Accuracy assessment of global ocean tide models in the South China Sea using satellite altimeter and tide gauge data

In this study, to meet the need for accurate tidal prediction, the accuracy of global ocean tide models was assessed in the South China Sea (0°–26°N, 99°–121°E). Seven tide models, namely, DTU10, EOT11a, FES2014, GOT4.8, HAMTIDE12, OSU12 and TPXO8, were considered. The accuracy of eight major tidal constituents (i.e., Q₁, O₁, P₁, K₁, N₂, M₂, S₂ and K₂) were assessed for the shallow water and coastal areas based on the tidal constants derived from multi-mission satellite altimetry (TOPEX and Jason series) and tide gauge observations. The root mean square values of each constituent between satellite-derived tidal constants and tide models were found in the range of 0.72–1.90 cm in the deep ocean (depth>200 m) and 1.18–5.63 cm in shallow water area (depth<200 m). Large inter-model discrepancies were noted in the Strait of Malacca and the Taiwan Strait, which could be attributable to the complicated hydrodynamic systems and the paucity of high-quality satellite altimetry data. In coastal regions, an accuracy performance was investigated using tidal results from 37 tide gauge stations. The root sum square values were in the range of 9.35–19.11 cm, with the FES2014 model exhibiting slightly superior performance.     

Ocean Tide Models Developed by Assimilating TOPEX/POSEIDON Altimeter Data into Hydrodynamical Model: A Global Model and a Regional Model around Japan

A global ocean tide model (NAO.99b model) representing major 16 constituents with a spatial resolution of 0.5° has been estimated by assimilating about 5 years of TOPEX/POSEIDON altimeter data into barotropic hydrodynamical model. The new solution is characterized by reduced errors in shallow waters compared to the other two models recently developed; CSR4.0 model (improved version of Eanes and Bettadpur, 1994) and GOT99.2b model (Ray, 1999), which are demonstrated in comparison with tide gauge data and collinear residual reduction test. This property mainly benefits from fine-scale along-track tidal analysis of TOPEX/POSEIDON data. A high-resolution (1/12°) regional ocean tide model around Japan (NAO.99Jb model) by assimilating both TOPEX/POSEIDON data and 219 coastal tide gauge data is also developed. A comparison with 80 independent coastal tide gauge data shows the better performance of NAO.99Jb model in the coastal region compared with the other global models. Tidal dissipation around Japan has been investigated for M₂ and K₁ constituents by using NAO.99Jb model. The result suggests that the tidal energy is mainly dissipated by bottom friction in localized area in shallow seas; the M₂ ocean tidal energy is mainly dissipated in the Yellow Sea and the East China Sea at the mean rate of 155 GW, while the K₁ energy is mainly dissipated in the Sea of Okhotsk at the mean rate of 89 GW. TOPEX/POSEIDON data, however, detects broadly distributed surface manifestation of M₂ internal tide, which observationally suggests that the tidal energy is also dissipated by the energy conversion into baroclinic tide.     

Absolute Sea Level Surface Modeling for the Mediterranean from Satellite Altimeter and Tide Gauge Measurements

Changes in the height of the ocean can be described through the relative and absolute sea level changes depending on the geodetic reference the sea level records are related to. Satellite altimetry provides absolute sea level (ASL) measurements related to the global geodetic reference, whereas tide gauges provide relative sea level (RSL) measurements related to the adjacent land. This study aims at computing the ASL surfaces for different time epochs from combined satellite altimeter and tide gauge records. A method of sea level data fusion is proposed to enable modeling of the impact of present and future sea level changes on the coast. Sea surface modeling was investigated for ten different gridding methods commonly used for the interpolation of altimeter data over the open ocean and extrapolation over the coastal zones. The performance of gridding methods was assessed based on the comparison of the gridded altimeter data and corrected tide gauge measurements. Finally, the sea level surfaces related to the GRS80 global reference ellipsoid were computed for the Mediterranean Sea over the altimeter period. In addition, the current sea level trends were estimated from both sea level measurements.     

