A regression method for estimating salinity in the Ocean

被引:5
|
作者
Korotenko, K. A. [1 ]
机构
[1] Russian Acad Sci, PP Shirshov Oceanol Inst, Moscow, Russia
关键词
Atlantic Ocean; World Ocean; Regression Curve; Salinity Data; Dynamic Height;
D O I
10.1134/S0001437007040030
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
Specification of salinity is an important problem in initialization of the global ocean circulation models. Unlike the temperature, the salinity data in the World Ocean are irregular and nonuniform; thus, methods for estimating the salinity using pleutiful temperature data are urgently needed. A new regression method for estimating the salinity in the ocean is suggested in this paper. Unlike similar currently known approaches, the method suggested applies a set of polynomials and their powers invariant for the entire ocean, while the latter is divided into a number of study subregions for the estimates. The best-fit regression curves and the minimal errors of the salinity estimates are found for each of the regions. The method uses the World Oceanographic Database (WOD-2001 NODC NOAA) to determine the regression coefficients and the confidence intervals (RCCI). A special RCCI database was organized on the basis of these data. The RCCI database makes possible determination of the salinity at any point in the ocean if the temperature data are provided (measurements, XBT profiles, etc.). A realization of the method suggested is demonstrated by the example of the Atlantic Ocean.
引用
收藏
页码:464 / 475
页数:12
相关论文
共 50 条
  • [1] A regression method for estimating salinity in the Ocean
    K. A. Korotenko
    Oceanology, 2007, 47 : 464 - 475
  • [2] Estimating the influence of salinity on sea level anomaly in the ocean
    National Centers for Environmental Predictions, National Oceanic and Atmospheric Administration, Washington, DC, United States
    不详
    不详
    Geophys. Res. Lett., 19 (3551-3554):
  • [3] Estimating the influence of salinity on sea level anomaly in the ocean
    Maes, C
    GEOPHYSICAL RESEARCH LETTERS, 1998, 25 (19) : 3551 - 3554
  • [4] Estimating Oxygen in the Southern Ocean Using Argo Temperature and Salinity
    Giglio, D.
    Lyubchich, V.
    Mazloff, M. R.
    JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2018, 123 (06) : 4280 - 4297
  • [5] The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state
    Lu, Zeting
    Cheng, Lijing
    Zhu, Jiang
    Lin, Renping
    JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2016, 121 (06) : 3672 - 3691
  • [6] Comparison of Three Brillouin Ocean Lidar Models for Estimating Temperature and Salinity
    Jia, Xiaohong
    Yan, Guoliang
    Wu, Xingxing
    Luo, Ningning
    Wang, Lei
    Shi, Jiulin
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2025, 13 (03)
  • [7] On regression method for estimating a population proportion
    Naik, VD
    Gupta, PC
    STATISTICAL PAPERS, 1996, 37 (01) : 85 - 92
  • [8] ESTIMATING OCEAN SUBSURFACE SALINITY FROM REMOTE SENSING DATA BY MACHINE LEARNING
    Su, Hua
    Yang, Xin
    Yan, Xiao-Hai
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 8139 - 8142
  • [9] Dependence of bimodality of ocean waves on estimating method
    Zhao, D
    PROCEEDINGS OF THE 10TH (2000) INTERNATIONAL OFFSHORE AND POLAR ENGINEERING CONFERENCE, VOL III, 2000, : 681 - 685
  • [10] USE OF REGRESSION METHOD IN ESTIMATING REGIONAL POPULATION
    CHU, SF
    INTERNATIONAL STATISTICAL REVIEW, 1974, 42 (01) : 17 - 28