DOI: 10.1002/2017JC013631
Scopus记录号: 2-s2.0-85040725973
论文题名: Retrieving Temperature Anomaly in the Global Subsurface and Deeper Ocean From Satellite Observations
作者: Su H. ; Li W. ; Yan X.-H.
刊名: Journal of Geophysical Research: Oceans
ISSN: 21699275
出版年: 2018
卷: 123, 期: 1 起始页码: 399
结束页码: 410
语种: 英语
英文关键词: global ocean
; Random Forest
; remote sensing observations
; satellite altimetry
; subsurface temperature anomaly
Scopus关键词: accuracy assessment
; Argo
; global climate
; global ocean
; machine learning
; observational method
; parameterization
; remote sensing
; satellite altimetry
; satellite data
; sea surface height
; sea surface temperature
; thermal structure
英文摘要: Retrieving the subsurface and deeper ocean (SDO) dynamic parameters from satellite observations is crucial for effectively understanding ocean interior anomalies and dynamic processes, but it is challenging to accurately estimate the subsurface thermal structure over the global scale from sea surface parameters. This study proposes a new approach based on Random Forest (RF) machine learning to retrieve subsurface temperature anomaly (STA) in the global ocean from multisource satellite observations including sea surface height anomaly (SSHA), sea surface temperature anomaly (SSTA), sea surface salinity anomaly (SSSA), and sea surface wind anomaly (SSWA) via in situ Argo data for RF training and testing. RF machine-learning approach can accurately retrieve the STA in the global ocean from satellite observations of sea surface parameters (SSHA, SSTA, SSSA, SSWA). The Argo STA data were used to validate the accuracy and reliability of the results from the RF model. The results indicated that SSHA, SSTA, SSSA, and SSWA together are useful parameters for detecting SDO thermal information and obtaining accurate STA estimations. The proposed method also outperformed support vector regression (SVR) in global STA estimation. It will be a useful technique for studying SDO thermal variability and its role in global climate system from global-scale satellite observations. © 2018. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/114831
Appears in Collections: 气候减缓与适应
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作者单位: Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National Engineering Research Centre of Geo-spatial Information Technology, Fuzhou University, Fuzhou, China; Laboratory for Regional Oceanography and Numerical Modeling, National Laboratory for Marine Science and Technology, Qingdao, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China; Center for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE, United States
Recommended Citation:
Su H.,Li W.,Yan X.-H.. Retrieving Temperature Anomaly in the Global Subsurface and Deeper Ocean From Satellite Observations[J]. Journal of Geophysical Research: Oceans,2018-01-01,123(1)