Tide Gauge Data Research Articles

Regional Mean Sea Level Variability Due to Tropical Cyclones: Insights from August Typhoons

This study investigates the interannual variations in regional mean sea levels (MSLs) of the northeast Asian marginal seas (NEAMS) during August, focusing on the role of typhoon activity from 1993 to 2019. The NEAMS are connected to the Pacific through the East China Sea (ECS) and narrow, shallow straits in the east, where inflow from the southern boundary (ECS), unless balanced by eastern outflow, leads to significant convergence or divergence, as well as subsequent changes in regional MSLs. Satellite altimetry and tide-gauge data reveal that typhoon-induced Ekman transport plays a key role in MSL variability, with increased inflow raising MSLs during active typhoon seasons. In contrast, weak typhoon activity reduces inflow, resulting in lower MSLs. This study’s findings have significant implications for coastal management, as the projected changes in tropical cyclone frequency and intensity due to climate change could exacerbate sea level rise and flooding risks. Coastal communities in the NEAMS region will need to prioritize enhanced flood defenses, early warning systems, and adaptive land use strategies to mitigate these risks. This is the first study to link typhoon frequency directly to NEAMS MSL variability, highlighting the critical role of wind-driven processes in regional sea level changes.

Tides in Complex Coastal Regions: Early Case Studies From Wide‐Swath SWOT Measurements

AbstractStudying ocean tides with satellite altimetry has traditionally been difficult in coastal regions. The 1 day repeat of the Cal/Val phase of SWOT provides a unique dataset that can be exploited for tidal analysis. In this work, KaRIn data from the SWOT Cal/Val phase are analyzed in two coastal regions to present a first look at the possibilities for tidal analysis from SWOT. The areas are: (a) Bristol Channel and (b) Great South Bay. When benchmarked against in situ measurements in these regions, substantial improvements over tide models, which typically report errors exceeding tens of centimeters and degrees, are seen. Specifically, the SWOT ocean‐tide estimates exhibit amplitude discrepancies ranging from 1.75 to 3 cm and phase lag discrepancies between 1.75° and 2.75° when compared with in situ tide gauge data. These findings underscore the value of SWOT for tidal research in complex coastal regions.

Accuracy assessment of recent global ocean tide models in coastal waters of the European North West Shelf

The accuracy of global ocean tide models is assessed in coastal waters of the European North West Shelf to ascertain where higher resolution local (forecast) models are most needed for geophysical and navigational applications, and which global models are most suitable for providing boundary conditions for regional and local tide models. Five recent global ocean tide models (FES2014b, EOT20, TPXO9-atlas-v5, GOT4.10c, and DTU16) are considered, with the models first compared by interpolating them onto common grids and computing the mean absolute deviation at each grid point. Coastline tide gauge and offshore bottom pressure sensor data were collated from several sources to give a total of 279 observation sites for evaluating model accuracy, including observational values from 137 locations that have not previously been released and have therefore not been assimilated into any of the global models tested. The residual errors between each model’s predicted phasor and the corresponding observed phasor were calculated at each observation location, and quantified using the root mean square (RMS) and median absolute residual (MAR) for the eight tidal constituents M2, S2, N2, O1, K1, K2, P1, and Q1. To avoid RMS values being biased by observation point density, a Voronoi-weighted RMS based on the water area of the Voronoi polygon about each observation location was also developed and used. Four zones were defined based on ocean depth to gauge model performance, and model inaccuracy is again demonstrated in near-shore regions. Seven further zones were defined based on geographical areas, which reveals inhomogeneity among the global models. The smallest overall root sum square (RSS) RMS error across all eight constituents arises with FES2014b, although TPXO9-atlas-v5 has the best performance when using the MAR and Voronoi-weighted RMS metrics. Using only the 137 observation sites that have not been assimilated by any model and therefore provide an independent accuracy assessment, FES2014b exhibits the smallest errors at the coastline, with an RSS RMS of 24.46 cm. All models exhibit larger errors with the 137 independent observation sites than with all 279 observation sites, with an average overall increase in RSS RMS error of 12%, and an increase of 30% for coastline tide gauges, highlighting the need for local model development in these areas.

