ARGO稀损数据插补与三维海洋要素场重构研究
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摘要
ARGO资料作为目前唯一实时/延时可用的全球尺度海洋三维观测资料,为分析揭示海洋环境温盐分布和流场结构、理解和深化海洋环境特性和气候变化规律提供了关键的观测事实基础,其蕴涵的丰富信息资源具有重要的科学意义和应用价值。论文针对ARGO资料稀疏、零散、缺损等空间不规则和时间不连续固有弱点和应用盲区,开展了ARGO稀疏资料插值、缺损数据拟合和三维温盐场重构等客观再分析研究；开展了基于卫星遥感资料和ARGO资料的海表流场估算以及三维流场的反演研究。为发掘和优化利用ARGO信息资源及ARGO资料再分析应用提供理论依据和技术手段；为深化海洋环境演变与气候变化研究,改进提高大气、海洋环境要素的数值预报能力提供数据信息支持。
论文主要成果和创新点包括：
(1)针对ARGO等稀疏资料的小尺度结构和细节特征刻画等实际问题,改进和发展了分形插值方法模型,通过引入遗传算法对分形插值参数(压缩因子)优化搜索,实现了插值参数的客观优选,改进了分形插值方法对海洋稀疏要素场插值的合理性和有效性。
(2)提出了一种小样本插值新方法-信息扩散插值。该方法基于模糊映射思想,通过对稀少数据点的信息进行模糊扩散和插值映射,实现有限数据点信息向其邻近区域点的概率插值；针对正态扩散模型在描述非对称结构数据的局限性,发展了非对称信息扩散插值算法模型-椭圆模型和概率模型。插值试验对比分析验证了该插值方法的合理性和有效性。
(3)三维海洋要素场重构。针对常规时空插值方法在分析处理ARGO资料和插值重构ARGO标准化网格产品中存在的问题和不足,提出了一种改进的时空插值算法,构建了三维温盐场反演的算法模型；基于改进的时空插值算法重构的三维要素场误差得到了有效的抑制。
(4)针对实际海洋要素场资料中普遍存在的数据缺测问题和常规奇异谱方法中迭代参数K、M选取的主观性和计算效率差等问题,提出和发展了模型参数分段寻优的新方法,能够避免模型参数陷入局部误差收敛进而合理搜索到全局最优解,且迭代计算效率和插补结果准确率得到有效改进和提高。
(5)利用卫星高度计资料和卫星遥感海面风场QuikSCAT资料,通过对不同纬度流场计算模型的分段处理,改进了地转流和Ekman流在赤道海区和赤道外海区的不连续问题,反演得到的海表地转流和Ekman流能够合理、准确地刻画全球海表流场的基本结构特征。
(6)基于ARGO浮标三维温盐场观测信息,通过将P矢量方法引入等密度面温盐场动力约束,反演得到三维海洋流场。该方法弥补了实际海流观测困难、海流资料难以准确获取的现实问题和不足,反演推算的三维流场可客观表现不同深度的海洋流场的基本结构特征。
Being the only real-time/delayed-time available three-dimensional global-scale ocean observation data, Argo data provides critical observation factual basis which contains a wealth of information resources for revealing the marine environment, temperature and salinity distribution and current structure, understanding and deepening of the marine environment features and climate change, and has important scientific significance. Aiming at the sparseness, fragments and discontinuousness of Argo data, the reanalysis research on fitting sparse data and reconstructing the three-dimensional temperature and salinity field is conducted. The surface current field is estimated and the three-dimensional current field is inversed based on the blending of Argo data and satellite remote sensing data. This study can provide a theoretical basis and technical means for exploring and optimizing the use of the Argo information resources and Argo data, and provide data and information support for deepening the research on the evolution of the marine environment and climate change and improving the numerical prediction capability of meteorological and oceanic numerical models.
The main achievements and creative results are as follows:
(1) Aiming at the small-scale detailed structures and characteristics of the sparse data like Argo data, the fractal interpolation method model is improved. By introducing genetic algorithm to the searching of fractal interpolation parameters (compression factor) to optimize the search, the objective optimization is achieved, and the rationality and effectiveness of the fractal interpolation method on the sparse data is improved.
(2) A small sample interpolation method-Information diffusion interpolation is put forward. Based on the idea of fuzzy mapping, by fuzzy diffusing and mapping the sparse data points, this technique can achieve the probability interpolation from the limited data points to its neighboring regions points. In order to the limitations of the normal diffusion model on describing the asymmetric structure of data, the nonnormal diffusion model-the elliptical model and probability model is developed. This model is verified by comparing the results of interpolation experiments.
(3) The reconstruction of three-dimensional ocean data field. Aiming at the deficiencies that exist in the Argo data and interpolation reconstruction Argo standardized grid products against the conventional space-time interpolation method, an improved space-time interpolation algorithm is developed, and the three-dimensional temperature and salinity model is conducted. This model can efficiently suppress the errors that exist in the conventional means.
(4) Aiming at the common missing data in the actual marine field data and the conventional singular spectrum of the iterative parameter K, M selected subjectivity and poor computational efficiency, a new model parameter segmenting method is put forward. This method can avoid the model parameters into the local error convergence and thus a reasonable search for the global optimal solution can be got. The iterative computational efficiency and accuracy of the interpolation results can be improved and enhanced.
(5) Based on the satellite altimeter data and satellite remote sensing of sea surface wind field QuikSCAT data, the inversed surface geostrophic flow and Ekman flow can be got by dealing with the flow model in different latitude segments using different ways to overcome the discontinuity near the equator.
(6) Based on the Argo observational three-dimensional temperature salinity field, the three-dimensional ocean flow field is inversed using the P-vector method based on the isopycnal temperature and salinity constraints. This method makes up the actual ocean current observation difficulty and practical problems and deficiencies in available current data. The inversed three-dimensional flow field can objectively reproduce the performance of currents on the different depths.
