基于变分法评估卫星海面信息对重构三维温盐及声场特性的影响

张明琪; 徐永生; 张庆君; 张利强; 向亮; 郭平; 杨亮; 黄超; 孙晗伟

doi:10.12284/hyxb2023163

基于变分法评估卫星海面信息对重构三维温盐及声场特性的影响

doi: 10.12284/hyxb2023163

张明琪¹,
徐永生^{1, 2, 5, ,},
张庆君³,
张利强³,
向亮¹,
郭平^{1, 5},
杨亮⁴,
黄超¹,
孙晗伟⁶

1.
中国科学院海洋研究所，山东青岛 266071
2.
崂山实验室，山东青岛 266061
3.
中国空间技术研究院遥感卫星总体部，北京 100094
4.
中国人民解放军 91001部队，北京 100841
5.
中国科学院大学，北京 100094
6.
北京无线电测量研究所，北京 100039

基金项目: 崂山实验室科技创新项目（LSKJ202201406）；国家自然科学基金项目（U1406401，41906027）；NSFC-山东省联合基金项目（U1406401）；中国科学院战略性先导科技专项（XDB42000000）。

详细信息

作者简介:
张明琪（1998—），男，甘肃省武威市人，研究方向为海洋三维温盐场重构。E-mail：mingqi_zhang502@163.com

通讯作者:
徐永生（1970—），男，山东省青岛市人，研究员，研究方向为物理海洋、海洋遥感。E-mail: yongsheng.xu@qdio.ac.cn

中图分类号: P714⁺.1
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出版历程
- 收稿日期: 2023-03-28
- 修回日期: 2023-07-17
- 网络出版日期: 2024-01-05
- 刊出日期: 2023-12-01

Variational method of ocean three-dimensional thermohaline structure and its acoustic performance evaluation

1.
Institute of Oceanography, Chinese Academy of Sciences, Qingdao 266071, China
2.
Laoshan Laboratory, Qingdao 266061, China
3.
Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China
4.
The People’s Liberation Army Troop 91001, Beijing 100841, China
5.
University of Chinese Academy of Sciences, Beijing 100094, China
6.
Beijing Institute of Radio Measurement, Beijing 100039, China

摘要

摘要: 基于卫星海面观测重构水下三维温盐场并获取声场特性的研究，在军事海洋等领域具有重要的实践应用价值，但其效果不但受到重构方法的影响，而且随所利用的卫星海面观测信息的不同而改变。本研究基于美国海军最新研发的利用变分法的温盐场重构，研究了利用卫星海面高度、海面温度或二者联合数据，及考虑温盐垂向梯度对重构三维温盐及声场特性的影响。结果发现，集合了海表温度、海表高度和温盐垂直梯度3个约束项的重构方案精度最高，其重构温度场和盐度场的平均误差分别为1.08℃、0.11，该方案也能更好地捕捉温盐场的空间特征。通过分析不同方案的空间特征，海面温度主要作用于捕捉混合层以浅区域的温盐特征，这对表面声学层（Sound Layer Depth, SLD）的影响较大；海面高度和温盐场的垂直梯度对混合层以深区域的反演精度都有较高提升，能够影响整个声速剖面的准确性。根据声学特征分析， SST、SSH与温盐垂直梯度同时约束时，SLD以浅声速与HYCOM相差最小，约为1 m/s；没有梯度约束时，SLD与HYCOM相差约为1.5 m/s，未能很好地反映海表面声道特征；表明声道特征对海表温度与梯度约束均较为敏感。
- 变分法 /
- 表面信息 /
- 梯度约束 /
- 三维温盐结构 /
- 声学特征
Abstract: Research on the reconstruction of underwater three-dimensional temperature and salinity fields and the acquisition of acoustic field characteristics based on satellite sea surface observations has significant practical value in military oceans and other fields. However, its effectiveness not only depends on the reconstruction method but also changes with different sea surface observations used. Although there are few reports on related research, it has significant guiding value for the design of satellite sea surface observation schemes. In this study, based on the latest variational method applied by the US Navy, the influence of the vertical gradient of temperature and salinity and the sea surface height, sea surface temperature, and their joint use on the reconstruction of three-dimensional temperature and salinity and acoustic field characteristics were investigated. The results showed that the reconstruction scheme incorporating the three constraint items had the highest accuracy, with average reconstruction errors of 1.08℃ for temperature field and 0.11 for salinity field, and could better capture the spatial features of the temperature and salinity fields. By analyzing the spatial characteristics of different schemes, the sea surface temperature mainly plays a role in capturing the temperature and salinity characteristics of the shallow region of the mixing layer, which has a great influence on the Sound Layer Depth (SLD). Both the sea surface height and the vertical gradient of warm salt field can improve the inversion accuracy of mixed layer and deep area, which can affect the accuracy of the whole sound velocity profile. According to the analysis of acoustic characteristics, when SST, SSH, and the gradient were constrained simultaneously, the SLD had the smallest difference from HYCOM in the shallow sound speed, which was about 1 m/s. When there was no gradient constraint, the SLD differed significantly from HYCOM and failed to reflect the surface duct characteristics. It can be seen that the surface duct is more sensitive to sea surface temperature and gradient constraints.
- variational method /
- sea surface observations /
- constraint of gradient /
- three-dimensional thermohaline structure /
- acoustic characteristics

