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Article

Coastal Eddy Detection in the Balearic Sea: SWOT Capabilities

by
Laura Fortunato
1,*,
Laura Gómez-Navarro
2,
Vincent Combes
2,3,
Yuri Cotroneo
1,
Giuseppe Aulicino
1 and
Ananda Pascual
2
1
Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, 80143 Naples, Italy
2
Institut Mediterrani d’Estudis Avançats (IMEDEA), Spanish National Research Council–University of the Balearic Islands (CSIC–UIB), 07190 Esporles, Spain
3
Departamento de Física, Universitat de les Illes Balears, 07120 Palma de Mallorca, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(15), 2552; https://doi.org/10.3390/rs17152552
Submission received: 16 June 2025 / Revised: 11 July 2025 / Accepted: 18 July 2025 / Published: 23 July 2025
(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring)
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Abstract

Mesoscale coastal eddies are key components of ocean circulation, mediating the transport of heat, nutrients, and marine debris. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution sea surface height data, offering a novel opportunity to improve the observation and characterization of these features, especially in coastal regions where conventional altimetry is limited. In this study, we investigate a mesoscale anticyclonic coastal eddy observed southwest of Mallorca Island, in the Balearic Sea, to assess the impact of SWOT-enhanced altimetry in resolving its structure and dynamics. Initial eddy identification is performed using satellite ocean color imagery, followed by a qualitative and quantitative comparison of multiple altimetric datasets, ranging from conventional nadir altimetry to wide-swath products derived from SWOT. We analyze multiple altimetric variables—Sea Level Anomaly, Absolute Dynamic Topography, Velocity Magnitude, Eddy Kinetic Energy, and Relative Vorticity—highlighting substantial differences in spatial detail and intensity. Our results show that SWOT-enhanced observations significantly improve the spatial characterization and dynamical depiction of the eddy. Furthermore, Lagrangian transport simulations reveal how altimetric resolution influences modeled transport pathways and retention patterns. These findings underline the critical role of SWOT in advancing the monitoring of coastal mesoscale processes and improving our ability to model oceanic transport mechanisms.

1. Introduction

Mesoscale and submesoscale oceanic structures—such as eddies, fronts, and filaments—play a crucial role in transporting heat, salt, and biogeochemical properties between coastal regions and the open ocean [1,2,3,4,5,6,7,8,9,10,11]. These dynamic features account for a significant portion of the ocean’s kinetic energy, and they contribute to the redistribution of tracers, such as nutrients, heat, and carbon, across different spatial and temporal scales [12,13,14,15,16,17]. Beyond their role in physical circulation, eddies play a significant part in ecological and environmental processes by facilitating the transport and retention of pollutants [18], plankton [19,20,21], and marine larvae [22,23,24]. Moreover, eddies influence the behavior and migration patterns of marine animals, including turtles [25], sharks [26], and birds [27,28,29].
Eddies are commonly detected using satellite altimetry data, which provides valuable observations of Sea Surface Height (SSH) at a global scale. Altimetric data has proven highly effective in tracking large-scale oceanic eddies and other mesoscale features [1,30]. In addition to SSH, satellite-derived sea surface temperature and ocean color imagery offer useful visual cues for identifying eddies. However, these surface signatures often provide only qualitative insights into the underlying dynamics [31,32]. While these observations help identify eddy structures, further quantitative analyses are needed to understand the dynamics of the associated currents and subsurface features.
Despite the advantages of altimetry, conventional nadir altimetric products are often limited by spatial resolution, particularly in coastal and regional areas where mesoscale features are more pronounced [33]. The lower resolution of these datasets can result in the underrepresentation of small-scale structures such as coastal eddies, which are critical for understanding local ocean dynamics [34]. To mitigate this issue, the integration of data from multiple altimetry missions has proven to be an effective strategy for enhancing the resolution and accuracy of sea level measurements. Studies in the Mediterranean Sea, for instance, have shown that merging multi-mission altimetric data significantly improves the detection and characterization of mesoscale features [35,36].
The SWOT (Surface Water and Ocean Topography) mission represents a major technological advancement, offering unprecedented spatial resolution capable of resolving mesoscale and submesoscale ocean structures, including coastal zones [37,38]. Previous efforts, including the FaSt-SWOT project, have validated SWOT data in the western Mediterranean Sea, demonstrating its ability to capture fine-scale coastal dynamics with high accuracy through combined observational and numerical approaches [39,40,41,42,43].
In light of these advances, the present study focuses on the Balearic Sea, a region characterized by a variety of mesoscale oceanic structures, including anticyclonic eddies previously identified in the literature [44,45,46,47,48,49]. This study specifically examines a coastal eddy southwest of Mallorca Island, utilizing the high-resolution capabilities of the SWOT mission. By uniquely combining high-resolution SWOT altimetry with cloud-free ocean color imagery over a focused 5-day period, we investigate the dynamics of this well-defined coastal eddy. Integrating these complementary datasets allows us to provide novel insights into fine-scale variability and demonstrate SWOT’s added value compared to traditional altimetric products in this region. The 5-day period from 23 June 2023 to 27 June 2023 was carefully selected to ensure uninterrupted satellite coverage, essential for a consistent and meaningful comparison between altimetric and optical data. The eddy under investigation is clearly visible in the ocean color imagery throughout this period, justifying its selection for detailed analysis.
While the core analysis focuses on these five days, the eddy was detectable in altimetric velocity fields as early as 11 June and remained coherent until around 1 July, with signs of weakening thereafter. The selected period corresponds to its mature phase, during which the structure and trapping behavior were most clearly expressed. The aim is to demonstrate the added value of SWOT observations over traditional altimetric products in resolving small-scale ocean features [50,51]. The aim is to demonstrate the added value of SWOT observations over traditional altimetric products in resolving small-scale ocean features [50,51].
The paper is organized as follows: First, we present the characteristics of the study area. Second, we describe the data and methods used, including satellite ocean color images, altimetric data, and Lagrangian simulations. Third, we present the results of our analysis, focusing on the identification and dynamics of the coastal eddy southwest of Mallorca. Finally, we discuss the implications of these results and the advantages and limitations of the different altimetric products in resolving coastal eddies and their associated transport dynamics.

2. Study Area

The Balearic Sea (Figure 1a) is a sub-basin of the western Mediterranean, located between the Iberian Peninsula, the Gulf of Lion, and the Balearic Islands (Ibiza, Mallorca, and Menorca). This region was selected for the present SWOT-based study because it hosts a range of mesoscale and submesoscale oceanic structures, including a well-defined coastal eddy southwest of Mallorca that exhibits clear surface signatures in both altimetric and ocean color data. The dynamic complexity of this coastal area, shaped by interactions between ocean currents and bathymetry, makes it a challenging environment for traditional altimetric products and thus ideal to highlight SWOT’s enhanced spatial resolution and ability to resolve fine-scale coastal features.
To the north, it is bounded by the Liguro–Provençal Basin, an area influenced by strong atmospheric forcings, while to the south, it is bounded by the Algerian Basin, which is dominated by intense mesoscale eddies and their interactions with the unstable Algerian current [46,47,48,52]. Consequently, the Balearic Basin can be considered a transitional region between the northern (Gulf of Lion) and southern (Algerian Sea) sections of the Western Basin’s cyclonic [52,53,54].
The general surface circulation of the Balearic Sea is mainly controlled by two permanent density fronts: the Catalan front, over the Iberian Peninsula slope, and the Balearic front, located over the insular slope (Figure 1b). The Catalan Front is a shelf/slope front that separates old Atlantic Water (AW) in the central Balearic subbasin from the less dense water transported by the Northern Current (NC), which, while also consisting of old AW, is influenced by the influx of continental freshwater from the Gulf of Lion and the Catalan shelves. The NC is a density-driven coastal current that flows southwestward from the Ligurian Sea into the Balearic Sea, where it either exits through the Ibiza Channel or retroflects cyclonically over the insular slope, forming the Balearic Current (BC). This current flows southward along the continental slope and is further fed by warm, fresh, recently modified AW from the Algerian Basin, entering through the Mallorca and Ibiza channels. The Balearic Front is a slope front formed by the recently modified AW entering the basin through the southern channels [55], separating older AW in the central basin from the less dense waters transported by the Balearic Current.
Beyond the general circulation at the basin scale, the Balearic subbasin is characterized by distinct frontal dynamics near the slope regions, particularly between the BC and the NC. These dynamics include the formation of mesoscale eddies [56,57,58], filaments, and changes in shelf-slope flows [55,59]. Such processes are known to influence not only local dynamics, leading to significant vertical motions [60], but also broader circulation patterns, as demonstrated by Pascual et al. [48] in their study of the blocking effect caused by a large anti-cyclonic eddy. Smaller-scale coastal eddies, such as the one analyzed in this study, often originate from instabilities in these frontal zones or interactions with complex bathymetry along the insular and continental slopes. These features are short-lived and spatially confined, yet they can play a disproportionate role in shaping local transport and mixing. Their detection and analysis require high-resolution observations, as their dynamics are often missed or smoothed out in conventional altimetric products.

3. Materials and Methods

3.1. Satellite Ocean Color

The approach of this study began with a qualitative analysis using ocean color data, particularly chlorophyll-a (Chla) concentration, to observe the presence of eddies. This analysis was based on a multi-modal satellite dataset from the Copernicus Marine Environment Monitoring Service (CMEMS, marine.copernicus.eu), specifically the Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations product (OCEANCOLOUR_MED_BGC_L3_MY_009_143), which provides biogeochemical data at a Level 3 (L3) processing level [60]. The dataset, characterized by daily images at a 1 km spatial resolution, integrates observations from several widely used satellites, including Sea-Viewing Wide Field-of-View Sensor (SeaWiFS); Moderate-Resolution Imaging Spectroradiometer (MODIS); Medium-Resolution Imaging Spectrometer (MERIS); Visible Infrared Imaging Radiometer Suite–Suomi National Polar-orbiting Partnership (VIIRS-SNPP); Joint Polar Satellite System-1 (JPSS1); and Ocean and Land Colour Instrument—Sentinel-3A and Sentinel-3B (OLCI-S3A and S3B). The product is available through DOI: https://doi.org/10.48670/moi-00299.

