Sea Surface Height Data Research Articles

Two-Dimensional Legendre Polynomial Method for Internal Tide Signal Extraction

This study employs the two-dimensional Legendre polynomial fitting (2-D LPF) method to fit M2 tidal harmonic constants from satellite altimetry data within the region of 53°E–131°E, 34°S–6°N, extracting internal tide signals acting on the sea surface. The M2 tidal harmonic constants are derived from the sea surface height (SSH) data of the TOPEX/Poseidon (T/P), Jason-1, Jason-2, and Jason-3 satellites via t-tide analysis. We validate the 2-D LPF method against the 300 km moving average (300 km smooth) method and the one-dimensional Legendre polynomial fitting (1-D LPF) method. Through cross-validation across 42 orbits, the optimal polynomial orders are determined to be seven for 1-D LPF, and eight and seven for the longitudinal and latitudinal directions in 2-D LPF, respectively. The 2-D LPF method demonstrated superior spatial continuity and smoothness of internal tide signals. Further single-orbit correlation analysis confirmed generally higher correlation with topographic and density perturbations (correlation coefficients: 0.502, 0.620, 0.245; 0.420, 0.273, −0.101), underscoring its accuracy. Overall, the 2-D LPF method can use all regional data points, overcoming the limitations of single-orbit approaches and proving its effectiveness in extracting internal tide signals acting on the sea surface.

OceanNet: a principled neural operator-based digital twin for regional oceans

While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for regional sea-suface height emulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill compared to a state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate initial steps for physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models.

Seafloor Topography Recovery Using the Observation Data of Tiangong-2 Interferometric Imaging Radar Altimeter

Tens of years of altimetry sea surface height (SSH) data has continuously contributed to the development of global seafloor topography models because it can be used to precisely retrieve the gravity variation caused by seafloor topography. The development and application of altimeter has come to a new era from conventional nadir-looking altimeters to interferometric wide-swath altimeters as the successful launch of the Interferometric Imaging Radar Altimeter on board Chinese Tiangong 2 space laboratory (TG2 InIRA). Compared with conventional altimeters, the observation efficiency of TG2 InIRA has been greatly improved. This paper presents for the first time the results of seafloor topography derived from TG2 InIRA data in west Pacific Ocean and South China Sea regions. The root mean squared errors (RMSE) of the recovered topographies by gravity-geologic method (GGM) referring to the shipborne measurements in these two regions are 69.882 and 50.110 m, respectively. The recovered topographies are also evaluated by comparing with five well-known topography models. Furthermore, two subareas in west Pacific Ocean region are selected to conduct a supplemental experiment showing that more revisits can help achieve better recovery. The RMSE of the more revisited subarea is 66.808 m, while the RMSE of the less revisited subarea is 79.273 m.

Seasonal and Fortnight Variations in Internal Solitary Waves in the Indonesian Seas From the SWOT Measurements

AbstractUsing the wide‐swath sea surface height (SSH) data from the 1‐day repeat calibration/validation phase of the Surface Water and Ocean Topography (SWOT) mission, we explored fine‐scale evolution of nonlinear internal solitary waves (ISWs) in the Banda/Molucca Seas in the Indonesian Archipelagos. Generated in the Ombai Strait and Lifamatola Passage through semidiurnal tide‐topography interaction, respectively, ISWs in the Banda/Molucca Seas have a SSH amplitude of 10–20 cm and exhibit multiple wave packets with the leading wave crest followed by a series of rank‐ordered secondary wave crests. Due to nonlinearity, the ISWs are observed to propagate northward at a speed faster than the regional mode‐1 internal gravity waves and their amplitudes are modulated fortnightly following the regional spring‐neap tidal cycle. On longer timescale, the observed ISW amplitudes are controlled by seasonal upper ocean stratification changes with a weaker stratification favoring a larger amplitude. By converting the SWOT‐measured SSH data to the interior ocean pressure signals, we quantified the depth‐integrated energy flux associated with the northward‐propagating ISWs to be 5 and 2 kW m−1 in the central Banda and Molucca Seas, respectively. Comparisons with past studies indicate that the ISWs contribute to close to 100% and 40% of the tidally‐induced, northward energy fluxes, respectively, across the Banda and Molucca Seas.

