https://foccus-project.eu/
Rip currents are a leading cause of coastal drowning accidents worldwide, yet their detection remains challenging due to their significant spatial variability and intermittent nature. While traditional in-situ methods provide high-fidelity but localized insights, optical imagery is often constrained by specific illumination and weather windows. To extend monitoring capabilities across broader areas and diverse environmental conditions, shore-based microwave radar offers a robust alternative. This study investigates the detection and characterization of rip current signatures using time-series microwave radar imagery, focusing on the development of an automated operational technology. Radar imagery captures the observed area by recording variations in backscatter intensity, which are primarily driven by wave breaking and surface roughness. In X-band radar, small-scale surface scatterers, such as breaking gravity waves, facilitate Bragg scattering, which is significantly modulated by rip current dynamics in the surf and outer surf zones.
Our proposed framework adopts a two-stage approach. In the first stage, conventional image processing techniques, including temporal averaging and filtering, are employed to identify candidate rip current patterns from radar sequences. To enhance detection robustness and mitigate false alarms, the second stage introduces an artificial intelligence-based recognition model trained to discriminate rip current signatures from transient wave breaking and background noise. Comparative analyses demonstrate that this AI-assisted approach significantly improves detection consistency across varying sea states. By combining the physical interpretability of traditional image processing with the predictive power of AI, this framework enables near-real-time, continuous rip current monitoring. These results highlight the potential of intelligent microwave radar systems to support coastal safety applications, including early warning systems and real-time hazard mitigation.
How to cite: Wu, L.-C., Chuang, L. Z.-H., and Lai, J.-W.: Operational Detection of Rip Currents Using Shore-Based Microwave Radar Imagery and Artificial Intelligence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3287, https://doi.org/10.5194/egusphere-egu26-3287, 2026.
Seagrass meadows play an important role in coastal water quality by regulating nutrient availability, reducing eutrophication pressure and stabilizing sediments. Their decline in many European coastal zones has intensified interest in restoration as a nature-based measure. However, the quantitative influence of seagrass on seasonal nutrient dynamics at the scale of whole estuarine systems remains insufficiently understood. In the Wadden Sea, excessive nutrient load and turbidity are persistent challenges; seagrass restoration is increasingly seen as a nature-based solution for improving nutrient uptake and ecosystem health. This study provides a novel, spatially explicit assessment of seagrass impacts on nutrient cycling across
an entire annual cycle in two hydrodynamically contrasting regions of the southern North Sea. The study is based on a validated three-dimensional hydrodynamic–biogeochemical modelling framework that reproduces observed water levels, temperature, salinity, waves, and nutrient
concentrations across the study area. Paired simulations with and without seagrass were used to quantify changes in dissolved inorganic nitrogen (NO3, NH4), phosphate (PO4), and dissolved organic carbon (DOC). In the Jade Bay, DOC increases by approximately 100–170% across seasons, PO4 decreases by 24–34%, and summer NO3 is reduced by up to 70%. In the Weser Estuary, the strength of vegetation effects is constrained by high riverine inputs
and rapid flushing. Although dissolved organic carbon increases by up to 17% and phosphate decreases by 3–10%, nitrogen responses are smaller and are significantly influenced by river discharge and mixing. Overall, the results show that seagrass restoration can substantially modify local nutrient cycling, but that its effectiveness strongly depends on hydrodynamic conditions and external nutrient load. The study shows that restoration provides ecological benefits in semi-enclosed, moderately flushed systems like the Jade Bay, where biological processes can influence local water quality. In river-dominated estuaries, the effect of seagrass remains more limited because external inputs and rapid transport constrain its influence,
unless accompanied by broader catchment-scale measures. The results highlight the potential of seagrass as a targeted nature-based measure for enhancing local water quality in suitable coastal settings, rather than as a stand-alone remedy for eutrophication at the estuarine scale.
How to cite: Villa Castrillón, L., Jacob, B., Pein, J., Wang, Z., and Staneva, J.: Influence of seagrass restoration on nutrient cycling across contrasting estuarine systems in the southern North Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3817, https://doi.org/10.5194/egusphere-egu26-3817, 2026.