3.2. Satellite Altimetry

Satellite altimetry data were used to derive the mean surface geostrophic circulation and analyze mesoscale features using a combination of conventional and SWOT-enhanced datasets. The study utilized multiple Level 3 and Level 4 products, including along-track and gridded datasets, encompassing near-real-time and experimental data incorporating SWOT’s Ka-band Radar Interferometer (KaRIn) wide-swath observations.
  • The conventional product used is the CMEMS product European Seas Gridded L4 Sea Surface Heights and Derived Variables NRT (SEALEVEL_EUR_PHY_L4_NRT_008_060, DOI: https://doi.org/10.48670/moi-00142), processed by the DUACS (Data Unification and Altimeter Combination System) multimission altimeter data processing system. This product provides daily gridded Level-4 (L4) Sea Level Anomaly (SLA) maps for European Seas at 1/8° × 1/8° spatial resolution, computed relative to a 20-year reference period (1993–2012). The processing methodology is based on Optimal Interpolation, integrating measurements from 10 altimeter missions (TOPEX/Poseidon, Jason series, ERS, ENVISAT, GFO, CryoSat-2, Saral/AltiKa, Haiyang-2A) as detailed in Pujol et al. [61].
  • The Experimental multimission gridded L4 sea level heights and velocities with SWOT is a gridded product derived from the along-track (or Level-3) SEA LEVEL products (DOI: doi.org/10.48670/moi-00147) provided by CMEMS for the satellites SARAL/AltiKa, CryoSat-2, HaiYang-2B, Jason-3, Copernicus Sentinel-3A & 3B, Sentinel-6A, SWOT nadir, and SWOT Level-3 KaRIn sea level products (DOI: https://doi.org/10.24400/527896/A01-2023.018). The product is processed by SSALTO/DUACS and distributed by AVISO (https://www.aviso.altimetry.fr, accessed on 20 November 2024), supported by CNES (version 1.0.0) (DOI: https://doi.org/10.24400/527896/A01-2024.007). This dataset has daily temporal resolution and a spatial resolution of 1/10° × 1/10°. It uses the MIOST (Multiscale Interpolation Ocean Science Topography) approach [62,63], which models various modes of ocean surface topography variability to improve the representation of mesoscale ocean variability [64].
  • Additionally, the study used European Seas Along Track L3 Sea Surface Heights Reprocessed 1993–Ongoing Tailored For Data Assimilation (SEALEVEL_EUR_PHY_L3_MY_008_061, DOI: https://doi.org/10.48670/moi-00139), processed by the DUACS multimission altimeter data processing system. This product provides along-track, Level-3 (L3) sea surface height (SSH) observations reprocessed for consistency and optimized for data assimilation applications. It processes data from all altimeter missions available (e.g., TOPEX/Poseidon, Jason-1/2/3, Sentinel-3A/B, HaiYang-2A/B, etc.) and covers the European Seas from 1993 to the present. This dataset includes SWOT-nadir Calibration/Validation (CalVal) data used to validate SWOT KaRIn measurements and support mesoscale structure interpretation.
  • Finally, the SWOT Expert Level 3 Low-Rate Sea Surface Height (L3_LR_SSH_Expert) product (DOI: https://doi.org/10.24400/527896/A01-2023.018) is a gridded ocean topography dataset derived from Level-2 KaRIn and nadir altimetry observations collected by the SWOT satellite. This expert-level product includes a variety of key variables for oceanographic and geodetic research, such as Sea Surface Height Anomaly (SSHA), Mean Dynamic Topography (MDT), geostrophic currents (both absolute and anomalies), backscatter coefficient (sigma0), and the Mean Sea Surface (MSS). Additionally, it integrates quality flags, altimetric corrections, and external model outputs as separate layers, ensuring data accuracy and flexibility. The data are mapped onto a regular grid with a spatial resolution of approximately 0.05° (~5 km), covering the KaRIn swath. This product is derived from the L2 SWOT KaRIn low-rate ocean data products (NASA/JPL and CNES) and is produced and made freely available by the AVISO and DUACS teams as part of the DESMOS Science Team project. It is designed to offer high-resolution global ocean surface topography measurements, which are particularly useful for studying mesoscale ocean variability.
For clarity and consistency throughout this study, the four satellite altimetry datasets will be referred to as follows: DUACS CMEMS for the near-real-time gridded L4 product (SEALEVEL_EUR_PHY_L4_NRT_008_060); MIOST AVISO+SWOT for the experimental gridded L4 product incorporating SWOT KaRIn data; Along-Track SWOT L3 for the reprocessed along-track L3 product; and SWOT L3 for the SWOT Level 3 Ocean Products. For a detailed summary of the datasets used in this study, see Table 1.

3.3. Altimetric Data Coverage and Uncertainty

To better illustrate the spatial coverage and quality of the altimetric data used in this study, Figure 2 provides an overview of the observations available in the Balearic Sea during the study period (23–26 June 2023). The first panel (Figure 2a) shows the distribution of altimetric tracks from the four satellites active in the region at that time—SWOT, HaiYang-2B, Sentinel-3B, and Sentinel-6A. The color of each point represents the Sea Level Anomaly (SLA) value recorded along the satellite ground tracks. This visualization highlights the dense spatial sampling achieved during the observation window, particularly due to the contribution of SWOT’s wide-swath coverage. Notably, the presence of SWOT enhances the resolution near coastal areas, where traditional altimetry often struggles with sparse sampling and land contamination.
The observations from these satellites directly contribute to the gridded SLA field from the DUACS CMEMS product shown in the second panel (Figure 2b), which represents the midpoint of the analysis period (25 June 2023). This multi-mission product combines data from all available satellite tracks to generate a continuous representation of sea level anomalies. While the overall circulation patterns are coherent, the product offers a relatively smooth representation of mesoscale features. Fine-scale gradients and eddy structures, particularly in the vicinity of the coast, appear less distinct compared to the along-track data, suggesting some limitations in the gridded product’s ability to resolve submesoscale variability. The third panel (Figure 2c) displays the corresponding gridded error field for the DUACS CMEMS product. Here, increased uncertainty is evident along coastal zones and dynamically active regions, confirming the known limitations of conventional altimetry in areas with complex topography and proximity to land. These spatial patterns of observational uncertainty further reinforce the need for higher-resolution observations, such as those provided by SWOT, in order to improve the detection and analysis of coastal eddy dynamics.

3.4. Lagrangian Simulator for Particle Tracking

Particle tracking was performed using OceanParcels (Probably A Really Computationally Efficient Lagrangian Simulator), a Python toolbox specifically designed for tracking particles using output from ocean general circulation models [65]. This tool can simulate the tracking of both passive particles, such as water and plastic, and active particles, like plankton and fish.
OceanParcels v3.1.1 [66] was employed to simulate surface particle trajectories in the northwestern Mediterranean Sea, between 2.0°E and 3.0°E and between 38.5°N and 39.7°N. Particles were released once on 23 June 2023 at 00:00 UTC and advected forward in time until 27 June 2023 at 00:00 UTC, for a total of 5 days.
Particles were initialized randomly across the sea surface, with 10 particles per sea-grid cell, resulting in a total of 10,180 particles. Sea-cells were defined using a custom sea-mask based on Absolute Dynamic Topography (ADT) to exclude land-influenced areas. The velocity field used in the simulation was derived from ADT altimetry data, and the simulation was restricted to the surface layer (2D simulation).
The simulation proceeded by using the advection-only method without incorporating any diffusion terms. Particles were advected with a timestep (Δt) of 1 min, meaning each particle’s position was updated every 60 s based on the velocity fields. The particles were advected according to the Eulerian velocity field, and their displacement was calculated using the fourth-order Runge–Kutta (RK4) method [67], which ensures accurate interpolation of the velocity fields and smooth particle tracking. The advection equation is defined as
x ( t + Δ t ) = x ( t ) + ∫ t t + Δ t v ( x ( τ ) , τ )   d τ
where x(t) represents the position of the particle at time t and v is the velocity field (u, v, w) interpolated from the ocean data. This equation is solved using the RK4 advection scheme as implemented in OceanParcels [66]. The simulation setup does not include explicit diffusion terms; thus, particle motion is solely governed by advection from the geostrophic velocity field derived from altimetry. However, subgrid-scale dispersion is implicitly represented through the particle release strategy by releasing 10 particles per grid cell. Particles reaching land were halted and remained stationary. It is important to note that OceanParcels was used primarily to illustrate the trapping behavior of particles within the mesoscale eddy. The simulations were limited to the surface layer (2D) and involved passive particles without vertical mixing, diffusion, or active behaviors. These simplifications may underestimate actual dispersion, particularly in coastal zones affected by turbulence and vertical processes. Moreover, the particle trajectories strongly depend on the quality of the underlying geostrophic velocity fields derived from ADT. For mesoscale eddies with Ro > 0.1, cyclostrophic corrections can reach 0.5 m/s and significantly improve velocity estimates [68], indicating a potential source of error in our trajectory simulation.
Non-geostrophic components such as wind-driven Ekman flows, tidal currents, and unbalanced motions (e.g., cyclostrophic flows or submesoscale dynamics) were not included due to data limitations. Such processes may influence near-surface transport, particularly in coastal and submesoscale regimes [69,70], and their exclusion represents a potential source of uncertainty in particle trajectory estimates [71].
While these processes may influence transport in the study region, especially near the coast or in shallow areas, their omission does not compromise the main objective of the Lagrangian analysis, which was to illustrate the eddy’s retention capability.