The Atlantic Niño Mode: A Thermodynamic or a Dynamic Phenomenon?

AbstractThe Atlantic Niño is an important source of the year‐to‐year variability of the tropical Atlantic, consisting in an irregular oscillation of the Sea Surface Temperature (SST) in the eastern tropical Atlantic. The physical mechanism underlying this oscillation is topic of debate. Some theories, known as dynamical, suggest that the Atlantic Niño is driven by internal wave dynamics. Other theories, termed thermodynamic, propose that the eastern tropical Atlantic SST variability is caused by thermodynamic processes associated with heat fluxes at the ocean surface and/or heat transport by oceanic currents. Here, we examine the SST variability from 1940 to 2022 and find that it can be described in terms of two spatial modes: one in the central and the other in eastern tropical Atlantic. However, focusing on the period from 1993 to 2022, these patterns behave differently. In 1993–2009 they concur in determining the eastern tropical Atlantic SST variability, while in 2010–2022 they act in opposite direction. Therefore, we use Sea Surface Height data and heat fluxes advected by oceanic currents to explore the relative contribution of wave dynamics and heat advection to the eastern tropical Atlantic SST fluctuation. We show that, between 1993 and 2010, the eastern tropical Atlantic SST variability is mainly determined by heat advected from the south by horizontal currents, while between 2010 and 2022 it can be explained in terms of wave dynamics. This finding suggests that the two spatial patterns determining the eastern tropical Atlantic SST variability, are two distinct physical phenomena forced by different physical mechanisms.

Sea Surface Height Measurements Based on Multi-Antenna GNSS Buoys.

Sea level monitoring is an essential foundational project for studying global climate change and the rise in sea levels. Satellite radar altimeters, which can sometimes provide inaccurate sea surface height data near the coast, are affected by both the instrument itself and geophysical factors. Buoys equipped with GNSS receivers offer a relatively flexible deployment at sea, allowing for long-term, high-precision measurements of sea surface heights. When operating at sea, GNSS buoys undergo complex movements with multiple degrees of freedom. Attitude measurements are a crucial source of information for understanding the motion state of the buoy at sea, which is related to the buoy's stability and reliability during its development. In this study, we designed and deployed a four-antenna GNSS buoy with both position and attitude measurement capabilities near Jimiya Wharf in Qingdao, China, to conduct offshore sea surface monitoring activities. The GNSS data were processed using the Precise Point Positioning (PPK) method to obtain a time series of sea surface heights, and the accuracy was evaluated using synchronous observation data from a small sea surface height radar. The difference between the GNSS buoy and the full-time radar was calculated, resulting in a root-mean-square error (RMSE) of 1.15 cm. Concurrently, the attitude of the GNSS buoy was calculated using multi-antenna technology, and the vertical elevation of the GNSS buoy antenna was corrected using the obtained attitude data. The RMSE between the corrected GNSS buoy data and the high ground radar was 1.12 cm, indicating that the four-antenna GNSS buoy can not only acquire high-precision coastal sea level data but also achieve synchronous measurement of the buoy's attitude. Furthermore, the data accuracy was also improved after the sea level attitude correction.

Advancing Sea Surface Height Retrieval through Global Navigation Satellite System Reflectometry: A Model Interaction Approach with Cyclone Global Navigation Satellite System and FengYun-3E Measurements

The measurement of sea surface height (SSH), which is of great importance in the field of oceanography, can be obtained through the innovative technique of GNSS-R for remote sensing. This research utilizes the dataset from spaceborne GNSS-R platforms, the Cyclone Global Navigation Satellite System (CYGNSS) and FengYun-3E (FY-3E), as the primary source of data for retrieving sea surface height (SSH). The utilization of artificial neural networks (ANNs) allows for the accurate estimation of ocean surface height with a precision of meter-level accuracy throughout the period of 1–17 August 2022. As a traditional machine learning method, an ANN is employed to extract pertinent data features, facilitating the acquisition of precise sea surface height estimations. Additionally, separate models are devised for both GNSS-R platforms, one based on constant velocity (CV) and the other on constant acceleration (CA). The Interactive Multiple Model (IMM) is utilized as the main method to combine the four models and convert the likelihood of each model. The transition between the models allows the filters to effectively adapt to dynamic changes and complex environments. This approach relies on the fundamental notion of the Kalman filter (KF), which showcases robust noise handling capabilities in predicting the SSH, separately. The results demonstrate that the model interaction technology is capable of efficiently filtering and integrating SSH data, yielding a Root Mean Square Error (RMSE) of 1.03 m. This corresponds to a 9.84% enhancement compared to the retrieved height from CYGNSS and a 37.19% enhancement compared to the retrieved height from FY-3E. The model proposed in this paper provides a potential scheme for the GNSS-R data fusion of multiple platforms and multiple models. In the future, more data sources and more models can be added to achieve more accurate adaptive fusion.