The improvement of remote sensing systems, together with the emergence of new model-fitting algorithms based on machine-learning techniques, has allowed the estimation of the partial pressure of carbon dioxide (pCO2,sw) and pH (pHT,sw) in the waters of the Canary Islands (13-19ºW; 27-30ºN). Continuous time series data from moored buoys and Voluntary Observing Ships (VOS) between 2019 and 2024 were used to train and validate the models, providing an observational foundation for the satellite-based estimations. Among all the fitted models, the most powerful one was the bootstrap aggregation (bagging), giving a RMSE of 2.0 µatm (R2 > 0.99) for pCO2,sw and RMSE of 0.002 for pHT,sw, although the multilinear regression (MLR), neural network (NN) and categorical boosting (catBoost) also have a good predictive performance, with RMSE ranging from 5.4 to 10 µatm for 360 < pCO2,sw < 481 µatm and from 0.004 and 0.008 for 7.97 < pHT,sw< 8.07. Using the most reliable model, it was determined that there is an interannual trend of 3.51 ± 0.31 µatm yr-1 for pCO2,sw (which surpasses the rate of increase for atmospheric CO2 of 2.3 µatm yr-1) and an increase in acidity of -0.003 ± 0.001 pH units yr-1. Over the 6 years (2019-2024), the rise in the atmospheric CO2 and the increase in sea surface temperature, which reached 0.2 ºC per year under the influence of the unprecedented 2023 marine head wave, contribute to this important rate. Considering the Canary Islands, the region has moved from a slight CO2 source of 0.90 Tg CO2 yr-1 in 2019 to 4.5 Tg CO2 yr-1 in 2024. After 2022, eastern locations that acted as an annual sink of CO2 switched to acting as a source.
How to cite: Gonzalez-Davila, M., Sánchez-Mendoza, I., González-Santana, D., Curbelo-Hernández, D., González-González, A., and Santana-Casiano, J. M.: From In Situ Observations to Satellites: Machine Learning–Based Modelling of Seawater pCO₂ and pH in the Canary Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4425, https://doi.org/10.5194/egusphere-egu26-4425, 2026.
Coastal and inland shallow-water environments are increasingly exposed to climate-change-related impacts such as sea-level rise, coastal erosion and ecosystem degradation. Reliable numerical hydrodynamic and morphological models are essential for assessing these impacts and supporting coastal adaptation strategies [1]. The performance of such models strongly depends on accurate bathymetric input data. Albeit providing a high accuracy, traditional shipborne acoustic surveys remain time-consuming, costly and operationally limited in shallow or hazardous environments, resulting in data gaps and infrequent recurring measurements [2,3].
Satellite-derived bathymetry (SDB) has therefore emerged as a cost-efficient and spatially continuous alternative for mapping optically shallow-waters [3]. Empirical and semi-empirical SDB approaches rely on statistical relationships between reflectance and depth, offering computational simplicity but limited transferability due to their dependence on site-specific calibration. In contrast, physics-based inversion models explicitly describe radiative transfer within the water column, accounting for wavelength-dependent light attenuation controlled by inherent optical properties of the water column. These approaches provide physically interpretable bathymetric retrievals that remain applicable across a range of optical water conditions, with to-be expected accuracies ranging from approximately 0.5 to 1.0 m RMSE for water depth up to 30 m [2,4].
This study implements and extends the physics-based inversion model described in [4] within an open-source Python framework for transparent and reproducible SDB and optical water quality retrieval from multispectral satellite data. The framework enables the simultaneous estimation of the physical water depth and potentially biologic parameters such as suspended matter concentration, chlorophyll-a concentration and colored dissolved organic matter absorption. Beyond the current state-of-the art, this study scrutinizes different implementation parameters to assess and improve computational stability and adaptability across varying optical environments, while maintaining a physically consistent radiative transfer formulation. The approach was validated at two optically contrasting sites: the semi-turbid Lake Constance (Untersee) in southern Germany and the clear-water One Tree Reef (Great Barrier Reef) in eastern Australia. Overall, this study demonstrates that the open-source development of a physics-based SDB approach can achieve competitive accuracy while remaining reproducible and adaptable, making a transferable, cost-efficient bathymetric mapping retrieval in operational shallow water monitoring available to a broader (scientific) audience.