4. Results

4.1. Comparison Between Altimetric Products

Several eddies were observed in the Balearic Sea using Chla ocean color imagery from 23 to 27 June 2023. Further analyses were performed using two satellite altimetry products (DUACS CMEMS and MIOST AVISO+SWOT) to investigate a mesoscale anticyclonic coastal eddy located southwest of Mallorca Island. In this section, we compare the performance of both products in capturing fine-scale oceanic features by examining key dynamic parameters: Velocity Magnitude, Eddy Kinetic Energy (EKE), relative vorticity, and Absolute Dynamic Topography (ADT) gradient. This comparison assesses the capability of each product to represent mesoscale and submesoscale dynamics, with a particular focus on the observed coastal eddy. Although the full analysis spans five days (23–27 June), we present results for two representative dates (23 and 26 June) to highlight the eddy’s temporal evolution. In addition to differences in magnitude and resolution, differences in the direction of geostrophic velocity vectors are also evident between the two products, particularly at the eddy boundaries and in coastal regions, further supporting the added value of higher-resolution datasets. Chla imagery for the entire study period (Figure S1) is provided in the Supplementary Material.

4.1.1. Absolute Dynamic Topography (ADT)

The comparison between MIOST AVISO+SWOT and DUACS CMEMS data reveals significant differences in the geostrophic Velocity Magnitude pattern. Further comparison with chlorophyll-a concentrations from ocean color imagery (Figure 3a,b) shows spatial coherence between the presence of the eddy and surface chlorophyll distribution, supporting the hypothesis that MIOST AVISO+SWOT is more effective in detecting coastal eddies, where ocean dynamics interact with coastal topography. Specifically, MIOST AVISO+SWOT shows a higher presence of coastal eddies, likely due to its superior spatial resolution, which enables the detection of smaller-scale oceanic structures. This is particularly evident in a well-defined eddy near the southwestern coastal area of Mallorca, visible in the MIOST AVISO+SWOT product but almost absent in the DUACS CMEMS data (Figure 3c,d). In the area around the coastal eddy, the average geostrophic Velocity Magnitude from the MIOST AVISO+SWOT product is 10 cm/s, which is higher than the 6 cm/s recorded by DUACS CMEMS. This difference represents an increase of 4 cm/s, or approximately 72.4%.
This suggests that DUACS CMEMS, with its lower resolution, may not fully capture small-scale oceanic features or may have limited capability to detect fine-scale dynamic details. In terms of Velocity Magnitude, the MIOST AVISO+SWOT data exhibit more pronounced velocity gradients, indicating a higher sensitivity in detecting current variations. The DUACS CMEMS product, on the other hand, shows a less detailed velocity field, with weaker gradients and less-defined structures. While the main eddies are visible in DUACS CMEMS, their intensity and size appear less distinct. In contrast, MIOST AVISO+SWOT provides a more detailed representation, with well-defined contours of ADT anomalies associated with vortical structures and sharper velocity gradients. High-velocity areas (>0.3 m/s) are clearer and better localized in the MIOST AVISO+SWOT maps.
Temporal analysis of the coastal eddy identified in the MIOST AVISO+SWOT data shows spatial stability over several days, suggesting that it is a dynamically relevant structure. These results suggest that MIOST AVISO+SWOT excels in capturing small-scale oceanic variability, particularly in coastal regions. Complete daily maps for the entire study period (Figure S2) are provided in the Supplementary Material.
These spatial observations are further supported by the temporal evolution of ADT values within the eddy region, as shown in the time series plot in Figure 3e. Additional analysis of the mean ADT values within the red-boxed eddy region (shown in Figure 3a,b) highlights further discrepancies between the two altimetric products (Figure 3e).
Quantitatively, over the full month of June, the ADT values from the MIOST AVISO+SWOT product within the eddy region ranged from 0.03 to 0.13 cm, with a mean of 0.09 cm and a standard deviation of 0.03 cm. The maximum daily increase recorded was approximately 0.07 cm/day, indicating short-term dynamic variability consistent with the development and evolution of mesoscale features. In contrast, DUACS CMEMS ADT values remained lower and more stable, fluctuating between –0.01 and 0.04 cm, with a mean of 0.02 cm and a standard deviation of 0.02 cm. These numerical descriptors strengthen the visual evidence of enhanced detection capabilities in the MIOST AVISO+SWOT product for coastal and mesoscale dynamics. Between 23 June and 27 June, when the signature of an anticyclonic eddy was clearest, the MIOST AVISO+SWOT product showed the most pronounced ADT increase, reinforcing the spatial evidence of a well-defined eddy structure (Figure 3c,d). On the other hand, the DUACS CMEMS data maintained low and relatively flat ADT throughout this period, reflecting a limited capability to resolve finer eddy signals, likely due to its lower spatial resolution.

4.1.2. Eddy Kinetic Energy

The EKE maps (Figure 4) provide quantitative information on the dynamics of the currents in the study domain. The maps representing the distribution of EKE reveal significant differences in both intensity and spatial details. The comparative analysis shows that MIOST AVISO+SWOT offers higher spatial resolution compared to DUACS CMEMS, capturing with greater detail the energy structures associated with eddies, particularly along the southern coast, around ~2°E–39.5°N, and southwest of Mallorca, where the coastal eddy studied in this article is clearly visible. The coastal eddy visible southwest of Mallorca represents an interesting element for comparing the performance of the DUACS CMEMS and MIOST AVISO+SWOT products. This is especially relevant in the study area, where land proximity modulates eddy dynamics. As previously mentioned, coastal interactions play a crucial role in the observed differences between the two datasets, especially in regions where the proximity to the coastline affects the eddy’s structure and energy distribution. The land–sea interface can introduce additional complexities that are better captured by MIOST AVISO+SWOT, which has higher spatial resolution and is more sensitive to such local variations, while DUACS CMEMS, with its coarser resolution, tends to smooth out these effects. From the maps, it is evident that the eddy is detected differently by the two datasets. In the MIOST AVISO+SWOT product, the eddy is depicted with a well-defined spatial structure, characterized by sharp gradients and higher EKE values. Thanks to its higher resolution, MIOST AVISO+SWOT captures sub-mesoscale details, including spatial variations in energy and eddy asymmetry. In contrast, DUACS CMEMS presents a more diffuse representation of the eddy, with lower EKE values and less defined contours. This difference is likely due to a stronger spatial filtering effect in the DUACS CMEMS product, which tends to remove smaller spatial scales and, consequently, reduces sensitivity to sub-mesoscale dynamics and local variations near the coast. While DUACS CMEMS can capture the general location and stability of the eddy, it provides a less detailed view of the energy dynamics, which may be more suitable for large-scale studies. On the other hand, MIOST AVISO+SWOT is better suited for studies requiring the precise monitoring of local energy dynamics, such as those focusing on coastal interactions.
In the area southwest of Mallorca Island, around the coastal eddy, the average Eddy Kinetic Energy (EKE) from the MIOST AVISO+SWOT product is 0.87 cm2/s2, which is higher than the 0.75 cm2/s2 recorded by DUACS CMEMS. This difference corresponds to an increase of 0.12 cm2/s2, or approximately 16%. The enhanced spatial resolution of MIOST AVISO+SWOT allows for a more detailed representation of local energy structures, revealing stronger eddy dynamics. This finding further reinforces the conclusion that MIOST AVISO+SWOT is more sensitive to sub-mesoscale features compared to DUACS CMEMS. The improved detection of local variations in energy intensity in MIOST AVISO+SWOT is particularly valuable for studies focused on coastal interactions, where fine-scale dynamics are critical. Conversely, DUACS CMEMS provides a more generalized depiction of the eddy, with lower EKE values and less detailed spatial features. Complete daily maps for the entire study period (Figure S3) are available in the Supplementary Material.

4.1.3. Relative Vorticity

Relative vorticity is a key parameter for diagnosing the rotational properties of ocean flows and for identifying mesoscale and submesoscale structures such as eddies and filaments [1]. The comparison of relative vorticity fields from the two altimetry products (Figure 5) highlights substantial differences in the representation of ocean dynamics. The relative vorticity field from DUACS CMEMS exhibits a more homogeneous and less structured distribution, with smoother transitions between regions of positive and negative vorticity. In contrast, the MIOST AVISO+SWOT product captures greater spatial variability, with more defined vorticity structures and enhanced contrasts, and exhibits more pronounced extreme values of vorticity compared to DUACS CMEMS, indicating a greater sensitivity to local variations in the geostrophic velocity field. DUACS CMEMS, while effective at providing a more uniform vorticity field, is less detailed, with smoother transitions that limit its ability to resolve small-scale vorticity structures. This suggests that MIOST AVISO+SWOT is better suited for studies requiring high-resolution data to analyze fine-scale oceanic dynamics. Another notable difference is observed in coastal regions and around Mallorca Island. MIOST AVISO SWOT highlights more distinct and detailed vorticity structures, suggesting a superior capability in detecting mesoscale and sub-mesoscale phenomena, potentially related to current–topography interactions. Conversely, DUACS CMEMS presents more gradual transitions, offering a less detailed representation of these dynamic features. Complete daily maps for the entire study period (Figure S4) are provided in the Supplementary Material.

4.2. Comparison Between Gridded and Along-Track SWOT Data

Given the interesting performance of the MIOST AVISO+SWOT gridded (L4) product in resolving coastal structures, an additional comparison was conducted with the SWOT nadir CalVal along-track product (L3). This analysis aims to investigate the consistency between gridded and along-track datasets and to explore the potential of native-resolution SWOT observations in capturing fine-scale coastal dynamics.
The maps in Figure 6 illustrate two moments of the daily evolution of the ADT, derived from the MIOST AVISO+SWOT product, along with orthogonal geostrophic velocity fields obtained from the Along-Track L3 SWOT data over the period from 23 to 27 June 2023. The continuous background shading represents the gridded ADT contours, while the along-track data from the SWOT nadir CalVal product show both the along-track ADT anomalies and their associated orthogonal geostrophic velocities.
The data reveal the presence of a coastal anticyclonic eddy southwest of Mallorca, which was consistently identified throughout the observed period. In particular, the 27 June dataset exhibits the most pronounced and coherent eddy structure, with a positive ADT centered around 2.5°E and 39°N and a velocity pattern indicative of clockwise rotation, consistent with an anticyclonic eddy in the Northern Hemisphere. Earlier days show progressively weaker but coherent signals, indicating a developing or translating eddy. This visual comparison between gridded and along-track datasets allows for an evaluation of the consistency between the two data representations and highlights the capability of SWOT’s native-resolution observations to resolve sub-mesoscale features in coastal environments. The combined analysis underlines not only the high spatial detail provided by the along-track dataset but also its potential contribution to a more accurate characterization of coastal eddy dynamics and associated transport processes. Complete daily maps for the entire study period (Figure S5) are provided in the Supplementary Material.