论文主要成果和创新点包括：
(1)针对ARGO等稀疏资料的小尺度结构和细节特征刻画等实际问题,改进和发展了分形插值方法模型,通过引入遗传算法对分形插值参数(压缩因子)优化搜索,实现了插值参数的客观优选,改进了分形插值方法对海洋稀疏要素场插值的合理性和有效性。
(2)提出了一种小样本插值新方法-信息扩散插值。该方法基于模糊映射思想,通过对稀少数据点的信息进行模糊扩散和插值映射,实现有限数据点信息向其邻近区域点的概率插值；针对正态扩散模型在描述非对称结构数据的局限性,发展了非对称信息扩散插值算法模型-椭圆模型和概率模型。插值试验对比分析验证了该插值方法的合理性和有效性。
(3)三维海洋要素场重构。针对常规时空插值方法在分析处理ARGO资料和插值重构ARGO标准化网格产品中存在的问题和不足,提出了一种改进的时空插值算法,构建了三维温盐场反演的算法模型；基于改进的时空插值算法重构的三维要素场误差得到了有效的抑制。
(4)针对实际海洋要素场资料中普遍存在的数据缺测问题和常规奇异谱方法中迭代参数K、M选取的主观性和计算效率差等问题,提出和发展了模型参数分段寻优的新方法,能够避免模型参数陷入局部误差收敛进而合理搜索到全局最优解,且迭代计算效率和插补结果准确率得到有效改进和提高。
(5)利用卫星高度计资料和卫星遥感海面风场QuikSCAT资料,通过对不同纬度流场计算模型的分段处理,改进了地转流和Ekman流在赤道海区和赤道外海区的不连续问题,反演得到的海表地转流和Ekman流能够合理、准确地刻画全球海表流场的基本结构特征。
(6)基于ARGO浮标三维温盐场观测信息,通过将P矢量方法引入等密度面温盐场动力约束,反演得到三维海洋流场。该方法弥补了实际海流观测困难、海流资料难以准确获取的现实问题和不足,反演推算的三维流场可客观表现不同深度的海洋流场的基本结构特征。
Being the only real-time/delayed-time available three-dimensional global-scale ocean observation data, Argo data provides critical observation factual basis which contains a wealth of information resources for revealing the marine environment, temperature and salinity distribution and current structure, understanding and deepening of the marine environment features and climate change, and has important scientific significance. Aiming at the sparseness, fragments and discontinuousness of Argo data, the reanalysis research on fitting sparse data and reconstructing the three-dimensional temperature and salinity field is conducted. The surface current field is estimated and the three-dimensional current field is inversed based on the blending of Argo data and satellite remote sensing data. This study can provide a theoretical basis and technical means for exploring and optimizing the use of the Argo information resources and Argo data, and provide data and information support for deepening the research on the evolution of the marine environment and climate change and improving the numerical prediction capability of meteorological and oceanic numerical models.
The main achievements and creative results are as follows:
(1) Aiming at the small-scale detailed structures and characteristics of the sparse data like Argo data, the fractal interpolation method model is improved. By introducing genetic algorithm to the searching of fractal interpolation parameters (compression factor) to optimize the search, the objective optimization is achieved, and the rationality and effectiveness of the fractal interpolation method on the sparse data is improved.
(2) A small sample interpolation method-Information diffusion interpolation is put forward. Based on the idea of fuzzy mapping, by fuzzy diffusing and mapping the sparse data points, this technique can achieve the probability interpolation from the limited data points to its neighboring regions points. In order to the limitations of the normal diffusion model on describing the asymmetric structure of data, the nonnormal diffusion model-the elliptical model and probability model is developed. This model is verified by comparing the results of interpolation experiments.
(3) The reconstruction of three-dimensional ocean data field. Aiming at the deficiencies that exist in the Argo data and interpolation reconstruction Argo standardized grid products against the conventional space-time interpolation method, an improved space-time interpolation algorithm is developed, and the three-dimensional temperature and salinity model is conducted. This model can efficiently suppress the errors that exist in the conventional means.
(4) Aiming at the common missing data in the actual marine field data and the conventional singular spectrum of the iterative parameter K, M selected subjectivity and poor computational efficiency, a new model parameter segmenting method is put forward. This method can avoid the model parameters into the local error convergence and thus a reasonable search for the global optimal solution can be got. The iterative computational efficiency and accuracy of the interpolation results can be improved and enhanced.
(5) Based on the satellite altimeter data and satellite remote sensing of sea surface wind field QuikSCAT data, the inversed surface geostrophic flow and Ekman flow can be got by dealing with the flow model in different latitude segments using different ways to overcome the discontinuity near the equator.
(6) Based on the Argo observational three-dimensional temperature salinity field, the three-dimensional ocean flow field is inversed using the P-vector method based on the isopycnal temperature and salinity constraints. This method makes up the actual ocean current observation difficulty and practical problems and deficiencies in available current data. The inversed three-dimensional flow field can objectively reproduce the performance of currents on the different depths.
引文
[1]JAMES O'DONNELL, ARTHUR A. ALLEN, DONALD L. MURPHY, An Assessment of the Errors in Lagrangian Velocity Estimates Obtained by FGGE Drifters in the Labrador Current[J]. J. Atmos. Oceanic Technol,1997:14,292～307.
[2]AMY S. BOWER, LAURENCE ARMI, ISABEL AMBAR, Lagrangian Observations of Meddy Formation during AMediterranean Undercurrent Seeding Experiment[J]. J. Phys. Oceanogr. 1997:27,2545～2575.
[3]JOHANNES R. GEMMRICH, DAVID M. FARMER, Near-Surface Turbulence and Thermal Structure in a Wind-Driven Sea[J]. J. Phys. Oceanogr.1999:29:480～499.
[4]ELISE A. RALPH AND PEARN P. NIILER, Wind-Driven Currents in the Tropical Pacific[J]. J. Phys. Oceanogr.1999:29,2121～2119.
[5]刘增宏,许建平,朱伯康ARGOs漂流浮标的若干观测结果[J].热带海洋学报,24(1)：21-27,2005.
[6]苏京志,王东晓,张人禾.南海ARGO浮标观测结果初步分析[J].海洋与湖沼,2008,45(2)：276-283.
[7]张艳慧,王凡,臧楠.基于ARGO浮标和历史资料的热带西太平洋次表层与中层水年代变化分析[J].海洋学报,30(6)：17-23,2008.
[8]王彦磊,黄兵,张韧等.基于ARGO资料的世界大洋温度跃层的分布特征[J].海洋科学进展,26(4)：428-435,2008.
[9]陈奕德,张韧等.利用ARGO浮标定位信息估算分析赤道太平洋中层流场[J].海洋预报,23(4)：37-46,2006.
[10]朱江,周广庆等.一个三维变分海洋资料同化系统的设计和应用[J].中国科学(D),2007,37(2)：242-249.
[11]张人禾,刘益民.利用ARGO资料改进海洋资料同化和海洋模式中物理过程[J].气象学报,62(5)：613-622,2004.
[12]吴宗敏.径向基函数散乱数据拟合与无网格偏微分方程数值解[J].工程数学学报,19(2)：1-12,2002.
[13]张伟强,唐泽圣.大规模散乱数据层次B-样条曲面表示[J].计算机学报,22(10)：1059-1064,1999.
[14]王国夫,孙尧等.基于节点插入原理大规模散乱数据插值[J].哈尔滨工程大学学报,22(1)：45-48,2001.
[15]Cressman,G.P,. An operational objective analysis system[J].Mon.Wea.Rev.,87,367-747,1959.
[16]廖洞贤,工两铭.数值天气预报原理及应用[M].北京：气象出版社,356-3641986.
[17]张韧,万齐林等.地球科学中的散乱数据优化与缺损信息恢复[J].数据采集与处理.21(2)：209-217,2006.
[18]M.F.Bamsley and S.G.Demko.Iterated function systems and the global construction of fractals[J].Proc.of the Royal Soc.London,A399,243-275,1986.
[19]M.F.Bamsley.Fractal functions and interpolations[M].Constructive Approxmation 2,1986.