Improving the Coastal Mean Dynamic Topography by Geodetic Combination of Tide Gauge and Satellite Altimetry

Abstract

The ocean mean dynamic topography (MDT) is the surface representation of the ocean circulation. The MDT may be determined by the ocean approach, which involves temporal averaging of numerical ocean circulation model information, or by the geodetic approach, wherein the MDT is derived using the ellipsoidal height of the mean sea surface (MSS), or mean sea level (MSL) minus the geoid as the geoid. The ellipsoidal height of the MSS might be estimated either by satellite or coastal tide gauges by connecting the tide gauge datum to the Earth-centred reference frame. In this article we present a novel approach to improve the coastal MDT, where the solution is based on both satellite altimetry and tide gauge data using new set of 302 tide gauges with ellipsoidal heights through the SONEL network. The approach was evaluated for the Northeast Atlantic coast where a dense network of GNSS-surveyed tide gauges is available. The typical misfit between tide gauge and satellite or oceanographic MDT was found to be around 9?cm. This misfit was found to be mainly due to small scale geoid errors. Similarly, we found, that a single tide gauge places only weak constraints on the coastal dynamic topography.     

Assessment of SARAL-ALTIKA Tidal Corrections in the Coastal Oceans Around India

The present tidal correction of sea level records of Satellite with ARgoes and ALtimeter (SARAL) is based on the finite element solution (FES) of global tide model FES2012 tidal solution. In this study, we examined the validity of the tidal corrections in the coastal oceans around India using tide gauge measurements and a regional tidal model. Our regional model is based on the barotropic version of the Princeton Ocean Model that is forced by the time-varying tidal levels at the open ocean end based on the global FES99 tidal solution. Tide charts prepared from the simulated tidal levels are very similar to the FES tidal solutions. Comparison with the tide gauge measurement shows close agreement with the regional tidal solutions. On the other hand, the agreement with the FES tide models differ significantly in the Gulf of Khambhat and the Gulf of Kutch on the northwest, and in the Hooghly estuary on the northeast continental shelf. However, the agreement is exceptional in other parts of the study domain. These tidal solutions are used in the SARAL-ALTIKA X-track data to assess the FES tidal correction and to draw some inferences associated with the coastal processes. It is revealed that these corrections are reasonably accurate for the coastal oceans around India except the aforementioned converging channels.     

中国沿海验潮站及其邻近地区陆地垂直运动分析

利用中国沿岸验潮站GNSS和邻近地区陆态网络GNSS基准站观测数据,结合卫星高度计和验潮站海平面观测数据分析了中国沿海验潮站及其邻近地区陆地垂直运动特征。中国沿海海平面观测以及验潮站和陆态网GNSS基准站观测结果显示,中国沿海省区市及沿海验潮站陆地垂直运动总体表现为：辽宁至江苏沿海上升、上海至福建泉州沿海沉降、福建厦门至广西沿海升降交替的格局,局部滨海平原地区如华北平原天津南部、河北平原的沧县则表现出显著的沉降特征。验潮站陆地的抬升与沉降是沿海相对海平面变化的重要组成部分,准确掌握验潮站及其邻近区域的陆地垂直运动特征,可为沿海相对海平面变化分析、海平面变化影响评估以及未来海平面上升预测提供依据。     

A Kalman Filter Approach to Realize the Lowest Astronomical Tide Surface

In this paper , we present a novel Kalman filter approach to combine a hydrodynamic model-derived lowest astronomical tide (LAT) surface with tide gauge record-derived LAT values. In the approach, tidal water levels are assimilated into the model. As such, the combination is guided by the model physics. When validating the obtained “Kalman-filtered LAT realization” at all tide gauges, we obtained an overall root-mean-square (RMS) difference of 15.1 cm. At the tide gauges not used in the data assimilation, the RMS is 17.9 cm. We found that the assimilation reduces the overall RMS difference by ～ 31% and ～ 22%, respectively. In the Dutch North Sea and Wadden Sea, the RMS differences are 6.6 and 14.8 cm (all tide gauges), respectively. Furthermore, we address the problem of LAT realization in intertidal waters where LAT is not defined. We propose to replace LAT by pseudo-LAT, which we suggest to realize similarly as LAT except that all water level boundary conditions and assimilated tidal water levels have to be enlarged by a constant value that is removed afterward. Using this approach, we obtained a smooth reference surface for the Dutch Wadden Sea that fits LAT at the North Sea boundary within a few centimeters.     

