Ocean velocities are essential for understanding how the ocean influences and responds to climate dynamics, making their accurate reconstruction crucial for both climate modeling and predictions. However, reconstructing interior ocean velocities remains a significant challenge due to the sparse distribution of velocity observations and the ocean’s complex dynamics. In this study, we introduce an efficient methodology for reconstructing interior ocean velocities by combining sea surface satellite data—including sea surface height (SSH), temperature, wind, and current—with Argo velocity observations, using the dynamic mode decomposition (DMD) technique. DMD offers the advantage of reducing the dimensionality of interior velocity fields, helping to address the limitations caused by sparse observations. The reconstructed velocity for the Southern Pacific Ocean (SPO) was validated against Argo and acoustic Doppler current profiler (ADCP) velocities, showing a strong correlation than GLORYS12V1 velocities. In particular, the reconstructed velocities have a mean correlation coefficient of 0.78 for the zonal component and 0.74 for the meridional component above 1000 m. Additionally, the reconstructed flow field exhibits a coherent pattern that closely aligns with the eddies observed in SSH. This research significantly contributes to the Global Ocean Monitoring and Observing Program by enhancing both the accuracy and resolution of ocean velocity measurements.

For the traditional 1-D nadir altimetry measurements, the spatial resolution of the sea surface height (SSH) measurements is restricted. However, the new wide-swath altimeters like Surface Water and Ocean Topography (SWOT) are anticipated to enhance spatial resolution and thereby improve the accuracy of derived marine gravity anomalies, particularly in high-frequency signals. The study investigates the efficacy of L2 KaRIn Beta data of SWOT in recovering high-frequency marine gravity signals. CryoSat-2 data was used for comparison. The first-order difference of shipborne gravity data along ship tracks was used as the reference to investigate the high-frequency accuracy of gravity derived from SWOT and CryoSat-2 at different point spacings, respectively. The research results show that compared to CryoSat-2 satellite data, when the point spacing is less than 2 km or greater than 6 km, the accuracy improvement is relatively small, less than 3.0%. However, within the 2–4 km range, the accuracy improvement ranges from 6.8% to 7.9%. When the point spacing is between 4 and 6 km, the accuracy improvement ranges from 5.0% to 6.7%. Therefore, when the point spacing is greater than 2 km and less than 6 km, the accuracy improvement is obvious. The results show that SWOT wide-range altimeter satellite has obviously improved the accuracy of traditional altimeter gravity inversion in high frequency (especially in the range of 2–6 km).

Predicting the 3-D ocean temperature field is a significant task that helps to understand global climate change and the state of ocean motion. Lots of numerical and data-driven models are used to predict ocean temperatures. However, these methods are restricted to the time-sequence prediction of discrete points or rely on convolutional layers to inefficiently capture local spatial dependencies for spatio-temporal prediction. In this letter, we propose a deep learning model named SA-PredRNN that combines attention mechanisms and predictive recurrent neural networks to capture global positional correlations and spatio-temporal features. Global gridded Argo temperature data with Barnes objective analysis (BOA-ARGO) are used to predict the future 3-D ocean temperature. The average RMSEs of the proposed model are promoted by at most 11% and 10%, which indicates that the SA-PredRNN model has better performance than the other baseline models.

