Sea Surface Height Data Research Articles

Northward shift of the Kuroshio Extension during 1993–2021

The Kuroshio Extension (KE) flows eastward at the northern boundary of the North Pacific subtropical gyre. By transporting large amounts of seawater with heat, the KE contributes significantly to the formation of sea surface temperature (SST) fields. Recently, poleward shifts of major ocean gyres in the world ocean, including the North Pacific subtropical gyre, have been highlighted based on basin-scale changes in SST and sea surface height (SSH) distributions. However, a detailed investigation of the long-term meridional KE movement has not been presented. Investigation of KE path changes helps provide insights into long-term changes in the physical fields in the western North Pacific. In this study, we identified the KE path from satellite-derived SSH and surface current velocity data using a front identification method and showed that the KE migrated northward by approximately 210 km during 1993–2021. We further explored the cause of the northward KE shift based on atmospheric reanalysis data and numerical experiments using a high-resolution ocean general circulation model. It was revealed that the northward KE shift is mostly caused by the trend of wind stress curl in the North Pacific during 1993–2021.

Key Factors for Improving the Resolution of Mapped Sea Surface Height from Multi-Satellite Altimeters in the South China Sea

A satellite altimeter measures sea surface height (SSH) along the nadir track. Multiple satellite altimeters have been in orbit, and the measurements been merged for mapping mesoscale eddies of ~100 km in size in the oceans. The capability of the mapped SSH for resolving mesoscale eddies depends on mapping algorithms. A two-dimensional variational (2DVAR) algorithm was implemented to generate mapped SSH at a grid size of 1/12° in the South China Sea. A range of comparisons were performed between the mapped SSH and the commonly used AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic satellite data) mapped SSH data product at a grid size of 1/8° and 1/4°. The effective resolution, which represents the spatial scale that the data can resolve, was examined. The effective resolution of the mapped SSH using the 2DVAR algorithm is approximately 100 km, while it is 250 km with the 1/8° and 1/4° AVISO data products. The difference in the effective resolution results from the difference in the background state and thus the background error. The result suggests that the effective resolution of the mapped data could be increased by choosing a background state so that the associated errors could have a smaller decorrelation length scale.

Mode-1 N2 internal tides observed by satellite altimetry

Abstract. Satellite altimetry provides a unique technique for observing the sea surface height (SSH) signature of internal tides from space. Previous studies have constructed empirical internal tide models for the four largest constituents M2, S2, K1, and O1 by satellite altimetry. Yet no empirical models have been constructed for minor tidal constituents. In this study, we observe mode-1 N2 internal tides (the fifth largest constituent) using about 100 satellite years of SSH data from 1993 to 2019. We employ a recently developed mapping procedure that includes two rounds of plane wave analysis and a two-dimensional bandpass filter in between. The results show that mode-1 N2 internal tides have millimeter-scale SSH amplitudes. Model errors are estimated from background internal tides that are mapped using the same altimetry data but with a tidal period of 12.6074 h (N2 minus 3 min). The global mean error variance is about 25 % that of N2, suggesting that the mode-1 N2 internal tides can overcome model errors in some regions. We find that the N2 and M2 internal tides have similar spatial patterns and that the N2 amplitudes are about 20 % of the M2 amplitudes. Both features are determined by the N2 and M2 barotropic tides. The mode-1 N2 internal tides are observed to propagate hundreds to thousands of kilometers in the open ocean. The globally integrated N2 and M2 internal tide energies are 1.8 and 30.9 PJ, respectively. Their ratio of 5.8 % is larger than the theoretical value of 4 % because the N2 internal tides contain relatively larger model errors. Our mode-1 N2 internal tide model is evaluated using independent satellite altimetry data in 2020 and 2021. The results suggest that the model can make internal tide correction in regions where the model variance is greater than twice the error variance. This work demonstrates that minor internal tidal constituents can be observed using multiyear multi-satellite altimetry data and dedicated mapping techniques.

