Comparison of Sea Surface Heights Derived from Satellite Altimetry and from Ocean Bottom Pressure Gauges: The SW Pacific MOTEVAS Project

A bottom pressure gauge (BPG) was installed in proximity (3.7 km at closest approach) of Jason-1 and formerly TOPEX/Poseidon (T/P) ground track No. 238 at the Wusi site, located ∼ 10 km offshore off the west coast of Santo Island, Vanuatu, Southwest (SW) Pacific. Sea level variations are inferred from the bottom pressure, seawater temperature, and salinity, corrected for the measured surface atmospheric pressure. The expansion of the water column (steric increase in sea surface height, SSH) due to temperature and salinity changes is approximated by the equation of state. We compare time series of SSH derived from T/P Side B altimeter Geophysical Data Records (GDR) and Jason-1 Interim Geophysical Data Records (IGDR), with the gauge-inferred sea level variations. Since altimeter SSH is a geocentric measurement, whereas the gauge-inferred observation is a relative sea level measurement, SSH comparison is conducted with the means of both series removed in this study. In addition, high-rate (1-Hz) bottom pressure implied wave heights (H_1/3) are compared with the significant wave height (SWH) measured by Jason-1. Noticeable discrepancy is found in this comparison for high waves, however the differences do not contribute significantly to the difference in sea level variations observed between the altimeter and the pressure gauge. In situ atmospheric pressure measurements are also used to verify the inverse barometer (IB) and the dry troposphere corrections (DTC) used in the Jason IGDR. We observe a bias between the IGDR corrections and those derived from the local sensors. Standard deviations of the sea level differences between T/P and BPG is 52 mm and is 48 mm between Jason and BPG, indicating that both altimeters have similar performance at the Wusi site and that it is feasible to conduct long-term monitoring of altimetry at such a site.     

Trends in Extreme Mean Sea Level Quantiles from Satellite Altimetry

Satellite altimetry allows the study of sea-level long-term variability on a global and spatially uniform basis. Here quantile regression is applied to derive robust median regression trends of mean sea level as well as trends in extreme quantiles from radar altimetry time series. In contrast with ordinary least squares regression, which only provides an estimate on the rate of change of the mean of data distribution, quantile regression allows the estimation of trends at different quantiles of the data distribution, yielding a more complete picture of long-term variability. Trends derived from basin-wide averaged regional mean sea level time series are robust and similar for all quantiles, indicating that all parts of the data distribution are changing at the same rate. In contrast, trends are not robust and diverge across quantiles in the case of local time series. Trends are under- (over-)estimated in the western (eastern) equatorial Pacific. Furthermore, trends in the lowermost quantile (0.05) are larger than the median trend in the western Pacific, while trends in the uppermost quantile (0.95) are lower than the median trend in the eastern Pacific. These differences in trends in extreme mean sea level quantiles are explained by the exceptional effect of the strong 1997–1998 El Niño–Southern Oscillation (ENSO) event.     

Assessment of Radar Waveform Retracked Jason-2 Altimetry Sea Surface Heights Near Taiwan Coastal Ocean

This study focuses on assessing the accuracy of 20-Hz waveform retracked Jason-2 (J-2) altimetry sea surface heights (SSHs) in the vicinity of Taiwan by comparisons with the TOPEX/Poseidon (T/P) 10-Hz SSHs and sea level data from the Anping tide gauge. The study areas exhibit high, medium, and low amplitudes of ocean tides and contain diverse bathymetries with depths of 0–4000 m. The performance of Offset Center of Gravity (OCOG), threshold, modified threshold, and ice retrackers was examined by comparing the retracked SSHs with Earth Gravitational Model 2008 (EGM08) geoid via the use of the improvement percentages (IMPs). The results indicate that both altimetry measurements are significantly improved by waveform retracking techniques, with a maximum IMP of 46.6% for T/P and 82.0% for J-2, and the optimal achievement of retrackers is influenced by the characteristics of the study areas. In addition, valid retracked J-2 SSHs are much closer to shorelines than T/P. A comparison of retracked J-2 data with Anping tide gauge records reveals that applying the optimal retracking algorithms reduces the root mean squares of differences and increases the number of valid measurements.     

