首站-论文投稿智能助手
典型文献
Surface available gravitational potential energy in the world oceans
文献摘要:
Satellite altimetry observations, including the upcoming Surface Water and Ocean Topography mission, provide snapshots of the global sea surface high anomaly field. The common practice in analyzing these surface elevation data is to convert them into surface velocity based on the geostrophic approximation. With increasing horizontal resolution in satellite observations, sea surface elevation data will contain many dynamical signals other than the geostrophic velocity. A new physical quantity, the available surface potential energy, is conceptually introduced in this study defined as the density multiplied by half of the squared deviation from the local mean reference surface elevation. This gravitational potential energy is an intrinsic property of the sea surface height field and it is an important component of ocean circulation energetics, especially near the sea surface. In connection with other energetic terms, this new variable may help us better understand the dynamics of oceanic circulation, in particular the processes in connection with the free surface data collected through satellite altimetry. The preliminary application of this concept to the numerically generated monthly mean Global Ocean Data Assimilation System data and Archiving, Validation, and Interpretation of Satellite Oceanographic altimeter data shows that the available surface potential energy is potentially linked to other dynamic variables, such as the total kinetic energy, eddy kinetic energy and available potential energy.
文献关键词:
作者姓名:
Ruixin Huang;Bo Qiu;Zhiyou Jing
作者机构:
Woods Hole Oceanographic Institution,Woods Hole,MA 02543,USA;Department of Oceanography,University of Hawaii at Manoa,Honolulu,HI 96822,USA;State Key Laboratory of Tropical Oceanography,South China Sea Institute of Oceanology,Chinese Academy of Sciences,Guangzhou 510301,China
引用格式:
[1]Ruixin Huang;Bo Qiu;Zhiyou Jing-.Surface available gravitational potential energy in the world oceans)[J].海洋学报(英文版),2022(04):40-56
A类:
Oceanographic
B类:
Surface,available,gravitational,energy,world,oceans,Satellite,altimetry,observations,including,upcoming,Water,Topography,mission,provide,snapshots,global,sea,surface,high,anomaly,field,common,practice,analyzing,these,elevation,data,convert,them,into,velocity,geostrophic,approximation,With,increasing,horizontal,resolution,satellite,will,contain,many,dynamical,signals,other,than,new,physical,quantity,conceptually,introduced,this,study,defined,density,multiplied,by,half,squared,deviation,from,local,mean,reference,This,intrinsic,property,height,important,component,circulation,energetics,especially,near,connection,terms,may,help,us,better,understand,dynamics,oceanic,particular,processes,free,collected,through,preliminary,application,numerically,generated,monthly,Global,Data,Assimilation,System,Archiving,Validation,Interpretation,altimeter,shows,that,potentially,linked,variables,such,total,kinetic,eddy
AB值:
0.574219
相似文献
The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China: 2020/21 La Ni?a and 2020 Arctic Sea Ice Loss
Fei ZHENG;Ji-Ping LIU;Xiang-Hui FANG;Mi-Rong SONG;Chao-Yuan YANG;Yuan YUAN;Ke-Xin LI;Jiang ZHU-International Center for Climate and Environment Science(ICCES),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science& Technology,Nanjing 210044,China;Department of Atmospheric and Environmental Sciences University at Albany,State University of New York,Albany,NY 12222,USA;Department of Atmospheric and Oceanic Sciences& Institute of Atmospheric Sciences,Fudan University,Shanghai 200438,China;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;School of Atmospheric Sciences,Sun Yat-sen University,Zhuhai 519082,China;National Climate Center,Beijing 100081,China;University of Chinese Academy of Sciences,Beijing 100049,China9Beijing Municipal Climate Center,Beijing 100089,China
Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning
Nian LIU;Zhongwei YAN;Xuan TONG;Jiang JIANG;Haochen LI;Jiangjiang XIA;Xiao LOU;Rui REN;Yi FANG-Key Laboratory of Regional Climate-Environment for Temperate East Asia(RCE-TEA),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;University of Chinese Academy of Sciences,Chinese Academy of Sciences,Beijing 100049,China;Center for Artificial Intelligence in Atmospheric Science,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;Qi Zhi Institute,Shanghai 200232,China;Beijing Meteorological Service Center,BMSC,Beijing 100089,China;School of Mathematical Sciences,Peking University,Beijing 100871,China;School of Science,Beijing University of Posts and Telecommunications,Beijing 100876,China;Lab of Meteorological Big Data,Beijing 100086,China
机标中图分类号,由域田数据科技根据网络公开资料自动分析生成,仅供学习研究参考。