Southwestern Atlantic ocean fronts detected from the fusion of multi-source remote sensing data by a deep learning model

In the Southwestern Atlantic, the Falkland Current intrudes onto the South American shelf, resulting in the meeting of two water masses which are completely different in temperature and dynamic characteristics, thus generating the Southwestern Atlantic Front (SAF). Therefore, the SAF has prominent characteristics of thermal and dynamics. The current ocean front detection is mainly by performing gradient operations on sea surface temperature (SST) data, where regions with large temperature gradients are considered as ocean fronts. The thermal gradient method largely ignores the dynamical features, leading to inaccurate manifestation of SAF. This study develops a deep learning model, SAFNet, to detect the SAF through the synergy of 10-year (2010-2019) satellite-derived SST and sea surface height (SSH) observations to achieve high accuracy detection of SAF with fused thermal and dynamic characteristics. The comparative experimental results show that the detection accuracy of SAFNet reaches 99.45%, which is significantly better than other models. By comparing the frontal probability (FP) obtained by SST, SSH and SST-SSH fusion data respectively, it is proved that the necessity of fusion multi-source remote sensing data for SAF detection. The detection results of fusion data can reflect the spatial distribution of SAF more comprehensively and accurately. According to the meridional variation of FP, the main reason for the seasonal variation of the SAF is the change in its thermal characteristics, and the SAF has stable dynamic characteristics.

Quantifying the Degree of Eddy Quasi‐Geostrophy by Generalizing Rossby Deformation

AbstractThe quasi‐geostrophy (QG) of oceanic eddies is scientifically challenging and observationally puzzling because of its marginally balanced yet resolvable nature. Here a quantitative analysis on the degree of eddy QG is carried out using altimetric sea surface height data from multiple missions. We propose a metric of Rossby period of deformation (Td) as an extension to the Rossby radius of deformation (Rd) and its related phase speed of the first baroclinic Rossby wave (Cd) which satisfies Td = Rd/Cd, implying that any robust eddy with a length scale of ∼Rd is supposed to survive for a period longer than the Rossby period ∼Td under an average propagation speed of ∼Cd if a quasi‐geostrophic balance can be maintained. In terms of a generalized Rossby deformation with regards to Rd, Td, and Cd, three indices are defined as baselines to characterize the spatial, temporal, and dynamic aspects of eddy QG according to their effective size, actual lifetime, and propagation speed, respectively.

Why Did the 2017 Kuroshio Large Meander Event Become the Longest in the Past 70 Years?

AbstractThe Kuroshio south of Japan is known to vacillate between a straight and a large meander (LM) path. Since 1950, eight LM events have been observed with different durations. The most recent/on‐going LM started in August 2017 and has become the longest event in record. By analyzing eddy‐resolving sea surface height data and by adopting a wind‐forced linear vorticity model, we demonstrate that the on‐going LM is maintained by an exceptionally stable dynamic state of the Kuroshio Extension (KE) forced by wind stresses across the Pacific basin. The highly‐stable KE system not only minimizes westward eddy perturbations from disrupting the upstream Kuroshio path, its strengthened southern recirculation gyre further helps to anchor the Kuroshio across the Izu Ridge. By contrasting the on‐going event to the 2004–2005 event, we argue that the LM duration is more sensitive to the wind‐forced KE dynamic state than the eastward Kuroshio transport south of Japan.

Sea surface height data reconstruction via inter and intra layer features based on dual attention

Understanding of geoscience relies on spatio-temporal continuous fields, such as Sea Surface Height (SSH). Data reconstruction for restoring spatio-temporal continuous and gridded maps from incomplete observations and inaccuracy interpolation products has long been a crucial challenge in the marine geoscience. Despite remarkable progress, most existing learning-based reconstruction methods neglect to fully and discriminatively utilize the inter-layer and intra-layer features. The inter-layer semantics are complementary, and the significance of intra-layer components varies with frequency. To address these issues, we propose a multi-layer Feature Combination Network based on Attention mechanism (FCANet) for SSH data reconstruction. Specifically, a novel trainable Multi-layer Feature Combination Block (MFCB) is developed to enrich the features through inter-layer dependencies. Dual attention mechanism is introduced into the MFCB to enhance saliency of intra-layer features by adaptively rescaling the spatial-wise and channel-wise features. Furthermore, experimental results on SSH variable demonstrate the superiority of our FCANet over state-of-the-art reconstruction methods in the metrics of RMSE and SSIM.

