卫星遥感海表温度与悬浮泥沙浓度的资料重构及数据同化试验
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摘要
目前,卫星遥感和数值模拟已经成为我们理解海洋过程的两大主要手段。卫星遥感具有周期性、宏观性、实时性和费用低等特点,被广泛应用于海洋的水体监测；数值模拟能够从整体上把握海洋现象的时空变化规律,在海洋预报中发挥着重要作用。由于海洋上空覆盖的云层、传感器扫描轨道变化等原因,使用可见光和红外波段反演的遥感数据往往存在较大比例的数据缺失区域；其次,难以准确检测的薄云会造成反演数据异常.数值模拟中控制方程是对现实世界的简化,模型、初始条件、边界条件的误差会导致预报时效的降低。结合卫星遥感和数值模拟两者的优势,利用数据同化方法,融合遥感观测和数值模拟数据,构建海洋数据同化系统,可以有效地提高数值预报的精度.
针对以上问题,本文首先提出了结合经验模态分解(EMD)与经验正交函数(EOF)的自适应EMD-EOF资料重构方法,并应用该方法对2003年长江口海域5天平均的海表温度(SST)与表层悬浮泥沙浓度(SSC)遥感产品进行了资料重构。
结果表明：(1)SST重构的均方根误差为0.9℃、SSC重构的对数均方根误差为0.137(log10mg/L); (2)相对于Alvera提出的DINEOF方法,EMD-EOF方法的计算时间不到DINEOF方法的50%,同时重构精度提高10%左右；(3) EMD-EOF方法可以有效的剔除遥感反演中薄云未准确检测导致的噪声点,提高原始遥感图像的准确度；(4) EMD-EOF方法可以有效的重构数据量极少的遥感图像,得到高空间分辨率、全覆盖的遥感再分析产品。
海温与悬浮泥沙是影响中国近海浮游植物生长的主要因素之一,也是进行海洋生态模拟与预报的基础。本文使用减秩卡尔曼滤波(SEEK)方法,结合COHERENS数值模型与遥感观测数据,初步建立了杭州湾三维海温与悬浮泥沙的数据同化系统,利用2003年春季的遥感SST与SSC数据对同化系统进行了后报同化实验。
结果表明：(1)相对于遥感SST,模拟数据、预报数据、分析数据的均方根误差分别为2.13、1.65和0.75℃,而相对于遥感SSC,三者的对数均方根误差分别为0.62、0.53和0.26(log10mg/L);(2)对分析数据与遥感数据、分析数据与预报数据的差异进行分析表明,分析数据在分布趋势上接近预报数据,在数值上接近观测数据,观测对同化的影响效果显著；(3)数据同化方法可以有效的结合遥感观测与数值模拟两者的优势,改进数值预报的精度。
为了更好的利用遥感数据,提高海洋数值预报的精度,还需要在以下两个方面开展工作：(1)使用EMD-EOF方法对其他遥感数据产品(如CHL-a,透明度等)进行资料重构,同时通过对EOF分解后的时间模态系数进行预测,构建一个基于统计方法的短期海洋遥感预测系统。(2)利用数据同化方法,同化CHL-a、颗粒有机碳等遥感数据,提高海洋生态模拟与预报的精度。
At present, satellite remote sensing and numerical simulation are the two major means by which we learn more about ocean processes. Satellite remote sensing is characterized by periodicity, macroscopy, real-time and low cost, which is the reason why it is widely used in ocean monitoring. Numerical simulation can grasp the rules of ocean spatial-temporal variations as a whole, playing an important role in ocean forecasting. Because of the clouds coverage over the ocean and changes in scanning orbit of sensors, the satellite remote sensing data obtained by the visible and infrared bands often show missing data in a large proportion. Besides, thin clouds which are difficult to precisely detect could result in abnormal data retrieval。The control functions in numerical simulation predigest the real world. And errors of model, initial conditions and boundary conditions will reduce the forecast abilities. Combining the advantages of satellite remote sensing and numerical simulation, we can make use of the data assimilation method, merge the remote data and simulated data, construct the ocean data assimilation system and improve the accuracy of ocean forecast.
In response to the above problems, we advance an EMD-EOF data reconstruction method, which combines empirical mode decomposition (EMD) and Empirical Orthogonal Function (EOF). By applying the new method, we reconstruct the five-day-average sea surface temperature (SST) and suspended sediment concentration (SSC) data of Changjiang estuary sea area in 2003.
The conclusions are as follows. Firstly, the root mean squared error(RMSE) of SST reconstruction is 0.9℃and log RMSE of SSC reconstruction is 0.137(log1O mg/L). Secondly, the calculating time of EMD-EOF method is less than half of that of the DINEOF method raised by Alvera, and the reconstruction precision is comparatively improved. Thirdly, the EMD-EOF method can effectively eliminate the abnormal data which result from undetected thin clouds in remote sensing retrieve, improving the precision of original remote sensing images. Lastly, the EMD-EOF method can effectively reconstruct remote sensing images of little data, which leads to reanalysis remote sensing products of high spatial-resolution and full coverage.
Sea temperature and suspended sediment affect the growth of phytoplankton in China Adjacent Seas and they are also the basis of ocean ecological simulation and forecast. Using singular evolutive extended kalman filter(SEEK), combined with the simulation result of COHERENS model and remote sensing observation data, we initially build the three-dimensional data assimilation system of sea surface temperature and suspended sediment in Hangzhou Bay. This system is further tested via hindcast validation experiment by using the remote sensing data of SST and SSC of Spring in 2003.
Our research results are as follows. Firstly, compared with the remote sensing SST, the RMSEs of simulated data, forecast data and analyzed data are 2.13,1.65 and 0.75℃respectively, and compared with the remote sensing SSC, the log RMSEs of simulated data, forecast data and analyzed data are 0.62,0.53 and 0.26 (log10 mg/L) respectively. Secondly, as the difference between the analyzed data and remote sensing data and the difference between the analyzed data and forecast data show, the analyzed data are identical to the forecast data in terms of distributing trend and the analyzed data are close to the observed data in terms of numerical value. Therefore, observation has obvious effect on assimilation. Lastly, the data assimilation method can effectively combine the advantages of both remote sensing observation and numerical simulation, improving the precision of numerical forecast.
In order to better utilize the remote sensing data and improve the precision of ocean numerical forecasting, further research work is to be complemented from two perspectives. On the one hand, other remote sensing data (CHL-a, SDD eg.) are to be reconstructed by using the EMD-EOF method. Meanwhile, by forecasting the time-coefficients of EOF decomposition, we can build a short ocean remote sensing forecasting system. On the other hand, to enhance the precision of ocean ecological simulation and forecast, the data assimilation method is to be used to assimilate such remote sensing data as CHL-a and Particulate Organic Carbon (POC).
