Océanopolis

Oceans remain severely under-sampled in aerosol optical properties, limiting our ability to assess marine aerosol–climate feedbacks, long-range transport and to validate satellite products over two-thirds of Earth’s surface. The Sun–sky–lunar photometer CIMEL CE318-T has been successfully adapted for autonomous shipborne operation through the PHOTONS Observation Service (ACTRIS, Univ. Lille, CNRS) in collaboration with CIMEL and within ACTRIS-CARS activities. The system is fully compatible with AERONET processing. Here we summarize recent developments and multi-year results supporting the deployment of a global network of automatic ship-based photometers.Since 2021, a CE318-T has operated continuously on the research vessel (R.V.) Marion Dufresne under the framework of MAP-IO program. Between July 2021 and June 2024, it collected >25,000 Level 1.5 measurements in the southwestern Indian Ocean, with mean AOD (Aerosol Optical Depth) of 0.09 ± 0.07 (440 nm) and 0.05 ± 0.03 (870 nm), and EAE (Extinction Angstrom Exponent) of 0.7 ± 0.4, typical of clean marine conditions. A biomass-burning aerosol (BBA) event allowed retrieval of microphysical properties (size distribution, refractive index), showing the capability of the system under real ship-motion.During TRANSAMA campaign (La Réunion–Barbados, Apr–May 2023), two CE318-T photometers combined with a micropulse lidar aboard R.V. Marion Dufresne provided complementary column and vertical information. Despite low AOD (0.08 ± 0.04 at 440 nm) in the remote South Atlantic, the lidar detected transported continental layers not evident from column-integrated data alone. Photometer intercomparison showed excellent agreement (R > 0.96; RMSE = 0.005–0.008). Motion-induced degradation on data quality highlighted the need for the ongoing instrumental tests, using a motion-simulation hexapode platform.A second permanent site aboard the R.V. Gaia Blu (CNR, Italy) has collected >20,000 measurements mainly in the Mediterranean since Feb 2024. Mean AOD (0.19 ± 0.14 at 440 nm) and EAE (1 ± 0.4) reflect more variable aerosol regimes, including BBA and Saharan dust. Comparisons with nearby AERONET ground-based stations validated retrieval quality. In addition, the installation of a 3D scanning lidar (Sep 2025) further enhances observations of aerosol vertical structure and air–sea interactions.These first results demonstrate the capability of ship-based photometers, and their synergy with lidar, to fill critical observational gaps over the oceans. Improved data acquisition strategies addressing vessel structure and motion are ongoing. To date, five ship-photometers operate across major maritime regions: R.V. Marion Dufresne-France (Indian Ocean), R.V. Gaia Blu-Italy (Mediterranean Sea), R.V. Sarmiento de Gamboa-Spain (Atlantic Ocean), R.V. Perseverance-France (currently Pacific Ocean), and MS Richard With-Norway (Arctic/Norwegian coast). These installations represent a major European step toward the first global network of automatic shipborne photometers to improve aerosol characterization over remote oceans and support satellite CAL/VAL.

Amongst the Thematic Assembly Centers (TACs) of the Copernicus Marine Service, the Wind TAC is in charge of providing L3 and L4 sea surface wind and wind stress observation products at a global and regional scale. From 2015, the Wind TAC has been successful in providing data coming from scatterometer observations both Near Real Time (NRT) and reprocessed (Multi Year, MY) data. Since the new phase of the project, starting in 2025, high-resolution (i.e. 1 km) data from C-band Synthetic Aperture Radar (SAR) Satellite have been included in the Wind TAC. As the Copernicus Marine Service evolution is mainly user-driven, we take this opportunity to announce that L3 daily products of Sentinel-1A winds are operationally processed and delivered in Near Real Time since June 2024. The associated Multi Year database of L3 daily products has also been uploaded to the Copernicus Marine Service and covers data from 2018 to 2025. To obtain wind data from SAR observations the ocean surface roughness measured by the satellite is transformed into wind speed and direction using an empirical inversion method called the "dual-pol" (Mouche et al. 2017, Mouche et al. 2019) and an a priori wind helps constrain the possible values. To this purpose, the SAR Wind Production Unit uses the L2 files produced by ESA's Mission Performance Center for Sentinel-1 (MPC-SAR) both for NRT and MY processing. Then, the only difference between the two processing chains (NRT and MY) lies in the a priori wind used to constrain the computed wind speed: while the NRT chain uses wind model from the European Center for Medium-range Weather and Forecasting (ECMWF) operational Integrated Forecast System (IFS), the MY chain uses wind model from ECMWF ReAnalysis v5 (ERA5). To quantify the impact of the input a priori wind, the NRT and MY data are compared over their common period. More generally, the SAR wind data from Wind TAC has been qualified through comparisons against Numerical Weather Forecast (NWP) models, in situ measurements from buoys and co-located measurements from scatterometer. The results of these comparisons are shown here, illustrating some limitations in the methodology and ways to overcome them. Overall, these global comparisons highlight the complementarity of high-resolution SAR wind data with other wind observations. In the near future, the integration (in 2026 and 2027 respectively) in the SAR Wind TAC of the recently launched Sentinel-1C and Sentinel-1D satellites will allow for better coverage, and the complementarity between observation should be further investigated through triple-collocation between SAR, scatterometer and buoy data.