Frontogenesis Elevates the Maximum Chlorophyll a Concentration at the Subsurface Near the Kuroshio During Well‐Stratified Seasons

AbstractThe contribution that fronts and frontogenesis make to subsurface chlorophyll a (CHL) concentration was investigated near the Kuroshio using data from multiple surveys across the Kuroshio during summer and fall and satellite sea surface height (SSH) data alongside high‐resolution reanalysis data set. An intensive survey was conducted during a period of frontogenesis as seen from the in‐situ and satellite‐derived velocity data as well as the analysis of the reanalysis data set. Physical and biological observation showed shoaling of the nitracline and a subsurface CHL maximum (SCM) near the evolving front. The CHL concentration at the SCM (CHLSCM) was large near the front compared with the concentration in ambient water. Sections of 10 repeat seasonal surveys also captured the frontal structure, the shoaling of the nitracline, and the increase in CHLSCM as well as their interrelationship. The CHLSCM was large, and the depth of the SCM (ZSCM) was shallow above a large horizontal density gradient (i.e., marked mesoscale frontal structure) in the ocean interior. In addition, we demonstrate that the lateral strain rate, which is a measure of frontogenesis obtained from the SSH, can be used to estimate ZSCM and CHLSCM. Frontogenesis, secondary circulation, symmetric instability, and vertical diffusion were considered to cause the nutrient supply to the subsurface layer. The surveys and analysis indicate that fronts and frontogenesis increase CHLSCM, and the physical data obtained from both in‐situ and satellite observations can be used to estimate CHL distribution in the ocean interior.

A Partial Reconstruction Method for SAR Altimeter Coastal Waveforms Based on Adaptive Threshold Judgment

Due to land contamination and human activities, the sea surface height (SSH) data retrieved from altimeter coastal waveforms have poor precision and cannot provide effective information for various tasks. The along-track high-resolution characteristic of the new synthetic aperture radar (SAR) altimeter makes the retracking methods of traditional coastal waveforms difficult to apply. This study proposes a partial reconstruction method for SAR altimeter coastal waveforms. By making adaptive threshold judgments of model matching errors and repairing the contaminated waveforms based on the nearest linear prediction, the success rate of retracking and retrieval precision of SSH are significantly improved. The data from the coastal experimental areas of the Sentinel-3B satellite altimeter are processed. The results indicate that the mean proportion of waveform quality improvement brought by partial reconstruction is 80.30%, the mean retracking success rate of reconstructed waveforms is 85.60%, and the mean increasing percentage is 30.98%. The noise levels of SSH data retrieved by different methods are calculated to evaluate the processing precision. It is shown that the 20 Hz SSH precisions of the original and reconstructed coastal waveforms are 12.75 cm and 6.32 cm, respectively, and the corresponding 1 Hz SSH precisions are 2.85 cm and 1.41 cm, respectively. The results validate that the proposed partial reconstruction method has improved the SSH precision by a factor of two, and the comparison results with mean sea surface (MSS) model data further verify this conclusion.

Dynamic Response of Atmospheric and Ocean Parameters and Their Relation to Typhoon Haikui (2012) Using Satellite Data

Typhoon Haikui (2012) occurred in the northwestern Pacific Ocean, and landfall on the east coast of China brought heavy rainfall with strong winds. Because of Typhoon Haikui, sea surface temperature (SST) cooling of 3 °C occurred on the right side of the track, mainly due to Ekman transport and upwelling. SST cooling on the left side was lower than on the right side, mainly due to the rainfall. Heavy precipitation occurred on both sides of the typhoon track; however, rainfall was higher on the left side of the typhoon track. This paper explains the dynamic process between atmospheric and oceanographic parameters and verifies the variations in chlorophyll and sea surface height data before, during, and after the typhoon. Typhoon Haikui demonstrates dynamic variations and intuitively illustrates the relationship between the ocean and atmospheric parameters.

Seasonal West‐East Seesaw of M2 Internal Tides From the Luzon Strait

AbstractSatellite altimetry sea surface height (SSH) measurements from 1993 to 2017 are used to investigate the seasonal variability of mode‐1 M2 internal tides from the Luzon Strait. The 25 years of SSH data are divided into four seasonal subsets, from which four seasonal internal tide models are constructed following the same mapping procedure. Climatological seasonal hydrography in the World Ocean Atlas 2013 is used to calculate two seasonally variable parameters required in the mapping procedure: Wavelength and the transfer function from the SSH amplitude to depth‐integrated energy flux. The M2 internal tides from the Luzon Strait are extracted using propagation direction determined in plane wave analysis. The satellite results show that the westward and eastward M2 internal tides both demonstrate significant seasonal variation. The westward and eastward internal tides seesaw seasonally: The westward internal tides strengthen (weaken) in summer and fall (winter and spring); while the eastward internal tides strengthen (weaken) in winter and spring (summer and fall). We suggest that the seasonal seesaw is mainly determined by ocean stratification and the Kuroshio Current; however, further studies are needed to quantify their relative contributions.