A comparison of global and regional ocean tide models with tide gauges in the East Asian marginal seas

To enhance storm surge forecasting and establish open boundary conditions for sophistical numerical simulation of tides and tidal currents in the Bohai Sea, Yellow Sea, and East China Sea, this study evaluated the accuracy of eight global and regional tide models (EOT20, FES2014, NAO.99Jb, TPXO9, DTU16, HAMTIDE12, OSU12, and GOT4.10c) based on 41 tide gauges in the region. The differences between tide models and 8 offshore tide gauges were notable, with the root mean square (RMS) values ranging from 12.67 to 25.97 cm for M2, 6.09–13.73 cm for S2, 2.96–6.56 cm for K1, and 2.50–4.20 cm for O1. For the offshore stations, the NAO.99Jb model demonstrated superior performance with the root square sum (RSS) value of 12.63 cm among the eight tide models. For the island and coastal stations, the EOT20 model performed the best for M2 (RMS 13.03 cm), the FES2014 model for S2 (RMS 6.26 cm), and the DTU16 model for K1 and O1 (RMS 2.73 cm and 2.26 cm, respectively). Overall, the EOT20 model also exhibited the lowest RSS value (15.22 cm) among the eight models for 24 island and coastal stations. The EOT20 model slightly outperformed others with the RSS of 14.88 cm across all 32 tide gauges. Regarding the Sa tidal component, great discrepancies were found between TIDAL CONSTANTS (TICON) harmonic constant data and tide gauges. The reason for this is that the Doodson number for the Sa tidal component derived from tide gauge data, which is influenced by meteorological factors, should be (0,0,1,0,0,0), while the TICON harmonic constant data employed the Doodson number of (0,0,1,0,0,−1) corresponding to the basic astronomical variables. A correction method was proposed to adjust tidal constants from inconsistent Doodson numbers. Comparing with the harmonic constants of the Sa tidal component at 28 coastal tide stations revealed large errors in the NAO.99b, FES2014, and EOT20 models, with RMS values of 19.03, 22.08, and 13.61 cm, respectively. Consequently, caution should be taken when using the Sa results from these ocean tide models.

Long-Term Sea Level Periodicities over the European Seas from Altimetry and Tide Gauge Data

This study investigates the long-term temporal patterns of sea level changes by analyzing monthly tide gauge data from 1950 to 2022 (42 to 72 years) along the European coastline and monthly altimetry data from 1992 to 2024 in the surrounding European seas. The primary focus is on signals with periods longer than 5 years. The application of wavelet-based approaches and multiresolution analysis has enabled the isolation of signals with periods of approximately 8 and 16 years. However, the latter has only been observed in tide gauge data, as the altimetry time series is not sufficiently long. The same analysis was applied to the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO) indices, which enabled the detection of the same signals. The reported multiyear signals of sea level are correlated with NAO and AMO indices, particularly during the period spanning from 1975 to 2010.

Seasonal amplitude of principal tidal constituents in shallow shelf regions

Understanding the seasonal variation (annual cycle) of tides is crucial for coastal management and the planning of coastal infrastructure, as well as pivotal for navigation safety, prediction of extreme sea levels and so on. Specifically, seasonal variations of M2 amplitudes, larger in summer and smaller in winter, have attracted widespread attention. Nevertheless, investigations into other tidal constituents, such as S2, K1, O1, remain comparatively sparse. Here, seasonal variations of the four principal tidal constituents are studied in the shallow shelf regions of the China Seas and the North Sea, both acknowledged for exhibiting the most pronounced seasonal modulations of M2 amplitude, attributable to vertical stratification. We perform a segmented Harmonic Analysis with moving average filter, to remove semi-annual cycle, at 25 tide gauge data and find a synchronized seasonal modulation of M2 and O1 in China, which can be explained by stratification. Whereas S2 manifest an inverse seasonal modulation, larger in winter and smaller in summer, attributed to the sun's varying proximity to the earth. The K1 tide displays two very different seasonal cycle, also influenced by astronomical modulation. This observed pattern basically persists in the North Sea, while O1 shows a negative seasonal amplitude but still exists high correlation coefficient with M2. Consequently, this study provides a deep insight into 4 principal tidal constituents' seasonal modulation and underlying mechanisms, thereby enhancing our ability to comprehend and predict water level extremes and flooding with greater precision.