Variability of Sea Surface Heights in the Baltic Sea: An Intercomparison of Observations and Model Simulations

Sea level changes in the Baltic Sea are dominated by internal, short-term variations that are mostly caused by the ephemeral nature of atmospheric conditions over the Baltic area. Tides are small and their influence decreases from western parts of the Baltic Sea to the Baltic Proper. Superimposed to the large short-term sea level changes (up to few decimeters from day to day) are seasonal and interannual variations (centimeters to decimeters). This study focuses on the comparison of sea surface heights obtained from observations and from a high resolution oceanographic model of the Baltic Sea. From this comparison, the accuracy of the modeled sea surface variations is evaluated, which is a necessary precondition for the further use of the oceanographic model in geodetic applications. The model reproduces all observed Baltic sea level variations very reliably with an accuracy of 5 to 9 cm (rms) for short-term variations (up to 2 months) and 8 cm (rms) for long-term variations (>2 months). An additional improvement of the model can be attained by including long-period sea level variations of the North Sea. The model performs well also in the case of extreme sea level events, as is shown for a major storm surge that occurred at the southern coast of the Baltic Sea in November 1995.     

卫星测高技术在监测海啸现象中的研究及应用

利用卫星测高技术,通过共线平差和沿轨、垂直轨迹测高大地水准面梯度的计算,得到了海啸发生2个小时后的海面波高和海面梯度异常现象,为利用卫星测高技术监测海啸、模拟海啸现象提供了依据。     

卫星测高正常点海面高度计算方法研究

正常点海面高计算是卫星测高数据处理的基础.从测高卫星飞行轨道的规律出发,提出了采用“距离加权平均”计算正常点海面高的新方法,阐述了“距离加权平均”这一方法计算正常点海面高的原理.并分别利用传统方法和新方法所计算出的中国海域内正常点海面高数据,进行交叉点海面高不符值及其平差计算,对所得结果进行精度评定.实际计算结果比对证明了利用“距离加权平均”法计算正常点海面高的可行性和优越性.     

联合卫星测高和卫星重力数据研究热容海平面变化

首先用卫星测高资料计算了1993～2009年6月的全球平均海平面变化。用GRACE(gravity recovery andclimate experiment)时变重力场系数反演了2003～2009年6月全球平均海水质量变化。联合GRACE和卫星测高资料计算了2003～2009年6月的热容海平面变化,该变化呈上升趋势。用日本气象局Ishii等提供的海温数据计算了1993～2006年的海水引起的平均热膨胀海平面变化,1993～2003年间,全球海洋热膨胀引起的热容海平面呈上升趋势,约占同期平均海平面变化的一半。利用ARGO温盐数据计算了2004～2009年6月平均热容海平面变化,也呈上升态势,只是变化速率有所减慢。     

Vertical Land Motion in the Mediterranean Sea from Altimetry and Tide Gauge Stations

We have computed estimates of the rate of vertical land motion in the Mediterranean Sea from differences of sea level heights measured by the TOPEX/Poseidon radar altimeter and by a set of tide gauge stations. The comparison of data at 16 tide gauges, using both hourly data from local datasets and monthly data from the PSMSL dataset, shows a general agreement, significant differences are found at only one location. Differences of near-simultaneous, monthly and deseasoned monthly sea level height time-series have been considered in order to reduce the error in the estimated linear-term. In a subset of 23 tide gauge stations the mean accuracy of the estimated vertical rates is 2.3 ± 0.8 mm/yr. Results for various stations are in agreement with estimates of vertical land motion from geodetic methods. A comparison with vertical motion estimated by GPS at four locations shows a mean difference of ?0.04 ± 1.8 mm/yr, however the length of the GPS time-series and the number of locations are too small to draw general conclusions.     