4.3. Comparison Between SWOT L3 Product and Ocean Color Imagery

Figure 7 presents the Velocity Magnitude and Sea Surface Height Anomaly (SSHA) derived from the SWOT L3 product, used to assess the presence of the anticyclonic coastal eddy southwest of Mallorca. The analysis compares altimetric data with ocean color imagery. The analysis focuses on 23 and 26 June, with additional analyses for other days available in the Supplementary Material (see Figures S6–S8).
In the SSHA maps (Figure 7b), a well-defined positive anomaly appears near 2.5°E and 39°N, a typical signature of anticyclonic eddies. This anomaly is spatially consistent with the Velocity Magnitude patterns (Figure 7a), which exhibit a circular flow structure consistent with eddy dynamics.
Interestingly, the ocean color map (Figure 3a,b) reveals a localized patch of elevated chlorophyll-a concentration within the eddy core. While anticyclonic eddies are typically associated with downwelling and reduced surface productivity, enhanced chlorophyll concentrations can occasionally be observed in their center, particularly in coastal or shelf regions. This may result from eddy trapping of nutrient-rich coastal water, interactions with bathymetric features, or transient upwelling events [1,4].
In support of this, several physical–biogeochemical mechanisms have been proposed to explain such anomalies in productivity. One prominent process is eddy trapping, where the eddy isolates and retains coastal or shelf waters rich in nutrients and phytoplankton, allowing biological activity to persist or intensify over time [13,72]. Additionally, when eddies interact with complex bathymetry, they may induce localized upwelling—even in anticyclonic systems—by perturbing isopycnal surfaces and generating vertical motions [11,73]. Another possible factor is the presence of submesoscale frontal instabilities along the eddy periphery, which enhances vertical nutrient fluxes toward the interior, potentially sustaining elevated chlorophyll concentrations [12]. These mechanisms are particularly relevant in semi-enclosed coastal basins like the Balearic Sea, where lateral stirring, shelf–edge interactions, and water mass contrasts are intensified.
The spatial concurrence between chlorophyll enhancement, positive SSHA, and rotational velocity patterns strongly supports the presence of a mesoscale anticyclonic eddy. Despite being an experimental product, SWOT L3 demonstrates a notable capacity to resolve fine-scale coastal features, consistent with independent satellite ocean color observations. This alignment further supports the utility of SWOT L3 data in studying mesoscale and sub-mesoscale coastal processes.

4.4. Lagrangian Simulations and Eddy Dynamics

To assess the influence of altimetric resolution on coastal transport dynamics, Lagrangian simulations were performed using OceanParcels with two different geostrophic velocity fields: one derived from the ADT of the conventional DUACS CMEMS dataset and the other from the MIOST AVISO+SWOT product.
The simulation using DUACS CMEMS data (Figure 8) resulted in a less organized particle motion pattern. Although a general anticyclonic circulation was discernible, the eddy appeared more diffuse, with less-defined streamlines and weaker rotational trapping. The spatial distribution of beached particles (Figure 8c) was more scattered, with a broader and less coherent probability (Figure 8b) footprint along the Mallorca coast. The reduced resolution of the conventional altimetric product limited its ability to resolve the fine-scale eddy structure and its associated transport effects.
In contrast, in the MIOST AVISO+SWOT configuration (Figure 9), the particle trajectories clearly reflected the presence of a well-defined anticyclonic eddy southwest of Mallorca Island. Between 23 and 27 June 2023, the high-resolution ADT fields captured a compact, circular structure with tight streamlines and strong rotational flow. This coherent eddy structure effectively trapped particles within its core, indicating strong retention capabilities typical of mesoscale features. The beached particle probability map (Figure 9b) revealed concentrated stranding zones along the northern Mallorca coast, and particle landings were spatially clustered (Figure 9c), highlighting the improved predictability and spatial accuracy afforded by SWOT-derived fields.
The comparison between the two simulations underscores the critical influence of altimetric resolution on Lagrangian transport modeling in coastal settings, as evidenced by the distinct differences in transport behavior revealed by each dataset.
The analysis of particle travel distances from the Lagrangian simulations further highlights these differences quantitatively. The SWOT-enhanced product yields an average particle travel distance of 29.57 km, approximately 7.8% greater than the 27.44 km found in the DUACS simulation. Similarly, the maximum distance traveled is higher with SWOT data, reflecting the enhanced spatial resolution and ability to capture finer-scale ocean dynamics. These factors contribute to a more realistic representation of particle dispersion and transport pathways.
The inclusion of SWOT data significantly enhances the representation of mesoscale and sub-mesoscale features, which in turn affects the trapping efficiency and transport pathways of passive tracers. In coastal zones, where traditional altimetry suffers from lower accuracy due to land contamination [74,75] and sparse data coverage, the SWOT-enhanced product provides a more realistic dynamical background for particle tracking applications.
These results clearly demonstrate that the improved altimetric resolution provided by SWOT data leads to significantly different—and more realistic—Lagrangian transport patterns compared to conventional altimetric products. This improvement has critical implications for operational applications: the enhanced representation of mesoscale and sub-mesoscale eddy dynamics translates directly into more accurate predictions of particle pathways, trapping efficiency, and coastal deposition patterns. Consequently, Lagrangian transport modeling benefits greatly from SWOT-derived data, improving operational applications such as pollutant dispersion forecasting, biological connectivity studies, and search-and-rescue missions. In this sense, the differences in altimetric resolution are not merely academic but have a tangible impact on the quality and reliability of coastal transport predictions.

5. Discussion

A deeper understanding of eddy formation, evolution, and dissipation is essential for evaluating the ocean’s role in the Earth’s climate system. The analysis focuses on a 5-day period (23–27 June 2023) selected for optimal satellite data availability, ensuring continuous cloud-free coverage essential for integrating altimetric and ocean color imagery. While this timeframe limits the observation to the eddy’s short-term behavior, it enables a robust and consistent comparison between datasets.
This study highlights the critical role of high-resolution satellite altimetry in observing and understanding coastal mesoscale and sub-mesoscale ocean dynamics. By leveraging ocean color imagery and comparing two altimetric products—conventional DUACS CMEMS and SWOT-enhanced MIOST AVISO+SWOT—we identified and characterized a mesoscale anticyclonic eddy southwest of Mallorca Island. The SWOT-enhanced dataset consistently outperformed DUACS CMEMS in resolving small-scale features across multiple dynamic variables, including Velocity Magnitude, eddy kinetic energy, relative vorticity, and Absolute Dynamic Topography gradients.
The enhanced spatial resolution of MIOST AVISO+SWOT allowed for a more detailed and accurate depiction of eddy structures, especially near the coast, where conventional altimetry often suffers from reduced reliability. These findings were supported by ocean color imagery and further reinforced through the comparison with SWOT L3 data, which revealed a strong spatial coherence between physical and biogeochemical signals—such as chlorophyll-a concentrations—further supporting the presence and persistence of the identified eddy.
Lagrangian particle simulations highlighted the strong dependence of transport modeling outcomes on the accuracy of the underlying velocity fields derived from altimetry. The MIOST AVISO+SWOT-based simulations reproduced more coherent and physically consistent particle trajectories, with well-defined trapping within the eddy core and localized beaching patterns along the Mallorca coast. In contrast, simulations using DUACS CMEMS resulted in more dispersed and less predictable transport behavior, underscoring the limitations of lower-resolution products for modeling coastal dispersion processes.
The Lagrangian simulations conducted with OceanParcels served primarily to demonstrate the trapping of particles within the eddy structure. While effective in illustrating retention, these simulations were restricted to surface-layer passive advection without specific diffusion or vertical processes, which may lead to an underestimation of dispersion in complex coastal environments. Nonetheless, the modeled particle trajectories coherently reflect the altimetric fields and capture the main dynamical features of the eddy. Future studies should include vertical mixing, diffusion, and other dynamical processes, such as wave effects, to better represent full coastal transport mechanisms.
It is also important to acknowledge that particle trajectories are highly sensitive to the quality of the velocity fields, as these directly drive the Lagrangian simulations. In this study, the focus was on geostrophic currents and on assessing the improvements brought by SWOT and KaRIn data. Non-geostrophic components—such as wind-driven Ekman flows, tidal currents, and unbalanced motions (e.g., cyclostrophic flows or submesoscale dynamics)—were not included due to data limitations. While these processes may contribute to coastal transport in the study region, especially near the coast, their omission does not compromise the main objective of our Lagrangian analysis, which is to demonstrate the eddy’s capacity for particle retention during its mature stage. Future research should aim to incorporate these additional forcings to provide a more comprehensive representation of coastal transport and enhance predictive capabilities.
However, it is important to recognize the practical challenges associated with operationalizing SWOT data in near-real-time applications. These include limited data availability due to satellite revisit intervals and inherent data latency, the need for rigorous filtering and quality control to reduce noise and measurement errors, and seasonal variations that may influence observation frequency and reliability [36,43,76]. Overcoming these limitations is essential to fully leverage SWOT’s capabilities for timely coastal monitoring and management.
Furthermore, although this study focuses on the Balearic Sea, the advantages demonstrated here for high-resolution altimetry in resolving mesoscale and sub-mesoscale coastal processes have broader implications. These findings can inform coastal monitoring and management strategies globally, especially in regions with complex coastal dynamics where conventional altimetry faces limitations [51,76,77,78].
Future efforts integrating SWOT data with complementary observations and models are expected to enhance global coastal oceanographic applications [79,80,81].
Our findings confirm that SWOT-enhanced altimetry markedly improves the detection and characterization of coastal dynamics, offering clear benefits for operational applications such as pollutant dispersion modelling and marine spatial planning.
In summary, this study demonstrates that high-resolution altimetry, particularly from SWOT-enhanced products, significantly improves the detection and understanding of coastal mesoscale dynamics. While limitations remain due to modeling assumptions and data constraints, these results lay a robust foundation for advancing coastal oceanographic research and operational monitoring in complex coastal regions.
Future research should aim to extend this analysis to broader spatial and temporal domains, integrate additional observational datasets, and develop hybrid methodologies that combine high-resolution altimetry with ocean color and in situ measurements to advance coastal oceanography.