[20]Arakawa K,Krotkov E.Fractal modeling natural terrain:analysis and surface reconstruction with range data[J].Graphical Models and Image Processing,58(5),413-436,1996.
[21]孙洪泉.分形插值曲面理论与岩石断裂表面的分形插值研究[D].北京：中国矿业大学,1998.
[22]谢和平,孙洪泉.分形插值曲面理论及其应用[J].应用数学和力学,19(4),297-306,1998.
[23]Wang Jinan.Morphology and mechanical behavior of rock joints[D].Gliwice Polands Silesian Technical University,1994.
[24]阮火军,沙震.分块分形插值拟合与遗传算法[J].高校应用数学学报A辑,16(3)：263-268,2001.
[25]黄崇福.自然灾害风险评价-理论与实践[M].北京：科学出版社,2006.
[26]Huang C.F.2002.Information diffusion technique and small sample problem. International Journal of Information Technology and Decision Making, 1(2):229-249.
[27]黄崇福.自然灾害风险分析的信息矩阵方法[J].自然灾害学报.2006,15(1)：1-10
[28]张继权,李宁.气象灾害风险评估与管理的数量化应用[M].北京：北京师范大学出版社,2008.
[29]张韧,洪梅.基于不完备数据样本的联合作战大气-海洋环境风险决策[J].军事运筹与系统工程.23(1)：48-52,2009
[30]张韧,徐志升.信息扩散理论在舰载导弹环境影响评估中应用[J].火力与指挥控制,35(2)：41-45,2010.
[31]Liu Wei(刘巍),Zhang Ren,Xu Zhisheng. An Interpolation Algorithm Based on Information Diffusing Principle and Its Application in Sparse Oceanic Environment Data.2010 International Conference on Remote Sensing. Hangzhou,China,Oct.2010.
[32]黄嘉佑,张镡.北京降水缺测资料恢复的研究[J].气象,22(7)：6-11,1996.
[33]黄嘉佑.气象统计分析与预报方法[M].气象出版社,2004.
[34]杨欣斌，孙京诰,黄道.基于Bayesian网络的缺损数据处理方法[J].华东理上大学学报,(增刊)：41-44,2002.
[35]缪报通,陈发来.径向基函数神经网络在散乱数据插值中应用[J].中国科学技术大学学报,(2)：135-142,2001.
[36]岳十弘,张可村.带有误差估计式的最优模糊插值系统研究[J].两安交通大学学报,34(6)：98-101,2000.
[37]薛领涛,李郝林.基于模糊推理的自由曲而插值方法[J].机械设计与制造工程,31(5)：60-62,2002.
[38]吴洪宝,吴蕾.气候变率诊断和预测方法[M].北京：气象出版社,2005.
[39]J.M.Beckers and M.Rixen.EOF calculations and data filling from incomplete oceanographic datasets[J]. Journal of Atmospheric and Oceanic Technology.,20,1839-1856,2003.
[40]王桂华,刘增宏,许建平.利用ARGO资料重构太平洋三维温盐场和流场[A]ARGO应用研究论文集[C].北京：海洋出版社,16-26,2006.
[41]D.Kondrashov and M.Ghil.Spatio-temporal filling of missing points in geophysical data sets[J].Nonlinear Processes in Geophysics.,13,151-159,2006.
[42]Guinehut, S., P.Y. Le Traon, G. Larnicol, S. Philipps(2004), Combining ARGO and remote-sensing data to estimate the ocean three-dimensional temperature fields-a first approach based on simulated observations, J. Mar. Syst.,46,85-98,.
[43]Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes and W. Wang(2002),An improved in situ and satellite SST analysis for climate, J. Climate,15,1609-1625.
[44]Willis, J. K., D. Roemmich, and B. Cornuelle (2003),Combining altimetric height with broadscale profile data to estimate steric height, heat storage, subsurface temperature, and sea-surface temperature variability, J. Geophys. Res.,108(C9),3292.
[45]Fox,D. N.,W. J. Teague.C. N. Barron,M. R. Carnes (2002),The Modular Ocean Data Assimilation System (MODAS) Journal of Atmospheric & Oceanic Technology,19(2),240-252.
[46]Sakurai,T., K.Yukio, T.Kuragano (2005),Merged Satellite and In-situ Data Global Daily SST, Geoscience and Remote Sensing Symposium,2005. IGARSS'05. Proceedings,4,2606-2608.
[47]Kondrashov D,Ghil M.Spatio-temporal filling of missing points in geophysical data sets[J]. Nonlinear Processes Geophysics,2006,13(2):151-159.
[48]Kondrashov D, Ghil M. Reply to T Schneider's comment on"Spatio-temporalfilling of missing points in geophysical data sets"[J].Nonlinear Processes Geophysics,2007,14(1):3-4.
[49]Gomis, D., S. Ruiz, and M. A. Pedder (2001), Diagnostic analysis of the 3D ageostrophic circulation from a multivariate spatial interpolation of CTD and ADCP data, Deep Sea Res., Part I, 48,269-295.
[50]Paris, C. B., R. K. Cowen, K. M. M. Lwiza, D.-P. Wang, and D. B. Olson(2002), Multivariate objective analysis of the coastal circulation of Barbados, West Indies:Implication for larval transport, Deep Sea Res., Part I,49,1363-1386.
[51]Troccoli,A., M.A.Balmaseda, J.Segschneider, J.Vialard, D.L.T. Anderson, K.Haines, T.Stockdale, F.Vitart and A.D. Fox (2002),Salinity adjustments in the presence of temperature data assimilation, Mon. Wea. Rev.,130,89-102.
[52]Fujii,Yosuke and Masafumi Kamachi 1(2003), A Reconstruction of Observed Profiles in the Sea East of Japan Using Vertical Coupled Temperature-Salinity EOF Modes, J. Oceanogr.,59, 173-186.
[53]Sparnocchia,S.,N. Pinardi, and E. Demirov(2003),Multivariate empirical orthogonal function analysis of the upperthermocline structure of the Mediterranean sea from observations and model simulations, Annales Geophysicae,21,167-187.
[54]张仁铎.空间变异理论及应用[M].北京：科学出版社,2005.
[55]Beckers J M, Rixen M. EOF calculations and data filling from incomplete oceanographic datasets[J]. Journal of Atmospheric and Oceanic Technology,2003,20(12):1839-1856.
[56]Peter C.Chu and G.H.Wang. Auto-correlation to determine the spatial and temporal scale in the Japan Sea. Journal of Physical Oceanography,2002,32,3596-3615.
[57]Alvera-Azca'rate, A., A. Barth, M. Rixen, and J. M. Beckers (2005), Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions:Application to the Adriatic Sea, Ocean Modell.,9,325-346.
[58]Alvera-Azcarate,A.,A.Barth,J.-M.Beckers. (2007),Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields, J. Geophys. Res.,112.
[59]Gaillard, F.,E.Autret, Vi.Thierry, and P.Galaup(2009), Quality Control of Large ARGO Datasets, Journal of Atmospheric and Oceanic Technology,26(2),337-351.
