Abstract
The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information. However, traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone. A successive corrections analysis using variational optimization method, called spatial multi-scale recursive filter (SMRF), has been designed in this paper to extract multi-scale information resolved by sea ice observations. It is a combination of successive correction methods (SCM) and minimization algorithms, in which various observational scales, from longer to shorter wavelengths, can be extracted successively. As a variational objective analysis scheme, it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time, and also, the specification of parameters is more convenient. Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals. Further, it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration (SIC) experiment with the real observations from Special Sensor Microwave/Imager SIC (SSMI).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Achtemeier G L. 1986. The impact of data boundaries upon a successive corrections objective analysis of limited-area datasets. Monthly Weather Review, 114(1): 40–49, doi: https://doi.org/10.1175/1520-0493(1986)114<0040:TIODBU>2.0.CO;2
Barnes S L. 1964. A technique for maximizing details in numerical weather map analysis. Journal of Applied Meteorology, 3(4): 396–409, doi: https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2
Barnes S L. 1973. Mesoscale objective map analysis using weighted time-series observations. Norman: National Severe Storms Laboratory, 60
Behringer D W, Ji Ming, Leetmaa A. 1998. An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: the ocean data assimilation system. Monthly Weather Review, 126(4): 1013–1021, doi: https://doi.org/10.1175/1520-0493(1998)126<1013:AICMFE>2.0.CO;2
Buzzi A, Gomis D, Pedder M A, et al. 1991. A method to reduce the adverse impact that inhomogeneous station distributions have on spatial interpolation. Monthly Weather Review, 119(10): 2465–2491, doi: https://doi.org/10.1175/1520-0493(1991)119<2465:AMTRTA>2.0.CO;2
Courtier P. 1997. Variational methods (gtSpecial IssueltData assimilation in meteology and oceanography: theory and practice). Journal of the Meteorological Society of Japan, 75(1B): 211–218, doi: https://doi.org/10.2151/jmsj1965.75.1B_211
Cressman G P. 1959. An operational objective analysis system. Monthly Weather Review, 87(10): 367–374, doi: https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
Derber J, Rosati A. 1989. A global oceanic data assimilation system. Journal of Physical Oceanography, 19(9): 1333–1347, doi: https://doi.org/10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2
Hayden C M, Purser R J. 1995. Recursive filter objective analysis of meteorological fields: applications to NESDIS operational processing. Journal of Applied Meteorology, 34(1): 3–15, doi: https://doi.org/10.1175/1520-0450-34.1.3
He Zhongjie, Xie Yuanfu, Li Wei, et al. 2008. Application of the sequential three-dimensional variational method to assimilating SST in a global ocean model. Journal of Atmospheric and Oceanic Technology, 25(6): 1018–1033, doi: https://doi.org/10.1175/2007JTECHO540.1
Huang Bohua, Kinter J L, Schopf P S. 2002. Ocean data assimilation using intermittent analyses and continuous model error correction. Advances in Atmospheric Sciences, 19(6): 965–992, doi: https://doi.org/10.1007/s00376-002-0059-z
Koch S E, desJardins M, Kocin P J. 1983. An interactive Barnes objective map analysis scheme for use with satellite and conventional data. Journal of Climate and Applied Meteorology, 22(9): 1487–1503, doi: https://doi.org/10.1175/1520-0450(1983)022<1487:AIBOMA>2.0.CO;2
Li Dong, Wang Xidong, Zhang Xuefeng, et al. 2011. Multi-scale 3D-VAR based on diffusion filter. Marine Science Bulletin (in Chinese), 30(2): 164–171
Li Wei, Xie Yuanfu, He Zhongjie, et al. 2008. Application of the multi-grid data assimilation scheme to the China Seas’ temperature forecast. Journal of Atmospheric and Oceanic Technology, 25(11): 2106–2116, doi: https://doi.org/10.1175/2008JTECHO510.1
Liu D C, Nocedal J. 1989. On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(1): 503–528, doi: https://doi.org/10.1007/BF01589116