Absolute sea level variability of Arctic Ocean in 1993–2018 from satellite altimetry and tide gauge observations

Arctic absolute sea level variations were analyzed based on multi-mission satellite altimetry data and tide gauge observations for the period of 1993–2018. The range of linear absolute sea level trends were found ?2.00 mm/a to 6.88 mm/a excluding the central Arctic, positive trend rates were predominantly located in shallow water and coastal areas, and negative rates were located in high-latitude areas and Baffin Bay. Satellite-derived results show that the average secular absolute sea level trend was (2.53±0.42) mm/a in the Arctic region. Large differences were presented between satellite-derived and tide gauge results, which are mainly due to low satellite data coverage, uncertainties in tidal height processing and vertical land movement (VLM). The VLM rates at 11 global navigation satellite system stations around the Arctic Ocean were analyzed, among which 6 stations were tide gauge co-located, the results indicate that the absolute sea level trends after VLM corrected were of the same magnitude as satellite altimetry results. Accurately calculating VLM is the primary uncertainty in interpreting tide gauge measurements such that differences between tide gauge and satellite altimetry data are attributable generally to VLM.     

潮汐测量与验潮技术的发展

潮汐是由各天体作用于地球上的引潮力所产生，不仅海洋中有潮汐，大气圈和地球固体部分也同样存在着潮汐。海道测量中的潮汐测量仅指海洋潮汐测量仅指海洋潮汐测量，潮汐测量的手段很多，主要包括采用水尺；浮子式、引压钟式、声学式、压力式验潮仪验潮。而GPS验潮及潮汐遥感测量等技术研究国内外正在开展。所有这些验潮技术各有自己的特点。     

世界大洋潮波特征的比较分析

对TOPEX/Poseidon(以下简称T/P)高度计资料直接分析得到4个主要分潮(M2,K1,S2和O1)的调和常数,并与5个全球大洋潮波模式的模拟结果和138个验潮站观测资料的分析结果进行了系统比较,得出如下结论:在深水大洋,高度计资料直接分析结果与潮波模式模拟结果较一致;模拟出的无潮点的个数和位置也较一致;半日分潮模拟结果比全日分潮要好。5个模式模拟结果之间的差异相对较小,振幅的绝对偏差在1.0 cm左右,迟角的绝对偏差在10(°)左右。在陆架浅海,不同模型结果差异相对较大。高度计资料直接分析结果比模式模拟结果普遍偏小,尤其在陆架浅海更是如此。     

全球大洋潮汐模式在北印度洋潮汐预报准确性的评估

为评估DTU10、TPXO8、GOT00.2和NAO.99b 4个全球大洋潮汐模式对北印度洋潮汐的预报能力,采用英国海洋资料中心提供的海区中部和沿岸站潮汐调和常数资料,检验了这些模式4个主要分潮(M_2、S_2、K_1、O_1)的准确度。它们的各分潮调和常数资料准确度都比较高,振幅绝均差的最大值仅5.61 cm,迟角绝均差的最大值仅9.13°。这些模式的调和常数给出潮波传播特征差别不大。基于这些模式提供的调和常数,分别建立了北印度洋4、8和16分潮潮汐预报模型,将预报结果与中国海事服务网提供的沿岸24个站潮汐表资料进行对比。各模式的8分潮(M_2、S_2、N_2、K_2、K_1、O_1、P_1、Q_1)潮汐预报模型均优于4分潮(M_2、S_2、K_1、O_1)潮汐预报模型,NAO.99b模式可以提供16分潮(M_2、S_2、N_2、K_2、K_1、O_1、P_1、Q_1、MU_2、NU_2、T_2、L_2、2N_2、J_1、M1、OO_1)潮汐预报模型,但是对预报结果改善不明显;在各模式中,GOT00.2模式的8分潮潮汐预报模型对北印度洋沿岸的预报效果最好,平均绝均差为14.97 cm。     

Sea level changes at Ascension Island in the last half century

An exercise in ‘data archaeology’ at Ascension Island has provided an estimate of sea level change between 1955 and 2001.5 (the mid-point of a recent dataset spanning 1993–2009). That average trend of 0.93 mm y^?1 (SE 0.69) compares to a larger rate during 1993–2009 itself of 2.55 (SE 0.13) and 2.07 (SE 0.30) mm y^?1 from tide gauge and altimeter data respectively, suggesting a recent acceleration in sea level rise. An ocean model and steric height datasets have been used for comparison to the measurements, with the conclusion that the acceleration was probably at least partly due to a steric height increase. This exercise is based on only one month of historical tide gauge data and is admittedly on the useful limit for long-term sea level studies. In addition, it is unfortunate that the tide gauge benchmark installed in 1955 has disappeared, even if one can estimate its height relative to modern marks. Nevertheless, the study does provide information of interest to climate studies, enables limits to be inferred on the real changes, and provides background information for other coastal studies. Most importantly, it is intended as a demonstration of the value of similar exercises where short historical records exist.     

