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Gravity anomalies (GAs) and vertical gravity gradient anomalies (VGGs) are regularly used to predict bathymetry. However, few studies have explored the combination of the GAs and VGGs to predict bathymetry. We introduce the back propagation (BP) neural network into bathymetric prediction field and propose a method to predict depth from GAs and VGGs. The method was tested in the Mariana Trench region, and a neural network bathymetry model was constructed using feature data obtained from GAs and VGGs as the input of the BP neural network. Additionally, single‐beam sounding data were used as label data. By comparing the neural network and the gravity‐geologic method (GGM), the neural network was found to provide better performance with an accuracy improvement of 19%. The root‐mean‐square of the absolute difference between the neural network bathymetry model and the single‐beam sounding data was 72.40 m, with a relative accuracy of 1.71%. Approximately 50% of the differences were distributed within ±20 m, and 90% were distributed within ±100 m. The neural network bathymetry model was also compared with the GGM bathymetry model for different depths and topographies, and the results verified the feasibility and effectiveness of the BP neural network method. Plain Language Summary: The gravity data currently used to predict bathymetry include gravity anomalies and vertical gravity gradient anomalies. We propose a new method that introduces the back propagation neural network into bathymetric prediction, based on two kinds of gravity data, which are used to predict depth. The results show that the depths predicted by the neural network are closer to the shipborne depths than are those estimated with the current method. We also test the adaptability of the neural network method for different depths and topographies (seamount, trench, and plain). Finally, we find that the BP neural network method is competent for various inversion tasks. Key Points: A new method is proposed to predict depth using gravity data derived from a back propagation neural networkBathymetric prediction using gravity anomalies and vertical gravity gradient anomaliesThe BP neural network method displays outstanding performance compared to the gravity‐geologic method for different depths and topographies [ABSTRACT FROM AUTHOR]

This study quantifies the absolute sea level (ASL) change along the coast of China from 1993 to 2019 using tide gauge (TG) and Global Navigation Satellite System (GNSS) data. The sea level change at 12 collocated TG and GNSS stations along the Chinese coast are first analyzed by TG + GNSS data. Then the ASL rise and its spatial variation along the coast of China and that in surrounding oceans are studied using TG + GNSS data and satellite altimetry products. The results show that the relative sea level rise along the Chinese coast varied from 2.61 ± 0.76 to 5.56 ± 0.70 mm/yr, while the effect of vertical land motion (VLM) ranged from −1.46 ± 0.30 to 1.07 ± 0.38 mm/yr. After correcting the effect of the VLM, the ASL rise ranged from 2.51 ± 0.86 to 5.03 ± 0.60 mm/yr, with a mean rate of 3.94 ± 0.88 mm/yr, which was 0.7 mm/yr higher than the global average. It is found that ASL rise rate is significantly higher in Chinese seas and neighboring oceans, with a meridional distribution in the East China Sea and a zonal distribution in the South China Sea. This may be related to factors such as sea surface temperature rise, atmospheric pressure decrease, freshwater runoff, zonal wind stress anomaly, and alongshore currents in the area. Plain Language Summary: As a consequence of climate change, the global mean sea level from tide gauges and satellite altimetry observations increased from 1.4 mm/yr over the period 1901–1990 to 3.2 mm/yr over the period 1993–2015 to 3.6 mm/yr over the period 2006–2015. An improved understanding of sea level rise is essential for the safety of society, the economy and the environment. This study quantifies the absolute sea level (ASL) change along the coast of China from 1993 to 2019 using tide gauge data with the vertical land motion corrected by Global Navigation Satellite System data. The derived ASL rise rate ranged from 2.51 ± 0.86 to 5.03 ± 0.60 mm/yr, with a mean rate of 3.94 ± 0.88 mm/yr, which was 0.7 mm/yr higher than the global average. It is also found that the ASL rise rate in Chinese seas and neighboring oceans is significantly higher than the global average, with a meridional distribution in the East China Sea and a zonal distribution in the South China Sea. The local influencing factors of sea level rise along the coast of China were analyzed, including the sea surface temperature rise, atmospheric pressure decrease, freshwater runoff, zonal wind stress anomaly and alongshore currents in the area. Key Points: Tide gauge and collocated Global Navigation Satellite System were first used to analyze the absolute sea level (ASL) changes along the coast of ChinaThe high ASL rise rate along the Chinese coast was a natural manifestation in all Chinese seas and neighboring oceansThe local influencing factors of sea level rise along the coast of China were preliminarily analyzed [ABSTRACT FROM AUTHOR]