[60]Eugenia Kalnay(蒲朝霞等译).大气模式、资料同化和可预报性.北京：气象出版社,2005.
[61]M.F.Bamsley and S.G.Demko. Iterated function systems and the global construction of fractals[J].Proc.of the Royal Soc.London,A399,243-275,1986.
[62]令锋,王继民.分形插值函数对垂直压缩因子连续依赖性[J].兰州铁道学院学报,14(4),64-68,1995.
[63]吴思源,周源华.三维IFS分形插值逆问题的局部迭代算法[J].上海交通大学学报,38(9),1519-1523,2004.
[64]王辉赞,张韧,王彦磊.遗传优化的分形插值及其在ARGO资料中的应用[C].2007年中国智能自动化会议论文集,2007.08,兰州.
[65]刘巍,张韧,王辉赞.分形插值参数的遗传优化及其ARGO海温场应用试验[J].大气科学学报,33(2)：186-192,2010.
[66]王小平,曹立明.遗传算法-理论、应用与软件实现[M].西安：西安交通大学出版社,2003.
[67]张韧,洪梅.经验正交函数与遗传算法结合的副高位势场非线性模型反演[J].应用数学和力学,27(12)：1438-1445,2006.
[68]张韧,洪梅.副热带高压与东亚季风指数非线性数学模型的遗传算法参数优化.工程数学学报,25(3)：381-389,2008.
[69]张韧,洪梅等.基于遗传算法优化的ENSO指数的动力预报模型反演.地球物理学报,51(5)：1346-1353,2008.
[70]许建平ARGO全球海洋观测探秘[M].北京：海洋出版社,2002.
[71]苏纪兰.如何正确认识ARGO计划[J].海洋技术,20(2)：1-3,2001.
[72]吴洪宝,吴蕾.气候变率诊断和预测方法[M].北京：气象出版社,2005.
[73]封国林,董文杰.观测数据非线性时空分布理论和方法[M].北京：气象出版社,2006.
[74]刘巍,张韧,徐志升,金炜东.基于信息扩散的稀疏数据插值算法[J].解放军理工大学学报(自然科学版),13(1)：114-121,2012.
[75]刘巍,张韧,徐志升.基于信息扩散的海洋稀疏观测资料插值算法-椭圆模型[J].计算力学学报录用.
[76]张韧,刘巍,徐志升.基于信息扩散的海洋稀疏观测资料插值算法-概率模型[J].计算力学学报录用.
[77]Liu,W.T.,Tang W.,Polito P.S.(1998), NASA scatterometer provides global ocean-surface wind fields with more structures than numerical prediction.Geophys. Res. Lett.,25(6),761-764.
[78]Zeng, L., and Levy G.(1995), Space and time aliasing structure in monthly mean polar-orbiting satellite data, J. Geophys. Res.,100(D3),5133-5142.
[79]Kutsuwada,K.(1998), Impact of wind/wind-stress field in the North Pacific constructed by ADEOS/NSCAT data,J. Oceanogr.,54,443-456.
[80]Gandin, L.S.(1963), Objective analysis of meteorological field. Translated(1965) from Russian by Israel Programme for Scientific Translation, Jerusalem,242 pp.
[81]Cressman,G.P.(1959),An operational objective analysis system, Mon.Wea.Rev.,87,367-374.
[82]Emery,W.J.,and Thomson R.E.(2004),Data Analysis Methods in Physical Oceanography, Second and revised edition, Elsevier:Amsterdam, The Netherlands,638 pp.
[83]Zhang,H.-M.,Reynolds R.W., and Bates J.J.(2006),Blended and Gridded High Resolution Global Sea Surface Wind Speed and Climatology from Multiple Satellites:1987- Present, American Meteorological Society 2006 Annual Meeting,Paper2.23,Atlanta,GA,2006.
[84]Yuan, X.(2004), High-wind-speed evaluation in the Southern Ocean, J. Geophys. Res.,109, D13101,doi:10.1029/2003JD004179.
[85]Kubota, M., and O'Brien J. J. (1988), Variability of the Upper Tropical Pacific Ocean Model,J. Geophys. Res.,93(C11),13,930-13,940.
[86]Levitus, S. (Ed.)(2006), World Ocean Atlas 2005. vol.1-4, NOAA Atlas NESDIS, vol.61-64, NOAA, Silver Spring, Md.
[87]Chang, Y.-S., Rosati A. J., Zhang S., and Harrison M. J. (2009), Objective analysis of monthly temperature and salinity for the world ocean in the 21st century:Comparison with World Ocean Atlas and application to assimilation validation, J. Geophys. Res.,114,C02014.
[88]吴洪宝,吴蕾.气候变率诊断和预测方法[M].北京：气象出版社,2005.
[89]干辉赞,张韧,刘巍.奇异谱迭代插补的改进算法及其在缺损数据恢复中的应用[J].应用数学和力学.(中、英文版)29(10)：1127-1136,2008.
[90]江志红,丁裕国,屠其璞.气象场序列几种插补方案的对比试验[J].南京气象学院学报, 22(3)：352-359,1999.
[91]J.M.Beckers and M.Rixen.EOF calculations and data filling from incomplete oceanographic datasets[J].Journal of Atmospheric and Oceanic Technology,20(12),1839-1856,2003.
[92]D.Kondrashov and M.Ghil.Spatio-temporal filling of missing points in geophysical data sets[J]. Nonlinear Processes in Geophysics.,13(2),151-159,2006.
[93]王桂华,刘增宏,许建平.利用ARGO资料重构太平洋三维温盐场和流场[A]ARGO应用研究论文集[C].北京：海洋出版社,16-25,2006.
[94]Alvera-Azca'rate, A., Barth A., Rixen M., and Beckers J. M. (2005), Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions:Application to the Adriatic Sea, Ocean Modell.,9,325-346.
[95]Vautard R and Ghil M. Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series[J],Physica D.,1989,35(3),395-424.
[96]Alvera-Azcarate,A.,Barth A.,Beckers J.-M.(2007).Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields,J. Geophys. Res.,112, C03008.
[97]Kondrashov D and Ghil M. Reply to T. Schneider's comment on "Spatio-temporal filling of missing points in geophysical data sets"[J]. Nonlinear Processes in Geophysics,2007,14(1):3-4.
[98]Broomhead D S and King G P. Extracting qualitative dynamics from experimental data,Physica D,1986,20(2-3),217-236.
[99]Liu Wei(刘巍), Jin Weidong,Wang Huizan.An Optimized Atmospheric Missed Data Recovery Algorithm Based on Singular Spectrum Iteration. (Proceeding) 2008.11, Chengdu,China.2008 IEEE International Conferences on Cybernetics & Intelligent Systems (CIS-2008) and Robotics, Automation & Mechatronics (RAM-2008) (EI 检索:20085211805424)
[100]Chang,Y.-S.,Rosati A. J.,Zhang S.,and Harrison M. J.(2009).Objective analysis of monthly temperature and salinity for the world ocean in the 21st century:Comparison with World Ocean Atlas and application to assimilation validation, J. Geophys. Res.,114, C02014.
