海洋环流与波动重点实验室知识库

当今社会，海洋经济贸易得到飞快的发展，海上进行的人类活动增多，为了贸易便利或者是人们的人身安全，那么水下的信息就要求更加精确。黑潮延伸体是西北太平洋的一支高温高盐的强劲的西边界流，其变化，特别是黑潮延伸体的变化对我们国家的气候变化有很重要的影响作用。海水温度是表征海洋物理、海洋化学性质的基本的海洋要素，是众多反映海洋气候环境特征的水文指标其中的一个。研究清楚黑潮延伸体海域的海洋温度的结构、时间空间变化规律，了解其与周围的海区的相互作用，才有可能对我国气候环境做出更深层次的研究和认识，为气候研究打下牢固的基础。

本文主要聚焦于黑潮延伸体海域海水温度的三维结构，联合Argo剖面数据资料和海表面温度（SST）数据资料开发了一个新的拟合三维温度场的算法。主要工作如下：

利用全球海洋Argo浮标温盐数据集提供的2002-2019年的Argo温度剖面数据拟合重构了黑潮延伸体的三维温度场。细致论述了利用Argo资料来拟合三维温度场的种种步骤：将Argo温度剖面通过五个深度划分为六层，分别命名为------混合层水层、夹层水层、温跃层水层、过渡层水层、第一深层水层和第二深层水层；然后通过线性回归得到海表面温度（SST）和Argo观测温度的回归方程，继而得到作为初始值的SST；对Argo温度数据通过各种处理得到第一猜想值；SST和初始值共同作为初始值带入拟合算法中，进行分段拟合，最终得到拟合的黑潮延伸体的三维温度场。文中将拟合温度剖面与Argo观测剖面进行了比较。详细汇总本文中的拟合三维温度场的基本理论和算法，并将文中的拟合剖面与已存在的其他数据产品作对比分析，验证了算法的可靠性，利用Argo数据计算得到拟合温度剖面与Argo观测剖面之间的误差是在0.53左右，与BOA-Argo数据集的相比较误差是在0.46左右，与EN4.2.1数据集的误差是在0.37左右。拟合温度剖面与Argo观测剖面的相关系数在0.9左右，与BOA-Argo数据集和EN4.2.1数据集的相关系数均在0.84以上。

本文借助该算法得到的关于温度剖面的主要参数，分析了黑潮延伸体各个水层为温度的季节上的变化规律。结果显示，黑潮延伸体海水表层温度有着冬天、春天是低温，夏天、秋天是高温的现象，并且在黑潮延伸体以北海域冬季温度升高，黑潮延伸体以南海区冬季温度降低；混合层深度（MLD）与海水表层温度（SST）在一年中有相反的趋势，在冬天MLD最深，而在夏季最浅，并且黑潮延伸体南部海域混合层深度较大，北部海域混合层深度较小；夹层不论在冬季还是夏季，黑潮延伸体作为南北海域的界限都比较清晰；温跃层温度下降剧烈，顶底温度差很大，温跃层底部深度大概在800 m，季节性温跃层在100-200 m；过渡层存在较强的南北深度差异。

Today, with the very fast growth of Marine economy and trade, the quantity of human activities on the sea has increased, for the goals of the trade convenience or people’spersonal safety, underwater needs to be more accurate. Kuroshio extension is a powerful western boundary sea current of high temperature and high salt in the Northwest Pacific Ocean. Temperature is one of the basic ocean elements, which represents the physical and chemical features of the ocean. It is also one of the representations reflecting the characteristics of the ocean hydrology and climate environment. To stud the charateristics of the climate of the Kuroshio extension area and the rule of temporal and spatial variations. And studying the correlation between the KE and the surrounding waters. In this way can we make a future study and knowing of China’s environmental climate and lay a foundation for climate research.