The Coastal Sea‐Level Response to Wind Stress in the Middle Atlantic Bight

AbstractAnalysis of 40 years of tide gauge data and reanalysis wind stresses from the Middle Atlantic Bight (MAB) indicate that along‐shelf wind stresses are a dominant driver of coastal dynamic sea level (sea level plus atmospheric pressure) variability at daily to yearly time scales. The sea‐level response to along‐shelf wind stress varies substantially along the coast and is accurately reproduced by a steady, barotropic, depth‐averaged model (Csanady, 1978, https://doi.org/10.1175/1520‐0485(1978)008<0047:tatw>2.0.co;2, Arrested Topographic Wave). The model indicates that the sea‐level response in the MAB depends primarily on the along‐shelf distribution of the along‐shelf wind stress, the Coriolis frequency, the bottom drag coefficient, and the cross‐shelf bottom slope. The along‐shelf wind stress varies along the MAB shelf due primarily to changes in the shelf orientation. The sea‐level response depends on both the local and upstream (in the sense of Kelvin wave propagation) along‐shelf wind stresses. Consequently, sea‐level variability at daily, monthly and yearly time scales along much of the central MAB coast is more strongly driven by upstream winds along the southern New England shelf than by local winds along the central MAB shelf. The residual coastal sea‐level variability, after removing the wind‐driven response and the trend, is roughly uniform along the MAB coast. The along‐coast average of the residual sea level at monthly and yearly time scales is caused by variations in shelf water densities primarily associated with the large annual cycle in water temperature and interannual variations in salinity.

Machine learning models applied to altimetry era tide gauge and grid altimetry data for comparative long-term trend estimation: A study from Shikoku Island, Japan

Estimation of sea level trends is essential for understanding sea level rise dynamics. In this study, the performance of traditional Ordinary Least Squares (OLS) linear trend forecasting is compared with modern machine learning techniques, specifically Random Forests (RF) and Least Squares Support Vector Machines (LS-SVM).These methods are applied to 50 years of long-term tide gauge (TG) data from six tide gauge stations off the coast of Shikoku Island, Japan, and CMEMS Grid Altimetry data from 1993 to the present. The analysis uses OLS, RF, and LS-SVM to estimate trends from both data sets and compares the results. The objective is to determine the consistency and accuracy of RF and LS-SVM methods compared to the OLS method. The results indicate that machine learning algorithms (LS-SVM) effectively estimate sea level trends, offering potential improvements in precision for both long-term and medium-term analyses. Shikoku Island's coastal sea level trend is determined as 2.91±1.44 mm/yr using TG data and 3.00±1.52 mm/yr using CMEMS Grid Altimeter data with the OLS approach. Using the LS-SVM approach, the trend is found as 2.96±1.58 mm/yr with TG data and 3.02±1.60 mm/yr with CMEMS Grid Altimetry data. The novelty of this study lies in its thorough comparison of traditional and machine learning approaches for sea level trend estimation, providing valuable insights for future predictions of the sea level rise.