[101]王辉赞,张韧,金宝刚.基于卫星高度计和遥感风场的海表流场重构研究[C]Proceedings of 2010 International Conference on Remote Sensing (ICRS 2010) Vol.4,2010-10-05,Hangzhou.
[102]苏京志.全球上层海流的漂流浮标资料分析[D].青岛：中国科学院海洋研究所硕士论文,2000.
[103]郭吉鸽,谢基平,朱江.利用ARGO浮标轨迹推断大洋表层和中层海流[C],ARGO应用研究论文集,北京：海洋出版社,2006,P111-127.
[104]陈奕德,张韧,蒋国荣,用ARGO浮标定位信息估算分析赤道太平洋中层流场状况[J],海洋预报,23(4)：37-46,2006.
[105]陈奕德,张韧.赤道太平洋中层流场诊断分析[C]ARGO应用研究论文集.北京：海洋出版社,2006，111-127.
[106]Xie Jiping,Zhu Jiang,Xu Li,Guo Pinwen,Evaluation of Mid-depth Currents of NCEP Reanalysis Data in the Tropical Pacific Using ARGO Float Position Information, Advances in Atmospheric Sciences,2005,22(5):677-684.
[107]Xie Jiping,Zhu Jiang, Estimation of the Surface and Mid-Depth Currents from ARGO Floats in the Pacific and Error Analysis, Journal of Marine Systems,2008,73, P61-75.
[108]周伟东,袁耀初,p螺旋方法在黑潮流速计算中的应用(台湾以东海域)[J],海洋学报,1990,12(4)：416-425.
[109]陈红霞,袁业立,刘娜,曲媛媛,非线性共轭梯度法在东海黑潮流计算中的应用[J],海洋学报,2003,25(6)：31-38.
[110]Chu P C, P-vector method for determining absolute velocity from hydrographic data [J], Marine Technology Society Journal,1995,29(3):3-14.
[111]安玉柱,张韧,王辉赞.基于卫星遥感数据的全球表层流场反演重构[J].海洋通报,31(1)：106-113,2012.
[112]冯士笮,李凤歧,李少箐.海洋科学导论[M].北京：高等教育出版社,2000.
[113]侍茂崇.物理海洋学[M].济南：山东教育出版社,2004.
[114]刘巍,张韧,安玉柱.基于卫星遥感资料的海表流场估算[J].地球物理学进展,录用.
[115]Large W, Pond S, Sensible and Latent Heat Flux Measurements over the Ocean [J], Journal of Physical Oceanography,1982,12(5), P464-482.
[116]Trenberth K, Large W, Olson J, The Effective Drag Coefficient for Evaluating Wind Stress over the Oceans[J],Journal of Climate,1989,2(12),P1507-1516.
[117]Lagerloef G S E, Mitchum G T, Lukas R B, Niiler P P, Tropical Pacific Near-surface Currents Estimated from Altimeter,Wind,and Drifter Data[J].J.Geophys.Res.,1999,104(C10),23,313.
[118]Ducet N, Le Traon P Y, Reverdin G, Global High-resolution Mapping of the Ocean Circulation from TOPEX/POSEIDON and ERS-1/2[J],J. Geophys. Res,2000,105, P19477-19498.
[119]Chelton D B, Schlax M G, Samelson R M, de Szoeke R A, Global Observations of Large Oceanic Eddies[J], Geophys. Res. Lett.,2007,34, L15606.
[120]Wang Guihua,Liu Zenghong,Xu Jianping,Three dimensional Pacific temperature,salinity and circulation reconstructions with ARGO data[C],Collection of ARGO Application Papers,Beijing:China Ocean Press,2006,P16-25(Ch).
[121]Guinehut, S., Traon P.Y.Le., Larnicol G, Philipps S.(2004):Combining ARGO and remote-sensing data to estimate the ocean three-dimensional temperature fields-a first approach based on simulated observations, J. Mar. Syst.,46,85-98.
[122]王桂华,刘增宏,许建平.利用ARGO资料重构太平洋三维温盐场和流场[A]ARGO应用研究论文集[C],北京：海洋出版社,2006.
[123]郭吉鸽,谢基平,朱江.利用ARGO浮标轨迹推断大洋表层和中层海流[C],ARGO应用研究论文集,北京：海洋出版社,2006.
[124]Xie, J. P., Zhu J.(2008):Estimation of the surface and mid-depth currents from ARGO floats in the Pacific and error analysis. J. Mar. Sys.,73,61-75.
[125]陈奕德,张韧.利用ARGO浮标定位信息估算分析赤道太平洋中层流场状况[J],海洋预报,23(4)：37-46,2006.
[126]陈奕德,张韧,蒋国荣.赤道太平洋中层流场诊断分析[C]ARGO应用研究论文集.北京：海洋出版社,2006.
[127]Chu, P. C.P-vector Spirals and Determination of Absolute Velocities[J].J.Oceanogr.,56, 591-599,2000.
[128]Chu, P. C,李荣凤,游小宝.等密度面P-矢量方法和南海北部环流[J].自然科学进展,2001,11(5)：499-506.
[129]张韧,黄志松,刘巍.基于ARGO浮标资料太平洋三维流场估算[J].水动力学研究与进展,27(3)：256-263,2012.
[130]王辉赞,张韧,王桂华,安玉柱,金宝刚(2010).全球ARGO浮标剖面观测资料质量再控制数据集用户手册,15pp.
[131]侍茂崇.物理海洋学[M].济南,山东教育出版社,2004.
[2]AMY S. BOWER, LAURENCE ARMI, ISABEL AMBAR, Lagrangian Observations of Meddy Formation during AMediterranean Undercurrent Seeding Experiment[J]. J. Phys. Oceanogr. 1997:27,2545～2575.
[3]JOHANNES R. GEMMRICH, DAVID M. FARMER, Near-Surface Turbulence and Thermal Structure in a Wind-Driven Sea[J]. J. Phys. Oceanogr.1999:29:480～499.
[4]ELISE A. RALPH AND PEARN P. NIILER, Wind-Driven Currents in the Tropical Pacific[J]. J. Phys. Oceanogr.1999:29,2121～2119.
[5]刘增宏,许建平,朱伯康ARGOs漂流浮标的若干观测结果[J].热带海洋学报,24(1)：21-27,2005.
[6]苏京志,王东晓,张人禾.南海ARGO浮标观测结果初步分析[J].海洋与湖沼,2008,45(2)：276-283.
[7]张艳慧,王凡,臧楠.基于ARGO浮标和历史资料的热带西太平洋次表层与中层水年代变化分析[J].海洋学报,30(6)：17-23,2008.
[8]王彦磊,黄兵,张韧等.基于ARGO资料的世界大洋温度跃层的分布特征[J].海洋科学进展,26(4)：428-435,2008.
[9]陈奕德,张韧等.利用ARGO浮标定位信息估算分析赤道太平洋中层流场[J].海洋预报,23(4)：37-46,2006.