Large cetaceans face several anthropogenic threats. Among these, collisions are a major cause of anthropogenic mortality. Assessing and limiting their impact on populations is essential, as these species play an essential ecological role. All types of vessels, including offshore racing vessels, can collide with cetaceans. When a collision occurs between an offshore racing vessel and a large cetacean, the consequences are severe for both the whale, which is often injured or even killed and the vessel, which can suffer severe damage and be forced to withdraw from the race. Our study aimed to develop an encounter model that takes the characteristics of both cetaceans and racing vessels into account to estimate the number of encounters along vessel routes. The model was applied to three different routes commonly used in offshore racing: the first between Newport, USA and Skagen, Denmark; the second between Dover, England and the Gibraltar Strait; and the third between the Gibraltar Strait and Genoa, Italy. The number of encounters was estimated to be 1.7 for Route 1, 4.1 for Route 2 and 2.6 for Route 3. The model was also used to estimate the impact of routing vessels away from any exclusion zones that may be established in areas of high cetacean abundance. This routing could significantly reduce the number of encounters and offer potential solutions to reduce collisions between cetaceans and all types of vessels. The issue of collisions is becoming increasingly important and requires the development of methods to reduce the number of collisions worldwide.

Abstract This paper presents the latest merchant ship concepts developed by the WHISPER Project Consortium under the Horizon Europe framework. The project is managed by Verkis and technically led by Stirling Design International (SDI), a naval architecture firm. It showcases innovative Panamax bulk carrier and container feeder designs, achieving up to 30% fuel consumption reduction through the integration of cutting-edge technologies, including OceanWings wingsails, Solbian solar panels, and Sidewind turbines. The paper provides a detailed analysis of the performance prediction, operational challenges, and associated risks. Mitigation strategies employed within the WHISPER Project will also be discussed. One key innovation is the integration of six tiltable OceanWings wingsails, each 363 m2, into a Panamax bulk carrier, based on insights from Ant Topic and Marfin Management ship owners. Additionally, the paper presents the integration of two OceanWings wingsails with elevator platform on a container feeder with an innovative hull design, highlighting results on ship commercial capacity and performance as informed by Samskip and Nav-Tech ship owners. Both designs developed by SDI are practical examples of how renewable wind and solar energy technologies can be effectively incorporated into the shipbuilding and ship repair sectors, offering substantial potential for sustainable maritime operations. Keywords wind propulsion; bulk carrier; wing sail; naval architecture

Understanding cetacean whistles is crucial for assessing their social interactions, behaviours, and responses to anthropic activities. Identifying the various types of whistles present in acoustic recordings is often challenging, but necessary for this purpose. To facilitate this process, we have developed a semi-automated deep learning approach called the “Draw Your Own Contours” (DYOC) method. This is available as an open-source software along with its associated dataset. It utilises YOLOv8m and ResNet18 to identify whistle contours. DYOC was applied to 808 minutes of audio recordings of wild, free-ranging short-beaked common dolphins from the Bay of Biscay, France. It enabled the annotation of 8,730 whistle contours, six times faster than manual annotation. These recordings were associated with observations of dolphin behaviour, the presence of fishing nets, and the activation of the DOLPHINFREE acoustic beacon. Analyses revealed that these variables affected the signal-to-noise ratio, the number of inflections, and the frequency and/or duration of recorded whistles. This study provides the first characterisation of whistle features for a population of short-beaked common dolphins in the Bay of Biscay. The annotation of whistle contours using the DYOC method helped reveal the complex acoustic behaviour of dolphins in response to external variables.