To address challenges in large-scale deployments and real-time wave observations using Global Navigation Satellite System (GNSS) buoys, we assessed the reliability and precision of the single-frequency L1 time-differenced carrier phase (TDCP) method for determining significant wave heights (SWHs) and average wave periods. Utilizing simulated dynamic velocity measurements derived from International GNSS Service static observations, this study demonstrates that the L1 TDCP method exhibits superior performance compared to dual-frequency TDCP, postprocessed kinematic (PPK), and Doppler algorithms. Notably, L1 TDCP achieves approximately five times greater precision than the Doppler method in vertical direction assessments. In the offshore waters of Qingdao, China, we deployed two GNSS buoys for wave observation experiments, complemented by a nearby accelerometer-equipped wave buoy (WB) for external validation. By integrating the velocity measurements obtained from L1 TDCP, we derived the sea surface displacement, which, after applying a high-pass filter, isolated the wave components. The experimental results confirmed a strong correlation between L1 TDCP and PPK methods in capturing SWH and wave periods. This study found a negligible difference between L1 TDCP and PPK in SWH (0 ± 7.1 mm, 99% correlation) and in average wave period (0 ± 0.13 s, 97% correlation). The wave observations from two independent GNSS buoys at the same location also exhibited remarkable consistency, with a difference in SWH of 0.0 ± 2.5 cm and in average wave period of 0.0 ± 0.1 s. Compared to the independent WB, the L1 TDCP method demonstrated a difference in SWH of$- 1.9\,\,\pm \,\,5.8\,\,\text {cm}$with a 93% correlation and in average period of 0 ± 0.1 s with a 94% correlation. Further, our analysis into GNSS data sampling frequencies highlighted the efficacy of 1-Hz GNSS data in wave inversion though certain spectral limitations were noted. Implementing the L1 TDCP method at 1 Hz offers a substantial reduction in associated hardware costs, storage requirements, and computational demands. Notably, in contrast to PPK, it negates the need for delayed precise ephemeris, facilitating real-time computations. This study highlights the potential of L1 TDCP for broader, real-time GNSS buoy wave observations, harmonizing with the United Nations Decade of Ocean Science’s aspirations for an augmented Global Ocean Observing System.

The topography of the seafloor is highly correlated with the local gravity through intrinsically nonlinear relationships across a particular wavelength band. The purpose of this study is to compare a fully connected deep neural network (FC-DNN) and a convolutional neural network (CNN) with the gravity-geologic method (GGM) to determine whether deep learning can provide superior predictions of bathymetry. We include the short-wavelength gravity and geological models as training parameters, and assess the performance of different models and parameter combinations using various inputs. Compared with the CNN method, the FC-DNN with the short-wavelength gravity as an input reduces the standard deviation of bathymetry differences from 118.6 m to about 73.5 m. The FC-DNN with short-wavelength gravity reduces the standard deviation of bathymetry differences by up to 13.3% compared with the conventional GGM. Furthermore, we demonstrate that the addition of geological information alongside the short-wavelength gravity does not significantly enhance the accuracy. Power spectral density analysis suggests that the FC-DNN is superior for predicting wavelengths shorter than 6 km.

This letter describes the new ability to measure wind-generated gravity waves using the airborne$Ka$-band interferometric altimeter (AirKaIA). Although the original definition of wave parameters is derived from wave-induced sea surface elevation (WSSE), it is difficult to directly measure the WSSE, so most remote sensing of waves use other types of signals to obtain wave parameters. Here, we present the measurement of 2-D WSSE from AirKaIA. A new wave retrieval method based on the 2-D WSSE has been developed, tested with simulation data, and applied to AirKaIA data. The results indicate that the retrieved dominant wave directions, dominant wavelengths, and significant wave heights from AirKaIA are in agreement within situmeasurements while highlighting the need for further method tests using more observations. The study of ocean signals from interferometric altimeter is an emerging research topic. AirKaIA’s measurements of wind-generated gravity waves with 2-D WSSE have implications for the assessment of future interferometric altimeter missions.

Global Navigation Satellite System (GNSS) buoys were used to retrieve the significant wave heights (SWHs) and dominant periods of waves in the Qingdao coastal waters off China. The precise point positioning (PPP) and postprocessed kinematic (PPK) techniques were used to obtain the absolute motion of the GNSS buoys. Even though PPP has relatively low absolute positioning accuracy, this accuracy has a minimal influence on the spectrum in the wave frequency band. SWH values calculated from the PPK and PPP modes are nearly identical, with correlation coefficients and symmetric regression slopes both higher than 0.99. The SWH values calculated from PPP also show good agreement with a dedicated wave buoy, with a correlation coefficient of 0.935 and a difference of 0.82 cm ± 4.63 cm. A GaoFen-1 satellite image was used to assess the GNSS PPP wave spectrum via the dispersion relationship; both exhibit wave spectra with two peaks, consistent with the wave characteristics in the area.