Lorenc A C. 1986. Analysis methods for numerical weather prediction. Quarterly Journal of the Royal Meteorological Society, 112(474): 1177–1194, doi: https://doi.org/10.1002/qj.49711247414
Lorenc A C. 1988. Optimal nonlinear objective analysis. Quarterly Journal of the Royal Meteorological Society, 114(479): 205–240, doi: https://doi.org/10.1002/qj.49711447911
Lorenc A. 1992. Iterative analysis using covariance functions and filters. Quarterly Journal of the Royal Meteorological Society, 118(505): 569–591, doi: https://doi.org/10.1002/qj.49711850509
Lu Chungu, Browning G L. 1998. The impact of observational errors on objective analyses. Journal of the Atmospheric Sciences, 55(10): 1791–1807, doi: https://doi.org/10.1175/1520-0469(1998)055<1791:TIOOEO>2.0.CO;2
Masina S, Pinardi N, Navarra A. 2001. A global ocean temperature and altimeter data assimilation system for studies of climate variability. Climate Dynamics, 17(9): 687–700, doi: https://doi.org/10.1007/s003820000142
Moré J J, Thuente D J. 1994. Line search algorithms with guaranteed sufficient decrease. ACM Transactions on Mathematical Software, 20(3): 286–307, doi: https://doi.org/10.1145/192115.192132
Pauley P M, Wu Xiaihua. 1990. The theoretical, discrete, and actual response of the Barnes objective analysis scheme for one- and two-dimensional fields. Monthly Weather Review, 118(5): 1145–1164, doi: https://doi.org/10.1175/1520-0493(1990)118<1145:TTDAAR>2.0.CO;2
Peng Shiqiu, Xie Lian, Liu Bin, et al. 2010. Application of scale-selective data assimilation to regional climate modeling and prediction. Monthly Weather Review, 138(4): 1307–1318, doi: https://doi.org/10.1175/2009MWR2974.1
Purser R J, Wu Wanshu, 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(8): 1524–1535, doi: https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2
Purser R J, Wu Wanshu, Parrish D F, et al. 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(8): 1536–1548, doi: https://doi.org/10.1175//2543.1
Seaman R S. 1983. Objective analysis accuracies of statistical interpolation and successive correction schemes. Australian Meteorological Magazine, 31(4): 225–240
Seaman R S, Hutchinson M F. 1985. Comparative real data tests of some objective analysis methods by withholding observations. Australian Meteorological Magazine, 33(1): 37–46
Wu Xinrong, Han Guijun, Li Dong, et al. 2011. A three-dimensional variational analysis using sequential filter. In: Proceedings of the 2011 4th International Joint Conference on Computational Sciences and Optimization. Yunnan: IEEE, 1016–1020, doi: https://doi.org/10.1109/CSO.2011.60
Xie Yuanfu, Koch S E, McGinley J A, et al. 2005. A sequential variational analysis approach for mesoscale data assimilation. In: Proceedings of the 21st Conference on Weather Analysis and Forecasting/17th Conference on Numerical Weather Prediction. Washington, DC: American Meteorological Society
Xie Y, Koch S, McGinley J, et al. 2010. A space-time multiscale analysis system: a sequential variational analysis approach. Monthly Weather Review, 139(4): 1224–1240, doi: https://doi.org/10.1175/2010MWR3338.1
Zhang Xuefeng, Li Dong, Chu P C, et al. 2015. Diffusion filters for variational data assimilation of sea surface temperature in an intermediate climate model. Advances in Meteorology, 2015: 751404, doi: https://doi.org/10.1155/2015/751404
Zhang S, Zhao M, Lin S J, et al. 2014. Retrieval of tropical cyclone statistics with a high-resolution coupled model and data. Geophysical Research Letters, 41(2): 652–660, doi: https://doi.org/10.1002/2013GL058879
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item
The National Key Research and Development Program of China under contract Nos 2017YFC1404103 and 2016YFC1401701; the National Programme on Global Change and Air-Sea Interaction of China under contract GASI-IPOVAI-04; the National Natural Science Foundation of China under contract Nos 41876014 and 41606039.
Rights and permissions
About this article
Cite this article
Zhang, X., Yang, L., Fu, H. et al. A variational successive corrections approach for the sea ice concentration analysis. Acta Oceanol. Sin. 39, 140–154 (2020). https://doi.org/10.1007/s13131-020-1654-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13131-020-1654-5