Implications of Variability and Trends in Coastal Extreme Water Levels

AbstractProbabilities of coastal extreme water levels (EWLs) are increasing as sea levels rise. Using a time‐dependent statistical model on tide gauge data along U.S. and Pacific Basin coastlines, we show that EWL probability distributions also shift on an annual basis from climate forcing and long‐period tidal cycles. In some regions, combined variability (>15 cm) can be as large or larger than the amount of sea level rise (SLR) experienced over the past 30 years and projected over the next 30 years. Considering SLR and variability by 2050 at a location like La Jolla, California suggests a moderate‐level (damaging) flood today with a 50‐year return level (2% annual chance) would occur about 3–4 times a year during an El Nino nearing the peak of the nodal tide cycle. If interannual variability is overlooked, SLR related impacts could be more severe than anticipated based solely upon decadal‐scale projections.

Evaluation of the sub-annual sea level anomalies in the continental shelf of the Southwestern Atlantic and their relation to wind variability

We have analyzed the relationship between wind variability and sea level anomalies (SLA) on the Southwestern Atlantic Continental Shelf, focusing on sub-annual temporal scales. For this, we tested the capability of gridded altimetry to represent wind-driven SLA and compared results using an oceanographic model and tide gauge data. The present study used coherence analysis to analyze frequencies for which SLA and wind stress are coherent. The altimetry-SLA were found to have less energy below the three-month period compared to the model SLA. The coherence of along-shore wind stress and altimetry SLA was only significant for > 50 days (d), while the model SLA showed significant agreement in all periods considered, 20 d to annual. We further showed that geostrophic velocities on the continental shelf agreed significantly with SLA for > 50 d. As a result of an Empirical Orthogonal Function (EOF) analysis, we found that the second mode is highly coherent with the along-shore wind stress and accounts for 18.1% and 10.7% of variability in the model and altimetry sea level anomalies, respectively.

Explanations for the positive storm surges on the left side of landfall typhoons in China

The coastal regions of Southeast China frequently experience unusual positive storm surges on the left side of landfalling typhoons, a phenomenon historically overlooked and inadequately explained by conventional circular wind field models. In this study, a high resolution, two-dimensional storm surge model based on ADCIRC along with tide gauge data were used to investigate the spatiotemporal distribution of these surges and proposes underlying mechanisms, informed by a comparative analysis of circular and ERA5 reanalysis wind fields during typical typhoon event 9711 Winnie. Analyzing tide gauge data spanning from 1986 to 2016, the study uncovers a distinct pattern of left-side positive storm surges along the southeastern coast, notably on the Fujian coast and within the Taiwan Strait, which are found to be comparable to those on the cyclone’s right side. The research also documents a significant escalation in both the frequency and intensity of these left-side surges over the past three decades. Simulation results highlights the inadequacies of circular wind field models in operational forecasting and emphasizes the necessity of accounting for topographic influences and the structural complexity of wind fields in storm surge predictions. This is particularly pertinent in semi-enclosed seas with intricate hydrodynamics, such as the Taiwan Strait. The insights gleaned from this study are pivotal for enhancing the real-time simulation and prediction of storm surges, which are vital for coastal safety and disaster prevention measures.

Non‐Linear Vertical Land Motion of Coastal Chile and the Antarctic Peninsula Inferred From Combining Satellite Altimetry, Tide Gauge and GPS Data

AbstractWe developed an enhanced Kalman‐based approach to quantify abrupt changes and significant non‐linearity in vertical land motion (VLM) along the coast of Chile and the Antarctic Peninsula using a combination of multi‐mission satellite altimetry (ALT), tide gauge (TG), and GPS data starting from the early 1990s. The data reveal the spatial variability of co‐seismic and post‐seismic subsidence at TGs along the Chilean subduction zone in response to the Mw8.8 Maule 2010, Mw8.1 Iquique 2014, and Mw8.3 Illapel 2015 earthquakes that are not retrievable from the interpolation of sparse GPS observations across space and time. In the Antarctic Peninsula, where continuous GPS data do not commence until ∼1998, the approach provides new insight into the ∼2002 change in VLM at the TGs of +5.3 ± 2.2 mm/yr (Palmer) and +3.5 ± 2.8 mm/yr (Vernadsky) due to the onset of ice‐mass loss following the Larsen‐B Ice Shelf breakup. We used these data to constrain viscoelastic Earth model parameters for the northern Antarctic Peninsula, obtaining a preferred lithosphere thickness of 115 km and upper mantle viscosity of 0.9 × 1018 Pa s. Our estimates of regionally‐correlated ALT systematic errors are small, typically between ∼±0.5–2.5 mm/yr over single‐mission time scales. These are consistent with competing orbit differences and the relative errors apparent in ALT crossovers. This study demonstrates that, with careful tuning, the ALT‐TG technique can provide improved temporal and spatial sampling of VLM, yielding new constraints on geodynamic models and assisting sea‐level change studies in otherwise data sparse regions and periods.