6. Conclusions

SWOT-enhanced altimetry markedly improves the detection and analysis of coastal dynamics. By enabling more accurate velocity fields and supporting the identification of physical–biogeochemical coupling, this approach proves to be a valuable asset for coastal oceanography. Our results underscore the importance of integrating next-generation satellite altimetry into both research and operational ocean monitoring frameworks. Specifically, SWOT data have great potential in several applications. One is oil spill modeling, where precise current information is crucial for predicting pollutant dispersion. Another is the monitoring of coastal storm surges and flooding, which benefits from better characterization of nearshore circulation and sea level changes. SWOT data can also support the management and conservation of marine protected areas by detecting environmental changes that affect biodiversity. Finally, they can improve the prediction of nutrient or contaminant dispersion, helping safeguard water quality and ecosystem health. These examples highlight how SWOT-based altimetry can enhance decision-making and environmental management in coastal regions.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/rs17152552/s1. Figure S1: Daily chlorophyll-a concentration derived from ocean color images from 23 June 2023 to 27 June 2023 in the Balearic Sea. The red rectangle highlights the mesoscale anticyclonic coastal eddy located southwest of Mallorca Island; Figure S2: Daily chlorophyll-a concentration derived from ocean color imagery from 23 June 2023 to 27 June 2023 in the Balearic Sea (left panels). ADT maps from the two altimetric products (right panels); Figure S3: Eddy Kinetic Energy (EKE) maps from 23 June 2023 to 27 June 2023; Figure S4: Relative Vorticity maps from 23 June 2023 to 27 June 2023; Figure S5: Daily evolution of the ADT, derived from the MIOST AVISO+SWOT product, along with orthogonal geostrophic velocity and ADT fields obtained from the Along-Track L3 SWOT data from June 23 to 27, 2023. A mesoscale anticyclonic eddy is consistently identified southwest of Mallorca Island, with a well-defined structure on 27 June (red rectangle); Figure S6: (a) Surface velocity magnitude [m/s] and (b) Sea Surface Height Anomaly (SSHA) [m] derived from the SWOT L3 product on 24 June 2023. (c) Chlorophyll-a concentration [log(mg/m3)] derived from ocean color imagery on 24 June 2023; Figure S7: (a) Surface velocity magnitude [m/s] and (b) Sea Surface Height Anomaly (SSHA) [m] derived from the SWOT L3 product on 25 June 2023. (c) Chlorophyll-a concentration [log(mg/m3)] derived from ocean color imagery on 25 June 2023; Figure S8: (a) Surface velocity magnitude [m/s] and (b) Sea Surface Height Anomaly (SSHA) [m] derived from the SWOT L3 product on 27 June 2023. (c) Chlorophyll-a concentration [log(mg/m3)] derived from ocean color imagery on 27 June 2023.

Author Contributions

L.F., V.C., A.P. and L.G.-N. conceptualized the study and designed the methodology. L.F. performed data curation and formal analysis. L.F. wrote the original draft of the manuscript. L.F., V.C., A.P., L.G.-N., Y.C. and G.A. contributed to the review and editing. A.P. supervised the project and acquired funding. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Laura Fortunato’s PhD grant in the “Environmental Phenomena and Risks” doctoral program at the University Parthenope of Naples, funded by the PNRR, Mission 4, Component 1, Investment 3.4 “Enhancement of educational services: from nurseries to universities” and Investment 4.1 “University teaching and advanced skills”, as well as “Extension of the number of PhD programs and innovative PhDs for public administration and cultural heritage.” This PhD grant was funded under Ministerial Decree No. 351/2022, CUP I61I22000080007. This study has received funding from the Spanish Ministry of Science, Innovation, and Universities, the Spanish Research Agency, the European Regional Development Fund (MCIN/AEI/10.13039/501100011033/FUE) under Grant PID2021-122417NB-I00 (FaSt-SWOT project). No APC funding was received.

Data Availability Statement

All datasets used in this study are publicly available from open-access satellite data repositories. Ocean color data were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS) under the product ID OCEANCOLOUR_MED_BGC_L3_MY_009_143, accessible at https://doi.org/10.48670/moi-00299. Conventional altimetry data were sourced from CMEMS, including the Level-4 gridded product SEALEVEL_EUR_PHY_L4_NRT_008_060 (https://doi.org/10.48670/moi-00142) and the along-track Level-3 product SEALEVEL_EUR_PHY_L3_MY_008_061 (https://doi.org/10.48670/moi-00139). SWOT-enhanced altimetry data, including the experimental L4 MIOST product and the Expert Level 3 Low-Rate Sea Surface Height dataset, were obtained from AVISO+ and are available via https://doi.org/10.24400/527896/A01-2024.007 and https://doi.org/10.24400/527896/A01-2023.018, respectively. All data are freely accessible and were used in compliance with the corresponding data usage licenses. The Lagrangian analyses were performed using the open-source OceanParcels framework (https://oceanparcels.org, accessed on 20 November 2024), version 3.1.1, which is available at https://github.com/OceanParcels/parcels (accessed on 20 November 2024) under the MIT license. Any custom scripts developed for preprocessing and analysis can be made available upon reasonable request to the corresponding author.

Acknowledgments

The authors acknowledge the internship opportunity of the first author at IMEDEA’s María de Maeztu Centre of Excellence (CEX2021-001198), where part of this study was conducted. The authors would like to thank the project “Un approccio multidisciplinare alla contaminazione da idrocarburi nei mitili allevati nel Golfo di Pozzuoli,” supported by the University of Naples Parthenope Local Research Project 2023, in which some of the authors are involved. Gratitude is also extended to the European Union—NextGenerationEU, National Recovery and Resilience Plan (PNRR), Mission 4, Component 2, Investment 1.4, “Strengthening research infrastructures and creation of national R&D sample centers on selected Key Enabling Technologies,” Code CN00000023—Sustainable Mobility Center (Centro Nazionale per la Mobilità Sostenibile—CNMS), Spoke 3 “Waterways” and Spoke 7 “CCAM, Connected Networks and Smart Infrastructure,” in which some of the authors also participate. The authors also acknowledge the Antarctic Circumpolar Current Eddies Survey and Simulations (ACCESS) project (PNRA 19_00032), part of the Italian National Antarctic Research Program (PNRA), with the authors’ involvement. V. Combes also acknowledges support from the Spanish Ramón y Cajal Program (RYC2020-029306-I) through Grant AEI/UIB—10.13039/501100011033. Additional support comes from the Copernicus Marine Service (Sea-Level Thematic Center 24251L02-COP-TAC SL-2100).