[1] Pacheco, A., Horta, J., Loureiro, C., and Ferreira, (2015). Retrieval of nearshore bathymetry from landsat 8 images: A tool for coastal monitoring in shallow waters. Remote Sensing of Environment, 159:102–116. http://dx.doi.org/10.1016/j.rse.2014.12.004.
[2] Ashphaq, M., Srivastava, P. K., and Mitra, D. (2021). Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research. Journal of Ocean Engineering and Science, 6(4):340–359. https://doi.org/10.1016/j.joes.2021.02.006.
[3] Liu, Z., Liu, H., Ma, Y., Ma, X., Yang, J., Jiang, Y., and Li, S. (2024). Exploring the most efective information for satellite-derived bathymetry models in diferent water qualities. Remote Sensing, 16(13):2371. http://dx.doi.org/10.3390/rs16132371.
[4] Albert, A. (2004). Inversion technique for optical remote sensing in shallow water. PhD thesis, University of Hamburg. Retrieved from https://ediss.sub.uni-hamburg.de/handle/ediss/812.
How to cite: Klein, A. and David, C. G.: Physics-based satellite-derived bathymetry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7737, https://doi.org/10.5194/egusphere-egu26-7737, 2026.
GNSS technology offers the capabilities of global coverage and all-weather positioning and velocity measurement. Deploying modular, low-cost GNSS equipment on small buoys enables ocean environment monitoring, such as of waves and tides, by utilizing precise displacement and velocity information obtained from GNSS signals. Furthermore, networking multiple buoys can capture subtle changes during air-sea exchange processes and support marine target detection. However, the limited anti-interference capability of low-cost GNSS buoys under complex sea states results in reduced measurement accuracy, poor robustness, and constrained sensing dimensions, which severely restrict their operational deployment.
To address these issues, this work conducts an in-depth investigation into the application of low-cost GNSS buoys for robust multi-element marine environment sensing. The main contributions are as follows:
- A low-cost GNSS buoy measurement system is designed. A multi-antenna GNSS buoy platform(MGB) is developed along with three core modules for precise GNSS positioning, high-precision velocity measurement, and marine environmental sensing, providing a reliable foundation for algorithm development and field validation.
- A tide level measurement model based on a multi-antenna GNSS buoy is developed. To tackle the issues of gross errors in GNSS-derived tide level measurements and high-frequency oceanic noise disturbances, a noise-processing model integrating an attitude error correction model with a robust Vondrak filtering algorithm is established.
- A robust wave inversion model based on low-cost GNSS buoys is established. To reduce distortion in wave parameter estimation caused by abnormal GNSS velocity measurements, a comprehensive velocity determination method is proposed. A mapping model based on random wave theory is developed to transform GNSS velocity sequences into the wave spectrum, accompanied by a spectral moment parameter estimation model.
- A ship sensing model based on low-cost GNSS buoys is proposed. We exploit the observation information from GNSS buoys and employs wavelet analysis for time–frequency transformation to extract ship Kelvin wake signatures.
How to cite: Di, M., Ma, D., Guo, B., and Liu, H.: Low-Cost Multi-Antenna GNSS Buoys for Marine Environment Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1773, https://doi.org/10.5194/egusphere-egu26-1773, 2026.
Kuroshio flows eastward along the southern coast of Japan and has a variety of flow paths such as straight and large meander paths south of Japan. The Kuroshio path variations cause substantial damage to fisheries, marine transport, and marine environment (e.g., Nakata et al. 2000; Barreto et al. 2021). Consequently, Japanese research institutions have conducted Kuroshio path predictions using regional ocean data assimilation systems with the Kalman filter (Hirose et al. 2013) and the three- and four-dimensional variational methods (Miyazawa et al. 2017; Kuroda et al. 2017; Hirose et al. 2019). However, these systems are not designed for ensemble forecasts, and the predictions have been limited to deterministic ones so far.
We have developed a new local ensemble transform Kalman filter (LETKF)-based regional ocean data assimilation system (Ohishi et al. 2022a, b) and released ensemble ocean analysis datasets called the LETKF-based Ocean Research Analysis (LORA) for the western North Pacific and Maritime Continent regions (Ohishi et al. 2023, 2024a, b). The LORA datasets are shown to have sufficient accuracy for geoscience research, especially in mid-latitude regions (Ohishi et al. 2023), and we can perform both deterministic and ensemble forecasts initialized by the LORA. Therefore, this study aims to compare the predictability of the Kuroshio path south of Japan between deterministic and ensemble forecasts.