针对以上问题,本文首先提出了结合经验模态分解(EMD)与经验正交函数(EOF)的自适应EMD-EOF资料重构方法,并应用该方法对2003年长江口海域5天平均的海表温度(SST)与表层悬浮泥沙浓度(SSC)遥感产品进行了资料重构。
结果表明：(1)SST重构的均方根误差为0.9℃、SSC重构的对数均方根误差为0.137(log10mg/L); (2)相对于Alvera提出的DINEOF方法,EMD-EOF方法的计算时间不到DINEOF方法的50%,同时重构精度提高10%左右；(3) EMD-EOF方法可以有效的剔除遥感反演中薄云未准确检测导致的噪声点,提高原始遥感图像的准确度；(4) EMD-EOF方法可以有效的重构数据量极少的遥感图像,得到高空间分辨率、全覆盖的遥感再分析产品。
海温与悬浮泥沙是影响中国近海浮游植物生长的主要因素之一,也是进行海洋生态模拟与预报的基础。本文使用减秩卡尔曼滤波(SEEK)方法,结合COHERENS数值模型与遥感观测数据,初步建立了杭州湾三维海温与悬浮泥沙的数据同化系统,利用2003年春季的遥感SST与SSC数据对同化系统进行了后报同化实验。
结果表明：(1)相对于遥感SST,模拟数据、预报数据、分析数据的均方根误差分别为2.13、1.65和0.75℃,而相对于遥感SSC,三者的对数均方根误差分别为0.62、0.53和0.26(log10mg/L);(2)对分析数据与遥感数据、分析数据与预报数据的差异进行分析表明,分析数据在分布趋势上接近预报数据,在数值上接近观测数据,观测对同化的影响效果显著；(3)数据同化方法可以有效的结合遥感观测与数值模拟两者的优势,改进数值预报的精度。
为了更好的利用遥感数据,提高海洋数值预报的精度,还需要在以下两个方面开展工作：(1)使用EMD-EOF方法对其他遥感数据产品(如CHL-a,透明度等)进行资料重构,同时通过对EOF分解后的时间模态系数进行预测,构建一个基于统计方法的短期海洋遥感预测系统。(2)利用数据同化方法,同化CHL-a、颗粒有机碳等遥感数据,提高海洋生态模拟与预报的精度。
At present, satellite remote sensing and numerical simulation are the two major means by which we learn more about ocean processes. Satellite remote sensing is characterized by periodicity, macroscopy, real-time and low cost, which is the reason why it is widely used in ocean monitoring. Numerical simulation can grasp the rules of ocean spatial-temporal variations as a whole, playing an important role in ocean forecasting. Because of the clouds coverage over the ocean and changes in scanning orbit of sensors, the satellite remote sensing data obtained by the visible and infrared bands often show missing data in a large proportion. Besides, thin clouds which are difficult to precisely detect could result in abnormal data retrieval。The control functions in numerical simulation predigest the real world. And errors of model, initial conditions and boundary conditions will reduce the forecast abilities. Combining the advantages of satellite remote sensing and numerical simulation, we can make use of the data assimilation method, merge the remote data and simulated data, construct the ocean data assimilation system and improve the accuracy of ocean forecast.
In response to the above problems, we advance an EMD-EOF data reconstruction method, which combines empirical mode decomposition (EMD) and Empirical Orthogonal Function (EOF). By applying the new method, we reconstruct the five-day-average sea surface temperature (SST) and suspended sediment concentration (SSC) data of Changjiang estuary sea area in 2003.
The conclusions are as follows. Firstly, the root mean squared error(RMSE) of SST reconstruction is 0.9℃and log RMSE of SSC reconstruction is 0.137(log1O mg/L). Secondly, the calculating time of EMD-EOF method is less than half of that of the DINEOF method raised by Alvera, and the reconstruction precision is comparatively improved. Thirdly, the EMD-EOF method can effectively eliminate the abnormal data which result from undetected thin clouds in remote sensing retrieve, improving the precision of original remote sensing images. Lastly, the EMD-EOF method can effectively reconstruct remote sensing images of little data, which leads to reanalysis remote sensing products of high spatial-resolution and full coverage.
Sea temperature and suspended sediment affect the growth of phytoplankton in China Adjacent Seas and they are also the basis of ocean ecological simulation and forecast. Using singular evolutive extended kalman filter(SEEK), combined with the simulation result of COHERENS model and remote sensing observation data, we initially build the three-dimensional data assimilation system of sea surface temperature and suspended sediment in Hangzhou Bay. This system is further tested via hindcast validation experiment by using the remote sensing data of SST and SSC of Spring in 2003.
Our research results are as follows. Firstly, compared with the remote sensing SST, the RMSEs of simulated data, forecast data and analyzed data are 2.13,1.65 and 0.75℃respectively, and compared with the remote sensing SSC, the log RMSEs of simulated data, forecast data and analyzed data are 0.62,0.53 and 0.26 (log10 mg/L) respectively. Secondly, as the difference between the analyzed data and remote sensing data and the difference between the analyzed data and forecast data show, the analyzed data are identical to the forecast data in terms of distributing trend and the analyzed data are close to the observed data in terms of numerical value. Therefore, observation has obvious effect on assimilation. Lastly, the data assimilation method can effectively combine the advantages of both remote sensing observation and numerical simulation, improving the precision of numerical forecast.
In order to better utilize the remote sensing data and improve the precision of ocean numerical forecasting, further research work is to be complemented from two perspectives. On the one hand, other remote sensing data (CHL-a, SDD eg.) are to be reconstructed by using the EMD-EOF method. Meanwhile, by forecasting the time-coefficients of EOF decomposition, we can build a short ocean remote sensing forecasting system. On the other hand, to enhance the precision of ocean ecological simulation and forecast, the data assimilation method is to be used to assimilate such remote sensing data as CHL-a and Particulate Organic Carbon (POC).
引文
[1]王海君.太湖水色遥感大气校正方法研究[硕士学位论文].南京：南京师范大学,2007.
[2]冯士榨,李凤岐,李少菁.海洋科学导论,北京：高等教育出版社,2006.
[3]潘德炉,白雁.我国海洋水色遥感应用工程技术的新进展.中国工程科学,2008,(09)：14～25.
[4]修鹏.渤海海域水色遥感的研究[博士学位论文].青岛：中国海洋大学,2008.
[5]Youzhuan D, Zhihui W, Zhihua M, et al. Reconstruction of incomplete satellite SST data sets based on EOF method. Acta Oceanologica Sinica,2009,28(2):36～44.
[6]付东洋,丁又专,雷惠等.台风对海表温度及水色环境影响的遥感分析.海洋学研究,2009,27(2)：64～70.
[7]Beckers J M, Rixen M. EOF calculations and data filling from incomplete oceanographic datasets. Journal of Atmospheric and Oceanic Technology,2003,20(12): 1839-1856.
[8]李新,黄春林,车涛等.中国陆面数据同化系统研究的进展与前瞻.自然科学进展,2007,(02)：163～173.
[9]潘德炉,王迪峰.我国海洋光学遥感应用科学研究的新进展.地球科学进展,2004,(04)：506-512.
