From 1/4° to 1/8°: Influence of Spatial Resolution on Eddy Detection Using Altimeter Data

A substantial portion of ocean eddies, especially small ones, may be missed due to insufficient spatial or temporal sampling by satellite altimetry. In order to illustrate the influence of spatial resolution on eddy detection, this study provides a comparison of eddy identification, tracking, and analysis between two sets of merged altimeter data with spatial resolutions of 1/4° and 1/8°. One main study area (the Mediterranean Sea), and three confirmatory areas (the South-China Sea, the North-West Pacific, and the South-East Pacific) are chosen. The results show that the number and density of eddies captured by the 1/8° data are about twice as much as those captured by the 1/4° data, while the ratios of corresponding eddy parameters, i.e., radius, amplitude, (eddy kinetic energy (EKE)) are about 0.6–0.8 (1.3) for the two datasets (1/8° ÷ 1/4°). Long-term eddy tracking (1993–2018) is conducted in the Mediterranean Sea, indicating that the improvement in spatial resolution will increase the observed values of both the lifetime and the propagation distance of robust eddies. The number of eddies identified using the 1/4° data only accounts for ~30% to 60% of those identified using the 1/8° data. However, for eddies that can be detected using the two datasets, ~5% to 10% present errors (i.e., confusion). In comparison between the four regions, we find that for the enclosed seas with complex conditions, the increase in spatial resolution may lead to more significant improvements in the efficiency and accuracy of eddy detection.

Performance Analysis of Ocean Eddy Detection and Identification by L-Band Compact Polarimetric Synthetic Aperture Radar

The automatic detection and analysis of ocean eddies has become a popular research topic in physical oceanography during the last few decades. Compact polarimetric synthetic aperture radar (CP SAR), an emerging polarimetric SAR system, can simultaneously acquire richer polarization information of the target and achieve large bandwidth observations. It has inherent advantages in ocean observation and is bound to become an ideal data source for ocean eddy observation and research. In this study, we simulated the CP data with L-band ALOS PALSAR fully polarimetric data. We assessed the detection and classification potential of ocean eddies from CP SAR by analyzing 50 CP features for 2 types of ocean eddies (“black”and “white”) based on the Euclidean distance and further carried out eddy detection and eddy information extraction experiments. The results showed that among the 50 CP features, the dihedral component power (Pd), shannon entropy (SEI), double bounce (Dbl), Stokes parameters (g0 and g3), eigenvalue (l1), lambda, RVoG parameter (ms), shannon entropy (SE), surface scattering component (Ps), and σHH all performed better for detecting “white” eddies. Moreover, the H-A combination parameter (1mHA), entropy, shannon entropy (SEP, SEI, and SE), probability (p2), polarization degree (m), anisotropy, probability (p1), double bounce (Dbl), H-A combination parameter (H1mA), circular polarization ratio (CPR), and σVV were better CP features for detecting “black” eddies.

Lagrangian eddy tracking reveals the Eratosthenes anticyclonic attractor in the eastern Levantine Basin

Statistics of anticyclonic eddy activity and eddy trajectories in the Levantine Basin over the 2000–2018 period are analyzed using the DYNED-Atlas database, which links automated mesoscale eddy detection by the Angular Momentum Eddy Detection and Tracking Algorithm (AMEDA) algorithm to in situ oceanographic observations. This easternmost region of the Mediterranean Sea, delimited by the Levantine coast and Cyprus, has a complex eddying activity, which has not yet been fully characterized. In this paper, we use Lagrangian tracking to investigate the eddy fluxes and interactions between different subregions in this area. The anticyclonic structure above the Eratosthenes Seamount is identified as hosting an anticyclone attractor, constituted by a succession of long-lived anticyclones. It has a larger radius and is more persistent (staying in the same position for up to 4 years with successive merging events) than other eddies in this region. Quantification of anticyclone flux shows that anticyclones that drift towards the Eratosthenes Seamount are mainly formed along the Israeli coast or in a neighboring area west of the seamount. The southeastern Levantine area is isolated, with no anticyclone transfers to or from the western part of the basin, defining the effective attraction basin for the Eratosthenes anticyclone attractor. Co-localized in situ profiles inside eddies provide quantitative information on their subsurface physical anomaly signature, whose intensity can vary greatly with respect to the dynamical surface signature intensity. Despite interannual variability, the so-called Eratosthenes anticyclone attractor stores a larger amount of heat and salt than neighboring anticyclones, in a deeper subsurface anomaly that usually extends down to 500 m. This suggests that this attractor could concentrate heat and salt from this subbasin, which will impact the properties of intermediate water masses created there.