We performed 6-month deterministic and ensemble forecasts initialized on the first day of every month from January 2016 to December 2018 (36 cases in total) using the initial conditions of the analysis ensemble mean and 128 analysis ensembles from the LORA dataset, respectively. The results show that the predictability limits of the Kuroshio path are 74 and 108 days in the deterministic and ensemble forecasts, respectively, indicating a significantly longer predictability limit of the ensemble forecasts than the deterministic forecasts.
How to cite: Ohishi, S., Miyoshi, T., and Kachi, M.: Deterministic and ensemble forecasts of the Kuroshio south of Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6025, https://doi.org/10.5194/egusphere-egu26-6025, 2026.
Reliable forecasting of ocean mesoscale eddies is essential for applications such as scientific investigation, ecosystem management, and environmental services. However, comprehensive, large-sample evaluations of eddy forecasts from dynamical ocean prediction systems remain largely absent.
This study evaluates the performance of the LICOM Forecast System (LFS), a global eddy-resolving ocean forecast system, in predicting mesoscale eddies over the Northwest Pacific. One year of 1–15 day sea level anomaly (SLA) forecasts was compared with observations using the GEM-M eddy identification and tracking algorithm. A novel distance-based matching framework is developed to objectively link forecasted and observed eddies. This framework pairs correctly forecasted eddies between observation and forecast, while the remaining eddies are classified as missing eddies or false eddies.
Statistically, the system slightly underestimates eddy number (~8%) and amplitude (~22%), while overestimating eddy radius (~4%) and velocity (~24%). Despite these biases, LFS reproduces the large-scale spatial distribution of mesoscale variability in both eddy-rich and eddy-poor regions. Further, the matching outcomes reveal that LFS successfully forecasts ~63% of observed eddies at a 1-day lead time, while 37% of the observed eddies were missed, and 31% of the forecasted eddies were false. A key finding is that forecast skill is strongly dependent on eddy dynamical characteristics. Eddies with larger amplitudes and slower propagation velocities are more likely to be correctly predicted and exhibit smaller location errors. Quantitative analysis reveals a significant relationship between eddy amplitude and forecast location errors, particularly for weak eddies (amplitude smaller than 1.1 cm), and a robust linear dependence between eddy propagation speed and forecast error. For eddies with amplitudes greater than 1 cm and velocities below 1 km/day, the mean location errors is reduced to ~71 km at a 1-day lead time, compared to ~80 km for the full sample. This provides practical guidance for the forecasting applications: for eddies with larger amplitudes and slower velocity, the forecast system demonstrated greater accuracy in predicting their location.
This study establishes a systematic and scalable framework for evaluating mesoscale eddy forecasts and demonstrates that eddy predictability is closely linked to intrinsic dynamical properties. Also, the proposed matching-based validation framework further distinguishes between correct, missing, and false forecast eddies, providing new insight into the structural limitations of dynamical ocean forecasts and offering a diagnostic tool for evaluating forecast system performance.
How to cite: Zhang, J., Liu, H., Ding, M., Meng, Y., Zheng, W., Lin, P., Yu, Z., Li, Y., Wang, P., and Chen, J.: Forecasting Ocean Mesoscale Eddies in the Northwest Pacific in a Dynamic Ocean Forecast System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7397, https://doi.org/10.5194/egusphere-egu26-7397, 2026.
In this study, we present ocean heat content/salt content results with and without gliders, in part concentrating on the western North Atlantic, including the continental shelf area where there are typically numerous glider observations. The impact that the disparity in the number of these two platforms has on the calculation of Upper Ocean Heat Content at NCEI is discussed. Because gliders (vs. profiling floats) generally occupy small geographic regions on short time scales, the impact on global estimates vs. local estimates is examined in the context of those two ocean observing systems. We also look at the impact of NAS UGOS profiling floats vs. non UGOS floats vs. gliders in the Gulf of Mexico. The NAS program has funded the effort that has resulted in the collection of over 9000 ocean in situ profiles of temperature and salinity since 2019, and the value of those profiles is assessed both in terms of Ocean Heat Content, as well as ocean model forecast skill. Again, the different space-time sampling of gliders vs. profiling floats is highlighted. Finally, an overview of fully blended ocean products, including glider observations that come through the IOOS glider DAC to NCEI, Argo, and other observations, is presented.