Impact of satellite and regional in-situ profile data assimilation on a high-resolution ocean prediction system in the Northwest Pacific

The impacts of observation data sets on the high-resolution (1/24°) Northwest Pacific prediction system were investigated with the model sensitivity tests. We compared the model experiments assimilating the different combinations of the observation data sets, such as the sea surface height derived from satellite altimetry, sea surface temperature, and in-situ profiles, based on the Ensemble Optimal Interpolation. Pseudo-profiles constructed by the method of Cooper and Haines (1996, CH96) were assimilated into the model to assimilate sea surface height data. CH96 applied a conservation principle to derive pseudo-profiles by rearranging preexisting profiles. The comparison of the model experiments suggests that each observation data set enhances the model performance. Especially, the assimilation of the sea surface height reduces the model error by more than 9.81% and 6.44%, respectively, in terms of the root-mean-square error of the ocean temperature and salinity in the subsurface layer. It is interesting that the assimilation of the in-situ temperature profiles in the Korean marginal seas contributes to improving the reproducibility of the subsurface temperature and salinity in the East/Japan Sea (EJS) as well as Kuroshio-Kuroshio Extension (K-KE) regions. The improvement in the K-KE region seems to be related to the reproducibility of the Kuroshio axis. As the water mass in the EJS flows into the Pacific Ocean through the Tsugaru Strait, it affects the front of the Sanriku confluence, and it seems to eventually control the Oyashio Current and Kuroshio axis. In conclusion, this study evaluated the contribution of each observation component to ocean analysis in the KOOS-OPEM and confirmed the role of the existing observation networks. This study also suggests that greater attention should be paid to the role of regional ocean observation networks to improve the forecast skill of the ocean prediction system not only in the region but also in the open ocean, such as the Pacific Ocean.

Slow Slip Detectability in Seafloor Pressure Records Offshore Alaska

AbstractIn subduction zones worldwide, seafloor pressure data are used to observe tectonic deformation, particularly from megathrust earthquakes and slow slip events (SSEs). However, such measurements are also sensitive to oceanographic circulation‐generated pressures over a range of frequencies that conflate with tectonic signals of interest. Using seafloor pressure and temperature data from the Alaska Amphibious Community Seismic Experiment, and sea surface height data from satellite altimetry, we evaluate the efficacy of various seasonal and oceanographic pressure signal proxy corrections and conduct synthetic tests to determine their impact on the timing and amplitude prediction of ramp‐like signals typical of SSEs. We find that subtracting out the first mode of the complex empirical orthogonal functions of the pressure records on either the shelf or slope yields signal root‐mean‐square error (RMS) reductions up to 73% or 80%, respectively. Additional correction with proxies that exploit the depth‐dependent spatial coherence of pressure records provides cumulative variance reductions up to 83% and 93%, respectively. Our detectability tests show that the timing and amplitude of synthetic SSE‐like ramps can be well constrained for ramp amplitudes ≥4 cm on the shelf and ≥2 cm on the slope, using a fully automated detector. The principal limits on detectability are residual abrupt changes in pressure that occur as part of the transition to and from summer to winter conditions but are not adequately characterized by our seasonal corrections, as well as the inability to properly account for instrumental drift, which is not readily separated from the seasonal signal.

Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height

The Indonesian Throughflow (ITF) connects the tropical Pacific and Indian Oceans and is critical to the regional and global climate systems. Previous research indicates that the Indo-Pacific pressure gradient is a major driver of the ITF, implying the possibility of forecasting ITF transport by the sea surface height (SSH) of the Indo-Pacific Ocean. Here we used a deep-learning approach with the convolutional neural network (CNN) model to reproduce ITF transport. The CNN model was trained with a random selection of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and verified with residual components of the CMIP6 simulations. A test of the training results showed that the CNN model with SSH is able to reproduce approximately 90% of the total variance of ITF transport. The CNN model with CMIP6 was then transformed to the Simple Ocean Data Assimilation (SODA) dataset and this transformed model reproduced approximately 80% of the total variance of ITF transport in the SODA. A time series of ITF transport, verified by Monitoring the ITF (MITF) and International Nusantara Stratification and Transport (INSTANT) measurements of ITF, was then produced by the model using satellite observations from 1993 to 2021. We discovered that the CNN model can make a valid prediction with a lead time of 7 months, implying that the ITF transport can be predicted using the deep-learning approach with SSH data.