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Chelton, D.B.; Schlax, M.G.; Samelson, R.M. Global observations of nonlinear mesoscale eddies. Prog. Oceanogr. 2011, 91, 167–216. [Google Scholar] [CrossRef]
  2. Cotroneo, Y.; Budillon, G.; Fusco, G.; Spezie, G. Cold core eddies and fronts of the Antarctic Circumpolar Current south of New Zealand from in situ and satellite data. J. Geophys. Res. Ocean. 2013, 118, 2653–2666. [Google Scholar] [CrossRef]
  3. Dong, C.; McWilliams, J.C.; Liu, Y.; Chen, D. Global heat and salt transports by eddy movement. Nat. Commun. 2014, 5, 3294. [Google Scholar] [CrossRef] [PubMed]
  4. Gaube, P.; McGillicuddy, D.J., Jr.; Chelton, D.B.; Behrenfeld, M.J.; Strutton, P.G. Regional variations in the influence of mesoscale eddies on near-surface chlorophyll. J. Geophys. Res. Ocean. 2014, 119, 8195–8220. [Google Scholar] [CrossRef]
  5. Gaube, P.; McGillicuddy, D.J. The influence of Gulf Stream eddies and meanders on near-surface chlorophyll. Deep Sea Res. Part I Oceanogr. Res. Pap. 2017, 122, 1–16. [Google Scholar] [CrossRef]
  6. Kahru, M.; Mitchell, B.G.; Gille, S.T.; Hewes, C.D.; Holm-Hansen, O. Eddies enhance biological production in the Weddell-Scotia confluence of the Southern Ocean. Geophys. Res. Lett. 2007, 34, L14601. [Google Scholar] [CrossRef]
  7. Mahadevan, A.; Archer, D. Modeling the impact of fronts and mesoscale circulation on the nutrient supply and biogeochemistry of the upper ocean. J. Geophys. Res. 2000, 105, 1209–1225. [Google Scholar] [CrossRef]
  8. Nagai, T.; Gruber, N.; Frenzel, H.; Lachkar, Z.; McWilliams, J.C.; Plattner, G.-K. Dominant role of eddies and filaments in the offshore transport of carbon and nutrients in the California Current System. J. Geophys. Res. Ocean. 2015, 120, 5318–5341. [Google Scholar] [CrossRef]
  9. Siegel, D.A.; Peterson, P.; McGillicuddy, D.J., Jr.; Maritorena, S.; Nelson, N.B. Bio-optical footprints created by mesoscale eddies in the Sargasso Sea. Geophys. Res. Lett. 2011, 38, L13605. [Google Scholar] [CrossRef]
  10. Wang, Y.; Zhang, H.-R.; Chai, F.; Yuan, Y. Impact of mesoscale eddies on chlorophyll variability off the coast of Chile. PLoS ONE 2018, 13, e0203598. [Google Scholar] [CrossRef] [PubMed]
  11. Zhang, Z.; Wang, W.; Qiu, B. Oceanic mass transport by mesoscale eddies. Science 2014, 345, 322–324. [Google Scholar] [CrossRef] [PubMed]
  12. Lévy, M. The modulation of biological production by oceanic mesoscale turbulence. In Transport in Geophysical Flow: Ten Years After; Weiss, J.B., Provenzale, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2008; Volume 744, pp. 219–261. [Google Scholar] [CrossRef]
  13. McGillicuddy, D.J., Jr.; Robinson, A.R.; Siegel, D.A.; Jannasch, H.W.; Johnson, R.; Dickey, T.D.; McNeil, J.; Michaels, A.F.; Knap, A.H. Influence of mesoscale eddies on new production in the Sargasso Sea. Nature 1998, 394, 263–265. [Google Scholar] [CrossRef]
  14. Volkov, D.L.; Lee, T.; Fu, L.-L. Eddy-induced meridional heat transport in the ocean. Geophys. Res. Lett. 2008, 35, L20601. [Google Scholar] [CrossRef]
  15. Bouffard, J.; Renault, L.; Ruiz, S.; Pascual, A.; Dufau, C.; Tintoré, J. Subsurface small-scale eddy dynamics from multi-sensor observations and modeling. Prog. Oceanogr. 2012, 106, 62–79. [Google Scholar] [CrossRef]
  16. Fu, L.L.; Chelton, D.B.; Le Traon, Y.; Morrow, R. Eddy dynamics from satellite altimetry. Oceanography 2010, 23, 14–25. Available online: http://www.jstor.org/stable/24860859 (accessed on 20 November 2024). [CrossRef]
  17. Fu, L.L.; Alsdorf, D.; Rodriguez, E.; Morrow, R.; Mognard, N.; Lambin, J.; Lafon, T. The SWOT (Surface Water and Ocean Topography) mission: Spaceborne radar interferometry for oceanographic and hydrological applications. In Proceedings of the OceanObs’09: Sustained Ocean Observations and Information for Society, Venice, Italy, 21–25 September 2009; Volume 9, pp. 21–25. [Google Scholar]
  18. Brach, L.; Deixonne, P.; Bernard, M.-F.; Durand, E.; Desjean, M.-C.; Perez, E.; Lambert, C.; Sire, O.; Baudrimont, M.; Gasperi, J.; et al. Mesoscale anticyclonic eddies increase accumulation of microplastic in the North Atlantic subtropical gyre. Mar. Pollut. Bull. 2018, 126, 191–196. [Google Scholar] [CrossRef] [PubMed]
  19. Condie, S.; Condie, R. Retention of plankton within ocean eddies. Glob. Ecol. Biogeogr. 2016, 25, 1274–1286. [Google Scholar] [CrossRef]
  20. Krom, M.D.; Brenner, S.; Kress, N.; Neori, A.; Gordon, L.I. Nutrient dynamics and new production in a warm-core eddy from the eastern Mediterranean Sea. Deep Sea Res. Part A Oceanogr. Res. Pap. 1992, 39, 467–480. [Google Scholar] [CrossRef]
  21. Salihoğlu, İ.; Saydam, C.; Başturk, O.; Yilmaz, K.; Gocmen, D.; Hatipoğlu, E.; Yilmaz, A. Transport and distribution of nutrients and chlorophyll-a by mesoscale eddies in the northeastern Mediterranean. Mar. Chem. 1990, 29, 375–390. [Google Scholar] [CrossRef]
  22. Lobel, P.S.; Robinson, A.R. Transport and entrapment of fish larvae by ocean mesoscale eddies and currents in Hawaiian waters. Deep Sea Res. Part A Oceanogr. Res. Pap. 1986, 33, 483–500. [Google Scholar] [CrossRef]
  23. Sabates, A.; Salat, J.; Palomera, I.; Emelianov, M.; Fernandez de Puelles, M.L.; Olivar, M.P. Advection of anchovy (Engraulis encrasicolus) larvae along the Catalan continental slope (NW Mediterranean). Fish. Oceanogr. 2007, 16, 130–141. [Google Scholar] [CrossRef]
  24. Sabates, A.; Salat, J.; Raya, V.; Emelianov, M. Role of mesoscale eddies in shaping the spatial distribution of the coexisting Engraulis encrasicolus and Sardinella aurita larvae in the northwestern Mediterranean. J. Mar. Syst. 2013, 111, 108–119. [Google Scholar] [CrossRef]
  25. Gaube, P.; Barceló, C.; McGillicuddy, D.J., Jr.; Domingo, A.; Miller, P.; Giffoni, B.; Marcovaldi, N.; Swimmer, Y. The use of mesoscale eddies by juvenile loggerhead sea turtles (Caretta caretta) in the southwestern Atlantic. PLoS ONE 2017, 12, e0172839. [Google Scholar] [CrossRef] [PubMed]
  26. Gaube, P.; Braun, C.D.; Lawson, G.L.; McGillicuddy, D.J., Jr.; Penna, A.D.; Skomal, G.B.; Fischer, C.; Thorrold, S.R. Mesoscale eddies influence the movements of mature female white sharks in the Gulf Stream and Sargasso Sea. Sci. Rep. 2018, 8, 7363. [Google Scholar] [CrossRef] [PubMed]
  27. Roda, S.B.; Rotger, A.; Santangeli, A.; Tavecchia, G.; Sola, A.; Catchot, S.; Sanz-Aguilar, A. Coping with ocean dynamics: Foraging strategy and implications for conservation of a small petrel. Biol. Conserv. 2025, 302, 110913. [Google Scholar] [CrossRef]
  28. Sánchez-Román, A.; Gómez-Navarro, L.; Fablet, R.; Oro, D.; Mason, E.; Arcos, J.M.; Ruiz, S.; Pascual, A. Rafting behaviour of seabirds as a proxy to describe surface ocean currents in the Balearic Sea. Sci. Rep. 2019, 9, 17775. [Google Scholar] [CrossRef] [PubMed]
  29. Gomez-Navarro, L.; Sánchez-Román, A.; Pascual, A.; Fablet, R.; Hernandez-Carrasco, I.; Mason, E.; Oro, D. Seabird drift as a proxy to estimate surface currents in the western Mediterranean? In EGU General Assembly Conference Abstracts; European Geosciences Union: Vienna, Austria, 2017; p. 9548. Available online: https://www.researchgate.net/publication/318211642_Seabird_drift_as_a_proxy_to_estimate_surface_currents_in_the_western_Mediterranean (accessed on 20 November 2024).
  30. Morrow, R.; Le Traon, P.-Y. Recent advances in observing mesoscale ocean dynamics with satellite altimetry. J. Adv. Space Res. 2011, 47, 1067–1082. [Google Scholar] [CrossRef]
  31. Lehahn, Y.; d’Ovidio, F.; Levy, M.; Heifetz, E. Stirring of the Northeast Atlantic spring bloom: A Lagrangian analysis based on multisatellite data. J. Geophys. Res. 2007, 112, C08005. [Google Scholar] [CrossRef]
  32. Ducet, N.; Le Traon, P.Y.; Reverdin, G. Global high-resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and -2. J. Geophys. Res. Ocean. 2000, 105, 19477–19498. [Google Scholar] [CrossRef]
  33. Dussurget, R.; Birol, F.; Morrow, R.A.; De Mey, P. Fine resolution altimetry data for a regional application in the Bay of Biscay. Mar. Geod. 2011, 34, 447–476. [Google Scholar] [CrossRef]
  34. Pascual, A.; Pujol, M.I.; Larnicol, G.; Le Traon, P.Y.; Rio, M.H. Mesoscale mapping capabilities of multisatellite altimeter missions: First results with real data in the Mediterranean Sea. J. Mar. Syst. 2007, 65, 190–211. [Google Scholar] [CrossRef]
  35. Pascual, A.; Faugère, Y.; Larnicol, G.; Le Traon, P.Y. Improved description of the ocean mesoscale variability by combining four satellite altimeters. Geophys. Res. Lett. 2006, 33. [Google Scholar] [CrossRef]
  36. Morrow, R.; Fu, L.-L.; Ardhuin, F.; Benkiran, M.; Chapron, B.; Cosme, E.; D’Ovidio, F.; Farrar, J.T.; Gille, S.T.; Lapeyre, G.; et al. Global observations of fine-scale ocean surface topography with the surface water and ocean topography (SWOT) mission. Front. Mar. Sci. 2019, 6, 232. [Google Scholar] [CrossRef]
  37. Fu, L.L.; Pavelsky, T.; Cretaux, J.F.; Morrow, R.; Farrar, J.T.; Vaze, P.; Dibarboure, G. The surface water and ocean topography mission: A breakthrough in radar remote sensing of the ocean and land surface water. Geophys. Res. Lett. 2024, 51, e2023GL107652. [Google Scholar] [CrossRef]
  38. Verger Miralles, E.; Mourre, B.; Barceló Llull, B.; Casas Pérez, B.; Combes, V.; Cutolo, E.; Pascual Ascaso, A. Study of fine-scale dynamics in the Balearic Sea through high-resolution observations and SWOT satellite data. Instrum. Viewp. 2024, 23, 21–22. Available online: https://hdl.handle.net/2117/411406 (accessed on 20 November 2024).
  39. Mourre, B.; Verger-Miralles, E.; Gómez-Navarro, L.; Barceló-Llull, B.; Casas, B.; Combes, V.; Cutolo, E.; Diaz-Barroso, L.; Garcia-Jove, M.; Lizarrán, I.; et al. FaSt-SWOT field campaigns combining high-resolution observations and modelling for SWOT validation in the Western Mediterranean Sea. In EGU General Assembly Conference Abstracts; European Geosciences Union: Vienna, Austria, 2024; p. 11336. [Google Scholar] [CrossRef]
  40. Pascual, A.; Mourre, B.; Balaguer, P.; Barceló-Llull, B.; Calafat, N.; Casas, B.; Combes, V.; Cutolo, E.; Díaz-Barroso, L.; Fernández, J.G.; et al. Fine-Scale Ocean Currents from Integrated Multi-Platform Experiments and Numerical Simulations: Contribution to the New SWOT Satellite Mission (FaSt-SWOT, PID2021-122417NB-I00); Cruise Plan; IMEDEA (CSIC-UIB): Esporles, Spain, 2023. [Google Scholar]
  41. Gómez-Navarro, L.; Cosme, E.; Le Sommer, J.; Papadakis, N.; Pascual, A. Development of an image de-noising method in preparation for the surface water and ocean topography satellite mission. Remote Sens. 2020, 12, 734. [Google Scholar] [CrossRef]
  42. Gómez-Navarro, L.; Fablet, R.; Mason, E.; Pascual, A.; Mourre, B.; Cosme, E.; Le Sommer, J. SWOT spatial scales in the western Mediterranean Sea derived from pseudo-observations and an Ad Hoc filtering. Remote Sens. 2018, 10, 599. [Google Scholar] [CrossRef]
  43. Aguiar, E.; Mourre, B.; Alvera-Azcárate, A.; Pascual, A.; Mason, E.; Tintoré, J. Strong long-lived anticyclonic mesoscale eddies in the Balearic Sea: Formation, intensification, and thermal impact. J. Geophys. Res. Ocean. 2022, 127, e2021JC017589. [Google Scholar] [CrossRef]
  44. Pessini, F.; Cotroneo, Y.; Olita, A.; Sorgente, R.; Ribotti, A.; Jendersie, S.; Perilli, A. Life history of an anticyclonic eddy in the Algerian basin from altimetry data, tracking algorithm and in situ observations. J. Mar. Syst. 2020, 207, 103346. [Google Scholar] [CrossRef]
  45. Cotroneo, Y.; Aulicino, G.; Ruiz, S.; Sánchez Román, A.; Torner Tomàs, M.; Pascual, A.; Budillon, G. Glider data collected during the Algerian basin circulation unmanned survey. Earth Syst. Sci. Data 2019, 11, 147–161. [Google Scholar] [CrossRef]
  46. Cotroneo, Y.; Aulicino, G.; Ruiz, S.; Pascual, A.; Budillon, G.; Fusco, G.; Tintoré, J. Glider and satellite high resolution monitoring of a mesoscale eddy in the Algerian basin: Effects on the mixed layer depth and biochemistry. J. Mar. Syst. 2016, 162, 73–88. [Google Scholar] [CrossRef]
  47. Aulicino, G.; Cotroneo, Y.; Ruiz, S.; Román, A.S.; Pascual, A.; Fusco, G.; Tamburini, A.; Budillon, G. Glider, satellite altimetry and numerical simulations for the monitoring of the Algerian Basin along a SARAL/AltiKa track. J. Mar. Syst. 2018, 179, 55–71. [Google Scholar] [CrossRef]
  48. Pascual, A.; Buongiorno Nardelli, B.; Larnicol, G.; Emelianov, M.; Gomis, D. A case of an intense anticyclonic eddy in the Balearic Sea (western Mediterranean). J. Geophys. Res. 2002, 107, 3183. [Google Scholar] [CrossRef]
  49. Bouffard, J.; Pascual, A.; Ruiz, S.; Faugère, Y.; Tintoré, J. Coastal and mesoscale dynamics characterization in the Balearic Sea. J. Geophys. Res. 2010, 115, C10029. [Google Scholar] [CrossRef]
  50. Malanotte-Rizzoli, P.; Artale, V.; Borzelli-Eusebi, G.L.; Brenner, S.; Crise, A.; Gacic, M.; Kress, N.; Marullo, S.; Ribera d’alcalà, M.; Sofianos, S.; et al. Physical forcing and physical/biochemical variability of the Mediterranean Sea: A review of unresolved issues and directions for future research. Ocean Sci. 2014, 10, 281–322. [Google Scholar] [CrossRef]
  51. Millot, C. Circulation in the Western Mediterranean Sea. J. Mar. Syst. 1999, 20, 423–442. [Google Scholar] [CrossRef]