[10]Mcclain E P, Pichel W G, Walton C C, et al. Multi-channel improvements to satellite-derived global sea-surface temperatures. Adv. Space Res,1983,2(6):43～47.
[11]黄海清,何贤强,王迪峰.神经网络法反演海水叶绿素浓度的分析.地球信息科学,2004,6(2)：31～36.
[12]廖迎娣,张玮,Y Deschamps P.运用SeaWiFS遥感数据探测中国东部沿海悬浮泥沙浓度的研究水动力学研究与进展.水动力学研究与进展,2005,20(5)：558-564.
[13]唐军武,丁静,田纪伟.黄东海二类水体三要素浓度反演的神经网络模型.高技术通讯,2005,15(3)：83～88.
[14]何贤强,潘德炉,毛志华.利用SeaW iFS反演海水透明度的模型研究.海洋学报,2004,26(5)：55～62.
[15]Delu P, Wenjiang G, Yan B. Ocean primary productivity estimation of China Sea by remote sensing. Progress in Natural Science,2005,15(7):627～632.
[16]张霄宇,林以安,唐仁友.遥感技术在河口颗粒态总磷分布及扩散研究中的应用初探.海洋学报,2005,27(1)：51-56.
[17]李小斌,陈楚群,施平.珠江口海域总无机氮的遥感提取研究.环境科学学报,2007,27(2)：313～318.
[18]白雁.中国近海固有光学量及有机碳卫星遥感反演研究[博士学位论文].北京：中国科学院,2007.
[19]朱江,徐迎春,王赐震.海温数值预报资料同化试验Ⅰ.客观分析的最优插值法试验.海洋学报,1995,17(6)：9～20.
[20]马寨璞,井爱芹.动态最优插值方法及其同化应用研究.河北大学学报：自然科学版,2004,24(6)：574～580.
[21]R Everson, P Cornillon, L Sirovich, etc. An empirical eigenfunction analysis of sea surface temperatures in the western North Atlantic. Journal of Physical Oceanography, 1997,27(2):468～479.
[22]William J. Emery, Richard E. Thomson. Data analysis methods in physical oceanography, Pergamon:Elsevier Science Pub. Co.,1998.
[23]刘成思.集合卡尔曼滤波资料同化方案的设计和研究[硕士学位论文].北京：中国气象科学研究院,2005.
[24]王跃山.数据同化-它的缘起、含义与主要方法.海洋预报,1999,16(1)：11-20.
[25]李新,黄春林.数据同化——一种集成多源地理空间数据的新思路.科技导报,2004,(12)：13～17.
[26]黄春林,李新.陆面数据同化系统的研究综述.遥感技术与应用,2004,(05)：424-430.
[27]韩旭军,李新.非线性滤波方法与陆面数据同化.地球科学进展,2008,(08)：813-820.
[28]朱伯康,许建平.全球Argo实时海洋观测网建设及应用进展.海洋技术,2007,(01)：69～76.
[29]麻常雷,高艳波.多系统集成的全球地球观测系统与全球海洋观测系统.海洋技术,2006,(03)：41～45.
[30]周雅静.东中国海环流特征的数值模拟与分析[硕士学位论文].青岛：中国海洋大学,2004.
[31]朱建荣.海洋数值计算方法和数值模型,北京：海洋出版社,2004.
[32]孙文心,江文胜,李磊.近海环境流体动力学数值模型,北京：科学出版社,2004.
[33]Halliwell G. Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid Coordinate Ocean Model (HYCOM). Ocean Model,2004,7:285～322.
[34]J L P, E J J, R P. COHERENS-a coupled hydrodynamical-ecological model for regional and shelf seas:user documentation:Management Unit of the Mathematical Models of the North Sea.1999.
[35]Chen C, Beardsley R C, Cowles G. An Unstructured Grid, Finite-Volume Coastal Ocean Model FVCOM User Manual:Umassd-06-0602 S.2006.
[36]Charney J G H M J R. Use of Incomplete Historical Data to Infer the Present State of Atmosphere. J Atmos Sci,1969,26:1160～1163.
[37]Gandin L S. Objective analysis of meteorological fields, Jerusalem:Israel Program for Scientific Translations,1965.
[38]Behringer D W, Ji M, Leetmaa A. An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part I:The Ocean Data Assimilation System. Monthly Weather Review,1998,126(4):1013～1021.
[39]Derber J, Rosati A. A Global Oceanic Data Assimilation System. Journal of Physical Oceanography,1989,19(9):1333～1347.
[40]James A Carton G C A X. A Simple Ocean Data Assimilation Analysis of the Global Upper Ocean 1950-95. Journal of Physical Oceanography,2000,30(2):294～ 309.
[41]Courtier P. Dual formulation of four-dimensional variational assimilation. The Quarterly Journal of the Royal Meteorological Society,1997,123(544):2449～2461.
[42]Daley R. Atmospheric data analysis, Cambridge:Cambridge University Press,1991. 457.
[43]Talagrand O, Courtier P. Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I:Theory. The Quarterly Journal of the Royal Meteorological Society,1987,113(478):1311 ～1328.
[44]Courtier P, Thepaut J-, Hollingsworth A. A strategy for operational implementation of 4D-VAR, using an incremental approach. Quart. J. Roy. Meteor. Soc.,1994,120: 1367-1387.
[45]Bennett A F, Thorburn M A. The Generalized Inverse of a Nonlinear Quasigeostrophic Ocean Circulation Model. Journal of Physical Oceanography,1992, 22(3):213～230.
[46]Kalman R. A new approach to linear filtering and prediction problems. Journal of basic Engineering,1960,82:35～45.
[47]Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research.C:Oceans,1994.
[48]Burgers G, van Leeuwen P J, Evensen G. Analysis scheme in the ensemble Kalman Filter. Mon. Wea. Rev.,1998,126:1719～1724.
[49]Houtekamer P L, Mitchell H L. Data Assimilation Using an Ensemble Kalman Filter Technique. Monthly Weather Review,1998,126(3):796～811.
[50]Anderson J L. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev.,2001,129:2884～2903.
[51]Whitaker J S, Hamill T M. Ensemble Data Assimilation without Perturbed Observations. Monthly Weather Review,2002,130(7):1913～1924.
[52]Eleveld M A, van der Woerd, H J B, et al. Using SPM observations derived from MERIS reflectances in a data assimilation scheme for sediment transport in the Dutch coastal zone:Proc. Joint 2007 EUMETSAT Meteorological Satellite Conference and the 15th American Meteorological Society (AMS) Satellite Meteorology & Oceanography Conference. Darmstadt:EUMETSAT:2007.
[53]Blaas M, E S G Y, van K T, et al. Data Model Integration of SPM transport in the Dutch coastal zone:Proceedings of the Joint 2007 EUMETSAT/AMS Conference.2007.
[54]Serafy E G Y H, Blaas M, Eleveld M A, et al. Data assimilation of satellite data of Suspended Particulate Matter in Delft3D-Delwaq for the North Sea:Proceedings of the Joint 2007 EUMETSAT/AMS Conference.2007.
