A Novel Method to Improve the Estimation of Ocean Tide Loading Displacements for K1 and K2 Components with GPS Observations

被引:7
作者
Pan, Haidong [1 ,2 ,3 ]
Xu, Xiaoqing [1 ,2 ,3 ]
Zhang, Huayi [1 ]
Xu, Tengfei [1 ,2 ,3 ]
Wei, Zexun [1 ,2 ,3 ]
机构
[1] Minist Nat Resources, Inst Oceanog 1, Key Lab Marine Sci & Numer Modeling, Qingdao 266061, Peoples R China
[2] Pilot Natl Lab Marine Sci & Technol, Lab Reg Oceanog & Numer Modeling, Qingdao 266237, Peoples R China
[3] Shandong Key Lab Marine Sci & Numer Modeling, Qingdao 266061, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
ocean tide loading; GPS; tidal harmonic analysis; tidal admittance; ocean tides;
D O I
10.3390/rs15112846
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The accurate estimation of ocean tide loading displacements is essential and necessary for geodesy, oceanic and geophysical studies. It is common knowledge that K-1 and K-2 tidal constituents estimated from Global Positioning System (GPS) observations are unsatisfactory because their tidal periods are nearly same to the revisit cycle or orbital period of GPS constellation. To date, this troublesome problem is not fully solved. In this paper, we revisit this important issue and develop a novel method based on the unique characteristic of tidal waves to separate GPS-system errors from astronomical K-1/K-2 tides. The well-known credo of smoothness indicates that tidal admittances of astronomical constituents in a narrow band can be expressed as smooth functions of tidal frequencies, while the interference of GPS-system errors seriously damages the smooth nature of observed tidal admittances. Via quadratic fitting, smooth functions of tidal frequencies for tidal admittances can be determined, thus, astronomical K-1 and K-2 tides can be interpolated using fitted quadratic functions. Three GPS stations are selected to demonstrate our method because of their typicality in terms of poor estimates of K-1/K-2 tidal parameters related to GPS-system errors. After removing GPS-systematical contributions based on our method, corrected K-1/K-2 tides at three GPS stations are much closer to the modeled K-1/K-2 tides from FES2014, which is one of the most accurate tide models. Furthermore, the proposed method can be easily applied to other areas to correct GPS-system errors because their smooth nature is valid for global tidal signals.
引用
收藏
页数:16
相关论文
共 26 条
[1]   Benefits of combining GPS and GLONASS for measuring ocean tide loading displacement [J].
Abbaszadeh, Majid ;
Clarke, Peter J. ;
Penna, Nigel T. .
JOURNAL OF GEODESY, 2020, 94 (07)
[2]   Ocean tide loading displacements in western Europe: 2. GPS-observed anelastic dispersion in the asthenosphere [J].
Bos, Machiel S. ;
Penna, Nigel T. ;
Baker, Trevor F. ;
Clarke, Peter J. .
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 2015, 120 (09) :6540-6557
[3]  
CARTWRIGHT DE, 1994, OCEANOL ACTA, V17, P453
[4]   Vertical displacement loading tides and self-attraction and loading tides in the Bohai, Yellow, and East China Seas [J].
Fang GuoHong ;
Xu XiaoQing ;
Wei ZeXun ;
Wang YongGang ;
Wang XinYi .
SCIENCE CHINA-EARTH SCIENCES, 2013, 56 (01) :63-70
[5]   DEFORMATION OF EARTH BY SURFACE LOADS [J].
FARRELL, WE .
REVIEWS OF GEOPHYSICS AND SPACE PHYSICS, 1972, 10 (03) :761-&
[6]   Nodal variations and long-term changes in the main tides on the coasts of China [J].
Feng, Xiangbo ;
Tsimplis, Michael N. ;
Woodworth, Philip L. .
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2015, 120 (02) :1215-1232
[7]   GLOBAL CHARTS OF OCEAN TIDE LOADING EFFECTS [J].
FRANCIS, O ;
MAZZEGA, P .
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 1990, 95 (C7) :11411-11424
[8]   Application of the Variational Mode Decomposition (VMD) method to river tides [J].
Gan, Min ;
Pan, Haidong ;
Chen, Yongping ;
Pan, Shunqi .
ESTUARINE COASTAL AND SHELF SCIENCE, 2021, 261
[9]   EOT20: a global ocean tide model from multi-mission satellite altimetry [J].
Hart-Davis, Michael G. ;
Piccioni, Gaia ;
Dettmering, Denise ;
Schwatke, Christian ;
Passaro, Marcello ;
Seitz, Florian .
EARTH SYSTEM SCIENCE DATA, 2021, 13 (08) :3869-3884
[10]   Deep-learning-based information mining from ocean remote-sensing imagery [J].
Li, Xiaofeng ;
Liu, Bin ;
Zheng, Gang ;
Ren, Yibin ;
Zhang, Shuangshang ;
Liu, Yingjie ;
Gao, Le ;
Liu, Yuhai ;
Zhang, Bin ;
Wang, Fan .
NATIONAL SCIENCE REVIEW, 2020, 7 (10) :1584-1605