HTML全文

图 1 卫星海面观测三维温盐场重建及评估的具体流程图

Fig. 1 The specific flow chart of reconstruction of three-dimensional temperature and salinity field and its assessment using satellite sea surface observations

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图 2 4种重构方案与Argo观测均方根误差对比

Fig. 2 Comparison of the root mean square error (RMSE) of the four reconstruction schemes with Argo observations

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图 3 4种重构方案与Argo观测数据平方相关系数对比

Fig. 3 Comparison of the square correlation coefficient of the four reconstruction schemes with Argo observations

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图 4 温度垂直断面对比

a: HYCOM；b: 方案1；c: 方案2；d: 方案3；e: 方案4

Fig. 4 Comparison of vertical sections of temperature

a: HYCOM; b: Scheme 1; c: Scheme 2; d: Scheme 3; e: Scheme 4

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图 5 盐度垂直断面对比

a: HYCOM；b: 方案1；c: 方案2；d: 方案3；e: 方案4

Fig. 5 Comparison of vertical sections of salinity

a: HYCOM; b: Scheme 1; c: Scheme 2; d: Scheme 3; e: Scheme 4

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图 8 2019年9月20日温盐垂直断面对比

a: HYCOM；b: 方案1；c: 方案2；d: 方案3；e: 方案4

Fig. 8 Comparison of vertical sections of temperature and salinity on September 20, 2019

a: HYCOM; b: Scheme 1; c: Scheme 2; d: Scheme 3; e: Scheme 4

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图 6 2019年3月20日温盐垂直断面对比

a: HYCOM；b: 方案1；c: 方案2；d: 方案3；e: 方案4

Fig. 6 Comparison of vertical sections of temperature and salinity on March 20, 2019

a: HYCOM; b: Scheme 1; c: Scheme 2; d: Scheme 3; e: Scheme 4

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图 7 2019年6月20日温盐垂直断面对比

a: HYCOM；b: 方案1；c: 方案2；d: 方案3；e: 方案4

Fig. 7 Comparison of vertical sections of temperature and salinity on June 20, 2019

a: HYCOM; b: Scheme 1; c: Scheme 2; d: Scheme 3; e: Scheme 4

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图 9 100 m、500 m、900 m深度处方案4和HYCOM模式数据的温盐度切面对比

Fig. 9 Comparison of temperature and salinity sections at depths of 100 m, 500 m and 900 m for Scheme 4 and HYCOM model data

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图 10 4种方案的反演数据与WOA18以及HYCOM数据计算的声速剖面对比

Fig. 10 Comparison of the sound velocity profiles calculated from the inversion data of the four schemes and WOA18 and HYCOM data

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图 11 声传播损失场（0~30 km）

a: HYCOM；b: 方案1；c: 方案2；d: 方案3；e: 方案4

Fig. 11 acoustic wave transmission loss field (0−30 km)

a: HYCOM; b: Scheme 1; c: Scheme 2; d: Scheme 3; e: Scheme 4

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表 1 4种研究方案及其主要约束条件

Tab. 1 Four research schemes and their main constraints

方案	主要约束条件	表达式
方案1	SST + SSH	J = J₁ + J₃ + J₅ + J₇ + J₉ + J₁₀
方案2	SST + Grad	J = J₁ + J₂ + J₃ + J₄ + J₅ + J₆ + J₇ + J₈ + J₉
方案3	SSH + Grad	J = J₁ + J₂ + J₃ + J₄ + J₅ + J₆ + J₇ + J₈ + J₁₀
方案4	SST + SSH + Grad	J = J₁ + J₂ + J₃ + J₄ + J₅ + J₆ + J₇ + J₈ + J₉ + J₁₀

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