[55]Chassignet E P, Hurlburt H E, Smedstad O M, et al. The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system.2007,65(1-4):60～83.
[56]Oke P R, Brassington G B, Griffin D A, et al. The Bluelink ocean data assimilation system (BODAS).2008,21(1-2):46～70.
[57]T P D, Verron J, Roubaud M C. A singular evolutive extended Kalman filter for data assimilation in oceanography. J. Mar.Syst.,1998,16(323-340).
[58]Wan L, Zhu J, Bertino L. Initial ensemble generation and validation for ocean data assimilation using HYCOM in the Pacific. Ocean Dynamics,2008,58:81～99.
[59]Luyten P, Andreu-Burillo I, Norro A. A new version of the European public domain code COHERENS.2006.
[60]Chen C, Malanotte-Rizzoli P, Wei J, et al. Application and comparison of Kalman filters for coastal ocean problems:An experiment with FVCOM. J. Geophys. Res.,2009, 114:C5011.
[61]孙湘平.中国近海区域海洋,北京：海洋出版社,2006.
[62]许东禹.中国近海简况,北京：地质出版社,1963.
[63]中国大百科全书——大气·海洋·水文卷,北京：中国大百科全书出版社,1987.
[64]陈冠贤.中国海洋渔业环境,杭州：浙江科学技术出版社,1991.
[65]苏纪兰.中国近海水文,北京：海洋出版社,2005.
[66]C B R, R L, H Y. Discharge of the Changjiang (Yangtze River) into the East China Sea. Continental Shelf Research,1985,4(1/2):57～76.
[67]程天文,赵楚年.我国沿岸入海河川径流量与输沙量的估算.地理学报,1984,39(4)：412～426.
[68]陈则实.渤、黄、东海海洋图集——水文分册,北京：海洋出版社,1992.
[69]毛汉礼.毛汉礼著作选集,北京：学苑出版社,1996.
[70]《中国海洋志》编委会.中国海洋志,郑州：大象出版社,2003.
[71]何贤强,潘德炉,朱乾坤等.海洋水色及水温扫描仪精确瑞利散射计算.光学学报,2005,(02)：145～151.
[72]何贤强,潘德炉,尹中林等.水色遥感卫星姿态对瑞利散射计算的影响.遥感学报,2005,(03)：242～246.
[73]何贤强,潘德炉,白雁等.通用型海洋水色遥感精确瑞利散射查找表.海洋学报,2006,28(1)：47～55.
[74]何贤强,潘德炉,白雁等.海洋水色水温扫描仪精确大气漫射透射比计算.光学学报,2008,(04)：626-633.
[75]何贤强,潘德炉,白雁等.基于矩阵算法的海洋-大气耦合矢量辐射传输数值计算模型.中国科学(D辑：地球科学),2006,(09)：860～870.
[76]何贤强,潘德炉,白雁等.基于辐射传输数值模型PCOART的大气漫射透过率精确计算.红外与毫米波学报,2008,(04)：303～307.[77]毛志华,黄海清,朱乾坤等.我国海区SeaWiFS资料大气校正.海洋与湖沼,2001,(06)：581～587.
[78]毛志华,朱乾坤,潘德炉.卫星遥感业务系统海表温度误差控制方法.海洋学报(中文版),2003,(05)：49～57.
[79]毛志华,朱乾坤,潘德炉等.卫星遥感速报北太平洋渔场海温方法研究.中国水产科学,2003,(06)：502～506.
[80]周科.基于AVHRR的区域海表温度反演算法研究[硕士学位论文].杭州：国家海洋局第二海洋研究所,2005.
[81]刘芳.南黄海及东海北部海域悬沙的遥感研究[硕士学位论文].青岛：中国科学院研究生院(海洋研究所),2005.
[82]陈吉余,陈沈良.长江口生态环境变化及对河口治理的意见.水利水电技术,2003,34(1)：19～25.
[83]Beckers J M, Rixen M. EOF Calculations and Data Filling from Incomplete Oceanographic Datasets. J. Atmos. Ocean Technol.,2003,20:1839～1856.
[84]Alvera-Azcarate A, Barth A, Rixen M, et al. Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions:application to the Adriatic Sea surface temperature. Ocean Modelling,2005,9(4):325～346.
[85]Alvera-Azcarate A, Barth A, Beckers J M, et al. Multivariate reconstruction of missing data in sea surface temperature, chlorophyll and wind satellite fields. J. Geophys. Res., accepted,2006.
[86]中华人民共和国水利部.2003年中国河流泥沙公报-长江版：2003.
[87]鲍献文,万修全等.渤海、黄海、东海AVHRR每表温度场的季节变化特征.海洋学报,2002,24(5)：125～133.
[88]Alvarez A, Lopez C, Riera M, et al. Forecasting the SST space-time variability of the Alboran Sea with genetic algorithms (Paper 1999GL011226). GEOPHYSICAL RESEARCH LETTERS,2000,27(17):2709～2712.
[89]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings: Mathematical, Physical and Engineering Sciences,1998,:903～995.
[90]刘科峰,张韧,姚跃等.EOF分解与Kalman滤波相结合的副高位势场数值预报优化.解放军理工大学学报：自然科学版,2006,7(3)：291-296.
[91]Luyten P J, J E Jones, R Proctor, et al. COHERENS-A coupled hydrodynamical-ecological model for regional and shelf seas:User documentation, MUMM Report,1999.911.
[92]范学平,曾远.COHERENS模型的三维潮流及物质输运数值模拟.人民长江,2008,(02)：23-25.
[93]韩树宗,林俊,张栋国等.青岛近海悬沙输运的三维数值模拟.中国海洋大学学报(自然科学版),2007,(06)：873-878.
[94]韩树宗,郑运霞,高志刚.9711号台风对日照近海悬沙浓度影响的数值模拟.中国海洋大学学报(自然科学版),2008,(06)：868-874.
[95]华祖林,顾莉,查玉含等.基于COHERENS模型的污染物质输运数值模拟.环境科学与技术,2009,(04)：14-18.
[96]李艳芸,李绍武.风暴潮预报模型在渤海海域中的应用研究.海洋技术,2006,(01)：101～106.
[97]武雅洁,梅宁,梁丙臣.高浓热盐水在胶州湾潮流作用下的输移扩散规律研究.中国海洋大学学报(自然科学版),2008,(06)：1029-1034.
[98]张洪龙,刘亚男,卢丽锋等.一个概化的潮汐河口羽状流动力学的初步研究.海洋通报,2007,(01)：3～11.
[99]Tett P. A three layer vertical and microbiological processes model for shelf seas: Proudman Oceanographic Laboratory Report 14.199085.
[100]全球海气耦合模型课题组.大气环流和海气相互作用的数值模拟,北京：研究生讲义,2007.
[101]马寨璞.海洋流场数据同化方法与应用的研究[博士学位论文].杭州：浙江大学,2002.
