D. K. Lee, 2012: Effect of length scale tuning of background error in WRF-3DVAR system on assimilation of high-resolution surface data for heavy rainfall simulation. Adv. Atmos. Sci., 29, 11421158, https://doi.org/10.1007/s00376-012-1183-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, G., J. Zhu, and G. Zhou, 2004: Salinity estimation using the T-S relation in the context of variational data assimilation. J. Geophys. Res., 109, C03018, https://doi.org/10.1029/2003JC001781.

    • Search Google Scholar
    • Export Citation
  • Han, G., W. Li, Z. He, K. Liu, and J. Ma, 2006: Assimilated tidal results of tide gauge and TOPEX/Poseidon data over the China seas using a variational adjoint approach with a nonlinear numerical model. Adv. Atmos. Sci., 23, 449460, https://doi.org/10.1007/s00376-006-0449-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, G., and et al. , 2011: A regional ocean reanalysis system for coastal waters of China and adjacent seas. Adv. Atmos. Sci., 28, 682690, https://doi.org/10.1007/s00376-010-9184-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, G., H. Fu, X. Zhang, W. Li, X. Wu, X. Wang, and L. Zhang, 2013: A global ocean reanalysis product in the China Ocean Reanalysis (CORA) project. Adv. Atmos. Sci., 30, 16211631, https://doi.org/10.1007/s00376-013-2198-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayden, C. M., and R. J. Purser, 1995: Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing. J. Appl. Meteor., 34, 315, https://doi.org/10.1175/1520-0450-34.1.3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, Z., Y. Xie, W. Li, D. Li, G. Han, K. Liu, and J. Ma, 2008: Application of the sequential three-dimensional variational method to assimilating SST in a global ocean model. J. Atmos. Oceanic Technol., 25, 10181033, https://doi.org/10.1175/2007JTECHO540.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X. Y., 2000: Variational analysis using spatial filters. Mon. Wea. Rev., 128, 25882600, https://doi.org/10.1175/1520-0493(2000)128<2588:VAUSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janeković, I., H. Mihanović, I. Vilibić, B. Grčić, S. Ivatek-Šahdan, M. Tudor, and T. Djakovac, 2020: Using multi-platform 4D-Var data assimilation to improve modeling of Adriatic Sea dynamics. Ocean Modell., 146, 101538, https://doi.org/10.1016/j.ocemod.2019.101538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., and et al. , 2012: Research on the temporal-spatial distributions and the physical mechanisms for the sound speed profiles in north-central Indian Ocean (in Chinese). Wuli Xuebao, 61, 282299.

    • Search Google Scholar
    • Export Citation
  • Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2008: A three-dimensional variational data assimilation scheme for the regional ocean modeling system. J. Atmos. Oceanic Technol., 25, 20742090, https://doi.org/10.1175/2008JTECHO594.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, D. C., and J. Nocedal, 1989: On the limited memory BFGS method for large scale optimization. Math. Program., 45, 503528, https://doi.org/10.1007/BF01589116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., 1988: Optimal nonlinear objective analysis. Quart. J. Roy. Meteor. Soc., 114, 205240, https://doi.org/10.1002/qj.49711447911.

  • Lorenc, A. C., 1992: Iterative analysis using covariance functions and filters. Quart. J. Roy. Meteor. Soc., 118, 569591, https://doi.org/10.1002/qj.49711850509.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., and et al. , 2000: The Met. Office global three-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 126, 29913012, https://doi.org/10.1002/qj.49712657002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lovett, J. R., 1978: Merged seawater sound-speed equations. J. Acoust. Soc. Amer., 63, 17131718, https://doi.org/10.1121/1.381909.

  • , L. G., X. Wang, H. Wang, L. Li, and G. Yang, 2013: The variations of zooplankton biomass and their migration associated with the Yellow Sea Warm Current. Cont. Shelf Res., 64, 1019, https://doi.org/10.1016/j.csr.2013.05.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mack, J., R. Arcucci, M. Molina-Solana, and Y. K. Guo, 2020: Attention-based convolutional autoencoders for 3D-variational data assimilation. Comput. Methods Appl. Mech. Eng., 372, 113291, https://doi.org/10.1016/j.cma.2020.113291.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., 2016: A review of targeted observations. Bull. Amer. Meteor. Soc., 97, 22872303, https://doi.org/10.1175/BAMS-D-14-00259.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Medwin, H., 1975: Speed of sound in water: A simple equation for realistic parameters. J. Acoust. Soc. Amer., 58, 13181319, https://doi.org/10.1121/1.380790.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mu, M., W. Duan, D. Chen, and W. Yu, 2015: Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting. Natl. Sci. Rev., 2, 226236, https://doi.org/10.1093/nsr/nwv021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Owens, W. B., and A. P. S. Wong, 2009: An improved calibration method for the drift of the conductivity sensor on autonomous CTD profiling floats by θS climatology. Deep. Res. I, 56, 450457, https://doi.org/10.1016/j.dsr.2008.09.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, S., Y. Zhu, Z. Li, Y. Li, and J. Yu, 2019: Improving the real-time marine forecasting of the northern South China Sea by assimilation of glider-observed T/S profiles. Sci. Rep., 9, 17845, https://doi.org/10.1038/s41598-019-54241-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ponsar, S., P. Luyten, and V. Dulière, 2016: Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea. Ocean Dyn., 66, 955971, https://doi.org/10.1007/s10236-016-0968-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purser, R. J., W. S. Wu, D. F. Parrish, and N. M. Roberts, 2003: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 15361548, https://doi.org/10.1175//2543.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snyder, C., 1996: Summary of an informal workshop on adaptive observations and FASTEX. Bull. Amer. Meteor. Soc., 77, 953961, https://doi.org/10.1175/1520-0477-77.5.953.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vendrasco, E. P., J. Sun, D. L. Herdies, and C. F. De Angelis, 2016: Constraining a 3DVAR radar data assimilation system with large-scale analysis to improve short-range precipitation forecasts. J. Appl. Meteor. Climatol., 55, 673690, https://doi.org/10.1175/JAMC-D-15-0010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., N. Liu, B. Li, and X. Li, 2014: An overview of ocean predictability and ocean ensemble forecast (in Chinese). Adv. Atmos. Sci., 29, 12121225.