In order to meet the needs of oceans research and survey, in this paper,a new algorithm for fitting 3-D temperature pattern by uniting Argo profile data and sea surface temperature (SST) data. The major studying work is as follows:

The three-dimensional temperature pattern of the kuroshio extension was rebulit by matching the Argo temperature profile data from 2002 to 2019 provided by the global ocean Argo buoy temperature salt data set. The way of fitting three-dimensional temperature field with Argo data is introduced in detail. The Argo temperature profile is divided into six layers through five depths, namely mixing layer, entrainment zone, thermocline, transition zone, the first deep layer and the second deep layer. Then, the regression equations of SST and Argo temperature are got through linear regression, and then the initial value of SST was obtained. The first guess value is obtained through various processing of Argo profile data. The SST and the initial value are both taken into the fitting means as the beginning value, and the piecewise fitting is carried out. Finally, the 3D temperature field of the fitted kuroshio extension body is obtained. The fitting temperature profile is compared with the Argo observation profile. A detailed summarizes the basic theory and algorithm of fitting three-dimensional temperature field by SST of Argo and satellite remote sensing data, and compares the fitted temperature profile calculated in this paper with the data sets of BOA-Argo and EN4.2.1, which have considerable consistency. The error between the fitted temperature profile and the Argo observation profile calculated with Argo data is about 0.53, about 0.46 with BOA-Argo data library, and about 0.37 with EN4.2.1 data set. The correlation of association between the fitted temperature profile and Argo observation profile was around 0.9, while the cor between the BOA-Argo dataset and EN4.2.1 dataset was above 0.84.

In this paper, the principal arguments of temperature profile obtained by this algorithm are used to analyze the four seasons change features and laws of water orders in kuroshio extension body. Interpretation of the results: the SST of the kuroshio extension body has obvious characteristics of cold in winter and spring, elevated temperature in summer and autumn. The monthly variation trend of mixed layer depth (MLD) is opposite to that of sea surface temperature (SST). The mixed layer depth is the deepest in winter and the shallowest in summer. Regardless of in winter or summer, the extension of kuroshio tide is the boundary between north and south sea area. The temperature of the thermocline drops sharply, and the temperature imparity between the top and bottom is large. The depth at the bottom of the thermocline is about 800 m, and the seasonal thermocline is 100-200 m. There is a strong north-south depth difference in the transition layer.

摘  要... I

ABSTRACT. Ⅲ

第1章  绪论... 1

1.1  研究背景及意义.. 1

1.2  研究现状.. 2

1.3  研究内容.. 3

1.4  章节介绍.. 4

第2章  Argo数据及处理... 6

2.1  Argo计划介绍.. 6

2.2  Argo数据的应用.. 7

2.3  Argo数据选取.. 8

2.4  Argo数据处理.. 10

第3章  卫星遥感数据及处理... 12

3.1  卫星传感器说明.. 12

3.1.1  NOAA卫星说明及AVHRR传感器说明.. 13

3.1.2  MODIS. 14

3.2  卫星遥感SST数据说明.. 15

3.2.1 OISST. 15

3.2.2  SST处理.. 16

第4章  其他数据资料... 18

4.1  验证数据集.. 18

4.2  其他数据.. 19

4.2.1  高度计的绝对动力地形数据.. 19

第5章  参数化的拟合算法实验及验证... 22

5.1  参数化拟合方法的来源.. 22

5.2  参数化的拟合算法.. 24

5.3  算法实验.. 27

5.4  验证分析.. 29

5.5  本章小结.. 34

第6章  三维垂直温度场的特征分析... 36

6.1  海表温度的时空特征.. 36

6.2  混合层的时空特征.. 38

6.3  夹层的时空特征.. 41

6.4  温跃层的时空变异特征.. 42

6.5  过渡层和深层的时空特征.. 45

6.6  本章小结.. 46

第7章  总结与期望... 47

7.1  总结.. 47

7.2  期望.. 49

参考文献... 50

致  谢... 56

作者简历及攻读学位期间发表的学术论文与研究成果   58