Temperature Structure Inversion of Mesoscale Eddies in the South China Sea Based on Deep Learning

Mesoscale eddies are common in global oceans, playing crucial roles in ocean dynamics, ocean circulation, and heat transport, and their vertical structures can affect the water layers from tens to thousands of meters. In this study, we integrated sea surface height and sea surface temperature data into deep learning methods to study the mesoscale eddy subsurface temperature structure and to explore the relationship between sea surface data and eddy subsurface layers. In this study, we introduce Dual_EddyNet, a deep learning algorithm designed to invert the subsurface temperature structure of mesoscale eddies. Using this algorithm, we explore the impact of the sea surface height and sea surface temperature on the subsurface temperature structure inversion of mesoscale eddies. Furthermore, we compare different data fusion strategies, namely single-stream neural networks and dual-stream neural networks, to validate the effectiveness of the dual-stream model. To capture the interrelations among surface data and integrate feature information across various dimensions, we introduce the Triplet Attention Mechanism. The experimental results demonstrate that the proposed Dual_EddyNet performs well in reconstructing the three-dimensional structure of mesoscale eddies in the South China Sea (within a depth of 1000 m), with an inversion accuracy of 91.44% for cyclonic eddies and 95.25% for anticyclonic eddies. This algorithm provides a new method for inverting the subsurface temperatures of mesoscale eddies, and can not only be directly deployed in systems, embedded in ship moving platforms, etc., but can also provide a data reference for assimilations and numerical simulations, demonstrating its rich application potential.

A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction

Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction.

Ocean Satellite Data Fusion for High-Resolution Surface Current Maps

Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum—high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images—in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing.

Numerical simulation study on hydrodynamics of Xisha Islands and Reefs driven by tide levels, sea surface heights and ocean currents

This study employs the FVCOM numerical model to investigate tide flow and hydrodynamics in the marine region surrounding Yongxing Island. The HYCOM model is utilized to obtain sea surface height and ocean current data at the boundary. Additionally, the astronomical tide level predicted by the TPXO9 global tide model is incorporated into the analysis. Through numerical simulation research, the paper aims to examine the aforementioned phenomena. The findings indicate a strong concurrence between the outcomes of the FVCOM numerical simulation and the observed data. The winter and summer tidal patterns in the seas around Yongxing Island exhibit a high degree of similarity. In winter, the current speed of rapid flood and ebb process, exhibits somewhat higher than that in summer. The maximum current speed occurs in the northern marine region of Yongxing Island, during the rapid flood process, whereas it occurs in the northwest marine region of Yongxing Island during the rapid ebb process.

A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion

In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results.

An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height Derived from Satellite Altimetry Data

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of sea level change predictions, this study introduced an improved variational mode decomposition and ensemble empirical mode decomposition–long short-term memory hybrid model (VMD–EEMD–LSTM). This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the intrinsic mode functions (IMFs) with various frequencies, along with a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results, obtained through a comparative analysis of six sets of Dutch coastal sea surface height data, confirm the excellent accuracy of the hybrid model proposed (root mean square error (RMSE) = 47.2 mm, mean absolute error (MAE) = 33.3 mm, coefficient of determination (R2) = 0.9). Compared to the VMD-LSTM model, the average decrease in RMSE was 58.7%, the average reduction in MAE was 60.0%, and the average increase in R2 was 49.9%. In comparison to the EEMD-LSTM model, the average decrease in RMSE was 27.0%, the average decrease in MAE was 28.0%, and the average increase in R2 was 6.5%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance. The model proposed in this study demonstrates a notable enhancement in global mean sea lever (GMSL) forecasting accuracy during testing along the Dutch coast.