利用验潮站数据进行三种潮汐模型的精度分析

采用全球分布的565个验潮站水位资料对NAO.99b,CSR4.0和TPXO7.2三种潮汐模型进行精度评估。结果表明:在全球海洋范围内,NAO.99b模型精度最高;在黄海海域,TPXO7.2模型的精度最高;在东海和南海海域,则是NAO.99b模型最优;在深海海域,三种模型精度差异不大;在浅海海域,采用同化方法的潮汐模型比采用经验方法的潮汐模型更有优势。     

山东邻海长周期分潮对深度基准面的影响分析

利用潮汐模型NAO.99Jb和FES2014确定了山东邻海的深度基准面模型并对其精度进行了评估,结果表明,NAO.99Jb模型确定的深度基准值L10的中误差为23.28 cm,FES2014模型确定的深度基准值L13的中误差为34.37 cm,长周期分潮的相对误差过大导致加入长周期分潮改正项后深度基准值中误差分别增大了11.04 cm和12.38 cm,较其他分潮对深度基准值精度的影响更明显,所以基于潮汐模型构建深度基准面模型时,长周期分潮部分必须加入实测数据改正。进一步采用山东邻海13个长期验潮站实测数据,定量地分析了长周期分潮对深度基准面确定的影响,结果表明,长周期分潮改正项的量值介于13.89~22.39 cm,平均改正值为18.03 cm,在深度基准值中占比达到15.15%。因此,长周期分潮改正对深度基准面的精确确定研究贡献较大,准确的长周期分潮模型是构建高精度深度基准面模型的基础。     

东印度洋海域最优深度基准面模型构建

利用东印度洋海域周边长期验潮站实测数据、TOPEX/Poseidon等系列卫星测高反演结果,评估了DTU10,EOT11a,FES2014,GOT4.8,OSU12和TPXO8六种全球潮汐模型精度,根据卫星测高结果给出了浅水分潮改正量和长周期分潮改正量的经验模型,又在此基础上分析并构建了研究区域精度最优的深度基准面模型。考虑到全球潮汐模型在近岸的影响因素及验潮站位置,将13个验潮站分成开阔海域与近海海域两类,与潮汐模型的对比,结果表明,DTU10和FES2014模型分别在开阔海域和近海海域精度最优。根据潮汐模型在不同分潮处的精度,如EOT11a模型在O1和K1分潮处精度较高,DTU10在N2,M2,S2和K2分潮处精度较高等,分别构建了开阔海域与近海海域的组合深度基准面模型,计算得知误差分别为11.33和20.95 cm,其精度显著提高。     

The state‐of‐the‐art in marine navigation

Tide gauges distributed all over the world provide valuable information for monitoring mean sea level changes. The statistical models used in estimating sea level change from the tide gauge data assume implicitly that the random model components are stationary in variance. We show that for a large number of global tide gauge data this is not the case for the seasonal part using a variate-differencing algorithm. This finding is important for assessing the reliability of the present estimates of mean sea level changes because nonstationarity of the data may have marked impact on the sea level rate estimates, especially, for the data from short records.     

Absolute Calibration of the Jason-1 Altimeter Using UK Tide Gauges

This article describes an “absolute” calibration of Jason-1 (J-1) altimeter sea surface height bias using a method developed for TOPEX/Poseidon (T/P) bias determination reported previously. The method makes use of U.K. tide gauges equipped with Global Positioning System (GPS) receivers to measure sea surface heights at the same time, and in the same geocentric reference frame, as Jason-1 altimetric heights recorded in the nearby ocean. The main time-dependent components of the observed altimeter-minus-gauge height-difference time series are due to the slightly different ocean tides at the gauge and in the ocean. The main harmonic coefficients of the tide differences are calculated from analysis of the copious TOPEX data set and then applied to the determination of T, P, and J-1 bias in turn. Datum connections between the tide gauge and altimetric sea surface heights are made by means of precise, local geoid differences from the EGG97 model. By these means, we have estimated Jason-1 altimeter bias determined from Geophysical Data Record (GDR) data for cycles 1-61 to be 12.9 cm, with an accuracy estimated to be approximately 3 cm on the basis of our earlier work. This J-1 bias value is in close agreement with those determined by other groups, which provides a further confirmation of the validity of our method and of its potential for application in other parts of the world where suitable tide gauge, GPS, and geoid information exist.     