[102]Larsen J. Ocean data assimilation and observing system design[博士学位论文].丹麦：K(?)BENHAVNS UNIVERSITET,2006.
[103]Bouttier F, Courtier P. Data assimilation concepts and methods:Meteorological Training Course Lecture Series. ECMWF,2002.
[104]Eliassen A. Provisional report on calculation of spatial covariance and autocorrelation of the pressure field:Videnskaps-Akademiets Institutt for Vaer-og Klimaforskning,1954:5,11.
[105]Gandin L S. The problem of optimal interpolation. Trudy Main Geophys. Obs. 1959,99:67～76.
[106]Gandin L S. On optimal interpolation and extrapolation of meteorological fields. Trudy Main Geophys. Obs.,1960,114:75～89.
[107]Lorenc A C. A global three-dimensional multivariate statistical interpolation scheme. Monthly Weather Review,1981,109:701～721.
[108]Mashkovich S A, Veyl I G. Numerical experiments on four-dimensional objective analysis on the basis of the spectral prediction model, Leningrad:Gidrometeoilat,1972. 3-6.
[109]屠伟铭,张跃堂.全球最优插值客观分析.气象学报,1995,53(2)：148-156.
[110]郭衍游.东中国海区域海浪同化系统设计与研究[博士学位论文].青岛：中国科学院研究生院(海洋研究所),2006.
[111]E K R. A new approach to linear filtering and prediction problems. Journal of Basic Engineering,1960,82D:34～45.
[112]高山红,吴增茂,谢红琴.Kalman滤波在气象数据同化中的发展与应用.地球科学进展,2000,15(5)：571～575.
[113]Nerger L, Hiller W, Ter J S. A comparison of error subspace Kalman filters. Tellus, 2005,57A:715～735.
[114]倪勇强,耿兆铨,朱军政.杭州湾水动力特性探讨.水动力学研究与进展,2003,18(4)：439～445.
[115]李宁.遥感和数值模拟方法在我国近岸水质研究中的应用[博士学位论文].天津：天津大学,2009.
[116]ETOPO11-minute global relief. http://www.ngdc.noaa.gov/mgg/gdas/gd_designagrid.html?dbase=GRDET2.
[117]Objectively Analyzed Air-Sea Fluxes(OAFLux) for the Global OCeans. http://oaflux.whoi.edu/data.html.
[118]长江水文网.http://www.cjh.com.cn/.
[119]中华人民共和国水利部.2003年中国河流泥沙公报-钱塘江：2003.
[120]HYCOM consortium. http://www.hycom.org/dataserver.
[121]张学峰.集合卡尔曼滤波数据同化方法在海温数值预报中的应用研究[硕士学位论文].杭州：浙江大学,2005.
[122]Barth A. Assimilation of sea surface temperature and sea surface height in a two-way nested primitive equation model of the Ligurian Sea:[University of Liege],2004.
[123]万莉颖.集合同化方法在太平洋海洋高度计资料同化中的应用研究[博士学位论文].北京：中国科学院研究生院(大气物理研究所),2006.
[124]Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res.,1994,99(5): 143～162.
[125]M N E M, D H J, J C P. The effect of sediment type on the relationship between reflectance and suspended sediment concentration. International Journal of Remote Sensing,1889,10(7):1283～1289.
[126]黄海军,李成治,郭建军.黄河口海域悬沙光谱特征的研究.海洋科学,1994,(5)：40～45.
[127]王芳,李国胜.海洋悬浮泥沙二元特征参数MODIS遥感反演模型研究.地理研 究,2007,26(6)：1186～1197.
[128]Huang N E, Shen Z, Long S R. A NEW VIEW OF NONLINEAR WATER WAVES. Annual Review of Fluid Mechanics,1999,31:417～457.
[129]孙晖.经验模态分解理论与应用研究[博士学位论文].杭州：浙江大学,2005.
[130]黄大吉,赵进平,苏纪兰.希尔伯特-黄变换的端点延拓.海洋学报(中文版),2003,(01)：1～11
[131]李培良,左军成,吴德星等.渤、黄、东海同化TOPEX/POSEIDON高度计资料的半日积分潮数值模拟.海洋与湖沼,2005,6(1)：24～30.
[132]张人禾,刘益民,殷永红.利用ARGO资料改进海洋资料同化和海洋模型中的物理过程.气象学报,2004,62(5)：613～622.
[133]曹艳华.四维变分资料同化的降维方法及在海洋资料同化中的应用,北京：首都师范大学,2006.
[134]朱江,周光庆,闰长香等.一个三维变分海洋资料同化系统的设计和初步应用中国科学D辑：地球科学,2007,37(2)：261～271.
[135]卢风顺.海洋资料变分同化系统优化及并行实现[博士学位论文].长沙：国防科学技术大学,2007.
[136]谈建国,周红妹,陆贤等.NOAA卫星云检测和云修复业务应用系统的研制和建立.遥感技术与应用,2000,15(04)：228～231.
[137]Kondrashov, D., Ghill, M.. Spatio-temporal filling of missing points in geophysical data sets. Nonlin. Processes Geophys,2006,13:151～159.
[138]H Gunes, Rist U. Spatial resolution enhancement/smoothing of stereo-particle-image-velocimetry data using proper-orthogonal-decomposition-based and Kriging interpolation methods, Physics of fluids,2007,19(6):64101-1～64101-19.
[139]魏凤英.现代气候统计诊断与预测技术.北京：气象出版社,1999.
[140]Bjornsson H, Venegas SA. A manual for EOF and SVD analyses of climatic data. CCGCR Report No.97-1,1997.
[141]Fang Wendong, W. W. Hsieh. Summer sea surface temperature variability off Vancouver Island from satellite data. Journal of Geophysical Research, 1993,98:143911～14405.
[142]SM Chiswell. Variability in sea surface temperature around New Zealand from AVHRR images. Zealand Journal of Marine and Freshwater New Research, 1994,28:179～192.
[143]Yunyue Yu, William J. Emery. Satellite derived sea temperature variability in the western Tropical Pacific Ocean. Remote Sensing of Environment,1996,58:299～310.
[143]王静,齐义泉等.基于TOPEX/Poseidon资料的南海海面高度场的时刻特征分析.热带海洋学报,2003,22(4)：26～32.
[144]Du BiLan, Song Xuejia. SST prediction for East China Sea and Adjacent Waters. Acta Oceanologica Sinica,1982,1(2):154～161.
[2]冯士榨,李凤岐,李少菁.海洋科学导论,北京：高等教育出版社,2006.
[3]潘德炉,白雁.我国海洋水色遥感应用工程技术的新进展.中国工程科学,2008,(09)：14～25.
[4]修鹏.渤海海域水色遥感的研究[博士学位论文].青岛：中国海洋大学,2008.
[5]Youzhuan D, Zhihui W, Zhihua M, et al. Reconstruction of incomplete satellite SST data sets based on EOF method. Acta Oceanologica Sinica,2009,28(2):36～44.
