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1、塑鲨叁堂氅主兰堡丝塞全苎塑垩全文摘要本文以三维陆架海模型(HAMSOM)作为数据同化系统的模型部分， 深入研究了数据同化的理论方法和在海洋流场数据处理中的应用，对海 洋场的数据同化方法提出了行之有效的改进，并将中国黄、东海区域内 的TOPEXPOSEIDON(TP)数据和理论模式的计算结果相结合，进行同 化处理，获得了更接近于真实的结果。对数据同化问题的研究，尤其针对目前我国海区的数据同化应用研 究较少这一情况。本文在前人文献的基础上，进行了方法及应用上的研 究。这些成果包括：建立考虑了时间相关的方差矩阵和时空相关的最优 插值算法：对卡尔曼滤波算法进行了SVD简化以及建立了显式的状态转 移矩阵
2、：将TP实时卫星数据进行调和分析并与数值模型进行同化处理。 具体内容为如下几个方面：1建立时间相关的协方差矩阵处理方法 在估值类的同化方法中，观测数据与理论模型结合的桥梁是各自的协方差矩阵。在确定协方差矩阵的时候，以往的做法是将所有不同观测 时刻的数据当作同一时刻的数据应用；本文在形成协方差矩阵时，不仅 考虑空I白J相关，而且应用了时间相关，对观测数据的应用依照其得到时 间的不同分别处理到协方差矩阵的建立中，并根据其在时间上的相关强 弱给予不同的权重值，使得对观测数据的应用更接近实际情况。2应用动态最优插值算法进行渤海海表温度同化 传统的最优插值算法没有考虑观测数据的时间错位。本文首先推导了既
3、考虑空间相关，又考虑时间相关的最优插值算法，由于应用了时间相关的协方差矩阵，就使这个算法具有动态处理时间错位的观测数据的 能力。随后，本文应用理论模型数据和模拟观测数据进行了并行试验， 将动态插值算法应用到渤海海表温度场的计算中，同化结果兼顾理论模 型结果和模拟观测结果，在方差意义上显示出最优特性，验证了本文所 提出的方法改进的正确性。浙江人学博十学位论文全文摘要3应用SVD分解，推导了简约形式的卡尔曼滤波方法，并直接建立了 显式的状态转移矩阵卡尔曼滤波法无法直接应用于海洋，其主要原因是增益矩阵的计算 量太大。本文根据“最大限度保留原来的主要信息，尽可能地简化计算” 的原则，应用SVD分解推导
4、了简约形式的卡尔曼滤波方法，得到了该方 法的简约表达公式，同时，还得到了截断误差部分的伴随发展公式，它 可用于探讨截差本身的动态发展。另外，本文成功应用反向量表述的方法将通常需要迭代得到的卡尔 曼状态转移矩阵表述为可直接计算的显式表达形式。上述两个方面的改进节省了数据同化卡尔曼滤波法的计算量，提高 了效率，对时效性很强的海洋预报计算很有意义。4对TP数据进行调和分析，并将其结果与理论模型数据进行同化处理， 使结果更接近于真实值对卫星数据如何进行处理和应用是一个重要的课题。本文首先使用 黄、东海沿岸60个验潮站实际观测得到的调和常数进行同化处理，有效 地改进了理论模式的结果。随后，本文将美国TP
5、卫星数据集的第1第 1 50周期的数据进行调和分析，获得了黄、东海区域的10条轨道上的 M2分潮的调和常数。在此基础上，本文进一步应用最优插值算法，将理 论模型结果和卫星数据调和结果进行同化处理。获得了更接近于真实值 的同化结果。关键词：数据同化，动态最优插值，卡尔曼滤波，TP数据浙江人学博十学位论文 全文摘要AbstractIn this thesis，studies were carried out on the theoretical schemes of data assimilation and their applications on sea fluid field，the th
6、reedimensional baroclinic primitiveequation model一一一Hamburg Shelf Ocean Model(HAMSOM)-being one part of the threeones that constitute the dam assimilation(DA)systemEtiective modification on schemes of DA were obtained and applications on TOPEXPOSEIDON(TP、data results that covering the area of Yellow
7、 Sea and East Sea were doneThe final results became closer tothe objective natureStudies on DA schemes and their applications were detailed on the basis of existed literatures，the studies of applications on China Sea being rather poor at the momentThe final results obtained in this PaDer contained t
8、hat the optimal interpolation scheme highlighted by the covariance that the correlation between difierent time and the correlation between difierent place being considered；that the simplification of Kalmarl Filter with the singular-value decomposition(SVD)and the direct construction of state transit
9、ion matrix preceded with“inverse vector expression”：and that the analysis of TP data and its blending with theoretical modelThey ale detailed aS follows：1To construct covariance matrix with time correlation and distanee correlationBoth observational data and theoretical model results haS its respect
10、ive errorsupon which both covariance matrix being produtednle matrices are the bridge that linkedd the observational data with the theoretical model results togethermen made covariance matrix，the observational data were always treated as one group without considering the time misfit，which did not ma
11、tch the true situationIn this thesis not only the distance，but also the time correlation was taken into account when constructed the covariance matrix，the observational data being treated differently according to its obtaining time and the weight，big or small，being assigned to differently according
12、to its time correlation，strong or weakAll that done mentioned above made the application of data be closer to practical situation2Applied the dynamic optimal interpolation to Bohai SSTThere iS no consideration of time misfit in traditional optimal interpolationIn this thesis、some researches were don
13、e on the methodology of optimal interpolation，in which a new foI111 of the felrmulae WaS developed that named dynamic optimal interpolation，the time corlelation being introducedWith the applications of the new method，the weightmatrix of observations Can be dealt with dynamically while without losing
14、 t11e merits of thetraditional onesand the time correlation Can be taken into account in real time whencomputing the weight matrix，which overcome the problem of time misfit between observations and forecastsA test arranged to cheek lately and the result indicates that the塑些叁兰堕堂堡笙苎 全塞塑垩new method can
15、 work efficiently,that the results were closer to the objective nature31o produce a new reduced KF scheme with SVDand construct thestate transition matrix directlyMany researchers have employed Kalman Filter(KF)scheme to data assimilation， which manifests its validity in blending the model results and dataHowever,the major difficulty in applying the KF scheme to data assimilation is its enormous size ofthe system， which leads tO the suboptimum in practical applicationsNowadays，many extended