Cross-Domain Submesoscale Eddy Detection Neural Network for HF Radar

With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments. Therefore, a cross-domain submesoscale eddy detection neural network (CDEDNet) based on the high-frequency radar (HFR) data of the Nansan and Xuwen region is proposed in this paper. Firstly, a fundamental deep eddy detection architecture CDEDNet-0 is constructed with a fully convolutional network (FCN). Secondly, for solving the problem of insufficient labeled eddy data, an instance-based domain adaption method is adopted in CDEDNet-1 to increase training samples. Thirdly, for tackling the problem of unable to inherit previous detection experience, parameter-based transfer learning is incorporated in CDEDNet-2 for multi-scene eddy detection. The experiment results demonstrate CDEDNet-1 and CDEDNet-2 perform better than CDEDNet-0 in terms of accuracy. Meanwhile, eddy characteristics including eddy type, radius, occurring time, merger, and dynamic trajectory are analyzed for the Nansan and Xuwen regions.

Application of Three Deep Learning Schemes Into Oceanic Eddy Detection

Recent years have witnessed the increase in applications of artificial intelligence (AI) into the detection of oceanic features. Oceanic eddies, ubiquitous in the global ocean, are important in the transport of materials and energy. A series of eddy detection schemes based on oceanic dynamics have been developed while the AI-based eddy identification scheme starts to be reported in literature. In the present study, to find out applicable AI-based schemes in eddy detection, three AI-based algorithms are employed in eddy detection, including the pyramid scene parsing network (PSPNet) algorithm, the DeepLabV3+ algorithm and the bilateral segmentation network (BiSeNet) algorithm. To justify the AI-based eddy detection schemes, the results are compared with one dynamic-based eddy detection method. It is found that more eddies are identified using the three AI-based methods. The three methods’ results are compared in terms of the numbers, sizes and lifetimes of detected eddies. In terms of eddy numbers, the PSPNet algorithm identifies the largest number of ocean eddies among the three AI-based methods. In terms of eddy sizes, the BiSeNet can find more large-scale eddies than the two other methods, because the Spatial Path is introduced into the algorithm to avoid destroying the eddy edge information. Regarding eddy lifetimes, the DeepLabV3+ cannot track longer lifetimes of ocean eddies.

Cyclone-Anticyclone asymmetry of eddy detection on gridded altimetry product in the Mediterranean Sea

<p>Thanks to a Observing System Simulation Experiment (OSSE) that simulate the along-track satellite measuring process on the sea surface of the high resolution model CROCO-MED60v40-15-16 we investigate how the reliability and the accuracy of the detected eddies are influenced by the satellite sampling and the mapping procedure. The main result of this study is that there is that there is a strong cyclone-anticyclone asymmetry of the eddy detection on the altimetry products AVISO/CMEMS in the Mediterranean Sea. Large scale cyclones having a characteristic radius larger than the local deformation radius are much less reliable than large scale anticyclones. We estimate, that less than 60% of these cyclones detected on gridded altimetry product are reliable, while more than 85% of mesoscale anticyclones are reliable. Besides, both the barycenter and the size of these mesoscale anticyclones are relatively accurate. This asymmetry comes from the difference of stability between cyclones and anticyclones. Large mesoscale cyclones often splits into smaller sub mesoscale structures hav ing a rapid dynamical evolution. The high resolution model CROCO-MED60v40 shows that this complex dynamic is too fast and too small to be accurately captured by the gridded altimetry products based on a strong spatio-temporal  interpolation. The later smooth out this sub mesoscale dynamics and tend to generate an excessive number of unrealistic (i.e. unreliable) mesoscale cyclones in comparison with the reference field. On the other hand, large mesoscale anticyclones,  which are more robust and that evolve more slowly, can be spatially resolved and accurately tracked by standard altimetry products.<span>  </span>However, we confirm that gridded altimetry products have a systematic bias on the eddy intensity and especially for anticyclones. The azimuthal geostrophic velocities are always underestimated on the AVISO/CMEMS products even for large mesoscale anticyclones.<span> </span></p>

Lagrangian eddy tracking reveals the Eratosthenes anticyclonicattractor in the eastern Levantine basin

<p>Statistics of anticyclone activity and trajectories in the southeastern Mediterranean sea over the period 2000-2018<br>is created using the DYNED atlas, which links the automated mesoscale eddy detection by the AMEDA algorithm with in<br>situ oceanographic observations. This easternmost region of the Mediterranean sea, delimited by the Levantine coast and<br>Cyprus, has a complex eddying activity, which has not yet been fully characterized. Using Lagrangian tracking<br>to investigate the eddy fluxes and interactions between different subregions in this area, we find that the southeastern Levantine<br>area is isolated, with no anticyclone exchanges with the western part of the basin. Moreover the anticyclonic structure above<br>the Eratosthenes seamount is identified as being an anticyclone attractor, differentiated from other anticyclones and staying<br>around this preferred position up to four years with successive mergings. Colocalized in situ profiles inside eddies provide<br>quantitative information on their subsurface structure and show that similar surface signatures correspond to very different<br>physical properties. Despite interannual variability, the so-called "Eratosthenes attractor" stores a larger amount of heat and<br>salt than neighboring anticyclones, in a deeper subsurface anomaly that usually extend down to 500 m. This suggests that this<br>attractor could concentrate heat and salt from this sub-basin, which will impact the properties of intermediate water masses<br>created there.</p>