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Assimilation of temperature and salinity using isotropic and anisotropic recursive filters in Tropic Pacific

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Abstract

A data assimilation scheme used in the updated Ocean three-dimensional Variational Assimilation System (OVALS), OVALS2, is described. Based on a recursive filter (RF) to estimate the background error covariance (BEC) over a predetermined scale, this new analysis system can be implemented with anisotropic and isotropic BECs. Similarities and differences of these two BEC schemes are briefly discussed and their impacts on the model simulation are also investigated. An idealized experiment demonstrates the ability of the updated analysis system to construct different BECs. Furthermore, a set of three years experiments is implemented by assimilating expendable bathythermograph (XBT) and ARGO data into a Tropical Pacific circulation model. The TAO and WOA01 data are used to validate the assimilation results. The results show that the model simulations are substantially improved by OVALS2. The inter-comparison of isotropic and anisotropic BEC shows that the corresponding temperature and salinity produced by the anisotropic BEC are almost as good as those obtained by the isotropic one. Moreover, the result of anisotropic RF is slightly closer to WOA01 and TAO than that of isotropic RF in some special area (e.g. the cold tongue area in the Tropic Pacific).

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References

  • Barker D M, Huang W, Guo Y R, et al. 2004. A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results. Monthly Weather Review, 132: 897–913

    Article  Google Scholar 

  • Bertino L, Evensen G, Wackernagel H. 2002. Combining geostatistics and Kalman filtering for data assimilation in an estuarine system. Inverse Methods, 18: 1–23

    Article  Google Scholar 

  • Cohn S E, Da Silva A, Guo J, et al. 1998. Assesing the effects of data selection with the DAO physicalspace statistical analysis system. Monthly Weather Review, 126: 2913–2926

    Article  Google Scholar 

  • Courtier P E, Anderson W, Heckley J, et al. 1998. The ECMWF implementation of three dimensional variational assimilation (3D-Var), Part I: Formulation. The Quarterly Journal of the Royal Meteorological Society, 124: 1783–1808

    Google Scholar 

  • Daley R. 1991. Atmospheric Data Assimilation. Cambridge University Press, 457

  • Derber J D, Rosati A. 1989. A global oceanic data assimilation system. Journal of Physical Oceanography, 19: 1333–1347

    Article  Google Scholar 

  • Dobricic S, Pinardi N. 2008. An oceanographic threedimensional variational data assimilation scheme. Ocean Modelling, 22: 89–105

    Article  Google Scholar 

  • Duchon C. 1979. Lanczos filtering in one and two dimensions. Journal of Applied Meteorology, 18, 1016–1022

    Article  Google Scholar 

  • Evensen G. 2003. The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53: 343–367

    Article  Google Scholar 

  • Evensen G, van Leeuwen P J. 2000. An Ensemble Kalman Smoother for nonlinear dynamics. Monthly Weather Review, 128: 1852–1867

    Article  Google Scholar 

  • Evensen G. 2004. Sampling strategies and square root analysis schemes for the EnKF. Ocean Dynamics, 54: 539–560

    Article  Google Scholar 

  • Fujii Y. 2005. Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar). Journal of Oceanography, 61: 167–181

    Article  Google Scholar 

  • Fujii Y, Kamachi M. 2003. A nonlinear preconditioned quasi-Newton method without inversion of a firstguess covariance matrix in variational analysis. Tellus, 55A: 450–454

    Google Scholar 

  • Gao S, Wang F, Li M K, et al. 2008. Application of altimetry data assimilation on mesoscale eddies simulation. Science in China Series D: Earth Sciences, 51(1): 142–151

    Article  Google Scholar 

  • Gao J D, Xue M, Brewster K, et al. 2004. A Threedimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars. Journal of Atmospheric and oceanic technology, 21: 457–469

    Article  Google Scholar 

  • Golub G H, Van Loan C F. 1996. Matrix computations. 3rd ed. Baltimore, USA: The Johns Hopkins University Press, 694

    Google Scholar 

  • Gauthier P C, Cherette L, Fillion L, et al. 1998. Implementation of a 3D variational data assimilation system at the Canadian Meteorological Center. Part I: The global analysis. Atmosphere-Ocean, 37: 103–156

    Google Scholar 

  • Haney R L. 1971. Surface thermal boundary conditions for ocean circulation model. Journal of Physical Oceanography, 1: 241–248

    Article  Google Scholar 

  • Huang X Y. 2000. Variational Analysis Using Spatial Filters. Monthly Weather Review, 128: 2588–2600

    Article  Google Scholar 

  • Hayden C M, Purser R J. 1995. Recursive Filter Objective Analysis of Meteorological Fields: Applications to NESDIS Operational Processing. Journal of Applied Meteorology, 34: 3–15

    Article  Google Scholar 

  • Heckley W A, Courtier P, Pailleux J, et al. 1992. The ECMWF variational analysis: general formulation and use of background information. In: ECMWF workshop proceedings. Variational assimilation with special emphasis on three-dimensional aspects. ECMWF, Reading, U.K., 49–94. Available from ECMWF, Shinfield Park, Reading, RG29AX, U. K.