How to cite: Hogan, P., Reagan, J., Mishonov, A., and Boyer, T.: Impact of Space-Time Sampling: Gliders vs. Profiling Floats, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22155, https://doi.org/10.5194/egusphere-egu26-22155, 2026.
The FOCCUS project (Forecasting and Observing the Open-to-Coastal Ocean for Copernicus Users) aims to advance seamless open-to-coastal ocean monitoring and forecasting within the Copernicus framework. FOCCUS focuses on strengthening the integration of multi-platform observations, including satellite, in situ, and land-based remote sensing data, with high-resolution coastal and shelf-sea models and Artificial Intelligence (AI) enabled methodologies to improve the consistency, accuracy, and usability of coastal information.
Building and improving existing coastal monitoring capabilities and developing innovative coastal products is vital for coastal protection in the face of climate change. The integration of coastal observations with advanced hydrodynamic and coastal models and unified coastal management systems is essential to enhance monitoring and forecasting across the open ocean–coastal continuum. Recent technological advances further enable the implementation of novel numerical modelling approaches and AI-based methods, allowing seamless solutions across spatial scales and supporting pan-European applications. Within FOCCUS, recent developments address key challenges related to open-to-coastal interactions, the generation of enhanced coastal products, and the application of AI-supported approaches for data fusion, downscaling, and gap filling. These developments contribute to improved representation of coastal processes and increased robustness of coastal forecasting systems.
FOCCUS outcomes support a wide range of coastal applications, including pollution hazard and risk mapping, coastal erosion assessment, sustainable resource management, harmful algal bloom monitoring, ecosystem protection, support to Marine Protected Areas, and the assessment of natural hazards and extreme events under climate change. By reinforcing the connection between Copernicus marine core services and coastal user needs, FOCCUS contributes to the development of scalable, pan-European coastal products and decision-support tools, enhancing Europe’s capacity to monitor, forecast, and adapt to increasing coastal risks.
How to cite: Staneva, J. and the FOCCUS EU Project Team: FOCCUS: Advances in Open-to-Coastal Ocean Monitoring and Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21107, https://doi.org/10.5194/egusphere-egu26-21107, 2026.
How to cite: Lo Iacono, M., Pattarino, M., Caligaris, F., Durin, G., Bordone, A., Raiteri, G., Ciuffardi, T., Lombardi, C., Pennecchi, F., and Coïsson, M.: Modeling dissolved oxygen for coastal monitoring and forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22599, https://doi.org/10.5194/egusphere-egu26-22599, 2026.
When applying sustainable Nature-based Solution (NbS) for coastal engineering, a major challenge lies in determining the effectiveness of these NbS approaches in mitigating coastal erosion. The efficacy of NbS is influenced by various factors, including the specific location, layout, and the scale of implementation. This study integrates artificial intelligence (AI) with hydro-morphodynamic numerical simulations to develop an AI-based emulator focused on predicting Bed Level Changes (BLC) as indicators of erosion and deposition dynamics. In particular, we explore the influence of seagrass meadows, which vary in their initial depth (hs) and depth range (hr), on the attenuation of coastal erosion during storm events.
The framework employs a hybrid approach combining the SCHISM-WWM hydrodynamic model with XBeach to simulate 180 depth range and starting depth combination (hr-hs) scenarios along the Norderney coast in the German Bight. A Convolutional Neural Network (CNN) architecture is used with two inputs—roller energy and Eulerian velocity—to efficiently predict BLC. The CNN shows high accuracy in replicating spatial erosion patterns and quantifying erosion/deposition volumes, achieving an R² of 0.94 and RMSE of 3.47 cm during validation.
This innovative integration of AI and NbS reduces computational costs associated with traditional numerical modelling and improves the feasibility of What-if Scenarios applications for coastal erosion management. The findings highlight the potential of AI-based approache to optimize seagrass transplantation layouts and inform sustainable coastal protection strategies effectively. Future advancements aim to further optimize model integration and scalability, thereby advancing NbS applications in enhancing coastal resilience against environmental stressors.