Vertical Land Motion in the Mediterranean Sea from Altimetry and Tide Gauge Stations

We have computed estimates of the rate of vertical land motion in the Mediterranean Sea from differences of sea level heights measured by the TOPEX/Poseidon radar altimeter and by a set of tide gauge stations. The comparison of data at 16 tide gauges, using both hourly data from local datasets and monthly data from the PSMSL dataset, shows a general agreement, significant differences are found at only one location. Differences of near-simultaneous, monthly and deseasoned monthly sea level height time-series have been considered in order to reduce the error in the estimated linear-term. In a subset of 23 tide gauge stations the mean accuracy of the estimated vertical rates is 2.3 ± 0.8 mm/yr. Results for various stations are in agreement with estimates of vertical land motion from geodetic methods. A comparison with vertical motion estimated by GPS at four locations shows a mean difference of -0.04 ± 1.8 mm/yr, however the length of the GPS time-series and the number of locations are too small to draw general conclusions.     

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.     

Extension of Satellite Altimetry Jason-2 Sea Level Anomalies Towards the Red Sea Coast Using Polynomial Harmonic Techniques

Satellite altimetry data are facing big challenges near the coasts. These challenges arise due to the fundamental difficulties of correction and land contamination in the foot print, which result in rejection of these data near the coast. Several studies have been carried out to extend these data towards the coast. Over the Red Sea, altimetry data consist of gaps, which extend to about 30–50 km from the coast. Two methods are used for processing and extending Jason-2 satellite altimetry sea level anomalies (SLAs) towards the Red Sea coast; Fourier Series Model (FSM), and the polynomial sum of sine model (SSM). FSM model technique uses Fourier series and statistical analysis reflects strong relationship with both the observation and AVISO data, with strong and positive correlation. The second prediction technique, SSM model, depends on the polynomial sum of sine, and does not reflect any relationship with the observations and AVISO data close to the coast and the correlation coefficient (CC) is weak and negative. The FSM model output results in SLA data significantly better and more accurate than the SSM model output.     

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.     

A New Methodology for Incorporating Tide Gauge Data in Sea Surface Topography Models

As part of the Vertical Offshore Reference Frames (VORF) project sponsored by the U. K. Hydrographic Office, a new model for Sea Surface Topography (SST) around the British Isles has been developed. For offshore areas (greater than 30 km from the coast), this model is largely derived from satellite altimetry. However, its accuracy and level of detail have been enhanced in coastal areas by the inclusion of not only the 60 PSMSL tide gauges with long-term records around the coasts of the United Kingdom and Ireland but also some 385 gauges established at different epochs and for different observation spans by the U. K. Admiralty. All tide gauge data were brought into a common reference frame by a combination of datum models and direct GPS observations, but a more significant challenge was to bring all short-term sea level observations to an unbiased value at a common epoch. This was achieved through developing a spatial-temporal correlation model for the variations in mean sea level around the British Isles, which in turn meant that gauges with long-term observation spans could be used as control points to improve the accuracy of Admiralty gauges. It is demonstrated that the latter can contribute point observations of mean sea level (MSL) with a precision of 0.078 m. A combination of least squares collocation and interpolation was developed to merge the coastal point and offshore gridded data sets, with particular algorithms having to be developed for different configurations of coastal topology. The resulting model of sea surface topography is shown to present a smooth transition from inshore coastal areas to offshore zones. Further benefits of the techniques developed include an enhanced methodology for detecting datum discontinuities at permanent tide gauges.     

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.     