Inversion method of deflection of the vertical based on SWOT wide-swath altimeter data

The deflection of the vertical (DOV) is the key information in the study of ocean gravity field. However, in most areas, the precision of the prime component of DOV is significantly lower than that of the meridian component. To obtain higher accuracy and resolution of ocean gravity information, researchers have proposed a novel altimeter called the wide-swath altimeter. This altimeter allows for the simultaneous acquisition of high-precision and high-resolution two-dimensional measurements of sea surface height (SSH). In this paper, the Surface Water and Ocean Topography (SWOT) mission with a wide-swath altimeter on board is selected for research. One cycle of SWOT sea surface height data is simulated to inverse the DOV in the Arabian Sea (45°E—80°E, 0°–30°N), and the inversion results are compared with those of conventional altimeter data. The results demonstrate that the difference between the meridian and prime components derived from the inversion of SWOT wide-swath data is minimal, significantly outperforming the inversion results of conventional nadir altimeter data. The advantage of SWOT wide-swath altimeter lies in its ability to use the multi-directional geoid slope at any sea surface measurement point to invert the components in the meridian and prime directions. To investigate the impact of this advantage on inversion precision, this paper employs a method to calculate the gradient of the geoid in multiple directions to invert DOV components. The improvement effect of calculating the gradient of the geoid in multiple directions on the precision of DOV component is analyzed. It is found that the accuracy of DOV inversion has significantly improved with the increase of geodetic gradient calculation direction. In addition, the effects of various errors and grid spacing in SWOT wide sea surface height data on the precision of DOV inversion are also analyzed.

The Impact of Charge-Neutral Atmospheric Propagation Path on the Altimetry Performance of Interferometric Radar Altimeter

Abstract The interferometric radar altimeter (IRA) is an innovative remote sensing sensor that enables the observation of mesoscale and submesoscale (meso–submesoscale) ocean dynamic phenomena. The charge-neutral atmosphere introduces path delay and bending in signal propagation. In this study, three types of sea surface height (SSH) errors caused by charge-neutral atmosphere propagation path for IRA were identified: differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Among them, DDE exhibits a proportionality to the negative zenith neutral delay (ZND) and demonstrates a significant increase with the incident angle; PDE is solely reliant on the ZND; PBE is like DDE in trend and magnitude resembling a ramp. Intriguingly, PBE exhibits insensitivity to variations in the charge-neutral atmosphere, behaving more like a systematic error. Theoretically, PBE leads to an increase in the SSH error of about 1.2 cm at far range for SWOT. The ZND spectrum fitted from the Jason-3 zenith delay correction data is additionally utilized to simulate the spatial distribution of ZND anomaly within the SWOT observation swaths. Then, the impact of PDE anomaly (PDEA), PBE, and DDE anomaly (DDEA) on the observation performance of SWOT is also considered in conjunction with SSH data provided by HYCOM. The findings indicate that both PDEA and PBE significantly reduce IRA’s performance in oceanic phenomena, while the impact of DDEA can be disregarded. The PBE can distort the sea surface trend and increases the mean sea level within the range, requiring further attention. Significance Statement This paper focuses on how the signal path affects the accuracy of measuring sea surface height (SSH) using the interferometric radar altimeter (IRA) in the charge-neutral atmosphere. The present paper defines three types of SSH errors caused by propagation path to IRA, namely, differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Three types of SSH errors will make a significant impact on the altimetry performance of IRA. Among them, PDE spatial anomaly and PBE will reduce the expected observation performance of the meso–submesoscale ocean phenomena in IRA’s swath, while PBE has not received enough attention in previous studies. Therefore, the research will provide a theoretical basis for IRA to correct the above SSH errors.