In recent years, new satellites and sensors have made it easier to observe various ocean properties and phenomena at different scales. For example, the Copernicus Space program includes several types of sensors that can monitor roughness, ocean color, temperature, and height from space. While each polar-orbiting sensor only revisits a given area occasionally, the constellation of satellite missions can observe the ocean more frequently. Other geostationary sensors, like the SEVIRI sensor, also contribute by providing hourly data on sea surface temperature, which helps capture changes even in cloudy areas. This data provides invaluable insights into ocean state, climatology and evolution. However, working with this diverse data is challenging. Accessing raw data, organizing it, and preparing it for analysis requires technical expertise, which can be a barrier for many potential users. To solve this, the Ocean Virtual Laboratory (OVL) team has developed open source tools to make ocean data more accessible and easy to analyze.Firstly, online data visualization websites, such as https://ovl.oceandatalab.com, have been made publicly accessible. These platforms empower users to explore various satellite, in-situ, and model data with just a few clicks. Users can navigate through time and space, easily compare hundreds of products (some in Near Real-Time), and utilize drawing and annotation features. The OVL web portal also embed tools like SEAShot to facilitate sharing interesting cases with fellow scientists and communicating about captivating oceanic structures.The second tool, SEAScope, is a freely available standalone application that provides additional data analysis capabilities. SEAScope, which works on Windows, Linux, and macOS, allows users to display data on a 3D globe and extract specific data for detailed analysis. Users can even link SEAScope with other applications, like Jupyter notebooks, to perform deeper data analysis and bring results back into SEAScope for visualization. Together, these tools make it easier for users to access, analyze, and share valuable ocean data. They are commonly used during at sea campaign, as it allows scientists to quickly and efficiently look at a variety of data in near time. This is particularly useful to make informed decisions to adapt cruise planning and compare the remote sensing measurements with in-situ observations in near real time .

The increasing adoption of AI-based approaches in Earth system sciences has led to breakthroughs in modeling and forecasting, exemplified by state-of-the-art performance of neural weather forecasting systems [Bi et al., 2023, Lam et al., 2022]. In oceanography, Deep Learning techniques show significant promise for advancing ocean state modeling by combining both modeled and observational ocean datasets [Febvre et al., 2023, Martin et al., 2023, Wang et al., 2024]. However, in the context of ocean forecasting, the deployment of neural forecasting approaches faces challenges such as sparse observational data and uncertainties in existing datasets. Despite advances in ocean observation systems, the ocean remains under-sampled, complicating the training of robust forecasting models [Wang et al., 2024].This study presents the application of the 4DVarNet framework [Fablet et al., 2021, Fablet et al., 2023] in forecast mode, specifically for 7-day sea surface height (SSH) prediction. 4DVarNet employs an end-to-end Deep Learning strategy to forecast future SSH state from sparse satellite observational data. Using a variational data assimilation formulation, the framework combines a UNet with a convolutional LSTM to iteratively reconstruct future ocean state. The model was trained on synthetic altimetry observations sampled from the GLORYS12 operational reanalysis (2010–2019) and evaluated on independent Nadir altimetry tracks from 2023.The results demonstrate that 4DVarNet outperforms traditional state-of-the-art operational forecasting system GLO12 [Lellouche et al., 2013], achieving a normalized RMSE (nRMSE) score of 0.92 for lead time 0 compared to 0.86 for the baseline GLO12. The model shows superior accuracy across all forecast lead times, highlighting its potential advantage in operational oceanography. The framework improves the accuracy of the SSH forecast by effectively using gappy satellite altimetry data. This demonstrates a particular interest of applying the proposed method to other data sources, such as the SWOT altimetry mission, but also of implementing alternative learning strategies, including training on synthetic datasets and fine-tuning with real-world altimetry observations.In addition to the improved predictive performance compared to the state-of-the-art operational forecast system, this study establishes a standardized workflow for data processing, training, and evaluation, inspired by OceanBench framework [Johnson et al., 2023].This research highlights the potential of the 4DVarNet framework for short-term neural ocean forecasting. The proposed method efficiently handles sparse altimetry data and achieves significant performance in predicting 7-days gap-free SSH state at global scale, improving the accuracy by almost 65% in average compared to the baseline GLO12. Future studies should focus on improving the model by incorporating additional data sources, evaluating the impact of input and output resolutions and associated learning strategies (eg. patching), and exploring its applicability to additional variables describing ocean state other than the SSH.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Marginal Ice Zone (MIZ) forms a critical transition region between the ocean and sea ice cover as it protects the close ice further in from the effect of the steepest and most energetic open ocean waves. As waves propagate through the MIZ, they get exponentially attenuated. Unfortunately, the associated attenuation coefficient is difficult to accurately estimate and model, and there are still large uncertainties around which attenuation mechanisms dominate depending on the conditions. This makes it challenging to predict waves in ice attenuation, as well as sea ice breakup and dynamics. Here, we report in-situ observations of strongly modulated waves-in-ice amplitude, with a modulation period of around 12 hours. We show that simple explanations, such as changes in the incoming open water waves, or the effect of tides and currents and bathymetry, cannot explain for the observed modulation. Therefore, the significant wave height modulation observed in the ice most likely comes from a modulation of the waves-in-ice attenuation coefficient. To explain this, we conjecture that one or several waves-in-ice attenuation mechanisms are periodically modulated and switched on and off in the area of interest. We gather evidence that sea ice convergence and divergence is likely the factor driving this change in the waves in ice attenuation mechanisms and attenuation coefficient, for example by modulating the intensity of floe-floe interaction mechanisms.