Calibration and validation (Cal/Val) of the sea surface height as measured by satellite radar altimeters is essential to understand altimeter biases, observation trends, and instrument aging. It also supports the long-term stability of the produced climate change records of sea level as determined by altimetry. In this article, we report the calibration of HY-2B and Jason-2/3 using the established research infrastructure and data sharing initiative introduced by the Altimetry Calibration Cooperation Plan (ACCP) of China. Currently, three ACCP calibration sites encompass the Wanshan Islands, and two national oceanic sites are located along the China coastline. For each Cal/Val site, the components of the facilities—the geodetic, sea level, and Global Navigation Satellite System (GNSS) infrastructure—and the followed monitoring procedures and calibration methods are described. The HY-2B performance was primarily evaluated using about two years data, which indicated a mean bias of −0.2 ± 4.2 cm. Confidence in the results is strong, because the HY-2B biases were cross compared and confirmed by all the three independent sites and the three satellite ground tracks. Compared with its predecessor HY-2A, HY-2B shows very stable observations with no linear drift at present. In addition, Jason-2 and Jason-3 were mainly assessed using Qianliyan site. Our results indicate that the Jason-3 sea-surface height bias is approximately 2–3 cm smaller than that of Jason-2 and that the long-term stability of Jason-2/3 shows no significant trend, which in good agreement with the international dedicated sites. The instrument noises of Jason-2/3 and HY-2B were estimated based on the ACCP sites. The results show that the instrument noise in the previous literature is underestimated. This was also consolidated by the result from wavenumber spectrum and global crossover point analysis. The code and Wanshan data used in these Cal/Val experiments are publicly available to facilitate further work in this domain (https://github.com/GenericAltimetryTools/CalAlti).

Validating the wet path delay (WPD) is essential to determining satellite microwave radiometer errors that may deteriorate sea level measurement accuracy. The global navigation satellite system (GNSS) is a favored method for assessing WPD for altimetry. Over Chinese coastal waters, however, validations have not been performed, primarily because of the large gaps between International GNSS Service sites. Herein, we report a long-term assessment of the Jason-2/3 radiometers and the initial performance of the Chinese HY-2B radiometer via comparisons of WPD values derived from three types of GNSS networks along the Chinese coast. A new method based on the second-order derivative of WPD along satellite tracks was developed to detect where land contamination first appears. The results indicate that this method is more sensitive to land influences; the distance at which land contamination first appears was estimated to be approximately 40–50 km over the China coast sea. To compare the WPD at different heights, such as GNSS sites and sea level surface, an exponential function containing the corresponding decay coefficients was adopted. The WPD decay coefficients for each GNSS site were recalculated using the 3-D ERA5 pressure level fields instead of the empirical value of 2000. The results show that the new coefficient may reduce the differences between WPD values at different heights. This WPD comparison over Chinese coastal waters indicates average WPD differences of 3 (Jason-2) and −1 mm (Jason-3), showing a high level of consistency between the GNSS sites and the satellite radiometers. The uncertainties in the WPD differences were estimated to be 14–21 and 7–24 mm for the Jason-2/3 and HY-2 radiometers, respectively, which is consistent with previous studies using global GNSS. After removing the GNSS uncertainty and the spatial decorrelation, the WPD uncertainties of the Jason-2, Jason-3, and HY-2 radiometers over the regional area were estimated to be 13, 14, and 11 mm, respectively. The long-term evaluation of the WPD differences with the GNSS sites indicates that the drift rates of the Jason-2/3 radiometers over China coast waters are 0.3 and −0.9 mm/y, respectively.