Altimeter Data Research Articles

Analysing the impact of SWOT observation errors on marine gravity recovery

SUMMARY The wide-swath altimeter satellite Surface Water and Ocean Topography (SWOT) will provide high spatiotemporal resolution sea surface heights (SSHs), which is crucial for studying the impact of observation errors on marine gravity recovery. This study uses simulated SWOT data to derive deflection of the vertical (DOV) and gravity anomalies in the northern South China Sea. We quantified the impact of SWOT errors on DOV and gravity anomalies, and analysed the contributions from different directions of geoid gradient. The results show that the geoid gradient in the cross-track direction significantly improves gravity field recovery by enhancing the precision of east component of DOV. For one-cycle SWOT observations, phase errors emerge as the most impactful error affecting both DOV and gravity anomalies, followed by random errors. 2-D Gaussian filtering and the tilt correction proposed in this study could effectively mitigate their impact. Using the corrected data for DOV computation, the precision in the east and north components improves by 75.32 and 46.80 per cent, respectively, while enhancing the accuracy of the gravity field by 70.23 per cent. For 17-cycle data, phase errors and random errors remain the predominant factors affecting DOV and gravity anomalies, but their impact diminishes with an increase in SWOT observations. Our results indicate that marine gravity accuracy improves by approximately 70 per cent compared to a single cycle. Whether for single-cycle or multicycle data, the impact of phase errors is roughly twice that of random errors. These data processing strategies can serve as valuable references for wide-swath altimeter data processing, aiming to advance the precision and resolution of marine gravity field recovery.

Comparison of wave spectrum assimilation and significant wave height assimilation based on Chinese-French oceanography satellite observations

Assimilating observation data into wave models can significantly improve the numerical simulation accuracy of ocean waves. Traditionally, assimilation efforts have predominantly focused on significant wave height (SWH). However, the launch of the China-France Oceanography SATellite (CFOSAT)has facilitated the acquisition to obtain global-scale wave spectrum data, introducing a transformative dimension to assimilation methodologies. This novel wave spectrum data holds substantial potential for refining global wave data assimilation results. In our study, a series of numerical experiments were conducted employing CFOSAT data to systematically contrast the impact of wave spectrum assimilation with that of SWH assimilation on global wave simulations. The assimilating impacts of SWH assimilation and spectrum assimilation were validated against wave data retrieved from the National Data Buoy Center (NDBC) buoys and Jason-3 altimeter. The validation included SWH, mean wave period (MWP), dominant wave period (DWP), the direction from which the waves at the DWP are coming (DWD), and the wave spectrum. When comparing with the buoy measurements, spectrum assimilation demonstrated superior effectiveness in improving mean wave period (MWP) compared to SWH assimilation. The assimilation index for spectrum assimilation reached 12.21%, escalating to 15.78% when MWP exceeded or equaled 8 s. In contrast, for SWH, DWP and DWD, SWH assimilation performed better. Validation against Jason-3 altimeter data revealed that spectrum assimilation surpassed SWH assimilation in terms of overall improvement. Notably, SWH assimilation exhibited a more favorable impact when SWH values were <2.5 m. However, for SWH values greater than or equal to 2.5 m, spectrum assimilation outperformed SWH assimilation, yielding assimilation indices of 8.10% and 6.18% compared to buoys and Jason-3, respectively. Overall, this work illustrates that utilizing CFOSAT data for spectrum assimilation and SWH assimilation can enhance wave simulation accuracy under different conditions, which may point to promising applications in hurricane forecasting and disaster reduction research.

Phase‐Accurate Internal Tides in a Global Ocean Forecast Model: Potential Applications for Nadir and Wide‐Swath Altimetry

AbstractInternal tides (ITs) play a critical role in ocean mixing, and have strong signatures in ocean observations. Here, global IT sea surface height (SSH) in nadir altimetry is compared with an ocean forecast model that assimilates de‐tided SSH from nadir altimetry. The forecast model removes IT SSH variance from nadir altimetry at skill levels comparable to those achieved with empirical analysis of nadir altimetry. Accurate removal of IT SSH is needed to fully reveal lower‐frequency mesoscale eddies and currents in altimeter data. Analysis windows of order 30–120 days, made possible by the frequent (hourly) outputs of the forecast model, remove more IT SSH variance than longer windows. Forecast models offer a promising new approach for global internal tide mapping and altimetry correction. Because they provide information on the full water column, forecast models can also help to improve understanding of the underlying dynamics of ITs.