[6]付东洋,丁又专,雷惠等.台风对海表温度及水色环境影响的遥感分析.海洋学研究,2009,27(2)：64～70.
[7]Beckers J M, Rixen M. EOF calculations and data filling from incomplete oceanographic datasets. Journal of Atmospheric and Oceanic Technology,2003,20(12): 1839-1856.
[8]李新,黄春林,车涛等.中国陆面数据同化系统研究的进展与前瞻.自然科学进展,2007,(02)：163～173.
[9]潘德炉,王迪峰.我国海洋光学遥感应用科学研究的新进展.地球科学进展,2004,(04)：506-512.
[10]Mcclain E P, Pichel W G, Walton C C, et al. Multi-channel improvements to satellite-derived global sea-surface temperatures. Adv. Space Res,1983,2(6):43～47.
[11]黄海清,何贤强,王迪峰.神经网络法反演海水叶绿素浓度的分析.地球信息科学,2004,6(2)：31～36.
[12]廖迎娣,张玮,Y Deschamps P.运用SeaWiFS遥感数据探测中国东部沿海悬浮泥沙浓度的研究水动力学研究与进展.水动力学研究与进展,2005,20(5)：558-564.
[13]唐军武,丁静,田纪伟.黄东海二类水体三要素浓度反演的神经网络模型.高技术通讯,2005,15(3)：83～88.
[14]何贤强,潘德炉,毛志华.利用SeaW iFS反演海水透明度的模型研究.海洋学报,2004,26(5)：55～62.
[15]Delu P, Wenjiang G, Yan B. Ocean primary productivity estimation of China Sea by remote sensing. Progress in Natural Science,2005,15(7):627～632.
[16]张霄宇,林以安,唐仁友.遥感技术在河口颗粒态总磷分布及扩散研究中的应用初探.海洋学报,2005,27(1)：51-56.
[17]李小斌,陈楚群,施平.珠江口海域总无机氮的遥感提取研究.环境科学学报,2007,27(2)：313～318.
[18]白雁.中国近海固有光学量及有机碳卫星遥感反演研究[博士学位论文].北京：中国科学院,2007.
[19]朱江,徐迎春,王赐震.海温数值预报资料同化试验Ⅰ.客观分析的最优插值法试验.海洋学报,1995,17(6)：9～20.
[20]马寨璞,井爱芹.动态最优插值方法及其同化应用研究.河北大学学报：自然科学版,2004,24(6)：574～580.
[21]R Everson, P Cornillon, L Sirovich, etc. An empirical eigenfunction analysis of sea surface temperatures in the western North Atlantic. Journal of Physical Oceanography, 1997,27(2):468～479.
[22]William J. Emery, Richard E. Thomson. Data analysis methods in physical oceanography, Pergamon:Elsevier Science Pub. Co.,1998.
[23]刘成思.集合卡尔曼滤波资料同化方案的设计和研究[硕士学位论文].北京：中国气象科学研究院,2005.
[24]王跃山.数据同化-它的缘起、含义与主要方法.海洋预报,1999,16(1)：11-20.
[25]李新,黄春林.数据同化——一种集成多源地理空间数据的新思路.科技导报,2004,(12)：13～17.
[26]黄春林,李新.陆面数据同化系统的研究综述.遥感技术与应用,2004,(05)：424-430.
[27]韩旭军,李新.非线性滤波方法与陆面数据同化.地球科学进展,2008,(08)：813-820.
[28]朱伯康,许建平.全球Argo实时海洋观测网建设及应用进展.海洋技术,2007,(01)：69～76.
[29]麻常雷,高艳波.多系统集成的全球地球观测系统与全球海洋观测系统.海洋技术,2006,(03)：41～45.
[30]周雅静.东中国海环流特征的数值模拟与分析[硕士学位论文].青岛：中国海洋大学,2004.
[31]朱建荣.海洋数值计算方法和数值模型,北京：海洋出版社,2004.
[32]孙文心,江文胜,李磊.近海环境流体动力学数值模型,北京：科学出版社,2004.
[33]Halliwell G. Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid Coordinate Ocean Model (HYCOM). Ocean Model,2004,7:285～322.
[34]J L P, E J J, R P. COHERENS-a coupled hydrodynamical-ecological model for regional and shelf seas:user documentation:Management Unit of the Mathematical Models of the North Sea.1999.
[35]Chen C, Beardsley R C, Cowles G. An Unstructured Grid, Finite-Volume Coastal Ocean Model FVCOM User Manual:Umassd-06-0602 S.2006.
[36]Charney J G H M J R. Use of Incomplete Historical Data to Infer the Present State of Atmosphere. J Atmos Sci,1969,26:1160～1163.
[37]Gandin L S. Objective analysis of meteorological fields, Jerusalem:Israel Program for Scientific Translations,1965.
[38]Behringer D W, Ji M, Leetmaa A. An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part I:The Ocean Data Assimilation System. Monthly Weather Review,1998,126(4):1013～1021.
[39]Derber J, Rosati A. A Global Oceanic Data Assimilation System. Journal of Physical Oceanography,1989,19(9):1333～1347.
[40]James A Carton G C A X. A Simple Ocean Data Assimilation Analysis of the Global Upper Ocean 1950-95. Journal of Physical Oceanography,2000,30(2):294～ 309.
[41]Courtier P. Dual formulation of four-dimensional variational assimilation. The Quarterly Journal of the Royal Meteorological Society,1997,123(544):2449～2461.
[42]Daley R. Atmospheric data analysis, Cambridge:Cambridge University Press,1991. 457.
[43]Talagrand O, Courtier P. Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I:Theory. The Quarterly Journal of the Royal Meteorological Society,1987,113(478):1311 ～1328.
[44]Courtier P, Thepaut J-, Hollingsworth A. A strategy for operational implementation of 4D-VAR, using an incremental approach. Quart. J. Roy. Meteor. Soc.,1994,120: 1367-1387.
[45]Bennett A F, Thorburn M A. The Generalized Inverse of a Nonlinear Quasigeostrophic Ocean Circulation Model. Journal of Physical Oceanography,1992, 22(3):213～230.
[46]Kalman R. A new approach to linear filtering and prediction problems. Journal of basic Engineering,1960,82:35～45.
[47]Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research.C:Oceans,1994.
[48]Burgers G, van Leeuwen P J, Evensen G. Analysis scheme in the ensemble Kalman Filter. Mon. Wea. Rev.,1998,126:1719～1724.
[49]Houtekamer P L, Mitchell H L. Data Assimilation Using an Ensemble Kalman Filter Technique. Monthly Weather Review,1998,126(3):796～811.
[50]Anderson J L. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev.,2001,129:2884～2903.
[51]Whitaker J S, Hamill T M. Ensemble Data Assimilation without Perturbed Observations. Monthly Weather Review,2002,130(7):1913～1924.
[52]Eleveld M A, van der Woerd, H J B, et al. Using SPM observations derived from MERIS reflectances in a data assimilation scheme for sediment transport in the Dutch coastal zone:Proc. Joint 2007 EUMETSAT Meteorological Satellite Conference and the 15th American Meteorological Society (AMS) Satellite Meteorology & Oceanography Conference. Darmstadt:EUMETSAT:2007.