Assessing the impact of sea level rise on the Indus delta in Pakistan: A comprehensive analysis of flooded areas and future vulnerabilities

This research investigates the impact of sea level rise (SLR) on the Indus Delta, a vital ecosystem increasingly vulnerable to climate change repercussions. The objective of this study is to comprehensively assess the flooded areas under various shared socioeconomic pathway (SSP) scenarios based on the Intergovernmental Panel on Climate Change's (IPCC) 6th Assessment Report. The study employs a GIS-based bathtub model, utilizing historical (1995–2014) and IPCC-projected (2020–2150) tide gauge data from Karachi, Kandla, and Okha stations to identify potential inundated areas threatened by coastal flooding. Additionally, it analyzes LANDSAT-derived multispectral images to identify coastal erosion hotspots and changes in the landscape. A supervised random forest classifier is used to classify major landforms and understand alterations in land cover. Furthermore, neural network-based cellular automata simulations are applied to predict future land cover for 2050, 2100, and 2150 at risk of inundation. The results indicate that under different SSP scenarios, the estimated inundated land area varies from 307.36 km2 (5 % confidence on SSP1-1.9) to 7150.8 km2 (95 % confidence on SSP5-8.5). By 2150, the region will lose over 550 km2 of agricultural land and 535 km2 of mangroves (mean SLR projection). This work emphasizes identifying sensitive land cover for SLR-induced coastal flooding. It might fuel future policy and modeling endeavors to reduce SLR uncertainty and build effective coastal inundation mitigation methods.

Sea-level change in coastal areas of China: Status in 2021

The sea level in coastal areas of China reached the second highest in 2021, just after that recorded in 2022. External force and dynamic analyses based on tide gauges, satellite observations, reanalysis data and regional numerical outputs were conducted to understand these abnormally high sea levels and determine their possible causes. Results show that the coastal sea level of China had increased at an annual rate of 3.4 ± 0.3 mm during 1980–2021, with an acceleration of 0.06 ± 0.02 mm per year2. The superposition of significant oscillations of quasi-2, 3–7, quasi-9, quasi-11, quasi-19 and 20–30 years contributed to the anomalously high sea levels. The negative-phased El Niño/Southern Oscillation was correlated with the anomalously high sea level and the north‒south anti-phase pattern of the coastal sea level in 2021. Meanwhile, phase lags of 1–4 months occurred with the sea-level response. On a decadal timescale, the Pacific Decadal Oscillation (PDO) was negatively correlated with the anomalous mean sea level (MSL), and the negative-phased PDO contributed to the anomalous sea-level change in 2021. Particularly, the monthly MSL peaked in April and July, and the contribution of wind stress to the anomalously high sea level was 38.5% in the south of the Taiwan Strait in April and 30% along the coast of China in July. These results were consistent with the tide gauge and satellite data. Close agreement was also observed between the coastal sea-level fingerprint and the air and sea surface temperatures.