    • Search Google Scholar
    • Export Citation
  • Wang, X., G. Han, W. Li, X. Wu, and X. Zhang, 2013: Salinity drift of global Argo profiles and recent halosteric sea level variation. Global Planet. Change, 108, 4255, https://doi.org/10.1016/j.gloplacha.2013.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, W. D., 1962: Extrapolation of the equation or the speed of sound in sea water. J. Acoust. Soc. Amer., 34, 866, https://doi.org/10.1121/1.1918215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wong, A. P. S., G. C. Johnson, and W. B. Owens, 2003: Delayed-mode calibration of autonomous CTD profiling float salinity data by θS climatology. J. Atmos. Oceanic Technol., 20, 308318, https://doi.org/10.1175/1520-0426(2003)020<0308:DMCOAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, X., F. Qiao, Y. Yang, C. Xia, and X. Chen, 2012: Argo data assimilation in ocean general circulation model of northwest Pacific Ocean. Ocean Dyn., 62, 10591071, https://doi.org/10.1007/s10236-012-0549-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, M., L. Zhou, H. Fu, L. Jiang, and X. Zhang, 2016: Assessment of intraseasonal variabilities in China Ocean Reanalysis (CORA). Acta Oceanol. Sin., 35, 90101, https://doi.org/10.1007/s13131-016-0820-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, C., X. Ding, J. Zhang, J. Yang, and Q. Ma, 2018: An evaluation of sea surface height assimilation using along-track and gridded products based on the Regional Ocean Modeling System (ROMS) and the four-dimensional variational data assimilation. Acta Oceanol. Sin., 37, 5058, https://doi.org/10.1007/s13131-018-1225-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 16 16 16
Full Text Views 5 5 5
PDF Downloads 6 6 6

Improving the Estimation of Temperature and Salinity by Assimilation of Observed Sound Speed Profiles

View More View Less
  • 1 a School of Marine Science and Technology, Tianjin University, Tianjin, China
  • | 2 b Department of Oceanography, Texas A&M University, College Station, Texas
  • | 3 c Key Laboratory of Marine Environmental Information Technology, National Marine Data and Information Service, Ministry of Natural Resources, Tianjin, China
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Sound speed profile (SSP) affecting underwater acoustics is closely related to the temperature and the salinity fields. It is of great value to obtain the temperature and the salinity information through the high-precision sound speed profiles. In this paper, a data assimilation scheme by introducing sound speed profiles as a new constraint is proposed within the framework of 3DVAR data assimilation [referenced as SSP-constraint 3DVAR (SSPC-3DVAR)], which aims at improving the analysis accuracy of initial fields of the temperature and salinity in coastal sea areas. To validate the performance of the new assimilation scheme, ideal experiments are first carried out to show the advantages of the new proposed SSPC-3DVAR. Then the temperature, the salinity, and the SSP observations from field experiments in a coastal area are assimilated into the Princeton Ocean Model to validate the performance of short-time forecasts, adopting the SSPC-3DVAR scheme. Results show that it is efficient to improve the estimate accuracy by as much as 14.6% and 11.1% for the temperature and salinity, respectively, when compared with the standard 3DVAR. It demonstrates that the proposed SSPC-3DVAR approach works better in practice than the standard 3DVAR and will primarily benefit from variously and widely distributed observations in the future.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xuefeng Zhang, xuefeng.zhang@tju.edu.cn

Abstract

Sound speed profile (SSP) affecting underwater acoustics is closely related to the temperature and the salinity fields. It is of great value to obtain the temperature and the salinity information through the high-precision sound speed profiles. In this paper, a data assimilation scheme by introducing sound speed profiles as a new constraint is proposed within the framework of 3DVAR data assimilation [referenced as SSP-constraint 3DVAR (SSPC-3DVAR)], which aims at improving the analysis accuracy of initial fields of the temperature and salinity in coastal sea areas. To validate the performance of the new assimilation scheme, ideal experiments are first carried out to show the advantages of the new proposed SSPC-3DVAR. Then the temperature, the salinity, and the SSP observations from field experiments in a coastal area are assimilated into the Princeton Ocean Model to validate the performance of short-time forecasts, adopting the SSPC-3DVAR scheme. Results show that it is efficient to improve the estimate accuracy by as much as 14.6% and 11.1% for the temperature and salinity, respectively, when compared with the standard 3DVAR. It demonstrates that the proposed SSPC-3DVAR approach works better in practice than the standard 3DVAR and will primarily benefit from variously and widely distributed observations in the future.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xuefeng Zhang, xuefeng.zhang@tju.edu.cn
Save