Precise Orbit Determination of the MESSENGER Spacecraft

The NASA MESSENGER mission explored Mercury for more than four years to investigate the properties of the planet. To safely operate in the harsh conditions around Mercury, the spacecraft was in a highly eccentric orbit with a low periapsis altitude. The radiation environment had a strong impact on the spacecraft orbit evolution because of the proximity of Mercury to the Sun. A detailed modeling of the nonconservative forces is then a key factor to enhance the precise orbit determination of the spacecraft. We present here refined models of the nonconservative forces, including thermal reradiation effects, that enabled significant improvements in the trajectory reconstruction. A crossover analysis based on the Mercury Laser Altimeter (MLA) data was carried out to cross-check the accuracy of the orbit determination results. The trajectories retrieved by using the refined spacecraft dynamical model provide reduced height misfit at crossover points, indicating a high-quality reconstruction. Our new solutions of the spacecraft orbits are then archived to be used as auxiliary information for the data analysis of other MESSENGER instruments.

Analysis of wave breaking on synthetic aperture radar at C-band during tropical cyclones

ABSTRACT The purpose of our work is to analyze the effect of wave breaking on dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) synthetic aperture radar (SAR) image in the C-band during tropical cyclones (TCs) based on the machine learning method. In this study, more than 1300 Sentinel-1 (S-1) interferometric-wide (IW) and extra wide (EW) mode SAR images are collocated with wave simulations from the WAVEWATCH-III (WW3) model during 400 TCs. The validation of the significant wave height (SWH) simulated using the WW3 model against Jason-2 altimeter data. The winds for S-1 SAR images are reconstructed using wind retrievals in VV and VH polarization. The non-polarized (NP) contribution σ wb caused by wave breaking is assumed to be the result of the SAR-measured normalized radar cross-section (NRCS) σ 0 minus the Bragg resonant roughness σ br without the distortion of rain cells during TCs. The σ br is simulated by imputing wave spectra from the WW3 model into the theoretical backscattering model. It is found that the ratio (σ wb /σ 0) in VV polarization is related to the wind speed, the wind direction relative with the flight orientation, and radar incidence angle. Following this rationale, the Adaptive Boosting (AdaBoost) model was used for the estimation of NP contribution σ wb during TCs and are implemented for more than 300 dual-polarized S-1 images to validate the model. It is found that for the comparison between the sum of simulation NRCS and SAR observations, the root mean squared error (RMSE) is 1.95 dB and the coefficient (COR) is 0.86, which is better than a 2.83 dB RMSE and a 0.67 COR by empirical model. It is concluded that the AdaBoost model has a good performance on NP component simulation during TCs.

The Rising Concern for Sea Level Rise: Altimeter Record and Geo-Engineering Debate

The Oceans from Space V Symposium, held in Venice, Italy, on 24–27 October 2022, devoted special sessions to sea level rise, as described by a series of satellite altimeters, and to remediations of consequent calamities in vulnerable mediterranean seas. It emerged that various aspects of climate change can be modelled in time as a Single Exponential Event (SEE), with a similar trend (a 54–year e–folding time) for CO2 concentration in the Earth’s atmosphere, global average sea surface temperature, and global average sea level. The sea level rise record, combining tide gauges data starting in 1850, as well as more recent altimeter data, for the last 30 years, is already 25 cm above historical values. If the curve continues to follow the exponential growth of the simple SEE model, it will reach about 40 cm by the year 2050, 1 m by 2100, and 2.5 m by 2150. As a result, dramatic impacts would be expected for most coastal areas in the next century. Decisive remediations, based on geo-engineering at the basin scale, are possible for semi-enclosed seas, such as the Mediterranean and Black Seas. Damming the Strait of Gibraltar would provide an alternative to the conclusion that coastal sites such as the City of Venice are inevitably doomed.