Absolute Calibration of the Jason-1 Altimeter Using UK Tide Gauges

This article describes an “absolute” calibration of Jason-1 (J-1) altimeter sea surface height bias using a method developed for TOPEX/Poseidon (T/P) bias determination reported previously. The method makes use of U.K. tide gauges equipped with Global Positioning System (GPS) receivers to measure sea surface heights at the same time, and in the same geocentric reference frame, as Jason-1 altimetric heights recorded in the nearby ocean. The main time-dependent components of the observed altimeter-minus-gauge height-difference time series are due to the slightly different ocean tides at the gauge and in the ocean. The main harmonic coefficients of the tide differences are calculated from analysis of the copious TOPEX data set and then applied to the determination of T, P, and J-1 bias in turn. Datum connections between the tide gauge and altimetric sea surface heights are made by means of precise, local geoid differences from the EGG97 model. By these means, we have estimated Jason-1 altimeter bias determined from Geophysical Data Record (GDR) data for cycles 1–61 to be 12.9 cm, with an accuracy estimated to be approximately 3 cm on the basis of our earlier work. This J-1 bias value is in close agreement with those determined by other groups, which provides a further confirmation of the validity of our method and of its potential for application in other parts of the world where suitable tide gauge, GPS, and geoid information exist.     

Determination of Ocean Tide Loading Displacement by GPS PPP with Priori Information Constraint of NAO99b Global Ocean Tide Model

The published global ocean tide models show good agreement in deep oceans and exhibit differences in complex coastal areas, along with subsequent Ocean Tide Loading Displacement (OTLD) modeling differences. Meanwhile, OTLD parameters (amplitudes and phase lags) derived by Global Positioning System (GPS) Precise Point Positioning (PPP) approach need long time to converge to a stable state and show poor precision of S2, K1, and K2 constituents. Based on the fact that no constraint is imposed in the current kinematic solution, a new method is put forward, in which global ocean tide model predictions are taken as the priori information constraints to speed up the convergence rate and improve the accuracy of the GPS-derived OTLD parameters. First, the data of tide gauge from 01 January 2014 to 31 December 2016 are used to generate the harmonic parameters to evaluate the accuracy of six global ocean tide models and a regional ocean tide model (osu.chinesea.2010). Osu.chinesea.2010 model shows good agreement with the tide gauge results, while NAO99b model presents relatively large difference. The predictions from osu.chinesea.2010 and NAO99b model are employed as reference and the prior information, respectively. Second, continuous observations of 12 GPS sites during 2006–2013 in Hong Kong are collected to generate three dimensional OTLD amplitudes and phase lags of eight constituents using PPP with prior information constraints approach and harmonic analysis. Third, comparing the convergence time of eight constituents from PPP without and with priori information constraints approaches, the results show that the new method can significantly improve the convergence rate of OTLD amplitude estimates which obtain a certain level of stability seven years earlier than that derived by the PPP without priori information constraints. Precision of OTLD parameters derived by the new method is about 1 mm. By comparing with the precision of single PPP approach, the accuracy of eight constituents has been improved, especially for S2, K1, and K2 constituents. Finally, through comparing the different correction effects of OTLD estimates on the coordinates and their time series of the ground GPS stations, the results show that OTLD estimates derived by the new approach have similar influence as the osu.chinasea.2010 ocean tide model. The new method provides an effective means to improve the convergence and precision of the GPS-derived OTLD parameters, and achieve a similar correction as the high precision ocean tide model.     

Are Global Tide Gauge Data Stationary in Variance?

Tide gauges distributed all over the world provide valuable information for monitoring mean sea level changes. The statistical models used in estimating sea level change from the tide gauge data assume implicitly that the random model components are stationary in variance. We show that for a large number of global tide gauge data this is not the case for the seasonal part using a variate-differencing algorithm. This finding is important for assessing the reliability of the present estimates of mean sea level changes because nonstationarity of the data may have marked impact on the sea level rate estimates, especially, for the data from short records.