[10]朱江,周广庆等.一个三维变分海洋资料同化系统的设计和应用[J].中国科学(D),2007,37(2)：242-249.
[11]张人禾,刘益民.利用ARGO资料改进海洋资料同化和海洋模式中物理过程[J].气象学报,62(5)：613-622,2004.
[12]吴宗敏.径向基函数散乱数据拟合与无网格偏微分方程数值解[J].工程数学学报,19(2)：1-12,2002.
[13]张伟强,唐泽圣.大规模散乱数据层次B-样条曲面表示[J].计算机学报,22(10)：1059-1064,1999.
[14]王国夫,孙尧等.基于节点插入原理大规模散乱数据插值[J].哈尔滨工程大学学报,22(1)：45-48,2001.
[15]Cressman,G.P,. An operational objective analysis system[J].Mon.Wea.Rev.,87,367-747,1959.
[16]廖洞贤,工两铭.数值天气预报原理及应用[M].北京：气象出版社,356-3641986.
[17]张韧,万齐林等.地球科学中的散乱数据优化与缺损信息恢复[J].数据采集与处理.21(2)：209-217,2006.
[18]M.F.Bamsley and S.G.Demko.Iterated function systems and the global construction of fractals[J].Proc.of the Royal Soc.London,A399,243-275,1986.
[19]M.F.Bamsley.Fractal functions and interpolations[M].Constructive Approxmation 2,1986.
[20]Arakawa K,Krotkov E.Fractal modeling natural terrain:analysis and surface reconstruction with range data[J].Graphical Models and Image Processing,58(5),413-436,1996.
[21]孙洪泉.分形插值曲面理论与岩石断裂表面的分形插值研究[D].北京：中国矿业大学,1998.
[22]谢和平,孙洪泉.分形插值曲面理论及其应用[J].应用数学和力学,19(4),297-306,1998.
[23]Wang Jinan.Morphology and mechanical behavior of rock joints[D].Gliwice Polands Silesian Technical University,1994.
[24]阮火军,沙震.分块分形插值拟合与遗传算法[J].高校应用数学学报A辑,16(3)：263-268,2001.
[25]黄崇福.自然灾害风险评价-理论与实践[M].北京：科学出版社,2006.
[26]Huang C.F.2002.Information diffusion technique and small sample problem. International Journal of Information Technology and Decision Making, 1(2):229-249.
[27]黄崇福.自然灾害风险分析的信息矩阵方法[J].自然灾害学报.2006,15(1)：1-10
[28]张继权,李宁.气象灾害风险评估与管理的数量化应用[M].北京：北京师范大学出版社,2008.
[29]张韧,洪梅.基于不完备数据样本的联合作战大气-海洋环境风险决策[J].军事运筹与系统工程.23(1)：48-52,2009
[30]张韧,徐志升.信息扩散理论在舰载导弹环境影响评估中应用[J].火力与指挥控制,35(2)：41-45,2010.
[31]Liu Wei(刘巍),Zhang Ren,Xu Zhisheng. An Interpolation Algorithm Based on Information Diffusing Principle and Its Application in Sparse Oceanic Environment Data.2010 International Conference on Remote Sensing. Hangzhou,China,Oct.2010.
[32]黄嘉佑,张镡.北京降水缺测资料恢复的研究[J].气象,22(7)：6-11,1996.
[33]黄嘉佑.气象统计分析与预报方法[M].气象出版社,2004.
[34]杨欣斌，孙京诰,黄道.基于Bayesian网络的缺损数据处理方法[J].华东理上大学学报,(增刊)：41-44,2002.
[35]缪报通,陈发来.径向基函数神经网络在散乱数据插值中应用[J].中国科学技术大学学报,(2)：135-142,2001.
[36]岳十弘,张可村.带有误差估计式的最优模糊插值系统研究[J].两安交通大学学报,34(6)：98-101,2000.
[37]薛领涛,李郝林.基于模糊推理的自由曲而插值方法[J].机械设计与制造工程,31(5)：60-62,2002.
[38]吴洪宝,吴蕾.气候变率诊断和预测方法[M].北京：气象出版社,2005.
[39]J.M.Beckers and M.Rixen.EOF calculations and data filling from incomplete oceanographic datasets[J]. Journal of Atmospheric and Oceanic Technology.,20,1839-1856,2003.
[40]王桂华,刘增宏,许建平.利用ARGO资料重构太平洋三维温盐场和流场[A]ARGO应用研究论文集[C].北京：海洋出版社,16-26,2006.
[41]D.Kondrashov and M.Ghil.Spatio-temporal filling of missing points in geophysical data sets[J].Nonlinear Processes in Geophysics.,13,151-159,2006.
[42]Guinehut, S., P.Y. Le Traon, G. Larnicol, S. Philipps(2004), Combining ARGO and remote-sensing data to estimate the ocean three-dimensional temperature fields-a first approach based on simulated observations, J. Mar. Syst.,46,85-98,.
[43]Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes and W. Wang(2002),An improved in situ and satellite SST analysis for climate, J. Climate,15,1609-1625.
[44]Willis, J. K., D. Roemmich, and B. Cornuelle (2003),Combining altimetric height with broadscale profile data to estimate steric height, heat storage, subsurface temperature, and sea-surface temperature variability, J. Geophys. Res.,108(C9),3292.
[45]Fox,D. N.,W. J. Teague.C. N. Barron,M. R. Carnes (2002),The Modular Ocean Data Assimilation System (MODAS) Journal of Atmospheric & Oceanic Technology,19(2),240-252.
[46]Sakurai,T., K.Yukio, T.Kuragano (2005),Merged Satellite and In-situ Data Global Daily SST, Geoscience and Remote Sensing Symposium,2005. IGARSS'05. Proceedings,4,2606-2608.
[47]Kondrashov D,Ghil M.Spatio-temporal filling of missing points in geophysical data sets[J]. Nonlinear Processes Geophysics,2006,13(2):151-159.
[48]Kondrashov D, Ghil M. Reply to T Schneider's comment on"Spatio-temporalfilling of missing points in geophysical data sets"[J].Nonlinear Processes Geophysics,2007,14(1):3-4.
[49]Gomis, D., S. Ruiz, and M. A. Pedder (2001), Diagnostic analysis of the 3D ageostrophic circulation from a multivariate spatial interpolation of CTD and ADCP data, Deep Sea Res., Part I, 48,269-295.
[50]Paris, C. B., R. K. Cowen, K. M. M. Lwiza, D.-P. Wang, and D. B. Olson(2002), Multivariate objective analysis of the coastal circulation of Barbados, West Indies:Implication for larval transport, Deep Sea Res., Part I,49,1363-1386.
[51]Troccoli,A., M.A.Balmaseda, J.Segschneider, J.Vialard, D.L.T. Anderson, K.Haines, T.Stockdale, F.Vitart and A.D. Fox (2002),Salinity adjustments in the presence of temperature data assimilation, Mon. Wea. Rev.,130,89-102.