How to cite: Dammak, N., Chen, W., and Staneva, J.: Toward an AI-enhanced hydro-morphodynamic model for nature-based solutions in coastal erosion mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5375, https://doi.org/10.5194/egusphere-egu26-5375, 2026.
The coastal environments of the Southern Baltic Sea are of high ecological and socio-economic importance. Understanding future changes along its extensive and complex shorelines can help us comprehend the climatic and natural pressures arising from extreme weather events of compound and cascading nature, providing valuable insights for effective coastal management and the prevention of future adverse erosional changes. Current shoreline forecasting methods have limited capabilities to capture nonlinear forcings, have limited temporal forecasting and lack explainability. We present sequence-aware LSTM-RNN framework with optimized lookback functionality designed for end-to-end recursive shoreline forecasting. The model integrates 15 environmental factors spanning climatic, hydrometeorological and geomorphological indicators to enhance spatiotemporal representation, capture compound characteristics and maintain physical consistency. Trained with ERA5 reanalysis products, Landsat satellite observations, and CMIP6 SLR projections, our LSTM-RNN model achieves high forecasting skill of over 25 years, yielding an RMSE of 10.40, MAE of 7.13, and R2 of 0.55. The model was then allowed to make predictions for three proposed sectors, revealing consistent increase in erosional tendencies from 2030 to 2050 across nearly whole study region. Explainable AI method, DeepSHAP reveals that the increasing erosion in these sectors is governed by rising sea levels under high emission scenario when combined with storm surges and maximum significant wave height which far outweigh the accretion caused by wind-wave variables. The progression aligns closely with the established theories of shoreline evolution under the influence of rising sea levels and storm surges, underscoring the model’s ability to identify physically meaningful drivers. The framework demonstrates strong potential for advancing explainable AI in Earth observation, combining predictive accuracy with physical explainability for operational shoreline monitoring and climate change mitigation applications.
How to cite: Tanwari, K., Terefenko, P., Giza, A., and Śledziowski, J.: Explainable deep learning based decadal shoreline forecasting in the Southern Baltic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6775, https://doi.org/10.5194/egusphere-egu26-6775, 2026.
Reliable monitoring of coastal ocean states is critical for understanding regional climate variability and managing marine resources. We developed a high-resolution regional ocean analysis system for the seas around Korea. The system is based on the Regional Ocean Modeling System (ROMS) with a horizontal resolution of approximately 5 km and 30 vertical layers. To resolve complex coastal physical processes, we incorporated tidal forcing from the TPXO9 model and atmospheric forcing from ECMWF ERA5, while boundary conditions were supplied by the global GLORYS-NRT product.
To minimize model errors and incorporate the observation data, we applied the Ensemble Optimal Interpolation (EnOI) method for data assimilation. The background error covariance was estimated from a long-term simulation (1980–2022) comprising 44 ensemble members. We implemented a localization radius of 50 km horizontally and 100 m vertically to eliminate spurious correlations. The system assimilates a wide range of observations, including Sea Surface Temperature (OSTIA), surface geostrophic currents from satellite altimetry, and in-situ vertical profiles of temperature and salinity CTD and Argo floats.
Comparison with independent observation data and the global ocean analysis (GLORYS-NRT) demonstrated the system's reliable performance. The analysis field showed a high correlation (0.99) for sea surface temperature and reduced RMSE compared to the global model. Notably, our system accurately reproduced the vertical structure of the Yellow Sea Bottom Cold Water (YSBCW) and tidal fronts in the Yellow Sea and the meandering path of the East Korea Warm Current and Kuroshio. Furthermore, validation of volume transport through the Korea and Jeju Straits confirmed that our system better captures seasonal variability compared to the global product, which tended to underestimate transport in the Korea Strait.
The regional ocean analysis system successfully tracked significant climate anomalies in 2025. The region experienced distinct warming, with surface temperatures 0.5–2.0°C higher than the climatological mean (1991–2020), a warming trend extending to 150 m depth. Additionally, surface freshening (0.1–0.3 psu decrease) was observed in the Yellow Sea. These results underscore the necessity of including tidal processes and assimilating high-resolution local observations for effective monitoring of ocean climate change in the coastal seas.