Quasi-Continuous Tidal Datum for Peninsular Malaysia using Tide Gauge, Satellite Altimetry, and Tide Model Driver (TMD) Data

Quasi-Continuous Tidal Datum for Peninsular Malaysia using Tide Gauge, Satellite Altimetry, and Tide Model Driver (TMD) Data

Estimating Trends of Sea-Level Fluctuations from Tide Gauge Data Analysis along Bangladesh Coast

The present study tries to determine the nature and magnitudes of relative sea-level to estimate present and future trends of mean sea-level (MSL) changes based on historical tide gauge data of Cox's Bazaar, Charchanga, Hiron Point, Khepupara and Sadarghat stations of Bangladesh. Annual PSMSL tide gauge dataset of Cox's Bazaar station shows 2.72 mm/year sea-level rise while CPA provided 'Metric' dataset of Sadarghat station demonstrate 3.93 mm/year sea-level rise along Chittagong coast. Accordingly, the sea­ levels are supposed to be increased 10.54 cm, 24.11 cm and 37.69 cm than the 1988 MSL (213.70 cm) at Cox's Bazaar by the years 2050, 2100 and 2150, respectively. Moreover, based on long-term PSMSL tide dataset of Charchanga, Hiron Point and Khepupara stations, the estimated rise of sea-levels along the respective coasts are 8.15.mm/year, 3:56 mm/year and 18.66 mm/year, respectively. Finally, 7.4 :mm/year average rate of sea-level rise has been calculated from all the studied datasets under present study. However, disregarding the exceptionally high rate (18.66 m.m/year) at Khepupara station determined average rate of sea level rise along Bangladesh coast is 4.59 mm/year which seems to be consistent with the global average. Consequently, the predicted sea level along Bangladesh coast are approximately 22 cm, 45 cm and 68 cm above their average MSL (210.4 cm) in the years 2050, 2100 and 2150, respectively. The Chittagong Univ. J. B. Sci. 8(1&2): 75-86, 2013

Evaluating of the accuracy of the DTU22MDT mean dynamic topography model based on the tidal gauge data in Vietnam’s coastal areas

In this study, the accuracy of the latest mean dynamic topography model (MDT), DTU22MDT, is determined based on data collected from 14 tide gauge stations along the coast of Vietnam, from the North to the South. The process of assessing the accuracy of the DTU22MDT model is carried out carefully and precisely, involving determining the model’s height at tide gauge stations, quality checks of the data, and evaluation of the model’s accuracy. The research results indicate that, compared to the local sea surface in Vietnam, the DTU22MDT model has a higher height of approximately 0.7 meters. This model represents the global MDT with the highest accuracy in Vietnam, with a mean square error of about 9.0 cm. This information provides a basis for applying the model in scientific research and practical applications, such as constructing a national height system, planning the economic development of coastal areas, generating specialized maps, designing structures, and other activities in the maritime regions of Vietnam. The method used to evaluate the accuracy of the MDT in this study can be applied to other research areas and to different MDTs.

Variability Assessment of Global Extreme Coastal Sea Levels Using Altimetry Data

This study assesses the variability of coastal extreme sea levels globally by utilizing nearly three decades of along-track, multi-mission satellite altimetry data. An altimetry-based global coastal database of the non-tidal residual sea level component has been produced. The climate variability of extremes is modeled through a parametric, non-stationary statistical model. This model captures intra-annual, inter-annual and long-term variations in non-tidal residual return levels. Comparisons with tide gauge data demonstrate the ability of altimetry data to capture the variability of coastal extreme sea levels. Our findings reveal a greater complexity in the monthly variability patterns of non-tidal residual extremes in tropical latitudes, often exhibiting multiple storm periods, contrasting with coasts in extratropical latitudes, which are mostly controlled by a winter–summer pattern. This study also highlights the significant influence of established climate circulation patterns on sea level extremes. The positive phase of the Arctic Oscillation pattern leads to increases of over 25% in non-tidal residual return levels in Northwestern Europe with respect to a neutral phase. Furthermore, return levels in the western coast of Central America could be 50% higher during El Niño compared to La Niña. Our results show a robust increasing trend in non-tidal residual return levels along most global coastlines. A comparative analysis shows that variations during the 1995–2020 period were primarily driven by intra-annual variations.