[52]Fujii,Yosuke and Masafumi Kamachi 1(2003), A Reconstruction of Observed Profiles in the Sea East of Japan Using Vertical Coupled Temperature-Salinity EOF Modes, J. Oceanogr.,59, 173-186.
[53]Sparnocchia,S.,N. Pinardi, and E. Demirov(2003),Multivariate empirical orthogonal function analysis of the upperthermocline structure of the Mediterranean sea from observations and model simulations, Annales Geophysicae,21,167-187.
[54]张仁铎.空间变异理论及应用[M].北京：科学出版社,2005.
[55]Beckers J M, Rixen M. EOF calculations and data filling from incomplete oceanographic datasets[J]. Journal of Atmospheric and Oceanic Technology,2003,20(12):1839-1856.
[56]Peter C.Chu and G.H.Wang. Auto-correlation to determine the spatial and temporal scale in the Japan Sea. Journal of Physical Oceanography,2002,32,3596-3615.
[57]Alvera-Azca'rate, A., A. Barth, M. Rixen, and J. M. Beckers (2005), Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions:Application to the Adriatic Sea, Ocean Modell.,9,325-346.
[58]Alvera-Azcarate,A.,A.Barth,J.-M.Beckers. (2007),Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields, J. Geophys. Res.,112.
[59]Gaillard, F.,E.Autret, Vi.Thierry, and P.Galaup(2009), Quality Control of Large ARGO Datasets, Journal of Atmospheric and Oceanic Technology,26(2),337-351.
[60]Eugenia Kalnay(蒲朝霞等译).大气模式、资料同化和可预报性.北京：气象出版社,2005.
[61]M.F.Bamsley and S.G.Demko. Iterated function systems and the global construction of fractals[J].Proc.of the Royal Soc.London,A399,243-275,1986.
[62]令锋,王继民.分形插值函数对垂直压缩因子连续依赖性[J].兰州铁道学院学报,14(4),64-68,1995.
[63]吴思源,周源华.三维IFS分形插值逆问题的局部迭代算法[J].上海交通大学学报,38(9),1519-1523,2004.
[64]王辉赞,张韧,王彦磊.遗传优化的分形插值及其在ARGO资料中的应用[C].2007年中国智能自动化会议论文集,2007.08,兰州.
[65]刘巍,张韧,王辉赞.分形插值参数的遗传优化及其ARGO海温场应用试验[J].大气科学学报,33(2)：186-192,2010.
[66]王小平,曹立明.遗传算法-理论、应用与软件实现[M].西安：西安交通大学出版社,2003.
[67]张韧,洪梅.经验正交函数与遗传算法结合的副高位势场非线性模型反演[J].应用数学和力学,27(12)：1438-1445,2006.
[68]张韧,洪梅.副热带高压与东亚季风指数非线性数学模型的遗传算法参数优化.工程数学学报,25(3)：381-389,2008.
[69]张韧,洪梅等.基于遗传算法优化的ENSO指数的动力预报模型反演.地球物理学报,51(5)：1346-1353,2008.
[70]许建平ARGO全球海洋观测探秘[M].北京：海洋出版社,2002.
[71]苏纪兰.如何正确认识ARGO计划[J].海洋技术,20(2)：1-3,2001.
[72]吴洪宝,吴蕾.气候变率诊断和预测方法[M].北京：气象出版社,2005.
[73]封国林,董文杰.观测数据非线性时空分布理论和方法[M].北京：气象出版社,2006.
[74]刘巍,张韧,徐志升,金炜东.基于信息扩散的稀疏数据插值算法[J].解放军理工大学学报(自然科学版),13(1)：114-121,2012.
[75]刘巍,张韧,徐志升.基于信息扩散的海洋稀疏观测资料插值算法-椭圆模型[J].计算力学学报录用.
[76]张韧,刘巍,徐志升.基于信息扩散的海洋稀疏观测资料插值算法-概率模型[J].计算力学学报录用.
[77]Liu,W.T.,Tang W.,Polito P.S.(1998), NASA scatterometer provides global ocean-surface wind fields with more structures than numerical prediction.Geophys. Res. Lett.,25(6),761-764.
[78]Zeng, L., and Levy G.(1995), Space and time aliasing structure in monthly mean polar-orbiting satellite data, J. Geophys. Res.,100(D3),5133-5142.
[79]Kutsuwada,K.(1998), Impact of wind/wind-stress field in the North Pacific constructed by ADEOS/NSCAT data,J. Oceanogr.,54,443-456.
[80]Gandin, L.S.(1963), Objective analysis of meteorological field. Translated(1965) from Russian by Israel Programme for Scientific Translation, Jerusalem,242 pp.
[81]Cressman,G.P.(1959),An operational objective analysis system, Mon.Wea.Rev.,87,367-374.
[82]Emery,W.J.,and Thomson R.E.(2004),Data Analysis Methods in Physical Oceanography, Second and revised edition, Elsevier:Amsterdam, The Netherlands,638 pp.
[83]Zhang,H.-M.,Reynolds R.W., and Bates J.J.(2006),Blended and Gridded High Resolution Global Sea Surface Wind Speed and Climatology from Multiple Satellites:1987- Present, American Meteorological Society 2006 Annual Meeting,Paper2.23,Atlanta,GA,2006.
[84]Yuan, X.(2004), High-wind-speed evaluation in the Southern Ocean, J. Geophys. Res.,109, D13101,doi:10.1029/2003JD004179.
[85]Kubota, M., and O'Brien J. J. (1988), Variability of the Upper Tropical Pacific Ocean Model,J. Geophys. Res.,93(C11),13,930-13,940.
[86]Levitus, S. (Ed.)(2006), World Ocean Atlas 2005. vol.1-4, NOAA Atlas NESDIS, vol.61-64, NOAA, Silver Spring, Md.
[87]Chang, Y.-S., Rosati A. J., Zhang S., and Harrison M. J. (2009), Objective analysis of monthly temperature and salinity for the world ocean in the 21st century:Comparison with World Ocean Atlas and application to assimilation validation, J. Geophys. Res.,114,C02014.
[88]吴洪宝,吴蕾.气候变率诊断和预测方法[M].北京：气象出版社,2005.
[89]干辉赞,张韧,刘巍.奇异谱迭代插补的改进算法及其在缺损数据恢复中的应用[J].应用数学和力学.(中、英文版)29(10)：1127-1136,2008.
[90]江志红,丁裕国,屠其璞.气象场序列几种插补方案的对比试验[J].南京气象学院学报, 22(3)：352-359,1999.
[91]J.M.Beckers and M.Rixen.EOF calculations and data filling from incomplete oceanographic datasets[J].Journal of Atmospheric and Oceanic Technology,20(12),1839-1856,2003.
[92]D.Kondrashov and M.Ghil.Spatio-temporal filling of missing points in geophysical data sets[J]. Nonlinear Processes in Geophysics.,13(2),151-159,2006.
[93]王桂华,刘增宏,许建平.利用ARGO资料重构太平洋三维温盐场和流场[A]ARGO应用研究论文集[C].北京：海洋出版社,16-25,2006.