    Google Scholar 

  • Kaplan A, Kushnir Y, Cane M A. 2000. Reduced space optimal interpolation of historical marine sea level pressure: 1854–1992. Journal of Climate, 13: 2987–3002

    Article  Google Scholar 

  • Kuragano T, Kamachi M. 2000. Global statistical spacetime scales of oceanic variability estimated from the TOPEX/POSEIDON altimeter data. Journal of Geophysical Research, 105: 955–74

    Article  Google Scholar 

  • Lorenc A C. 1986. Analysis methods for numerical weather prediction. The Quarterly Journal of the Royal Meteorological Society, 112: 1177–1194

    Article  Google Scholar 

  • Lorenc A C. 1988. Optimal nonlinear objective analysis. The Quarterly Journal of the Royal Meteorological Society, 114: 205–240

    Article  Google Scholar 

  • Lorenc A C, Ballard R S, Ingleby N B, et al. 2000. The Met. Office global three-dimensional variational data assimilation scheme. The Quarterly Journal of the Royal Meteorological Society, 126: 2991–3012

    Article  Google Scholar 

  • Liu Y, Zhu J, She J, et al. 2009. Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model. Ocean Modelling, 30: 75–87

    Article  Google Scholar 

  • Liu H, Xue M. 2006. Retrieval of moisture from slantpath water vapor observations of a hypothetical GPS network using a three-dimensional variational scheme with anisotropic background error. Monthly Weather Review, 134: 933–949

    Article  Google Scholar 

  • Liu Y, Yan C X. 2010. Application of recursive filter to a three dimensional variational ocean data assimilation system. Advance of Atmospheric Sciences, 27(2): 293–302

    Article  Google Scholar 

  • Maes C, Behringer D, Reynolds R W, et al. 2000. Retrospective analysis of the salinity variability in the western Tropical Pacific Ocean using an indirect minimization approach, Journal of Atmospheric and oceanic technology, 17: 512–524

    Article  Google Scholar 

  • Pham D T. 2001. Stochastic methods for sequential data assimilation in strongly nonlinear systems. Monthly Weather Review, 129: 1194–1207

    Article  Google Scholar 

  • Oke P R, Sakov P, Corney Stuart P. 2007. Impacts of localisation in the EnKF and EnOI: experiments with a small model. Ocean Dynamics, 57: 32–45

    Article  Google Scholar 

  • Parrish D F, Derber J C. 1992. The National Meteorological Center’s spectral statistical-interpolation analysis system. Monthly Weather Review, 120: 1747–1763

    Article  Google Scholar 

  • Purser R J, Wu W S, Parrish D F, et al. 2003a. Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Monthly Weather Review, 131: 1524–1535

    Article  Google Scholar 

  • Purser R J, Wu W S, Parrish D F. 2003b. Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Monthly Weather Review, 131: 1536–1548

    Article  Google Scholar 

  • Riishøjgaard L P. 1998. A direct way of specifying flowdependent background error correlations for meteorological analysis systems. TellusA, 50: 42–57

    Article  Google Scholar 

  • Wang X D, Xu D F, Xu X H. 2008. Incremental 4D-VAR assimilation scheme based on Lorenz model. Acta Oceanologica Sinica, 27: 93–100

    Google Scholar 

  • Wu W S, Purser R J, Parrish D F. 2002. Three dimensional variational analysis with spatially inhomogeneous covariances.. Monthly Weather Review, 130: 2905–2916

    Article  Google Scholar 

  • Xiao X J, Wang D X, Yan C X, et al. 2008. Evaluation of a 3dVAR system for the South China Sea. Progress in Nature sciences, 18: 547–554

    Article  Google Scholar 

  • Yan C X, Zhu J, Zhou G Q. 2007. Impacts of XBT, TAO, Altimetry and ARGO Observations on the Tropical Pacific Ocean Data Assimilation. Advances in Atmospheric Sciences, 24(3): 383–398

    Article  Google Scholar 

  • Zhang R H, Endoh M. 1992. A free surface general circulation model for the tropical Pacific Ocean. Journal of Geophysical Research, 97(C7): 11237–11255

    Article  Google Scholar 

  • Zheng F, Zhu J. 2008. Balanced multivariate model errors of an intermediate coupled model for ensemble Kalman filter data assimilation. Journal of Geophysical Research, 113, C07002, doi:10.1029/2007JC004621

    Article  Google Scholar 

  • Zheng F, Zhu J, Zhang R H. 2006. Improved ENSO forecasts by assimilating sea surface temperature observations into an intermediate coupled model. Advances in Atmospheric Sciences, 23(4): 615–624

    Article  Google Scholar 

  • Zhu J, Zhou G Q, Yan C X, et al. 2006. A threedimensional variational ocean data assimilation system: Scheme and preliminary results. Science in China Series D: Earth Sciences, 49(11): 1212–1222

    Article  Google Scholar 

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Authors and Affiliations

  1. National Marine Environmental Forecasting Center, Beijing, 100081, China

    Ye Liu

  2. No 61741 Army Troop, Beijing, 10081, China

    Yanling Zhao

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  1. Ye Liu
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  2. Yanling Zhao
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Correspondence to Ye Liu.

Additional information

Foundation item: Major National Scientific Research Project on Global Change under contract No. 2010CB951901; the National Science Foundation of China under contract No. 40821092; Special Fund for Public Welfare Industry (Meteorology) under contract No.GYHY200906018.

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Liu, Y., Zhao, Y. Assimilation of temperature and salinity using isotropic and anisotropic recursive filters in Tropic Pacific. Acta Oceanol. Sin. 30, 15–23 (2011). https://doi.org/10.1007/s13131-011-0086-7

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  • DOI: https://doi.org/10.1007/s13131-011-0086-7

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