Verification of tidal trends in Cananeia (SP) and Santos (SP)

Recent studies indicate an imminent global rise in mean sea level. However, comprehending variations in tidal patterns is imperative before analyzing the scenario of extreme events, typically associated with the combination of high tides and intense meteorological influences. This study presents local trends in sea level, amplitude, and phase of the two main tidal components in Cananeia (SP) and Santos (SP), utilizing tide gauge data from records of sufficient length to avoid the need for nodal correction in the tide components. Using more up-to-date tide gauge data allowed the investigation of observed trends. Long-term tidal data series revealed an upward trend in mean level of approximately 4,324 mm/year in Cananeia and 0,538 mm/year in Santos. Both locations were affected by human activities along their estuaries, with Santos experiencing significant variations in the amplitude and phase values of the tidal constituents annually.

Altimeter Calibrations in the Preliminary Four Years’ Operation of Wanshan Calibration Site

In order to accomplish the calibration and validation (Cal/Val) of altimeters, the Wanshan calibration site (WSCS) has been used as a calibration site for satellite altimeters since its completion in August 2019. In this paper, we introduced the WSCS and the dedicated equipment including permanent GNSS reference stations (PGSs), acoustic tide gauges (ATGs), and dedicated GNSS buoys (DGB), etc. placed on Zhi’wan, Wai’ling’ding, Dan’gan, and Miao’Wan islands of the WSCS. The PGSs data of Zhi’wan and Wai’ling’ding islands were processed and analyzed using the GAMIT/GLOBK (Version 10.7) and Hector (Version 1.9) software to define the datum for Cal/Val of altimeters in WSCS. The DGB was used to transfer the datum from the PGSs to the ATGs of Zhi’wan, Wai’ling’ding, and Dan’gan islands. Separately, the tidal and mean sea surface (MSS) corrections are needed in the Cal/Val of altimeters. We evaluated the global/regional tide models of FES2014, HAMTIDE12, DTU16, NAO99jb, GOT4.10, and EOT20 using the three in situ tide gauge data of WSCS and Hong Kong tide gauge data (No. B329) derived from the Global Sea Level Observing System. The HAMTIDE12 tide model was chosen to be the most accurate one to maintain the tidal difference between the locations of the ATGs and the altimeter footprints. To establish the sea surface connections between the ATGs and the altimeter footprints, a GPS towing body and a highly accurate ship-based SSH measurement system (HASMS) were used to measure the sea surface of this area in 2018 and 2022, respectively. The global/regional mean sea surface (MSS) models of DTU 2021, EGM 2008 (mean dynamic topography minus by CLS_MDT_2018), and CLS2015 were accurately evaluated using the in situ measured data and HY-2A altimeter, and the CLS2015 MSS model was used for Cal/Val of altimeters in WSCS. The data collected by the equipment of WSCS, related auxiliary models mentioned above, and the sea level data of the hydrological station placed on Dan’gan island were used to accomplish the Cal/Val of HY-2B, HY-2C, Jason-3, and Sentinel-3A (S3A) altimeters. The bias of HY-2B (Pass No. 375) was −16.7 ± 45.2 mm, with a drift of 0.5 mm/year. The HY-2C biases were −18.9 ± 48.0 mm with drifts of 0.0 mm/year and −5.6 ± 49.3 mm with −0.3 mm/year drifts for Pass No. 170 and 185, respectively. The Jason-3 bias was −4.1 ± 78.7 mm for Pass No. 153 and −25.8 ± 85.5 mm for Pass No. 012 after it has changed its orbits since April 2022, respectively. The biases of S3A were determined to be −16.5 ± 46.3 mm with a drift of −0.6 mm/year and −9.8 ± 30.1 mm with a drift of 0.5 mm/year for Pass No. 260 and 309, respectively. The calibration results show that the WSCS can commercialize the satellite altimeter calibration. We also discussed the calibration potential for a wide swath satellite altimeter of WSCS.