How to cite: Choi, J.-S., Choi, B.-J., Makatabi, A., Jeong, K.-Y., and Seo, G.-H.: Implementation of a High-Resolution Regional Ocean Analysis System for Northwest Pacific Using an Ensemble Data Assimilation Method, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15344, https://doi.org/10.5194/egusphere-egu26-15344, 2026.
The rapid advancement of unmanned maritime systems (UMS) necessitates rigorous validation protocols within complex and non-linear marine environments. This study presents the comprehensive development and operational framework of an integrated metocean monitoring system at the at the Pohang Maritime Unmanned Systems Testbed, Republic of Korea. The system is specifically engineered to generate high-fidelity environmental datasets, which are pivotal for the systematic performance validation and reliability assessment of unmanned surface vehicles (USVs) and unmanned underwater vehicles (UUVs). To bridge the gap between controlled simulations and highly dynamic real-sea conditions, an integrated observation infrastructure comprising four core components has been established to capture multi-scale environmental variables.
Specifically, the infrastructure incorporates four synergistic core components: (1) onshore meteorological stations equipped with high-precision sensors to collect critical atmospheric parameters, including wind vectors, precipitation, and solar radiation; (2) offshore observation buoys deployed at strategic locations to monitor real-time wave dynamics, including significant wave height and sea surface temperature (SST); (3) bottom-mounted Acoustic Doppler Current Profiler (ADCP) utilized to acquire high-resolution vertical profiles of current velocity and direction across the water column, alongside hydrostatic pressure and wave parameters; and (4) mobile observation platforms integrated with vessel-mounted ADCP, conductivity-temperature-depth (CTD) sensors for high-resolution vertical profiling, and an automatic weather station (AWS). These mobile units are instrumental for ensuring spatial flexibility and mitigate observational gaps that stationary sensors, thereby achieving a holistic 3D characterization of the marine environment.
Crucially, all observation data from these multifaceted platforms are synchronized and transmitted in real-time to a centralized onshore integrated control system via high-speed telemetry. This unified framework facilitates real-time situational awareness, enabling operators to visualize and analyze metocean trends instantaneously. By quantifying precise sea state levels and providing continuous environmental telemetry, the infrastructure significantly enhances operational safety during field trials. This allows for proactive risk mitigation and informed decision-making against hazardous maritime conditions. Ultimately, this multidimensional system facilitates the characterization of environmental variables, enabling a rigorous analysis of the operational envelopes and autonomous navigation efficiency of unmanned systems. This infrastructure is expected to serve as a cornerstone for the international standardization of marine unmanned technologies and the development of extensive empirical databases for machine learning-based motion control algorithms.
How to cite: Jung, M., Park, J., Park, Y., Moon, G., Kim, J., Jang, I., and Seo, J.: Development and Implementation of an Integrated Metocean Monitoring Infrastructure at the Pohang Maritime Unmanned Systems Testbed, Republic of Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20032, https://doi.org/10.5194/egusphere-egu26-20032, 2026.
High-resolution coastal ocean modelling is essential for understanding and managing complex coastal systems under increasing environmental and socio-economic pressures. We present the development of an unstructured FVCOM numerical model for the Shetland and Orkney archipelagos in the north of Scotland, with a hindcast run covering a 30-year period at unprecedented high resolution (~70m around the Shetland coast and hourly output), nested in the Scottish Shelf Model and fully-forced with 5.5km CERRA atmospheric data at hourly frequency. The unstructured grid allows to resolve the complex coastline and bathymetry that characterizes these areas. This region is paramount for the aquaculture industry, with Shetland alone making up for more than 20% of the Scottish salmon and more than 80% of Scottish mussel production, yet its energetic circulation, complex bathymetry, and strong coastal–ocean interactions make monitoring and prediction of potential environmental impacts particularly challenging. Combining numerical modelling with data science tools, we explore the system variability and complexity. This allows the identification of emergent patterns, dominant modes and changes that may otherwise be overlooked. Our work helps supporting more effective long-term monitoring and sustainable use of marine resources in a region increasingly affected by climate change.
How to cite: Novi, L., De Dominicis, M., O’Hara Murray, R. B., Hills, A., Gallego, A., and Waldman, S.: Improving long-term monitoring around the Shetland and Orkney archipelagos with high resolution modelling and data science. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20285, https://doi.org/10.5194/egusphere-egu26-20285, 2026.