Remote Sensing of the Subtropical Front in the Southeast Pacific and the Ecology of Chilean Jack Mackerel Trachurus murphyi

The Subtropical Front (STF) plays a key role in the ecology of Chilean jack mackerel Trachurus murphyi. Nonetheless, there are few remote sensing studies of the STF in the open Southeast Pacific, and almost all of them have been conducted by satellite oceanographers in Russia and Ukraine to support respective large-scale fisheries of jack mackerel in this region. We reviewed these studies that documented long-term seasonal and interannual variability of the STF from sea surface temperature (SST) and sea surface height (SSH) data. We also mapped the STF from satellite sea surface salinity (SSS) data of the SMOS mission (2012–2019). The Subtropical Front consists of two fronts–North and South STF about 500 km apart–that border the Subtropical Frontal Zone (STFZ) in-between. The STF is density-compensated, with spatially divergent manifestations in temperature and salinity. In the temperature field, the STF extends in the WNW to ESE direction in the Southeast Pacific. In the salinity field, the STFZ appears as a broad frontal zone, extending zonally across the entire South Pacific. Three major types of satellite data-SST, SSH, and SSS-can be used to locate the STF. The SSH data is most advantageous with regard to the jack mackerel fisheries, owing to the all-weather capability of satellite altimetry and the radical improvement of the spatial resolution of SSH data in the near future. Despite the dearth of dedicated in situ studies of the South Pacific STFZ, there is a broad consensus regarding the STFZ being the principal spawning and nursing ground of T. murphyi and a migration corridor between Chile and New Zealand. Major data/knowledge gaps are identified, and key next steps are proposed to mitigate the data/knowledge gaps and inform fisheries management.

Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies

The space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents operational schemes from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between sea surface temperature and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations, namely satellite-derived SSH and SST data. The proposed multimodal 4DVarNet schemes combine a variational formulation involving trainable observation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> terms with a trainable gradient-based solver. An observing system simulation experiment for a Gulf Stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error and resolved space-time scales. We discuss further the potential and the limitations of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.

Inversion of Deflection of the Vertical in the South China Sea Using ICESat-2 Sea Surface Height Data

The traditional altimetry satellites based on pulse-limited radar altimeter only calculate along-track deflection of the vertical (DOV), which results in poorer precision of the prime vertical component than that of the meridian component and limits the precision of the marine gravity field inversion. We expect an improvement in the higher precision prime vertical component using the Ice, Cloud and land Elevation Satellite 2 (ICESat-2) sea surface height (SSH) data. In this paper, the 2′ × 2′ gridded DOVs derived from along-beam DOVs, cross-beam DOVs, and joint along-cross beam DOVs in the South China Sea (SCS; 0°–23°N, 103°–120°E) are calculated with the weighted least squares method, respectively. The inverse Vening–Meinesz (IVM) formula is applied to derive 2′ × 2′ gravity anomalies over the SCS from ICESat-2-derived gridded DOVs. In addition, the XGM2019e_2159-DOV and SIO V31.1-DOV models are used to assess the precision of the gridded DOVs. The XGM2019e_2159-GRA, SIO V31.1-GRA models, and ship-borne gravity anomalies are also adopted to evaluate the quality of gravity anomalies. The results show that the gridded DOVs calculated by the joint along-cross beam DOVs have the highest precision among the three gridded DOVs determined by ICESat-2. The precision of difference between gravity anomalies derived from the joint along-cross beam DOV and the above verification data are higher than those derived from the along-beam and cross-beam DOVs. We conclude that the joint along-cross beam DOV can effectively improve the precision of the gridded DOV, which is conducive to the inversion of a high-precision marine gravity field.