Forecasting of Mesoscale Eddies in the Kuroshio Extension Based on Temporal Modes-Enhanced Neural Network

Mesoscale eddies are a common occurrence in the Kuroshio Extension (KE) that have a major impact on the levels of salinity and heat transport in the Northwest Pacific, the strength of the Kuroshio jet, and the fluctuations of the Kuroshio’s trajectory. In this study, a purely data-driven machine learning model, Temporal Modes-Enhanced Neural Network (TMENN), is proposed to forecast the spatiotemporal variation of mesoscale eddies based on daily sea surface height (SSH) data over a 20-year period (2000–2019) in the Kuroshio Extension. To reduce computational costs and facilitate faster forecasting, raw SSH data are decomposed into spatial modes and temporal modes (principal components, PCs) by empirical orthogonal functions (EOF) analysis, and the first 117 PCs (a total of 8384 PCs), wherein the cumulative variance contribution rate reaches 95%, are selected solely as the predictors of TMENN to train and forecast. Forecasting reconstruction results show that the model can reliably forecast the evolution of the eddy in the KE for about 30 days. Additionally, three classical mesoscale eddy processes are selected to verify the accuracy of the model, namely cold eddy attachment, warm eddy shedding, and attachment, and the results indicate that the model can well capture the evolution process of mesoscale eddies.

Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning

Abstract. Mesoscale eddies, including surface-intensified eddies (SEs) and subsurface-intensified eddies (SSEs), significantly influence phytoplankton distribution in the ocean. Nevertheless, due to the sparse in situ data, understanding of the characteristics of SSEs and their influence on chlorophyll a (Chl a) concentration is still unclear. Consequently, the study utilized a deep learning model to extract SEs and SSEs in the northern Indian Ocean (NIO) from 2000 to 2015, using satellite-derived sea surface height (SSH) and sea surface temperature (SST) data. The analysis revealed that SSEs accounted for 39 % of the total eddies in the NIO, and their SST signatures exhibited opposite behaviour compared to SEs. Furthermore, by integrating ocean colour remote-sensing data, the study investigated the contrasting impacts of SEs and SSEs on Chl a concentration in two basins of the NIO, the Arabian Sea (AS) and the Bay of Bengal (BoB), known for their disparate biological productivity. In the AS, SEs induced Chl a anomalies that were 2 to 3 times higher than those caused by SSEs. Notably, there were no significant differences in Chl a anomalies induced by the same type of eddies between summer and winter. In contrast, the BoB exhibited distinct seasonal variations, where SEs induced slightly higher Chl a anomalies than SSEs during the summer, while substantial differences were observed during the winter. Specifically, subsurface-intensified anticyclonic eddies (SSAEs) led to positive Chl a anomalies, contrasting the negative anomalies induced by surface-intensified anticyclonic eddies (SAEs) with comparable magnitudes. Moreover, while both subsurface-intensified cyclonic eddies (SSCEs) and surface-intensified cyclonic eddies (SCEs) resulted in positive Chl a anomalies during winter in the BoB, the magnitude of SSCEs was only one-third of that induced by SCEs. Besides, subsurface Chl a induced by SSAEs (SSCEs) is ∼0.1 mg m−3 greater (less) than that caused by SAEs (SCEs) in the upper 30 (50) m using Biogeochemical Argo profiles. The distinct Chl a between SEs and SSEs can be attributed to their contrasting subsurface structures revealed by Argo profiles. Compared to SAEs (SCEs), SSAEs (SSCEs) enhance (decrease) production via the convex (concave) of the isopycnals that occur around the mixed layer. The study provides a valuable approach to investigating subsurface eddies and contributes to a comprehensive understanding of their influence on chlorophyll concentration.

Steric heights of submesoscale processes and internal gravity waves in the subtropical northwestern Pacific and northern South China Sea as revealed by moored observations