[53]Blaas M, E S G Y, van K T, et al. Data Model Integration of SPM transport in the Dutch coastal zone:Proceedings of the Joint 2007 EUMETSAT/AMS Conference.2007.
[54]Serafy E G Y H, Blaas M, Eleveld M A, et al. Data assimilation of satellite data of Suspended Particulate Matter in Delft3D-Delwaq for the North Sea:Proceedings of the Joint 2007 EUMETSAT/AMS Conference.2007.
[55]Chassignet E P, Hurlburt H E, Smedstad O M, et al. The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system.2007,65(1-4):60～83.
[56]Oke P R, Brassington G B, Griffin D A, et al. The Bluelink ocean data assimilation system (BODAS).2008,21(1-2):46～70.
[57]T P D, Verron J, Roubaud M C. A singular evolutive extended Kalman filter for data assimilation in oceanography. J. Mar.Syst.,1998,16(323-340).
[58]Wan L, Zhu J, Bertino L. Initial ensemble generation and validation for ocean data assimilation using HYCOM in the Pacific. Ocean Dynamics,2008,58:81～99.
[59]Luyten P, Andreu-Burillo I, Norro A. A new version of the European public domain code COHERENS.2006.
[60]Chen C, Malanotte-Rizzoli P, Wei J, et al. Application and comparison of Kalman filters for coastal ocean problems:An experiment with FVCOM. J. Geophys. Res.,2009, 114:C5011.
[61]孙湘平.中国近海区域海洋,北京：海洋出版社,2006.
[62]许东禹.中国近海简况,北京：地质出版社,1963.
[63]中国大百科全书——大气·海洋·水文卷,北京：中国大百科全书出版社,1987.
[64]陈冠贤.中国海洋渔业环境,杭州：浙江科学技术出版社,1991.
[65]苏纪兰.中国近海水文,北京：海洋出版社,2005.
[66]C B R, R L, H Y. Discharge of the Changjiang (Yangtze River) into the East China Sea. Continental Shelf Research,1985,4(1/2):57～76.
[67]程天文,赵楚年.我国沿岸入海河川径流量与输沙量的估算.地理学报,1984,39(4)：412～426.
[68]陈则实.渤、黄、东海海洋图集——水文分册,北京：海洋出版社,1992.
[69]毛汉礼.毛汉礼著作选集,北京：学苑出版社,1996.
[70]《中国海洋志》编委会.中国海洋志,郑州：大象出版社,2003.
[71]何贤强,潘德炉,朱乾坤等.海洋水色及水温扫描仪精确瑞利散射计算.光学学报,2005,(02)：145～151.
[72]何贤强,潘德炉,尹中林等.水色遥感卫星姿态对瑞利散射计算的影响.遥感学报,2005,(03)：242～246.
[73]何贤强,潘德炉,白雁等.通用型海洋水色遥感精确瑞利散射查找表.海洋学报,2006,28(1)：47～55.
[74]何贤强,潘德炉,白雁等.海洋水色水温扫描仪精确大气漫射透射比计算.光学学报,2008,(04)：626-633.
[75]何贤强,潘德炉,白雁等.基于矩阵算法的海洋-大气耦合矢量辐射传输数值计算模型.中国科学(D辑：地球科学),2006,(09)：860～870.
[76]何贤强,潘德炉,白雁等.基于辐射传输数值模型PCOART的大气漫射透过率精确计算.红外与毫米波学报,2008,(04)：303～307.[77]毛志华,黄海清,朱乾坤等.我国海区SeaWiFS资料大气校正.海洋与湖沼,2001,(06)：581～587.
[78]毛志华,朱乾坤,潘德炉.卫星遥感业务系统海表温度误差控制方法.海洋学报(中文版),2003,(05)：49～57.
[79]毛志华,朱乾坤,潘德炉等.卫星遥感速报北太平洋渔场海温方法研究.中国水产科学,2003,(06)：502～506.
[80]周科.基于AVHRR的区域海表温度反演算法研究[硕士学位论文].杭州：国家海洋局第二海洋研究所,2005.
[81]刘芳.南黄海及东海北部海域悬沙的遥感研究[硕士学位论文].青岛：中国科学院研究生院(海洋研究所),2005.
[82]陈吉余,陈沈良.长江口生态环境变化及对河口治理的意见.水利水电技术,2003,34(1)：19～25.
[83]Beckers J M, Rixen M. EOF Calculations and Data Filling from Incomplete Oceanographic Datasets. J. Atmos. Ocean Technol.,2003,20:1839～1856.
[84]Alvera-Azcarate A, Barth A, Rixen M, et al. Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions:application to the Adriatic Sea surface temperature. Ocean Modelling,2005,9(4):325～346.
[85]Alvera-Azcarate A, Barth A, Beckers J M, et al. Multivariate reconstruction of missing data in sea surface temperature, chlorophyll and wind satellite fields. J. Geophys. Res., accepted,2006.
[86]中华人民共和国水利部.2003年中国河流泥沙公报-长江版：2003.
[87]鲍献文,万修全等.渤海、黄海、东海AVHRR每表温度场的季节变化特征.海洋学报,2002,24(5)：125～133.
[88]Alvarez A, Lopez C, Riera M, et al. Forecasting the SST space-time variability of the Alboran Sea with genetic algorithms (Paper 1999GL011226). GEOPHYSICAL RESEARCH LETTERS,2000,27(17):2709～2712.
[89]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings: Mathematical, Physical and Engineering Sciences,1998,:903～995.
[90]刘科峰,张韧,姚跃等.EOF分解与Kalman滤波相结合的副高位势场数值预报优化.解放军理工大学学报：自然科学版,2006,7(3)：291-296.
[91]Luyten P J, J E Jones, R Proctor, et al. COHERENS-A coupled hydrodynamical-ecological model for regional and shelf seas:User documentation, MUMM Report,1999.911.
[92]范学平,曾远.COHERENS模型的三维潮流及物质输运数值模拟.人民长江,2008,(02)：23-25.
[93]韩树宗,林俊,张栋国等.青岛近海悬沙输运的三维数值模拟.中国海洋大学学报(自然科学版),2007,(06)：873-878.
[94]韩树宗,郑运霞,高志刚.9711号台风对日照近海悬沙浓度影响的数值模拟.中国海洋大学学报(自然科学版),2008,(06)：868-874.
[95]华祖林,顾莉,查玉含等.基于COHERENS模型的污染物质输运数值模拟.环境科学与技术,2009,(04)：14-18.
[96]李艳芸,李绍武.风暴潮预报模型在渤海海域中的应用研究.海洋技术,2006,(01)：101～106.
[97]武雅洁,梅宁,梁丙臣.高浓热盐水在胶州湾潮流作用下的输移扩散规律研究.中国海洋大学学报(自然科学版),2008,(06)：1029-1034.