Sea level rise estimation and projection from long-term multi-mission satellite altimetry and tidal data in the Southeast Asia region

ABSTRACT The Southeast Asian (SEA) region represents a complex coastal geographical location. Most countries in SEA are encircled by the ocean, with lowlands coastal areas. The advantages of long-term satellite altimetry and tide gauge measurements have enabled climate-related research, particularly sea level rise, to be examined extensively in spatial and temporal scales. In this study, sea level anomaly (SLA) derived from 26 years of multi-mission satellite altimeter data plus approximately 28 years of tidal data have been utilized. Then, the projection for every 10 years starting from 2030 until 2100 has been analysed using machine learning regression. The projections are then evaluated based on the global model of the Intergovernmental Panel on Climate Change (IPCC). The findings presented that sea level rise trends around SEA with overall means of 4.70 ± 0.37 mm yr−1 for the satellite altimeter and 3.41 ± 0.24 mm yr−1 for the coastal tide gauge. Sea level projection from coastal tide gauge and satellite altimeter are expected to rise up to 34.87 and 33.71 cm, respectively, in 2100. These outcomes prove that the altimetry data from Radar Altimeter Database System (RADS) and the redundant tidal data are capable in measuring the sea level in SEA. These results are also expected to be valuable for multidisciplinary environmental studies in SEA, such as coastal flooding, coastal erosion, and other effect of global warming. Thus, it would make a meaningful contribution towards Sustainable Development Goal (SDG) 13, climate action, by providing the sea level rate and prediction information.

Performance of ERA5 wind speed and significant wave height within Extratropical cyclones using collocated satellite radar altimeter measurements

ABSTRACT Similar in strength to hurricanes, Extratropical Cyclones (ECs) are responsible for innavigable sea states, coastal inundation and erosion, and subsequent destruction to coastal infrastructure. Across modern operational wave models, there exists a known systematic underestimation of wave heights during these extreme events. Using a global database of EC storm tracks and 36 years of satellite altimeter data, we examine EC structure and assess model performance through storm centered composite analyses of significant wave height ( H s ) and U10 wind speed ( | U 10 | ). Through the collocation of satellite altimeter observations with a state-of-the-art reanalysis product (ERA5), we investigate model performance with respect to | U 10 | and H s within ECs of varying intensities. By rotating our data reference frames, we align all storm directions to account for asymmetry in EC wind fields and subsequent increased wave growth This rotation results in the organization of the strongest wind speed and H s , as well as trends in the ERA5 performance. A characteristic EC radii is calculated and used to normalize data coordinates in the storm centered reference frame, which highlights the organization of EC structures. Performance metrics are then compared within different EC quadrants to explore the relationship between wind forcing accuracy and underestimation of H s .

A New Model for Elevation Change Estimation in Antarctica From Photon-Counting ICESat-2 Altimetric Data

A New Model for Elevation Change Estimation in Antarctica From Photon-Counting ICESat-2 Altimetric Data

Review of studies on the distribution of Antarctic krill accumulations in the Scotia Sea and analysis of mesoscale dynamics of its habitat

This article studies the mesoscale dynamics of the waters of the Scotia Sea, located at the northern tip of the Southern Ocean on its border with the Southern Atlantic Ocean. The Scotia Sea is one of the most promising fishing areas where the industrial catch of Antarctic krill (Euphausia superba) is produced. Since the distribution of commercial krill accumulations is largely determined by oceanological conditions, this paper analyzes mesoscale vortex dynamics, frontal zones, and topography in the Scotia Sea, affecting the creation of favorable oceanological conditions for the formation of commercial krill accumulations. The study is conducted using satellite and model data. It is shown that the distribution of sea level according to the GLORYS12v1 reanalysis data in the Scotia Sea has a zonal character. In winter and spring, sea level anomalies are positive (up to 20 cm), and in summer and autumn, they are negative (up to -180 cm). In the northwestern part of the Scotia Sea, according to the data of the GLORYS12v1 reanalysis for 1993-2020, thermal frontal zones with high repeatability (&gt; 80%) are distinguished. Dynamic frontal zones are also distinguished in the northwestern part of the water area and have a repeatability reaching 100%. This area in the Scotia Sea is the most dynamically active: increased kinetic energy values are observed here. The increased dynamic activity in the northwestern part of the Scotia Sea, considered above by the average and average seasonal distributions of characteristics, is also confirmed in the synoptic range of periods. Spatial distributions of long-lived mesoscale vortices according to META 3.2 data for 1993-2021 show the localization areas of these vortices, with cyclones forming significantly more than anticyclones. Analysis of the trajectories of these vortices showed that the direction of their propagation is mainly eastern. The intense vortex dynamics in the Scotia Sea is also confirmed by reconstructed altimetric data for 1970-1992.

Modeling and Correcting Building Boundary in ICESat-2 Spaceborne Laser Altimeter Data Considering the Extended Laser Spot Effect

Modeling and Correcting Building Boundary in ICESat-2 Spaceborne Laser Altimeter Data Considering the Extended Laser Spot Effect

A modified Goldstein filter for interferogram denoising of interferometric imaging radar altimeter based on multiple quality-guided graphs.