The Surface Water and Ocean Topography (SWOT) altimeter mission provides a good opportunity to globally detect submesoscale processes (submesoscales) from high-resolution sea surface height (SSH) measurements. However, in addition to the balanced mesoscale eddies and submesoscales, the measured SSH also contains signals of unbalanced internal gravity waves (IGWs) including internal tides (ITs) and supertidal IGWs (IGWs with frequencies of 3–8 cycles per day). Therefore, a quantitative understanding of the contributions of the above multiscale dynamic processes to the SSH is a prerequisite for exploring submesoscales from the SWOT-derived SSH data. Here, this issue was investigated through analyzing one-year moored observation-derived near-surface steric heights (SHs) in the subtropical northwestern Pacific (NWP) and northern South China Sea (SCS) which have different dynamic backgrounds. In the sense of annual mean, the root-mean-squared (RMS) SHs of submesoscales (defined as processes with periods of 2–15 days here) and IGWs in the SCS are 1.30 and 5.54 cm, respectively, while they are only 0.70 and 3.19 cm in the NWP. Seasonally, the RMS SH of submesoscales is larger in winter than summer in both regions; with respect to the IGWs, their RMS SH is larger in summer than winter in the SCS but it is opposite in the NWP. Generally, the SHs of diurnal and semidiurnal ITs are dominated by the stationary component in both regions but the proportion of the nonstationary component is much larger in the NWP than the SCS. The sum of the SHs by nonstationary diurnal and semidiurnal ITs exceeds that of submesoscales except in winter in the SCS. Furthermore, we also found that the SHs of supertidal IGWs are always larger than those of submesoscales in both regions and both seasons. By decomposing the IGWs with frequencies of 1–8 cpd (i.e., cycles per day) into different baroclinic modes, we found that for all of the IGWs bands, the SHs are overwhelmingly attributed to the first mode. The results in this study will provide useful information for the application of SWOT data in regions with active IGWs.

Regional mapping of energetic short mesoscale ocean dynamics from altimetry: performances from real observations

Abstract. For over 25 years, satellite altimetry has provided invaluable information about the ocean dynamics at many scales. In particular, gridded sea surface height (SSH) maps allow us to estimate the mesoscale geostrophic circulation in the ocean. However, conventional interpolation techniques rely on static optimal interpolation schemes, hence limiting the estimation of non-linear dynamics at scales not well sampled by altimetry (i.e., below 150–200 km at mid-latitudes). To overcome this limitation in the resolution of small-scale SSH structures (and thus small-scale geostrophic currents), a back-and-forth nudging algorithm combined with a quasi-geostrophic model, a technique called BFN-QG, has been successfully applied on simulated SSH data in observing system simulation experiments (OSSEs). The result is a significant reduction in interpolation error and an improvement in the space–time resolutions of the experimental gridded product compared to those of operational products. In this study, we propose that the BFN-QG be applied to real altimetric SSH data in a highly turbulent region spanning a part of the Agulhas Current. The performances are evaluated within observing system experiments (OSEs) that use independent data (such as independent SSH, sea surface temperature and drifter data) as ground truth. By comparing the mapping performances to the ones obtained with operational products, we show that the BFN-QG improves the mapping of short, energetic mesoscale structures and associated geostrophic currents both in space and time. In particular, the BFN-QG improves (i) the spatial effective resolution of the SSH maps by a factor of 20 %, (ii) the zonal and (especially) the meridional geostrophic currents, and (iii) the prediction of Lagrangian transport for lead times up to 10 d. Unlike the results obtained in the OSSEs, the OSEs reveal more contrasting performances in low-variability regions, which are discussed in the paper.

Preliminary Results of Marine Gravity Recovery by Tiangong-2 Interferometric Imaging Radar Altimeter

This paper presents for the first time the results of marine gravity recovery using the ocean observation data acquired by Tiangong-2 interferometric imaging radar altimeter (TG2 InIRA) which demonstrate not only the balanced accuracies of the north and east components of deflection of the vertical (DOV) as envisaged, but also the improved spatial resolutions of DOV compared with that by conventional altimeters (CAs). Moreover, much higher measurement efficiency owing to the wide-swath capability and the great potential in accuracy improvement of marine gravity field are also demonstrated. TG2 InIRA adopts the interferometry with short baseline and takes small incidence angles, by which wide-swath sea surface height (SSH) can be measured with high accuracy. Gravity recovery experiments in the Western Pacific area are conducted to demonstrate the performance, advantages and capability of TG2 InIRA. SSH data processing algorithms and DOV calculation have been designed by taking the wide-swath feature into account, based on which, the gravity anomalies are then calculated using the inverse Vening Meinesz formula. The derived gravity anomalies are compared with both the published gravity models and the shipborne gravity measurements. The results show that the accuracy of TG2 InIRA is equivalent to, or even a little better than, that of CAs. The fused gravity result using equal TG2 InIRA data and CAs data performs better than those using TG2 InIRA data alone or CAs data alone. Due to the signal bandwidth of TG2 InIRA is only 40 MHz which is much smaller than that of CAs, much higher accuracy can be hopefully achieved for future missions if larger signal bandwidth is used.