  52. Millot, C.; Taupier-Letage, I. Circulation in the Mediterranean Sea. In The Mediterranean Sea. Handbook of Environmental Chemistry; Saliot, A., Ed.; Springer: Berlin/Heidelberg, Germany, 2005; Volume 5K, pp. 29–66. [Google Scholar] [CrossRef]
  53. López García, M.J.; Millot, C.; Font, J.; García-Ladona, E. Surface circulation variability in the Balearic Basin. J. Geophys. Res. 1994, 99, 3285–3296. [Google Scholar] [CrossRef]
  54. La Violette, P.E.; Tintoré, J.; Font, J. The surface circulation of the Balearic Sea. J. Geophys. Res. 1990, 95, 1559–1568. [Google Scholar] [CrossRef]
  55. Wang, F.; Vieira, M.; Salat, J.; Tintoré, J.; La Violette, P.E. A shelf/slope filament off the Northeast Spanish Coast. J. Mar. Res. 1988, 46, 321–332. Available online: https://elischolar.library.yale.edu/journal_of_marine_research/1891 (accessed on 20 November 2024). [CrossRef]
  56. Pinot, J.M.; López-Jurado, J.L.; Riera, M. The CANALES experiment (1996–1998). Interannual, seasonal, and mesoscale variability of the circulation in the Balearic Channels. Prog. Oceanogr. 2002, 55, 335–370. [Google Scholar] [CrossRef]
  57. Tintoré, J.; Wang, D.P.; La Violette, P.E. Eddies and thermohaline intrusions of the shelf/slope front off Northeast Spain. J. Geophys. Res. 1990, 95, 1627–1633. [Google Scholar] [CrossRef]
  58. Pascual, A.; Gomis, D.; Haney, R.L.; Ruiz, S. A quasigeostrophic analysis of a meander in the Palamós canyon: Vertical velocity, geopotential tendency and a relocation technique. J. Phys. Oceanogr. 2004, 34, 2274–2287. [Google Scholar] [CrossRef]
  59. Rubio, A.; Barnier, B.; Jordà, G.; Espino, M.; Marsaleix, P. Origin and dynamics of mesoscale eddies in the Catalan Sea (NW Mediterranean): Insight from a numerical model study. J. Geophys. Res. 2009, 114, C06009. [Google Scholar] [CrossRef]
  60. Volpe, G.; Colella, S.; Brando, V.E.; Forneris, V.; Padula, F.L.; Cicco, A.D.; Santoleri, R. Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Sci. 2019, 15, 127–146. [Google Scholar] [CrossRef]
  61. Pujol, M.-I.; Faugère, Y.; Taburet, G.; Dupuy, S.; Pelloquin, C.; Ablain, M.; Picot, N. DUACS DT2014: The new multi-mission altimeter data set reprocessed over 20 years. Ocean Sci. 2016, 12, 1067–1090. [Google Scholar] [CrossRef]
  62. Ubelmann, C.; Dibarboure, G.; Gaultier, L.; Ponte, A.; Ardhuin, F.; Ballarotta, M.; Faugère, Y. Reconstructing ocean surface current combining altimetry and future spaceborne Doppler data. J. Geophys. Res. Ocean. 2021, 126, e2020JC016560. [Google Scholar] [CrossRef]
  63. Ubelmann, C.; Carrere, L.; Durand, C.; Dibarboure, G.; Faugère, Y.; Ballarotta, M.; Briol, F.; Lyard, F. Simultaneous estimation of ocean mesoscale and coherent internal tide sea surface height signatures from the global altimetry record. Ocean Sci. 2022, 18, 469–481. [Google Scholar] [CrossRef]
  64. Ballarotta, M.; Ubelmann, C.; Bellemin-Laponnaz, V.; Le Guillou, F.; Meda, G.; Anadon, C.; Dibarboure, G. Assimilation of wide-swath altimetry in level-4 multi-mission satellite maps. Ocean Sci. 2025, 21, 63–80. [Google Scholar] [CrossRef]
  65. OceanParcels Project. What Is Oceanparcels? Available online: http://oceanparcels.org/ (accessed on 16 October 2019).
  66. Delandmeter, P.; van Sebille, E. The Parcels v2.0 Lagrangian framework: New field interpolation schemes. Geosci. Model Dev. 2019, 12, 3571–3584. [Google Scholar] [CrossRef]
  67. Butcher, J.C. Numerical Methods for Ordinary Differential Equations, 3rd ed.; John Wiley & Sons: Hoboken, NJ, USA, 2016. [Google Scholar] [CrossRef]
  68. Ioannou, A.; Zervakis, V.; Tzortziou, M. Cyclostrophic corrections of AVISO/DUACS surface velocities and its application to mesoscale eddies in the Mediterranean Sea. J. Geophys. Res. Ocean. 2019, 124, 8995–9012. [Google Scholar] [CrossRef]
  69. Lévy, M.; Klein, P.; Treguier, A.-M. Impact of sub-mesoscale physics on production and subduction of phytoplankton in an oligotrophic regime. J. Mar. Res. 2001, 59, 535–565. [Google Scholar] [CrossRef]
  70. Rossi, V.; Ser-Giacomi, E.; López, C.; Hernández-García, E. Hydrodynamic provinces and oceanic connectivity from a transport network help designing marine reserves. Geophys. Res. Lett. 2014, 41, 2883–2891. [Google Scholar] [CrossRef]
  71. Beron-Vera, F.J.; Wang, Y.; Olascoaga, M.J.; Goni, G.J.; Haller, G. Objective detection of oceanic eddies and the Agulhas leakage. J. Phys. Oceanogr. 2013, 43, 1426–1438. [Google Scholar] [CrossRef]
  72. McWilliams, J.C. Submesoscale currents in the ocean. Proc. R. Soc. A Math. Phys. Eng. Sci. 2016, 472, 20160117. [Google Scholar] [CrossRef] [PubMed]
  73. Mahadevan, A. The impact of submesoscale physics on primary productivity of plankton. Annu. Rev. Mar. Sci. 2016, 8, 161–184. [Google Scholar] [CrossRef] [PubMed]
  74. Vignudelli, S.; Kostianoy, A.G.; Cipollini, P.; Benveniste, J. (Eds.) Coastal Altimetry; Springer Science and Business Media: Berlin/Heidelberg, Germany, 2011; pp. 3–50. [Google Scholar] [CrossRef]
  75. Dibarboure, G.; Boy, F.; Desjonqueres, J.D.; Labroue, S.; Lasne, Y.; Picot, N.; Poisson, J.C.; Thibaut, P. Investigating short-wavelength correlated errors on low-resolution mode altimetry. J. Atmos. Ocean. Technol. 2014, 31, 1337–1362. [Google Scholar] [CrossRef]
  76. Barceló-Llull, B.; Pascual, A. Recommendations for the design of in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission. Front. Mar. Sci. 2023, 10, 1082978. [Google Scholar] [CrossRef]
  77. Tintoré, J.; Pinardi, N.; Álvarez-Fanjul, E.; Aguiar, E.; Álvarez-Berastegui, D.; Bajo, M.; Balbin, R.; Bozzano, R.; Nardelli, B.B.; Cardin, V.; et al. Challenges for sustained observing and forecasting systems in the Mediterranean Sea. Front. Mar. Sci. 2019, 6, 568. [Google Scholar] [CrossRef]
  78. Escudier, R.; Renault, L.; Pascual, A.; Brasseur, P.; Chelton, D.; Beuvier, J. Eddy properties in the Western Mediterranean Sea from satellite altimetry and a numerical simulation. J. Geophys. Res. Ocean. 2016, 121, 3990–4006. [Google Scholar] [CrossRef]
  79. Mason, E.; Pascual, A. Multiscale variability in the Balearic Sea: An altimetric perspective. J. Geophys. Res. Ocean. 2013, 118, 3007–3025. [Google Scholar] [CrossRef]
  80. Barceló-Llull, B.; Pascual, A.; Sánchez-Román, A.; Cutolo, E.; d’Ovidio, F.; Fifani, G.; Ser-Giacomi, E.; Ruiz, S.; Mason, E.; Cyr, F.; et al. Fine-scale ocean currents derived from in situ observations in anticipation of the upcoming SWOT altimetric mission. Front. Mar. Sci. 2021, 8, 679844. [Google Scholar] [CrossRef]
  81. De Mey-Frémaux, P.; Ayoub, N.; Barth, A.; Brewin, R.; Charria, G.; Campuzano, F.; Ciavatta, S.; Cirano, M.; Edwards, C.A.; Federico, I.; et al. Model-observations synergy in the coastal ocean. Front. Mar. Sci. 2019, 6, 436. [Google Scholar] [CrossRef]
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Figure 1. (a) Study area. (b) Main fronts and sea surface currents in the Balearic Sea. (c) Bathymetry of a zoomed-in area within the study region.
Figure 1. (a) Study area. (b) Main fronts and sea surface currents in the Balearic Sea. (c) Bathymetry of a zoomed-in area within the study region.
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Figure 2. (a) Sea Level Anomaly (SLA) from 4 nadir altimeter missions from 23 to 27 June. (b) Gridded SLA at ⅛° resolution from DUACS CMEMS product. (c) Gridded SLA error at ⅛° resolution from DUACS CMEMS product.
Figure 2. (a) Sea Level Anomaly (SLA) from 4 nadir altimeter missions from 23 to 27 June. (b) Gridded SLA at ⅛° resolution from DUACS CMEMS product. (c) Gridded SLA error at ⅛° resolution from DUACS CMEMS product.
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Figure 3. (a,b) Daily chlorophyll-a concentrations from ocean color imagery on 23 and 26 June 2023 in the Balearic Sea. (c,d) ADT fields from DUACS CMEMS and MIOST AVISO+SWOT altimetric products for the corresponding dates. (e) Time series of the mean ADT within the eddy region (red rectangle) comparing the two altimetric products. The MIOST AVISO+SWOT data show a marked increase in ADT values from 23 June to 27 June, consistent with the presence of an anticyclonic eddy, in contrast to the DUACS CMEMS product, which shows lower and more stable ADT values.
Figure 3. (a,b) Daily chlorophyll-a concentrations from ocean color imagery on 23 and 26 June 2023 in the Balearic Sea. (c,d) ADT fields from DUACS CMEMS and MIOST AVISO+SWOT altimetric products for the corresponding dates. (e) Time series of the mean ADT within the eddy region (red rectangle) comparing the two altimetric products. The MIOST AVISO+SWOT data show a marked increase in ADT values from 23 June to 27 June, consistent with the presence of an anticyclonic eddy, in contrast to the DUACS CMEMS product, which shows lower and more stable ADT values.
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Figure 4. Eddy Kinetic Energy (EKE) maps from 23 June 2023 to 27 June 2023 from DUACS CMEMS and MIOST AVISO+SWOT altimetric products. The white area near the coast corresponds to regions where data are unavailable due to the altimetric product’s lower spatial resolution in coastal zones.
Figure 4. Eddy Kinetic Energy (EKE) maps from 23 June 2023 to 27 June 2023 from DUACS CMEMS and MIOST AVISO+SWOT altimetric products. The white area near the coast corresponds to regions where data are unavailable due to the altimetric product’s lower spatial resolution in coastal zones.
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Figure 5. Relative Vorticity maps on 23 and 26 June 2023 from the DUACS CMEMS and MIOST AVISO+SWOT altimetric products. The white area near the coast corresponds to regions where data are unavailable due to the altimetric product’s lower spatial resolution in coastal zones.
Figure 5. Relative Vorticity maps on 23 and 26 June 2023 from the DUACS CMEMS and MIOST AVISO+SWOT altimetric products. The white area near the coast corresponds to regions where data are unavailable due to the altimetric product’s lower spatial resolution in coastal zones.
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Figure 6. Evolution of the ADT, derived from the MIOST AVISO+SWOT product, along with orthogonal geostrophic velocity (purple arrows) and ADT fields obtained from the Along-Track L3 SWOT data for 23 and 26 June 2023. A mesoscale anticyclonic eddy is consistently identified southwest of Mallorca Island, with a well-defined structure (red rectangle).
Figure 6. Evolution of the ADT, derived from the MIOST AVISO+SWOT product, along with orthogonal geostrophic velocity (purple arrows) and ADT fields obtained from the Along-Track L3 SWOT data for 23 and 26 June 2023. A mesoscale anticyclonic eddy is consistently identified southwest of Mallorca Island, with a well-defined structure (red rectangle).
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Figure 7. (a) Surface Velocity Magnitude [m/s] and (b) Sea Surface Height Anomaly (SSHA) [m] derived from the SWOT L3 product on 23 June 2023. (c) Surface Velocity Magnitude [m/s] and (d) Sea Surface Height Anomaly (SSHA) [m] derived from the SWOT L3 product on 26 June 2023.
Figure 7. (a) Surface Velocity Magnitude [m/s] and (b) Sea Surface Height Anomaly (SSHA) [m] derived from the SWOT L3 product on 23 June 2023. (c) Surface Velocity Magnitude [m/s] and (d) Sea Surface Height Anomaly (SSHA) [m] derived from the SWOT L3 product on 26 June 2023.
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Figure 8. (a) Particle simulation trajectory with ADT pattern from the DUACS CMEMS product; (b) probability of particle beaching; (c) position of beached particles.
Figure 8. (a) Particle simulation trajectory with ADT pattern from the DUACS CMEMS product; (b) probability of particle beaching; (c) position of beached particles.
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Figure 9. (a) Particle simulation trajectory with ADT pattern from the MIOST AVISO+SWOT product; (b) probability of particle beaching; (c) position of beached particles.
Figure 9. (a) Particle simulation trajectory with ADT pattern from the MIOST AVISO+SWOT product; (b) probability of particle beaching; (c) position of beached particles.
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Table 1. Summary of satellite altimetry datasets used in this study. See text for further details.
Table 1. Summary of satellite altimetry datasets used in this study. See text for further details.
AbbreviationFull Product NameProcessing LevelTemporal ResolutionSpatial ResolutionSource/Processor
DUACS CMEMSEuropean Seas Gridded L4 Sea Surface Heights and Derived Variables (NRT)L4Daily1/8° × 1/8°CMEMS/DUACS
MIOST AVISO+SWOTExperimental multimission gridded L4 sea level heights and velocities with SWOTL4Daily1/10° × 1/10°CMEMS, AVISO, SSALTO/DUACS, CNES
Along-Track SWOT L3European Seas Along Track L3 Sea Surface Heights Reprocessed—Tailored for Data AssimilationL3Along-track (varies by altimeter)Along-track (varies by altimeter)CMEMS, DUACS, AVISO, CNES
SWOT L3SWOT Level3 experimental productsL3Daily2 km × 2 km, covering the KaRIn swathAVISO, DUACS, NASA/JPL, CNES
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MDPI and ACS Style

