基于遥感数据的三维温度场参数化分析方法研究

邢霄波; 徐永生; 贾永君; 黄超

doi:10.3969/j.issn.0253-4193.2020.11.005

基于遥感数据的三维温度场参数化分析方法研究

doi: 10.3969/j.issn.0253-4193.2020.11.005

邢霄波^{1, 2, 3,},
徐永生^{1, 2, 3, ,},
贾永君⁴,
黄超^{1, 2, 3}

1.
中国科学院海洋研究所，山东青岛 266071
2.
中国科学院大学，北京 100049
3.
青岛海洋科学与技术试点国家实验室海洋动力过程与气候功能实验室，山东青岛 266237
4.
国家卫星海洋应用中心，北京 100081

基金项目: 国家重点研发计划专项（2016YFC1401004）；国家自然科学基金（41676168，41376028）；国家自然科学基金创新研究群体项目（41421005）；基金委–山东省联合基金项目（U1406401）。

详细信息

作者简介:
邢霄波（1994—），女，河北省石家庄市人，从事物理海洋学海洋遥感研究。E-mail：15632779253@163.com

通讯作者:
徐永生，男，研究员，主要从事物理海洋学和海洋遥感方面的研究。E-mail：yongsheng.xu@qdio.ac.cn

中图分类号: P731.11
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- 被引次数: 0
出版历程
- 收稿日期: 2019-11-13
- 修回日期: 2020-01-15
- 网络出版日期: 2020-12-03
- 刊出日期: 2020-11-25

Research on parameterized analysis method of 3D temperature field based on remote sensing data

Xing Xiaobo^{1, 2, 3
,},
Xu Yongsheng^{1, 2, 3
, ,},
Jia Yongjun⁴,
Huang Chao^{1, 2, 3}

1.
Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Laboratory for Ocean Dynamics and Climate, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
4.
National Satellite Ocean Application Service, Beijing 100081, China

摘要

摘要: 为了满足海洋研究以及海洋调查的需求，本文基于Argo剖面和海表面温度数据开发了一个新的拟合三维温度场的算法。选取西北太平洋区域作为验证算法有效的实验海区。该水域的经纬度范围设定为：30°～40°N, 140°～155°E, 水平分辨率为0.25°。深度方向为从海表到2 000 m水深，水域划分为29层。拟合算法首先将Argo温度剖面以5个深度划分为6层，分别为混合层、夹层、温跃层、过渡层、第一深层、第二深层，然后以第一猜想值和线性回归得到的海表面温度作为初始条件重构三维温度场。重构的三维温度场的剖面与原观测剖面的均方根误差较小，相关性较好，表明该算法是合理有效的。
- 三维温度场 /
- 遥感数据 /
- 参数化方法
Abstract: To meet the needs of marine research and marine surveys, a new algorithm for fitting three-dimensional temperature fields was developed based on the Argo profile and sea surface temperature data. The Northwest Pacific region was selected as the experimental sea area to validate the algorithm. The latitude and longitude range of the water area was set as: 30°–40°N, 140°–155°E, and the horizontal resolution was 0.25°. The depth direction was from the sea surface to 2 000 m underwater, and the water area was divided into 29 layers. First the fitting algorithm divided the Argo temperature profile into six layers with five depths. The layers were mixed layer, inter layer, thermocline, transition layer, the first deep layer, and the second deep layer. The first guess and the sea surface temperature obtained by linear regression were used as the initial conditions to reconstruct the three-dimensional temperature field. The RMSE of the reconstructed three-dimensional temperature field was small and well correlated with the original observation profile, indicating that the algorithm is reasonable and effective.
- three-dimensional temperature field /
- remote sensing data /
- parameterization method

HTML全文

图 1 西北太平洋海域及研究区域地形分布

黑色虚线框表示实验海区

Fig. 1 Topography of the northwest Pacific Ocean and the study area

The black dotted box indicates the experimental sea area

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图 2 2002年至2019年5月Argo数据年分布

Fig. 2 Annual distribution of Argo data from 2002 to May, 2019

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图 3 Argo观测数据表征的温度剖面

Fig. 3 Temperature profile characterized by Argo observation data

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图 4 温度剖面表征的模型特征

Fig. 4 Model characteristics represented by temperature profile

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图 5 基于回归分析的遥感SST与Argo剖面最浅测量值之间的线性关系

Fig. 5 Linear relationship between remote sensing SST and the shallowest measured value of Argo profile based on regression analysis

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图 6 拟合结果误差分析（a），拟合结果与观测数据反应的垂直温度随深度的变化（b）

Fig. 6 Error analysis of the fitting results (a), and the vertical temperature of the fitting result and the observed data changes with depth (b)

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图 7 通过参数方程拟合得到的黑潮延伸体不同月份（a. 2018年2月; b. 2018年3月）拟合剖面与Argo观测剖面的对比

Fig. 7 Comparison of the Kuroshio extension body in different months (a. February 2018; b. March 2018) between the fitting profile and the Argo observation profile obtained through parametric equation fitting

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图 8 重构的4个季节剖面结果

Fig. 8 Profile results reconstructed in 4 seasons

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图 10 TFR结果与BOA-Argo（a），TFR结果与EN4产品（b），TFR结果与BOA-Argo、EN4产品的温度–水深散点图（c）

Fig. 10 Scatter plots between TFR results and BOA-Argo (a), TFR results and EN4 products (b), TFR results and BOA-Argo, EN4 products (c)

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图 9 来自TFR、BOA-Argo和EN4数据集的表层、100×10⁴ Pa和500×10⁴ Pa深度的温度分布

Fig. 9 Temperature distributions at the surface, 100×10⁴ Pa, and 500×10⁴ Pa depths from the TFR, BOA-Argo, and EN4 datasets

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表 1 参数式中各变量名及其含义

Tab. 1 Variable names and their meanings in the parameter formula

变量	变量含义	变量	变量含义	变量	变量含义
T^（m）	混合层温度	G_T^（m）	混合层梯度	d1	混合层深度
T^（en）	夹层温度	$ {\overline G _T}^{\left( {en} \right)}$	夹层平均梯度	d2	温跃层顶的深度
T^（th）	温跃层温度	G_T^（th）	温跃层梯度	d3	温跃层底的深度
T^（tr）	过渡层温度	$ {\overline G _T}^{\left( {tr} \right)}$	过渡层平均梯度	d4	第一深层顶部的深度
T^（de1）	第一深层温度	$ {\overline G _T}^{\left({de1} \right)}$	第一深层平均梯度	d5	第一深层底部的深度
T^（de2）	第二深层温度	${\overline G_T}^{\left( {de2} \right)}$	第二深层平均梯度	H	水的深度

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