Estimation of nitrate concentration and its distribution in the northwestern Pacific Ocean by a deep neural network model

doi:10.1016/j.dsr.2023.104005

IOCAS-IR > 海洋环流与波动重点实验室

	Estimation of nitrate concentration and its distribution in the northwestern Pacific Ocean by a deep neural network model
	Wang, Lixin 1,2; Xu, Zhenhua 2,3,4,5,6; Gong, Xiang 1; Zhang, Peiwen2,3,4 ; Hao, Zhanjiu 2,3,4,5; You, Jia2,3,4,5 ; Zhao, Xianzhi 1; Guo, Xinyu7
	2023-05-01
发表期刊	DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS
ISSN	0967-0637
卷号	195 页码:11
通讯作者	Xu, Zhenhua([email protected]) ; Gong, Xiang([email protected])
摘要	As a fundamental nutrient for marine biogeochemical processes, the magnitude and spatial distribution of nitrate concentrations are insufficiently measured in the interior ocean. In the present study, a deep neural network (DNN) model was developed to estimate nitrate concentrations in the upper northwestern Pacific Ocean (NPO). This model takes the temperature and salinity profiles as the primary input variables. Since the subtropical and tropical regions are featured by different spatial patterns of nitrate concentrations, we separately trained the model to improve the prediction skill. The predictive results indicate that the DNN model performs well in depicting both spatial and seasonal variability of nitrate concentrations. The sensitivity experiments show that temperature is the dominant factor for the nitrate estimation, while salinity has a relatively small effect, but it cannot be ignored in improving the prediction accuracy. Furthermore, using the temperature and salinity data from World Ocean atlas (2018), we found our DNN model has a good generalization ability on nitrate estimation in NPO. This model can be applied to further studies on nitrate's spatiotemporal variability and mechanism around the global ocean.
关键词	Nitrate Spatial distribution Deep neural network Northwestern pacific ocean
DOI	10.1016/j.dsr.2023.104005
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences; National Nature Science Foundation of China-Shandong Joint Fund[XDA22050202]; National Nature Science Foundation of China-Shandong Joint Fund[U1906215]; National Nature Science Foundation of China-Shandong Joint Fund[XDB42000000]; National Natural Science Foundation of China[92058202]; Laoshan Laboratory[LSKJ202202502]; Key Deployment Project of Center for Ocean Mega Research of Science; Chinese Academy of Sciences[COMS2020Q07]; CAS; CSIRO[133244KYSB20190031]
WOS研究方向	Oceanography
WOS类目	Oceanography
WOS记录号	WOS:001029214300001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS关键词	SEA-SURFACE NITRATE ; MIXED-LAYER ; THERMOCLINE ; TEMPERATURE ; NUTRIENTS ; FLUX
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/182656
专题	海洋环流与波动重点实验室海洋生态与环境科学重点实验室
通讯作者	Xu, Zhenhua; Gong, Xiang
作者单位	1.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 3.Pilot Natl Lab Marine Sci & Technol, Qingdao, Peoples R China 4.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao, Peoples R China 5.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China 6.Chinese Acad Sci, Inst Oceanol, CAS Engn Lab Marine Ranching, Qingdao, Peoples R China 7.Ehime Univ, Ctr Marine Environm Study, 2-5 Bunkyo cho, Matsuyama 7908577, Japan
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Wang, Lixin,Xu, Zhenhua,Gong, Xiang,et al. Estimation of nitrate concentration and its distribution in the northwestern Pacific Ocean by a deep neural network model[J]. DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS,2023,195:11.
APA	Wang, Lixin.,Xu, Zhenhua.,Gong, Xiang.,Zhang, Peiwen.,Hao, Zhanjiu.,...&Guo, Xinyu.(2023).Estimation of nitrate concentration and its distribution in the northwestern Pacific Ocean by a deep neural network model.DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS,195,11.
MLA	Wang, Lixin,et al."Estimation of nitrate concentration and its distribution in the northwestern Pacific Ocean by a deep neural network model".DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS 195(2023):11.