利用卫星测高技术监测厄尔尼诺和拉尼娜现象

利用美国宇航局和法国空间局联合公布的T／P数据（1993-2000年）和Jason-1数据（2002—2006年），由共线法计算了热带太平洋地区海平面高度的变化。根据T／P和Jason-1数据计算的海面月变化异常图，分别研究了和分析了1997-1998年的厄尔尼诺和拉尼娜现象、2002-2003年厄尔尼诺现象的变化发展过程。     

2004年苏门答腊地震前后海面高趋势的变化

利用苏门答腊附近海域T/P、Jason-1测高卫星近20年的海面高连续观测资料,分别计算了该区域(80°E~105°E,5°S~20°N)在2004年苏门答腊大地震前后的海面高变化趋势,并与该区域对应时间段GRACE重力卫星反演的地表质量迁移结果进行比较。研究结果表明,由卫星测高观测资料估算的震区地震前后海平面趋势的变化与卫星重力反演结果基本一致。由于卫星测高沿轨观测具有高精度、高时空分辨率的特性,卫星测高资料估算的海平面趋势变化可以更为准确地反映震区海平面变化的局部特征。还对该区域震后形变特征进行了初步分析。     

Estimating the Kuroshio Axis South of Japan Using Combination of Satellite Altimetry and Drifting Buoys

By tracking the locally strongest part of the sea-surface velocity field, which was obtained by integrating data of satellite altimeters and surface drifting buoys, we extracted the Kuroshio axis south of Japan every 10 days from October 1992 to December 2000. The obtained axes clearly express the effect of the bottom topography; three modes were observed when the Kuroshio ran over the Izu Ridge. The axis was very stable to the south of ‘Tosa-bae,’ off the Kii Channel. Mean current speed at the Kuroshio axis gradually increased from 0.65 m/s south of Kyushu to 1.45 m/s off Enshu-nada. This revised version was published online in July 2006 with corrections to the Cover Date.     

Influence of the Kuroshio Fluctuations on Sea Level Variations along the South Coast of Japan

The correlation between the Kuroshio and coastal sea level south of Japan has been examined using the altimetry and tide gauge data during the period 1992–2000. The sea level varies uniformly in a region bounded by the coast and the mean Kuroshio axis, which stretches for several hundred kilometers along the coast. These variations are related with the Kuroshio velocity, as coastal sea level decreases (or increases) when the Kuroshio is faster (or slower). To the east of the Kii Peninsula, where sea level variations are different from these to the west, movement of the Kuroshio axis additionally affects coastal sea level variations.     

15年测高资料反演南海北部潮汐结果的分析

在调和分析的基础上,结合差比关系的运用,利用T／P和Jason-1卫星测高数据进行了南海北部潮汐状况的反演、分析。其中沿轨数据和交点数据的海面异常（SLA）样本标准差（STD）值计算表明,对于15年资料而言,潮汐分析结果已基本一致。对3年轨道改变后的T／P资料分析发现,运用差比分析的办法能使其分析精度有20％左右的提高,达到与15年资料相近的分析效果。最后文章将近岸的反演结果与实测值作了对比,得出在大多数地区（除水动力复杂区域外）,沿轨数据的反演水位与实测值的误差均值小于19cm,STD值小于14cm。     

Calibration of TOPEX/Poseidon and Jason Altimeter Data to Construct a Continuous Record of Mean Sea Level Change

Jason, the successor to the TOPEX/POSEIDON (T/P) mission, has been designed to continue seamlessly the decade-long altimetric sea level record initiated by T/P. Intersatellite calibration has determined the relative bias to an accuracy of 1.6 mm rms. Tide gauge calibration of the T/P record during its original mission shows a drift of -0.1 ± 0.4 mm/year. The tide gauge calibration of 20 months of nominal Jason data indicates a drift of -5.7 ± 1.0 mm/year, which may be attributable to errors in the orbit ephemeris and the Jason Microwave Radiometer. The analysis of T/P and Jason altimeter data over the past decade has resulted in a determination of global mean sea level change of +2.8 ± 0.4 mm/year.