Aiming at the characteristics that the signal noise ratio (SNR) gradually decreases from the near to far range of the swath, an adaptive phase filtering algorithm based on Goldstein filtering and combined with multiple quality-guided graphs was proposed. Firstly, the components used to determine the filtering parameters were obtained through residue density, pseudo-coherence coefficient and pseudo-SNR, the three quality-guided graphs. Then, the filter parameters were calculated by weighting the three components. Finally, the size of filtering window was determined according to the account of residues, and the interferometric phase noise was removed in frequency domain. Simulated data, TSX/TDX data and airborne interferometric imaging radar altimeter data were used to verify the performance of the new algorithm. Compared with the results of Goldstein filtering and its improved algorithms, the results showed that the proposed algorithm can effectively filter out phase noise while maintaining the edge characteristics of interferometric fringe. The section of filtering result can well match with the section of simulated pure interfeometric phase. Moreover, the algorithm proposed in this paper can effectively remove the noise in the interferogram of TSX/TDX sea ice data, and the residues' filtering rate was above 86%, which can effectively remove the phase residues of the sea ice surface while maintaining the characteristics of the sea ice edge. Experimental results showed that the new algorithm provides an effective phase noise filtering method for imaging radar altimeter data processing.

Wet Tropospheric Correction for the HY-2C Altimeter over the Coastal Ocean Based on GNSS and ERA5 Data

For the impact of land, sea ice, rainfall and strong wind, the wet tropospheric correction (WTC) of HY-2C (the third marine dynamic environmental monitoring satellite of China) altimeter cannot be obtained accurately by the calibration microwave radiometer (CMR) over the coastal ocean. The conducted evaluation of the HY-2C CMR WTC over the coastal ocean by the Global Navigation Satellite System (GNSS) and ECMWF Reanalysis v5 (ERA5) data showed that the CMR WTC is abnormal or invalid. To solve this issue, a new method to determine the WTC of the HY-2C altimeter based on objective analysis (OA) by combining the GNSS WTC, ERA5 WTC and valid CMR WTC is developed. The OA WTC and HY-2C CMR WTC are compared to the Integrated Global Radiosonde Archive (IGRA) WTC to validate the OA method. The results show that the RMSE of the WTC differences significantly decreases and the correlation coefficient increases. The number of available WTC data increases over the open ocean. The variances of sea level anomaly corresponding to the OA WTC in most areas are significantly smaller than those of the HY-2C CMR WTC. In general, the availability and accuracy of HY-2C altimeter data are largely enhanced with the OA WTC.

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.

Inclination Trend of the Agulhas Return Current Path in Three Decades

The Agulhas Return Current (ARC), as a primary component of the Agulhas system, contributes to water exchange and mass transport between the southern portions of the Indian and Atlantic Ocean basins. In this study, satellite altimeter data and reanalysis datasets, and a new set of criteria for the piecewise definition of the jet axis are used to explore the long-term change of the ARC’s axis position in recent three decades. It is found that the ARC axis exhibits a significant slanting trend with its western part (35–48°E) migrating northward and the eastern part (48–70°E) migrating southward. The meridional movement of the ARC path could be attributed to large-scale wind forcing. The anomalous surface wind stress curl, by Ekman pumping mechanism, leads to positive–negative–positive sea surface height anomalies in the western section and negative–positive–negative anomalies in the eastern section, thus the ARC axis tilts accordingly, in a northwest–southeast direction. Further analysis suggests that this ARC slanting trend is more dependent on the southward shift of the downstream axis and less on the topographic steering upstream. The downstream axis is more likely to interact with the ACC fronts and its migration could dominate the local EKE pattern by changing the background circulation and energy cascade direction. For the headstream west of 35°E, the ARC axis is more subject to topography, thus the EKE change is more dominated by eddy activity processes, including shedding, propagation and merging. This study provides some new insights into the long-term change of ARC and its interaction with the local EKE variability.