[94]Alvera-Azca'rate, A., Barth A., Rixen M., and Beckers J. M. (2005), Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions:Application to the Adriatic Sea, Ocean Modell.,9,325-346.
[95]Vautard R and Ghil M. Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series[J],Physica D.,1989,35(3),395-424.
[96]Alvera-Azcarate,A.,Barth A.,Beckers J.-M.(2007).Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields,J. Geophys. Res.,112, C03008.
[97]Kondrashov D and Ghil M. Reply to T. Schneider's comment on "Spatio-temporal filling of missing points in geophysical data sets"[J]. Nonlinear Processes in Geophysics,2007,14(1):3-4.
[98]Broomhead D S and King G P. Extracting qualitative dynamics from experimental data,Physica D,1986,20(2-3),217-236.
[99]Liu Wei(刘巍), Jin Weidong,Wang Huizan.An Optimized Atmospheric Missed Data Recovery Algorithm Based on Singular Spectrum Iteration. (Proceeding) 2008.11, Chengdu,China.2008 IEEE International Conferences on Cybernetics & Intelligent Systems (CIS-2008) and Robotics, Automation & Mechatronics (RAM-2008) (EI 检索:20085211805424)
[100]Chang,Y.-S.,Rosati A. J.,Zhang S.,and Harrison M. J.(2009).Objective analysis of monthly temperature and salinity for the world ocean in the 21st century:Comparison with World Ocean Atlas and application to assimilation validation, J. Geophys. Res.,114, C02014.
[101]王辉赞,张韧,金宝刚.基于卫星高度计和遥感风场的海表流场重构研究[C]Proceedings of 2010 International Conference on Remote Sensing (ICRS 2010) Vol.4,2010-10-05,Hangzhou.
[102]苏京志.全球上层海流的漂流浮标资料分析[D].青岛：中国科学院海洋研究所硕士论文,2000.
[103]郭吉鸽,谢基平,朱江.利用ARGO浮标轨迹推断大洋表层和中层海流[C],ARGO应用研究论文集,北京：海洋出版社,2006,P111-127.
[104]陈奕德,张韧,蒋国荣,用ARGO浮标定位信息估算分析赤道太平洋中层流场状况[J],海洋预报,23(4)：37-46,2006.
[105]陈奕德,张韧.赤道太平洋中层流场诊断分析[C]ARGO应用研究论文集.北京：海洋出版社,2006，111-127.
[106]Xie Jiping,Zhu Jiang,Xu Li,Guo Pinwen,Evaluation of Mid-depth Currents of NCEP Reanalysis Data in the Tropical Pacific Using ARGO Float Position Information, Advances in Atmospheric Sciences,2005,22(5):677-684.
[107]Xie Jiping,Zhu Jiang, Estimation of the Surface and Mid-Depth Currents from ARGO Floats in the Pacific and Error Analysis, Journal of Marine Systems,2008,73, P61-75.
[108]周伟东,袁耀初,p螺旋方法在黑潮流速计算中的应用(台湾以东海域)[J],海洋学报,1990,12(4)：416-425.
[109]陈红霞,袁业立,刘娜,曲媛媛,非线性共轭梯度法在东海黑潮流计算中的应用[J],海洋学报,2003,25(6)：31-38.
[110]Chu P C, P-vector method for determining absolute velocity from hydrographic data [J], Marine Technology Society Journal,1995,29(3):3-14.
[111]安玉柱,张韧,王辉赞.基于卫星遥感数据的全球表层流场反演重构[J].海洋通报,31(1)：106-113,2012.
[112]冯士笮,李凤歧,李少箐.海洋科学导论[M].北京：高等教育出版社,2000.
[113]侍茂崇.物理海洋学[M].济南：山东教育出版社,2004.
[114]刘巍,张韧,安玉柱.基于卫星遥感资料的海表流场估算[J].地球物理学进展,录用.
[115]Large W, Pond S, Sensible and Latent Heat Flux Measurements over the Ocean [J], Journal of Physical Oceanography,1982,12(5), P464-482.
[116]Trenberth K, Large W, Olson J, The Effective Drag Coefficient for Evaluating Wind Stress over the Oceans[J],Journal of Climate,1989,2(12),P1507-1516.
[117]Lagerloef G S E, Mitchum G T, Lukas R B, Niiler P P, Tropical Pacific Near-surface Currents Estimated from Altimeter,Wind,and Drifter Data[J].J.Geophys.Res.,1999,104(C10),23,313.
[118]Ducet N, Le Traon P Y, Reverdin G, Global High-resolution Mapping of the Ocean Circulation from TOPEX/POSEIDON and ERS-1/2[J],J. Geophys. Res,2000,105, P19477-19498.
[119]Chelton D B, Schlax M G, Samelson R M, de Szoeke R A, Global Observations of Large Oceanic Eddies[J], Geophys. Res. Lett.,2007,34, L15606.
[120]Wang Guihua,Liu Zenghong,Xu Jianping,Three dimensional Pacific temperature,salinity and circulation reconstructions with ARGO data[C],Collection of ARGO Application Papers,Beijing:China Ocean Press,2006,P16-25(Ch).
[121]Guinehut, S., Traon P.Y.Le., Larnicol G, Philipps S.(2004):Combining ARGO and remote-sensing data to estimate the ocean three-dimensional temperature fields-a first approach based on simulated observations, J. Mar. Syst.,46,85-98.
[122]王桂华,刘增宏,许建平.利用ARGO资料重构太平洋三维温盐场和流场[A]ARGO应用研究论文集[C],北京：海洋出版社,2006.
[123]郭吉鸽,谢基平,朱江.利用ARGO浮标轨迹推断大洋表层和中层海流[C],ARGO应用研究论文集,北京：海洋出版社,2006.
[124]Xie, J. P., Zhu J.(2008):Estimation of the surface and mid-depth currents from ARGO floats in the Pacific and error analysis. J. Mar. Sys.,73,61-75.
[125]陈奕德,张韧.利用ARGO浮标定位信息估算分析赤道太平洋中层流场状况[J],海洋预报,23(4)：37-46,2006.
[126]陈奕德,张韧,蒋国荣.赤道太平洋中层流场诊断分析[C]ARGO应用研究论文集.北京：海洋出版社,2006.
[127]Chu, P. C.P-vector Spirals and Determination of Absolute Velocities[J].J.Oceanogr.,56, 591-599,2000.
[128]Chu, P. C,李荣凤,游小宝.等密度面P-矢量方法和南海北部环流[J].自然科学进展,2001,11(5)：499-506.
[129]张韧,黄志松,刘巍.基于ARGO浮标资料太平洋三维流场估算[J].水动力学研究与进展,27(3)：256-263,2012.
[130]王辉赞,张韧,王桂华,安玉柱,金宝刚(2010).全球ARGO浮标剖面观测资料质量再控制数据集用户手册,15pp.
[131]侍茂崇.物理海洋学[M].济南,山东教育出版社,2004.