Satellite Estimates of Mode-1 M2 Internal Tides Using Nonrepeat Altimetry Missions

Abstract Previous satellite estimates of internal tides are usually based on 25 years of sea surface height (SSH) data from 1993 to 2017 measured by exact-repeat (ER) altimetry missions. In this study, new satellite estimates of internal tides are based on 8 years of SSH data from 2011 to 2018 measured mainly by nonrepeat (NR) altimetry missions. The two datasets are labeled ER25yr and NR8yr, respectively. NR8yr has advantages over ER25yr in observing internal tides because of its shorter time coverage and denser ground tracks. Mode-1 M2 internal tides are mapped from both datasets following the same procedure that consists of two rounds of plane wave analysis with a spatial bandpass filter in between. The denser ground tracks of NR8yr make it possible to examine the impact of window size in the first-round plane wave analysis. Internal tides mapped using six different windows ranging from 40 to 160 km have almost the same results on global average, but smaller windows can better resolve isolated generation sources. The impact of time coverage is studied by comparing NR8yr160km and ER25yr160km, which are mapped using 160-km windows in the first-round plane wave analysis. They are evaluated using independent satellite altimetry data in 2020. NR8yr160km has larger model variance and can cause larger variance reduction, suggesting that NR8yr160km is a better model than ER25yr160km. Their global energies are 43.6 and 33.6 PJ, respectively, with a difference of 10 PJ. Their energy difference is a function of location. Significance Statement Our understanding of internal tides is mainly limited by the scarcity of field measurements with sufficient spatiotemporal resolution. Satellite altimetry offers a unique technique for observing and predicting internal tides on a global scale. Previous satellite observations of internal tides are mainly based on 25 years of data from exact-repeat altimetry missions. This paper demonstrates that internal tides can be mapped using 8 years of data made by nonrepeat altimetry missions. The new dataset has shorter time coverage and denser ground tracks; therefore, one can examine the impact of window size and time coverage on mapping internal tides from satellite altimetry. A comparison of models mapped from the two datasets sheds new light on the spatiotemporal variability of internal tides.

Overlapping-calibration of wide-swath altimeter baseline errors using two satellites formation flying design

The sea surface height (SSH) measured by future wide-swath altimetry satellites contains observation error due to baseline measurement, which is called SSH_Error_Baseline. It is a huge challenge for satellite engineering to maintain such high accuracies of the baseline roll and length in orbit to ensure that the SSH_Error_Baseline can be maintained below 1 cm. Therefore, how to effectively reduce the SSH_Error_Baseline is extremely important. In order to solve the existing problem, an innovative overlapping-calibration method is proposed based on two-satellite formation flight design with overlapping swath. In this study, the differences of SSH data observed by these two satellites in the overlapping area is so small that it can be ignored, and the SSH_Error_Baseline dominates the difference. Then, the baseline parameters of the two satellites can be well estimated by adopting an optimal inverse method. A total of 9 groups of observing system simulation experiments (OSSEs) have been carried out, and they respectively indicate different pessimistic theoretical scenarios of baseline measurement systems. According to the results, this design can effectively reduce the SSH_Error_Baseline in most scenarios. Regarding the advantage of this method, it can be applied to all along-track observation data without requiring the application of additional auxiliary data (i.e. model data, SSH derived from nadir altimetry). Therefore, when two wide-swath altimetry satellites are simultaneously designed, the formation flight scheme proposed in this paper is recommended, especially when the measurements of the baseline cannot meet the expected accuracies.

Detection and Identification of Mesoscale Eddies in the South China Sea Based on an Artificial Neural Network Model—YOLOF and Remotely Sensed Data

Mesoscale eddies are typical mesoscale ocean phenomena that exist widely in all oceans and marginal seas around the world, playing important roles in ocean circulation and material transport. They also have important impacts on the safe navigation of ships and underwater acoustic communications. Traditional mesoscale eddy identification methods are subjective and usually depend on parameters that must be pre-defined or adjusted by experts, meaning that their accuracy cannot be guaranteed. With the rise of deep learning, the “you only look once” (YOLO) series target recognition models have been shown to present certain advantages in eddy detection and recognition. Based on sea level anomaly (SLA) data provided over the past 30 years by the Copernicus Marine Environment Monitoring Service (CMEMS), as well as deep transfer learning, we propose a method for oceanic mesoscale eddy detection and identification based on the “you only look once level feature” (YOLOF) model. Using the proposed model, the mesoscale eddies in the South China Sea from 1993 to 2021 were detected and identified. Compared with traditional recognition methods, the proposed model had a better recognition effect (with an accuracy of 91%) and avoided the bias associated with subjectively set thresholds; to a certain extent, the model also improved the detection of and the identification speed for mesoscale eddies. The method proposed in this paper not only promotes the development of deep learning in the field of oceanic mesoscale eddy detection and identification, but also provides an effective technical method for the study of mesoscale eddy detection using sea surface height data.