Fortunato, L.; Gómez-Navarro, L.; Combes, V.; Cotroneo, Y.; Aulicino, G.; Pascual, A. Coastal Eddy Detection in the Balearic Sea: SWOT Capabilities. Remote Sens. 2025, 17, 2552. https://doi.org/10.3390/rs17152552

AMA Style

Fortunato L, Gómez-Navarro L, Combes V, Cotroneo Y, Aulicino G, Pascual A. Coastal Eddy Detection in the Balearic Sea: SWOT Capabilities. Remote Sensing. 2025; 17(15):2552. https://doi.org/10.3390/rs17152552

Chicago/Turabian Style

Fortunato, Laura, Laura Gómez-Navarro, Vincent Combes, Yuri Cotroneo, Giuseppe Aulicino, and Ananda Pascual. 2025. "Coastal Eddy Detection in the Balearic Sea: SWOT Capabilities" Remote Sensing 17, no. 15: 2552. https://doi.org/10.3390/rs17152552

APA Style

Fortunato, L., Gómez-Navarro, L., Combes, V., Cotroneo, Y., Aulicino, G., & Pascual, A. (2025). Coastal Eddy Detection in the Balearic Sea: SWOT Capabilities. Remote Sensing, 17(15), 2552. https://doi.org/10.3390/rs17152552

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