A variational successive corrections approach for the sea ice concentration analysis

Xuefeng Zhang; Lu Yang; Hongli Fu; Dong Li; Zheqi Shen; Lianxin Zhang; Xuhui Hu

doi:10.1007/s13131-020-1654-5

Volume 39 Issue 9

Sep. 2020

Turn off MathJax

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2020 > 39(9): 140-154

Xuefeng Zhang, Lu Yang, Hongli Fu, Dong Li, Zheqi Shen, Lianxin Zhang, Xuhui Hu. A variational successive corrections approach for the sea ice concentration analysis[J]. Acta Oceanologica Sinica, 2020, 39(9): 140-154. doi: 10.1007/s13131-020-1654-5

Citation:

Xuefeng Zhang, Lu Yang, Hongli Fu, Dong Li, Zheqi Shen, Lianxin Zhang, Xuhui Hu. A variational successive corrections approach for the sea ice concentration analysis[J]. Acta Oceanologica Sinica, 2020, 39(9): 140-154. doi: 10.1007/s13131-020-1654-5

Citation:

PDF( 1323 KB)

A variational successive corrections approach for the sea ice concentration analysis

doi: 10.1007/s13131-020-1654-5

1.
School of Marine Science and Technology, Tianjin University, Tianjin 300110, China
2.
Key Laboratory of Marine Environmental Information Technology, National Marine Data and Information Service, Ministry of Natural Resources, Tianjin 300171, China
3.
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

Funds: 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.

More Information

Corresponding author: E-mail: fhlkjj@163.com
Received Date: 2019-06-03
Accepted Date: 2020-01-13

Available Online: 2020-12-28

Publish Date: 2020-09-25

Abstract

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).
- variational successive corrections,
- spatial multi-scale recursive filter,
- sea ice concentration

FullText(HTML)

References(32)

References

[1]	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: 10.1175/1520-0493(1986)114<0040:TIODBU>2.0.CO;2
[2]	Barnes S L. 1964. A technique for maximizing details in numerical weather map analysis. Journal of Applied Meteorology, 3(4): 396–409. doi: 10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2
[3]	Barnes S L. 1973. Mesoscale objective map analysis using weighted time-series observations. Norman: National Severe Storms Laboratory, 60
[4]	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: 10.1175/1520-0493(1998)126<1013:AICMFE>2.0.CO;2
[5]	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: 10.1175/1520-0493(1991)119<2465:AMTRTA>2.0.CO;2
[6]	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: 10.2151/jmsj1965.75.1B_211
[7]	Cressman G P. 1959. An operational objective analysis system. Monthly Weather Review, 87(10): 367–374. doi: 10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
[8]	Derber J, Rosati A. 1989. A global oceanic data assimilation system. Journal of Physical Oceanography, 19(9): 1333–1347. doi: 10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2
[9]	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: 10.1175/1520-0450-34.1.3
[10]	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: 10.1175/2007JTECHO540.1
[11]	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: 10.1007/s00376-002-0059-z
[12]	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: 10.1175/1520-0450(1983)022<1487:AIBOMA>2.0.CO;2
[13]	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
[14]	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(11): 2106–2116. doi: 10.1175/2008JTECHO510.1
[15]	Liu D C, Nocedal J. 1989. On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(1): 503–528. doi: 10.1007/BF01589116
[16]	Lorenc A C. 1986. Analysis methods for numerical weather prediction. Quarterly Journal of the Royal Meteorological Society, 112(474): 1177–1194. doi: 10.1002/qj.49711247414
[17]	Lorenc A C. 1988. Optimal nonlinear objective analysis. Quarterly Journal of the Royal Meteorological Society, 114(479): 205–240. doi: 10.1002/qj.49711447911
[18]	Lorenc A. 1992. Iterative analysis using covariance functions and filters. Quarterly Journal of the Royal Meteorological Society, 118(505): 569–591. doi: 10.1002/qj.49711850509
[19]	Lu Chungu, Browning G L. 1998. The impact of observational errors on objective analyses. Journal of the Atmospheric Sciences, 55(10): 1791–1807. doi: 10.1175/1520-0469(1998)055<1791:TIOOEO>2.0.CO;2
[20]	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: 10.1007/s003820000142
[21]	Moré J J, Thuente D J. 1994. Line search algorithms with guaranteed sufficient decrease. ACM Transactions on Mathematical Software, 20(3): 286–307. doi: 10.1145/192115.192132
[22]	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: 10.1175/1520-0493(1990)118<1145:TTDAAR>2.0.CO;2
[23]	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: 10.1175/2009MWR2974.1
[24]	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: 10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2
[25]	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: 10.1175//2543.1
[26]	Seaman R S. 1983. Objective analysis accuracies of statistical interpolation and successive correction schemes. Australian Meteorological Magazine, 31(4): 225–240
[27]	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
[28]	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: 10.1109/CSO.2011.60
[29]	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
[30]	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: 10.1175/2010MWR3338.1
[31]	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: 10.1155/2015/751404
[32]	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: 10.1002/2013GL058879