In the framework of the MAXSS project, a multi-mission (MM) wind product under tropical cyclone conditions has been generated for the period 2010-2020, i.e., a synergistic product that combines the European Center for Medium-range Weather Forecast fifth reanalysis (ERA5) output with several scatterometer and radiometer wind data adjusted to the wind scale of hurricane hunter in situ observations. The errors of the satellite and MM wind products have been estimated with triple collocation analysis, while the different spatial representation of the datasets (i.e., representativeness error r<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) is accounted for and computed through spatial variance analysis. The error analysis shows that C-band scatterometers have the lowest standard deviation errors (0.9 m/s) compared to those of the Ku-band scatterometers (1.4-2.1 m/s), while radiometers have the largest errors (2.0-2.9 m/s). Finally, the analysis reveals that the MM wind product has a lower error (1.6 m/s) compared to ERA5 (2.6 m/s) under tropical cyclone conditions.

Background: Medial meniscus ramp lesions, involving the posterior horn and its meniscocapsular attachments, occur in up to 40% of anterior cruciate ligament (ACL) reconstructions. While these injuries are associated with increased rotatory laxity and anterior tibial translation in ACL-deficient knees, their specific impact on meniscal kinematics and extrusion under axial loading remains insufficiently characterized. Objective: This cadaveric study aimed to quantify and compare medial meniscal mobility and extrusion between intact knees and those with grade 4 ramp lesions using 7-Tesla MRI and digital volume correlation (DVC) under varying axial loads. Key Points: Two cadaveric specimens were subjected to axial loads of 0N, 750N, and 1500N in full extension. Following the creation of a grade 4 ramp lesion via a posteromedial arthroscopic approach, DVC analysis of 33,619,968 data points demonstrated significant increases in meniscal displacement. At 1500N, the mean lateromedial displacement (extrusion) increased from 2.11–2.81 voxels in native knees to 4.48–4.71 voxels in injured knees. Similarly, mean anteroposterior displacement increased from 0.48–2.11 voxels to 3.29–7.31 voxels post-injury. These findings indicate that disruption of the meniscotibial and meniscocapsular attachments significantly compromises the stabilizing "hoop" function of the medial meniscus. Conclusion: Grade 4 medial meniscus ramp lesions significantly increase meniscal extrusion and posterior mobility during axial compression, even in ACL-intact states. Because meniscal extrusion is a known precursor to compartment hyper-pressure and cartilage degeneration, these results suggest that surgical repair of ramp lesions may be necessary to restore joint kinematics and prevent secondary osteoarthritis.