Dynamics of the Agulhas Current Influenced by the North–South Shift of Subtropical Front

Abstract The influence of meridional shift of the oceanic subtropical front (STF) on the Agulhas Current (AC) regime shifts is studied using satellite altimeter data and a 1.5-layer ocean model. The satellite observations suggest the northward shift of the STF leads to the AC leaping across the gap with little Agulhas leakage, and the southward shift of the STF mainly results in the AC intruding into the Atlantic Ocean in the forms of a loop current and an eddy-shedding path, while there are three flow patterns of AC for moderate latitude of the STF. The ocean model results suggest no hysteresis (associated with multiple equilibrium states) exists in the AC system. The model reproduces similar AC regimes depending on different gap widths as in the observations, and model results can be used to explain the observed Agulhas leakage well. We also present the parameter space of the critical AC strength that results in different AC flow patterns as a function of the gap width. The vorticity dynamics of the AC regime shift suggests that the β term is mainly balanced by the viscosity term for the AC in the leaping and loop current paths, while the β and instantaneous vorticity terms are mainly balanced by the advection and viscosity terms for the AC in the eddy-shedding path. These findings help explain the dynamics of the AC flowing across the gateway beyond the tip of Africa affected by the north–south shift of the STF in the leaping regime or penetrating regime.

Above Ground Level Estimation of Airborne Synthetic Aperture Radar Altimeter by a Fully Supervised Altimetry Enhancement Network

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm.

Machine learning based classification of lake ice and open water from Sentinel-3 SAR altimetry waveforms

The aim of the study was to evaluate, for the first time, the capability of different machine learning (ML) algorithms in classifying along-track lake surface conditions (open water and ice types) across ice seasons (freeze-up, ice growth and break-up periods) from Sentinel-3 A/B synthetic aperture radar altimeter (SRAL) data. To achieve this goal, over 107,500 radar waveforms extracted from 11 large lakes across the Northern Hemisphere and three ice seasons (2018–2021) were manually labelled using complementary satellite data (Sentinel-1 imaging Synthetic Aperture Radar (SAR), Sentinel-2 Multispectral Instrument (MSI) Level 1C, and MODIS Aqua/Terra data) for the training and testing of the ML algorithms in discriminating between open water, young (thin) ice, growing ice and melting ice. The four ML algorithms tested include Random Forest (RF), Gradient Boosting Trees (GBT), K Nearest Neighbor (KNN) and Support Vector Machine (SVM). To characterize the waveforms, seven waveform parameters were derived: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient (Sigma0), late tail to peak power (LTPP), early tail to peak power (ETPP) and the maximum value of the echo power (Max). Accuracies >95% were achieved across all classifiers using a 4-parameter combination (Sigma0, PP, OCOG Width, and LEW). Among all waveform parameters, Sigma0, OCOG width and PP were found to be the most important parameters for discriminating between lake ice types and open water. Despite showing comparable classification performances in the overall classification, RF and KNN are found to be a better fit for global lake ice mapping as both are less sensitive to their internal hyperparameters. Additionally, consistent results (>93.7% accuracy in all classifiers) achieved on the accuracy assessment carried out for each lake (out-of-sample testing) revealed the strength of the classifiers for spatial transferability. Implementation of RF and KNN could be valuable in a pre-or post-processing step for identifying lake surface conditions under which the retrieval of water level and ice thickness may be limited or not possible and, therefore, inform algorithms currently used for the generation of operational or research products. While the research focused on 11 of the largest lakes of the Northern Hemisphere, the classification approach presented herein has potential for application on smaller lakes too since data in SAR mode (∼300 m along-track resolution) are used.

Systematic Bias Correction in Ocean Mesoscale Forecasting Using Machine Learning

AbstractThe ocean circulation is modulated by meandering currents and eddies. Forecasting their evolution is a key target of operational models, but their forecast skill remains limited. We propose a machine learning approach that improves the output of an ocean circulation model by learning and predicting its systematic biases. This method can be applied a priori to any region, and is tested in the Gulf of Mexico, where the Loop Current (LC) and the large anticyclonic eddies that detach from it are major forecasting targets. The LC dynamics are recurrent and lie on a low‐dimensional dynamical attractor. Building upon the information gained analyzing this low dimensional attractor, we improve the representation of sea surface anomalies in model outputs through information from satellite altimeter data using a Sequence‐to‐Sequence model, which is a special class of Recurrent Neural Network. Building upon the HYCOM‐NCODA analysis system, we deliver a correction to the forecast at the observation resolution. For at least 15 days the proposed method learns to forecast the systematic bias in the HYCOM‐NCODA, outperforming persistence, and improving the forecast. This data‐driven approach is fast and can be implemented as an added step to any dynamical hindcasting or forecasting model. It offers an interesting avenue for further developing hybrid modeling tools. In these tools, fundamental physical conservations are preserved through the integration of partial differential equations which obey them. In addition, the method highlights specific deficiencies of the hindcast system that deserve further investigation in the future.

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.