The SailingBox is a novel, reliable and user-friendly citizen-science device. Thanks to its compact, low-cost design, it can simultaneously measure up to six essential ocean variables. The SailingBox was tested on two separate sailing vessels in 2025, and here we present the preliminary results from a deployment on a Monaco Explorations sailing catamaran.
Surface water measurements were conducted in the Mediterranean Sea, in September-October 2025, during the Greece Mission, coordinated by Monaco Explorations. We deployed the SailingBox alongside a Pocket FerryBox system used for reference measurements, including temperature, salinity and pH. The resulting data provide insights into the variability of surface water properties along the boat's route from Monaco to Volos, Greece, and back. This study compares the data between the two platforms to assess the quality and consistency of the measurements, and to investigate the characteristics of the surface water dynamic during the stormy fall weather in the Mediterranean. Preliminary analysis indicates good agreement between the two measurement systems for temperature, salinity and density.
We present here the first demonstration of the citizen science version of the SailingBox on sailing vessels across variable conditions, and we demonstrate the potential of this miniaturized flow-through observation system for conducting autonomous, low-power and reliable observations in the surface coastal ocean.
How to cite: Deschner, S.: Field Validation of the Low-Cost SailingBox for Reliable Ocean Monitoring in the Mediterranean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21484, https://doi.org/10.5194/egusphere-egu26-21484, 2026.
Ocean surface currents are controlled by upper ocean dynamics, atmospheric conditions, as well as air-sea momentum exchanges. In the context of oil spill drift forecasting, this diversity of driving mechanisms imprints various sources of uncertainty, each of which is characterized by specific spatio-temporal patterns. Focusing on French coastal area (i.e. Bay of Biscay and English Channel), we aim at quantifying these uncertainties through ensemble and stochastic modeling approaches. We will present recent model developments within MANGA (MANche-GAscogne) Shom’s operational forecasting system, and discuss preliminary results in this direction. We will pay a particular attention to air-sea momentum exchanges, discussing strategies to model it with a stochastic approach. Such a source of uncertainty includes both large-scale components associated with atmospheric conditions and small-scale components associated with upper ocean dynamics, which a stochastic model should account for.
How to cite: Jamet, Q., Gourvès, D., Raynaud, S., Weiss, L., and Brankart, J.-M.: Quantifying surface currents uncertaintiesin French coastal area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16650, https://doi.org/10.5194/egusphere-egu26-16650, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 1a
EGU26-11166 | ECS | Posters virtual | VPS20
Improving coastal monitoring and forecasting systems through interoperable OGC API EDR-based data servicesTue, 05 May, 14:51–14:54 (CEST) vPoster spot 1a
Effective coastal monitoring and forecasting systems rely on the availability and timeliness of interoperable, standardized, and accessible marine data across observational, modelling and service layers. Fragmented data formats, legacy infrastructures, and non-standardized access mechanisms remain significant barriers to the seamless integration of ocean observations into operational monitoring and forecasting systems and downstream applications.
This study presents the development of a standards-based data workflow designed to enhance interoperability, scalability, and facilitate marine data integration, through the adoption of international standards and best practices. The proposed approach focuses on establishing robust data flows that transform, validate, and harmonize heterogeneous datasets (e.g., in situ near-real-time observations and numerical model outputs) into NetCDF format. Standardized and programmatic access to these datasets is enabled though the OGC API Environmental Data Retrieval protocol, implemented using the pygeoapi platform. By adopting open standards and service-oriented architectures, this framework enables efficient spatio-temporal querying of ocean variables, facilitating their assimilation into forecasting systems, decision-support tools, and customized applications. In parallel, geoportal interfaces were updated to integrate the new OGC API EDR services, ensuring that interoperable data access is available both through machine-to-machine interfaces and user-friendly graphical tools, supporting a broad range of user profiles and promoting citizen involvement and ocean literacy.
By addressing interoperability at the data, service, and user-interface levels, this work demonstrates how standardized data infrastructures are key enablers for improved, scalable, and sustainable coastal monitoring and forecasting capabilities.
How to cite: Dias, T., Videira, C., Lobo, V., Costa, A. C., and Lourenço Baptista, M.: Improving coastal monitoring and forecasting systems through interoperable OGC API EDR-based data services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11166, https://doi.org/10.5194/egusphere-egu26-11166, 2026.