[98]张洪龙,刘亚男,卢丽锋等.一个概化的潮汐河口羽状流动力学的初步研究.海洋通报,2007,(01)：3～11.
[99]Tett P. A three layer vertical and microbiological processes model for shelf seas: Proudman Oceanographic Laboratory Report 14.199085.
[100]全球海气耦合模型课题组.大气环流和海气相互作用的数值模拟,北京：研究生讲义,2007.
[101]马寨璞.海洋流场数据同化方法与应用的研究[博士学位论文].杭州：浙江大学,2002.
[102]Larsen J. Ocean data assimilation and observing system design[博士学位论文].丹麦：K(?)BENHAVNS UNIVERSITET,2006.
[103]Bouttier F, Courtier P. Data assimilation concepts and methods:Meteorological Training Course Lecture Series. ECMWF,2002.
[104]Eliassen A. Provisional report on calculation of spatial covariance and autocorrelation of the pressure field:Videnskaps-Akademiets Institutt for Vaer-og Klimaforskning,1954:5,11.
[105]Gandin L S. The problem of optimal interpolation. Trudy Main Geophys. Obs. 1959,99:67～76.
[106]Gandin L S. On optimal interpolation and extrapolation of meteorological fields. Trudy Main Geophys. Obs.,1960,114:75～89.
[107]Lorenc A C. A global three-dimensional multivariate statistical interpolation scheme. Monthly Weather Review,1981,109:701～721.
[108]Mashkovich S A, Veyl I G. Numerical experiments on four-dimensional objective analysis on the basis of the spectral prediction model, Leningrad:Gidrometeoilat,1972. 3-6.
[109]屠伟铭,张跃堂.全球最优插值客观分析.气象学报,1995,53(2)：148-156.
[110]郭衍游.东中国海区域海浪同化系统设计与研究[博士学位论文].青岛：中国科学院研究生院(海洋研究所),2006.
[111]E K R. A new approach to linear filtering and prediction problems. Journal of Basic Engineering,1960,82D:34～45.
[112]高山红,吴增茂,谢红琴.Kalman滤波在气象数据同化中的发展与应用.地球科学进展,2000,15(5)：571～575.
[113]Nerger L, Hiller W, Ter J S. A comparison of error subspace Kalman filters. Tellus, 2005,57A:715～735.
[114]倪勇强,耿兆铨,朱军政.杭州湾水动力特性探讨.水动力学研究与进展,2003,18(4)：439～445.
[115]李宁.遥感和数值模拟方法在我国近岸水质研究中的应用[博士学位论文].天津：天津大学,2009.
[116]ETOPO11-minute global relief. http://www.ngdc.noaa.gov/mgg/gdas/gd_designagrid.html?dbase=GRDET2.
[117]Objectively Analyzed Air-Sea Fluxes(OAFLux) for the Global OCeans. http://oaflux.whoi.edu/data.html.
[118]长江水文网.http://www.cjh.com.cn/.
[119]中华人民共和国水利部.2003年中国河流泥沙公报-钱塘江：2003.
[120]HYCOM consortium. http://www.hycom.org/dataserver.
[121]张学峰.集合卡尔曼滤波数据同化方法在海温数值预报中的应用研究[硕士学位论文].杭州：浙江大学,2005.
[122]Barth A. Assimilation of sea surface temperature and sea surface height in a two-way nested primitive equation model of the Ligurian Sea:[University of Liege],2004.
[123]万莉颖.集合同化方法在太平洋海洋高度计资料同化中的应用研究[博士学位论文].北京：中国科学院研究生院(大气物理研究所),2006.
[124]Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res.,1994,99(5): 143～162.
[125]M N E M, D H J, J C P. The effect of sediment type on the relationship between reflectance and suspended sediment concentration. International Journal of Remote Sensing,1889,10(7):1283～1289.
[126]黄海军,李成治,郭建军.黄河口海域悬沙光谱特征的研究.海洋科学,1994,(5)：40～45.
[127]王芳,李国胜.海洋悬浮泥沙二元特征参数MODIS遥感反演模型研究.地理研 究,2007,26(6)：1186～1197.
[128]Huang N E, Shen Z, Long S R. A NEW VIEW OF NONLINEAR WATER WAVES. Annual Review of Fluid Mechanics,1999,31:417～457.
[129]孙晖.经验模态分解理论与应用研究[博士学位论文].杭州：浙江大学,2005.
[130]黄大吉,赵进平,苏纪兰.希尔伯特-黄变换的端点延拓.海洋学报(中文版),2003,(01)：1～11
[131]李培良,左军成,吴德星等.渤、黄、东海同化TOPEX/POSEIDON高度计资料的半日积分潮数值模拟.海洋与湖沼,2005,6(1)：24～30.
[132]张人禾,刘益民,殷永红.利用ARGO资料改进海洋资料同化和海洋模型中的物理过程.气象学报,2004,62(5)：613～622.
[133]曹艳华.四维变分资料同化的降维方法及在海洋资料同化中的应用,北京：首都师范大学,2006.
[134]朱江,周光庆,闰长香等.一个三维变分海洋资料同化系统的设计和初步应用中国科学D辑：地球科学,2007,37(2)：261～271.
[135]卢风顺.海洋资料变分同化系统优化及并行实现[博士学位论文].长沙：国防科学技术大学,2007.
[136]谈建国,周红妹,陆贤等.NOAA卫星云检测和云修复业务应用系统的研制和建立.遥感技术与应用,2000,15(04)：228～231.
[137]Kondrashov, D., Ghill, M.. Spatio-temporal filling of missing points in geophysical data sets. Nonlin. Processes Geophys,2006,13:151～159.
[138]H Gunes, Rist U. Spatial resolution enhancement/smoothing of stereo-particle-image-velocimetry data using proper-orthogonal-decomposition-based and Kriging interpolation methods, Physics of fluids,2007,19(6):64101-1～64101-19.
[139]魏凤英.现代气候统计诊断与预测技术.北京：气象出版社,1999.
[140]Bjornsson H, Venegas SA. A manual for EOF and SVD analyses of climatic data. CCGCR Report No.97-1,1997.
[141]Fang Wendong, W. W. Hsieh. Summer sea surface temperature variability off Vancouver Island from satellite data. Journal of Geophysical Research, 1993,98:143911～14405.
[142]SM Chiswell. Variability in sea surface temperature around New Zealand from AVHRR images. Zealand Journal of Marine and Freshwater New Research, 1994,28:179～192.
[143]Yunyue Yu, William J. Emery. Satellite derived sea temperature variability in the western Tropical Pacific Ocean. Remote Sensing of Environment,1996,58:299～310.
[143]王静,齐义泉等.基于TOPEX/Poseidon资料的南海海面高度场的时刻特征分析.热带海洋学报,2003,22(4)：26～32.
[144]Du BiLan, Song Xuejia. SST prediction for East China Sea and Adjacent Waters. Acta Oceanologica Sinica,1982,1(2):154～161.
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