ZHANG Chunling, XU Jianping, BAO Xianwen, WANG Zhenfeng. An effectivemethod for improving the accuracy of Argo objective analysis[J]. Acta Oceanologica Sinica, 2013, 32(7): 66-77. doi: 10.1007/s13131-013-0333-1
Citation: ZHANG Chunling, XU Jianping, BAO Xianwen, WANG Zhenfeng. An effectivemethod for improving the accuracy of Argo objective analysis[J]. Acta Oceanologica Sinica, 2013, 32(7): 66-77. doi: 10.1007/s13131-013-0333-1

An effectivemethod for improving the accuracy of Argo objective analysis

doi: 10.1007/s13131-013-0333-1
  • Received Date: 2012-04-11
  • Rev Recd Date: 2012-10-15
  • Based on the optimal interpolation objective analysis of the Argo data, improvements are made to the empirical formula of a background error covariancematrixwidely used in data assimilation and objective analysis systems. Specifically, an estimation of correlation scales that can improve effectively the accuracy of Argo objective analysis has been developed. Thismethod can automatically adapt to the gradient change of a variable and is referred to as “gradient-dependent correlation scalemethod”. Its effect on the Argo objective analysis is verified theoretically with Gaussian pulse and spectrumanalysis. The results of one-dimensional simulation experiment show that the gradient-dependent correlation scales can improve the adaptability of the objective analysis system, making it possible for the analysis scheme to fully absorb the shortwave information of observation in areas with larger oceanographic gradients. The new scheme is applied to the Argo data objective analysis systemin the Pacific Ocean. The results are obviously improved.
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  • Akima H. 1970. A new method for interpolation and smooth curve fitting based on local procedures. J Assoc Comput Mech, 17: 589-602
    Behrinoer David W, Ji Ming, Leetmaa Ants. 1998. An improved coupled method for ENSO prediction and implications for ocean initialization: Part I. The ocean data assimilation system. MonthlyWeather Review, 126: 1013-1021
    Bonekamp H G, Oldenborgh J V, Burgers G. 2001. Variational assimilation of TAO and XBT data in the HOPE OGCM: adjusting the surface fluxes in the tropical ocean. J Geophys Res, 106: 16693-16709
    Carton J A, Chepurin G, Cao X. 2000. A simple ocean data assimilation analysis of the global upper ocean 1950-95: Part I.Methodology. J Phys Oceanogr, 30: 294-309
    Dean Roemmich, Gilson John. 2009. The 2004-2008 mean and annual cycle of temperature, salinity and steric height in the global ocean from the Argo program. Progress in Oceanography, 82: 81-100
    DeeD P, Silva A Da. 1998. Data assimilation in the presence of forecast bias. Quart J Roy Meteor Soc, 124: 269-295
    Derber J, Rosati A. 1989. A global oceanic data assimilation system. Journal of Physical Oceanography, 19: 1333-1347
    Fu Weiwei, Zhou Guangqing, Wang Huijun. 2004. Ocean data assimilation with background error covariance: derived from OGCM outputs. Advances in Atmospheric Sciences, 21(2): 181-192
    Gaillard F. 2010. ISAS-Tool Version 5.3: Method and Configuration. Brest: Laboratoere de Physique de Oceans, 12
    Gandin L S. 1963. Objective analysis of meteorological fields. Leningrad: Gidromet, 242
    Gao Li, Liu Yuguang, Rong Zengrui. 2007. The sea level anomaly and analysis of mesoscale eddies in Kuroshio extensions. Transactions of Oceanology and Limnology, 1: 14-23
    Gaspari G, John S E. 1999. Construction of correlation functions in two and three dimensions. Q J RMeteorol Soc, 125: 723-757
    He Zhongjie, Xie Yuanfu, Li Wei. 2007. Application of the sequential three-dimensional variation method to assimilating SST in a global ocean model. Journal of Atmospheric and oceanic Technology, 25: 1018-1033
    Hollingsworth A, Lonnberg P. 1986. The statistical structure of shortrange forecast errors as determined from radiosonde data: Part I. The wind field. Tellus, 38A: 111-136
    Kenneth H, Bergman. 1979. Multivariate analysis of temperature and wind using optimums interpolation. Mon Wea Rev, 107(11): 1423-1444
    Kuragano T, Kamachi M. 2000. Global statistical space-time scales of oceanic variability estimated from the TOPEX/POSEIDON altimeter data. J Geophys Res, 105: 955-974
    Li Wei, Xie Yuanfu, He Zhongjie, et al. 2008. Application of the multigrid data assimilation scheme to the China seas’ temperature forecast. Journal of Atmospheric and Oceanic Technology, 25: 2106-2116
    Meyers G H, Phillips N S, Springtall J. 1991. Space and timescales for optimal interpolation of temperature tropical Pacific. Progr Oceanogr, 28: 189-218
    Reynolds R W, Smith T M. 1994. Improved global sea surface temperature analysis. J Clim, 6: 929-948
    Riishogaard L P. 1998. A direct way of specifying flow-dependent background error correlations for meteorological analysis system. Tellus, 50A: 42-57
    Shigeki H, Tsuyoshi O, Tomoaki N. 2008. A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations. JAMSTEC Rep Res Dev, 8: 47-59
    Weaver A, Courtier P. 2001. Correlation modeling on the sphere using a generalized diffusion equation. Q J RMeteorol Soc, 127: 1815-1846
    Xie Y, Koch S E, McGinley J A, et al. 2010. A space-time multi-scale analysis system: a sequential variational analysis approach. MonWea Rev, 139: 1224-1239
    Xu Luping. 2007. Digital Image Processing (in Chinese). Beijing: Science Press, 80
    Zhang Weimin, Cao Xiaoqun, Xiao Qingnong, et al. 2010. Variational data assimilation using wavelet background error covariance: initialization of Typhoon Kaemi (2006). Journal of Tropical Meteorology, 16(4): 334-340
    Zhuang Zhaorong, Xue Jishan, Zhuang Shiyu, et al. 2006. Research for background error statistical analysis of geopotential height. Atmospheric Science (in Chinese), 30(3): 533-544
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