As the chart datum of China, the lowest normal low water (LNLW) was calculated using three tidal constituents, major (Q1, O1, P1, K1, N2, M2, S2 and K2), shallow water (M4, MS4 and M6) and long-period tidal (Sa and Ssa). The construction of a tidal datum is mainly concerned with improvements in the major tidal constituents, and the contribution of the long-period tidal component has been generally neglected. In this study, long-term tide gauge observations and multi-mission satellite altimetry data were used to investigate the spatial distribution of the long-period tidal contribution in Chinese seas and analyze the relative long-period tidal contribution rate into four regions. The results showed that the mean contribution in Chinese seas is 7.63 %, with the largest contribution in the Bohai Sea (11.33 %) and smallest in the East China Sea (5.27 %). Differences between tide gauge and satellite-derived results were compared in detail. The Sa and M2 tidal amplitudes are the main factors affecting the long-period tidal contribution to the LNLW. The relative long-period tidal contribution can be up to 34.18 % when tide gauge observations record small M2 and large Sa amplitudes. These results indicate that the long-period tidal constituent cannot be neglected in the establishment of the LNLW datum. Therefore, to improve tidal datum precision, precise extraction and accuracy assessments of long-period tidal constituents should be a research focus. [ABSTRACT FROM AUTHOR]

In this study, to meet the need for the accurate prediction of sea level anomaly (SLA), a hybrid model is proposed. In this model, empirical mode decomposition is combined with singular spectrum analysis and least-squares extrapolation to predict satellite-derived SLA. Each intrinsic mode function series of an empirical mode decomposition is decomposed and reconstructed using singular spectrum analysis. The reconstructed components and the residual series are predicted using least-squares extrapolation. This hybrid model was used for satellite-derived SLAs that were obtained using multi-mission along-track satellite altimetry data from September 1992 to January 2018, and the prediction errors for 3 years lead times were analysed. The observations and predictions of the principal components for annual or interannual periods correlated well, and the proposed hybrid model effectively predicted the SLAs. For the 3 years lead time predictions, the mean absolute error and root-mean-square error were 1.03 and 1.32 cm, respectively, which were less than those reported for existing methods. [ABSTRACT FROM AUTHOR]

Calibration/Validation (Cal/Val) of satellite altimeters is fundamental for monitoring onboard sensor performance and ensuring long-term data quality. As altimeter technology has been evolving rapidly from profile to wide swath and interferometric altimetry, different requirements regarding Cal/Val have emerged. Most current Cal/Val technology has been developed for conventional profile altimeters, whereby satellite observations are compared against measurements at one point along orbit lines. However, the application of this type of Cal/Val technique to swath interferometric altimeters with two-dimensional measurements is difficult. Here, we propose a new strategy for the evaluation of interferometric altimeters based on comparison of wave-induced sea surface elevation (WSSE) spectra from one- and two-dimensional measurements. This method assumes that the WSSE variance of an equilibrium wave field is uniform and can be measured equivalently in the space or time domains. The method was first tested with simulated data and then used to evaluate the performance of an airborne interferometric radar altimeter system (AIRAS) using Global Navigation Satellite System (GNSS) buoy measurements. The differences between the WSSE variances from the AIRAS and two GNSS buoys were below 8 cm2, corresponding to a standard deviation of 2.8 cm, which could serve as a reference for the WSSE error over the scale range of waves. The correlation coefficient between the AIRAS and GNSS buoys was approximately 0.90, indicating that the error was small relative to the WSSE signals. In addition, the sea surface height (SSH) difference measured by the AIRAS was compared with that derived from the GNSS buoys at two sites. The results indicated that the error of the SSH difference was 3 cm. This approach represents a possible technique for the Cal/Val of future spaceborne/airborne interferometric altimeters; however, additional experiments and applications are needed to verify the feasibility of this method. [ABSTRACT FROM AUTHOR]